*2.1. Macroscopic Traffic Flow Model and Vehicle Velocity Prediction*

Macroscopic traffic flow models use a mathematical model to describe traffic dynamics. The mathematical model evaluates the unmeasured area based on real-time input data. This model is usually based on empirical relationship, in which the parameters to be evaluated are either obtained from external calculation of historical data or generated from internal evaluation of an algorithm. The macroscopic traffic flow model method has been widely used in traffic state assessment.

Literature [16,17] introduces in detail the current developments and trends of the above macro traffic flow model, as well as traffic modeling, evaluation, and control methods based on intelligent networked vehicles. The macroscopic traffic flow model method has the following advantages: First, this method explains the mechanism of traffic, expands the observation data, and provides additional information. Therefore, this method can use less data to predict accurate traffic conditions. Second, it has higher interpretability. This means that even if the prediction is not accurate, the reason may be found in a certain confidence interval. Third, it can be directly integrated with traffic control practices, such as using model predictive control. The macroscopic traffic flow model method also has the following shortcomings: First, inaccurate or uncalibrated models will lead to poor performance of traffic state assessment results. Therefore, in actual application, the macroscopic traffic flow model traffic state assessment method must be carefully selected and calibrated. In this case, checking the validity of a model or calibrating a model requires a big set of data [18]. Second, since the macro-traffic flow model cannot be adjusted adaptively, it cannot reflect the changes in traffic flow brought by random traffic interference alone. Therefore, in the absence of real-time traffic information, it is more suitable for the prediction of traffic flow that does not change rapidly, such as the prediction of traffic parameters in expressways and urban loops.

Traffic flow (simulation) models are used to characterize complex traffic flow systems in order to understand, describe, and predict traffic flow [19]. It is a basic tool for analysis and experimental research of transportation systems. The traffic flow model is not only used in the traditional fields of traffic system design, testing, management, and personnel training. With hot research direction on intelligent vehicles and intelligent transportation systems, it has also been used to evaluate and predict the state of the transportation system [18]. Macroscopic traffic flow models can be classified according to the degree of detail they represent in traffic systems. This classification can be implemented by considering different levels of traffic entities in their respective flow models. Literature [20] classifies the traffic flow model as follows:

The microscopic traffic flow model not only describes the time and space behavior of system entities (vehicles, drivers, etc.), but also describes the interaction between them in detail. For example, a lane change behavior of each vehicle in the flow is described as a series of driver decisions.

Similar to the microscopic model, the submicroscopic model describes the characteristics of a single vehicle in the traffic flow. However, in addition to the detailed description of the driving behavior, the control behaviors (transmission shifting, ESP, etc.) of the vehicle in response to the surrounding conditions are also modeled. Moreover, the sub-modules of the vehicle are also modeled by mathematical equations.

The mesoscopic traffic flow model neither identifies nor tracks individual vehicles, but it lists the behavior of individual vehicles (for example, in the form of probability). In view of this, traffic is represented by small groups of traffic entities. The model does not describe the behavior and interaction of these groups in detail. For example, the lane change of a single-vehicle is described as a transient event. The decision to change lanes

is based on parameters such as the relative density of the lane and the speed difference. Some mesoscopic models analogous to the theory of gas motion have been pushed out. These gas motion models describe the dynamic distribution of model velocity.

The macroscopic traffic flow model describes the traffic flow at an overall level without identifying its constituent details. For example, traffic flow is aggregated (represented by flow, density, and speed). The behavior of individual vehicles (lane changes, etc.) is usually not displayed. A macroscopic traffic flow model assumes that it is appropriately allocated to the road lanes and uses approximate methods to achieve it. Macroscopic traffic flow models are usually classified based on the number of their partial differential equations. Usually, one side of the equation is the model representation, and the other side is the order of the equation. Here are some typical traffic flow models and their extended models [18].

#### 2.1.1. One-Dimensional Flow Model

The Lighthill-Whitham-Richards (LWR) model [21] is a famous innovation in the mesoscopic traffic flow model. It uses a conservation law of vehicles in the traffic flow and assumes that the traffic parameters (traffic speed and density) follow a fundamental diagram (FD) in equilibrium. This traffic model has the ability to identify traffic congestion and distinguish between traffic congestion and free flow. Moreover, because of its relatively simple equations, it can be quickly calculated and solved. However, due to the limitations of the model, it cannot reproduce some complicated phenomena, for example: unstable flow and the stop-and-go phenomenon.

After the LWR model, higher-order models were developed in order to show phenomena different from equilibrium traffic state (usually referred to as micro disturbance and non-equilibrium state), higher-order models usually use another momentum equation to describe the evolutionary relationship of traffic speed instead of the FD. The Payne-Whitham (PW) model [22] is the first well-known high-order modeling attempt. The PW model and its extension [23] successfully reproduce some well-known traffic phenomena, such as hysteresis, reduced capacity. However, it still has some shortcomings, such as negative speed [17].

The Aw-Rascle-Zhang (ARZ) model [24] is another high-order model. It allows the equilibrium state of traffic to be transformed into other states. It also overcomes the limitations of the PW model. It can be derived from the LWR model. The ARZ model is further developed and extended to the following models: general second-order model [25], phase change model [26], and generalized ARZ (GARZ) model [27].

Recently, the theory of explaining the traffic flow model has made new progress. Some models can be represented using Hamilton-Jacobi partial differential equations (HJ-PDE). HJ-PDE has been studied in detail in the field of partial differential equations and physics [28], and its theory can effectively solve this kind of model [29]. For example, the traffic density state variable ρ(t, x) of the traditional LWR model can be transformed into the state variable N(t, x) of the accumulation flow. In addition, due to the nature of the accumulated flow, this model can implicitly express vehicle trajectory and travel time. The relationship between macro and micro can be shown. Then, a Lagrangian coordinate system was proposed and used accumulated flow and vehicle trajectories in the mesoscopic traffic flow model [30]. Similar topics have also been discussed in other traffic flow models [31].

#### 2.1.2. Multi-Lane Models, Multi-Class Models, and Random Models

Actual traffic often has multiple lanes and multiple categories, different from the model introduced in Section 2.1.1. The studies [32,33] proposed some models that consider multi-lane and multi-category traffic behavior. For these extended models, modeling lane change behavior is one of their main challenges. Regarding lane changes, human behavior is quite important.

For the random properties of the traffic and the uncertainty of input/output data, a stochastic model [34] based on the LWR model is proposed. In the references [35,36], the source of randomness in dynamics explains the heterogeneity of vehicles, which means the relationship between multi-class models and random models. Moreover, the work [37] developed an LWR stochastic model based on HJ PDE.

#### 2.1.3. Development Trend

The traffic flow model is not only used in the traditional fields of traffic system design, testing, management, and personnel training [20]. As the research on intelligent vehicles and intelligent transportation systems is a hotspot, it is also used to evaluate and predict the state of the transportation system [18]. It extends from ensuring the stability and energy efficiency optimization of a single vehicle to the safety and efficiency of the entire system.

The main principle of the prediction algorithm is to combine numerical simulation, traffic model, real-time data, and historical data to predict the evolution of future traffic conditions. Designing a fast, scalable, and accurate road traffic forecasting tool is the key to overcome the lack of forecasting ability of the existing traffic management information system [18], and it can be applied to the prediction and planning of vehicle path and speed in the future. At present, there is no hybrid traffic flow model that combines the advantages of macro traffic flow model and data-based traffic flow model. The hybrid traffic flow model is of great significance to improve the accuracy, robustness, and real-time performance of prediction. In the future, the traffic flow model will not be used individually, but a multi-layered hybrid model, as well as possible complementary and combined use of model-based traffic flow models and data-based learning models.

#### *2.2. Data-Based Traffic Flow Model and Vehicle Velocity Prediction*

Traffic parameter prediction method based on big data and machine learning has attracted the research interest of many scholars in recent years [38,39]. The traffic management department realizes the prediction of traffic flow by collecting and analysising the current and historical traffic data. Big data analysis can effectively predict the occurrence of traffic accidents. Big data analysis mainly solves the following three problems: data storage, data analysis, and data management [40]. The collected traffic big data is trained into a prediction model by machine learning method to analyze the evolution trend of traffic. Machine learning models can be divided into: supervised learning, unsupervised learning, reinforcement learning, deep learning and entity-based algorithms [40]. The labeled training data is used for supervised learning algorithms. Linear regression, decision tree, neural network and support vector machine are typical supervised learning methods.

The data-based traffic flow model can be divided into historical data-based traffic flow model and real-time big data-based traffic flow model. Among them, the traffic condition assessment method that widely relies on historical data uses a statistical method or machine learning method to find the relationship between historical data. Traffic conditions are evaluated based on this correlation and real-time data, meaning that it does not require prior knowledge of explicit modeling in the macroscopic traffic flow model. This method usually requires a big amount of historical data.

The traffic flow model based on historical data has the following advantage: less time for model selection and calibration. The disadvantages are: first, based on historical data means that the model may fail when unexpected events occur or when a relatively long trend is predicted. Second, the computational consumption required for training and learning will be very high. Third, the method can be regarded as a "black box", which means it is unable to properly explain the model decisions [18]. Fourth, if the information from real traffic flow is greatly different from that stored in the data used for the training model, the prediction accuracy will not be guaranteed.

Compared with the macro traffic flow model and the traffic flow model method based on historical data, the method based on real-time big data is defined as a method that does not rely on the empirical relationship that appears in the macro traffic flow model but depends on real-time data flow. This means that this method relies less on the prior knowledge of transportation. Its advantage is that it is robust to uncertain phenomena or unpredictable accidents. In an era of ubiquitous sensors (smart phones) and the emergence of a large number of intelligent networked vehicles, methods based on real-time big data streams may become popular in the future [41,42]. This method is more suitable for the prediction of urban road network traffic flow.

#### 2.2.1. Research Status of Traffic Flow Evolution Using History Traffic Data and Artificial Intelligence Methods

It has become a hot research direction using deep learning method to predict the change of traffic parameters, for example in [43–47]. The unsupervised incremental machine learning, deep learning, and deep reinforcement learning was adopted by Dinithi Nallaperuma et al. to structure an expansive smart traffic management platform [43]. It can successfully model traffic flow with fluctuation; however, the method proposed in the literature is not effective for predicting traffic flow with high frequency fluctuation. An improvement to this problem is to increase the amount of data used to train the deep learning model. The restricted Boltzmann Machine method was used to predict traffic [44]. This method has a better nonlinear fitting ability and high prediction accuracy for typical chaotic time series. Di Zang et al. [45] solved the task of long-term traffic speed prediction for elevated highways by coupling convolutional long-short-term memory and convolutional neural network (CNN) into a single framework.

For the problem of uncertain data used in the training model, a common solution is to combine fuzzy rules with deep learning [48] and neural network [49]. By introducing the fuzzy representation into the deep learning model to lessen the impact of data uncertainty, a deep convolutional network model was established to explore the spatiotemporal connection of traffic flow to promote traffic flow prediction in [48]. The experimental results show that the combination of deep learning and fuzzy theory can improve the prediction accuracy, compared with other methods, such as autoregressive integrated moving average (ARIMA), deep learning-based prediction model for Spatial-Temporal data, CNN, fully convolutional neutral network, and fatigue detection convolutional network. The Takagi-Sugeno system was used for fuzzy reasoning, and two learning processes were proposed to update the membership function of the fuzzy system [49]. The proposed method has advantages over the six traditional models, such as artificial neural network, support vector machine, ARIMA model, and vector autoregressive model.

In addition to representing uncertainty with fuzzy rules, another approach is to point out exactly what the uncertainties are and then label those uncertainties with contextual factors. The relationship between traffic flow values in a time interval is investigated based on a combination of contextual factors from historical data [50]. From the analysis results, forecasting accuracy can be better improved by the proposed new method. On the other hand, the design is slightly inferior to the conventional method due to inconsistent points. This can be interpreted to the high volatility degree associated with low-traffic-flow periods.

There are several ways to improve the prediction accuracy of deep learning methods: training data are screened [51,52] or training parameters are optimized [53]. A deep belief network (DBN) model and a kernel extreme learning machine classifier is combined as a prediction model, wherein the important features of the traffic flow data are extracted through DBN at the bottom of the network. To predict the traffic flow, the extracted results are inputted into the kernel extreme learning machine classifier [52]. Automatically use those highly correlated spatiotemporal points to train the deep learning network, and reduce the use of less correlated data [51]. This explains the interaction between past and future data to some extent. Traffic flow theory and its application on urban transportation networks with more efficient deep learning architectures is a promising study field [51]. This is the valuable research direction recommended by this paper.

#### 2.2.2. Research Status of Velocity Prediction Using Real-Time Traffic Data

Real-time prediction model of vehicle travel speed is helpful to improve vehicle safety, maneuverability, and fuel economy. In order to achieve these effects, an accurate velocity prediction model needs to be established and can be successfully implemented in the real

system. There are two main types of vehicle speed prediction models: the prediction model based on Markov chain and the prediction model based on recursive neural network. Markov chains are stochastic data-driven models that predict future states from state transition matrices and current states. Transient probabilities are aggregated into state transition matrices. The structure of measurement based on transfer conditions is intuitive and easy to implement. There are three kinds of Markov chain models: interval coding, fuzzy coding, and velocity constraint model [54]. The speed prediction models are based on recurrent neutral network (RNN) including standard RNN, long short-term memory, and gated recurrent unit (GRU) models [54]. Among the three models based on Markov chain, the model combining the fuzzy coding method and the constraint model has the highest prediction accuracy. Of the three RNN-based models, GRU has the highest prediction accuracy due to the appropriate structure of long-term dependent learning by combining the amount of previously determined state data [54].

#### 2.2.3. Development Trend

More research study needs to be performed in the following aspects for future databased vehicle speed prediction methods [15]:

#### *2.3. Influence of Vehicle Lateral Dynamic on Speed Prediction*

The influence of vehicle lateral dynamics on vehicle speed prediction is based on the traffic risk assessment from drivers or intelligent vehicles. Traffic risk assessment models can be divided into two types: longitudinal and lateral models. The longitudinal traffic risk assessment model mainly evaluates vehicle collision accidents caused by untimely braking or insufficient braking force. It includes a risk assessment model based on traffic accident data or simulation data [55,56] and a model-based risk assessment model [57–59]. Model-based work often requires parameters and empirical assumptions, while data-based methods only focus on extracting the relationship between images and road safety without considering other influencing factors such as drivers; thus, it has certain limitations.

Lateral traffic risk assessment models often build lane change risk models based on road traffic environment and lane change path factors, such as building a highway exit lane change risk model based on a proportional advantage model [60]. The existing literature rarely considers the impact of the traffic environment and vehicle lateral handling characteristics [61]. How to dynamically and accurately analyze the impact of traffic conditions on the prediction of vehicle velocity change and intermittent road safety (vehicle lateral stability) is a huge challenge for road safety analysis in practical application [60].

In order to study the stability characteristics of different vehicles and generate their stability criteria (lateral traffic risk assessment model), it is necessary to study the structural characteristics of vehicles and their handling stability control methods. For example, distributed drive control (also known as torque vector control) enhances the vehicle's dynamic performance [62], so its traffic risk assessment should be different from that of ordinary front-wheel steering vehicles. In a similar work, Kun jiang et al. [63] presented a method to estimate and predict individual tire forces based on a vehicle dynamics model and observer with low-cost sensors and driver assistance map, which is in close relation to the speed of the vehicle. Yu-Chen Lin et al. [64] developed an ecological cruise control based on an adaptive prediction-based control strategy. The design guarantees the safety of driving, riding comfort, as well as fuel efficiency simultaneously when running on roads with curves and up-down slopes. However, the above two literature did not involve the personalized assessment of vehicle traffic risk, nor did they pay attention to the relationship between traffic risk and predicted speed.

Some researches combine vehicle lateral safety with longitudinal speed prediction. To this end, a three-degree-of-freedom vehicle lateral dynamics model with lateral load transfer ratio index is derived for a rollover speed prediction model in [65]; similarly [66,67]. Lin Li et al. [68] combined the energy management of HEVs with vehicle speed prediction and planning before entering the corner. Pan Song et al. [69] demonstrated an improved optimal speed adjusting method based on the vehicle handling stability and path following performance. Med Krid et al. [70] presented a model predictive control (MPC) strategy for an active anti-roll system, which aims to minimize the load transfer during cornering and the consumed energy by the actuators. Hongbin Ren et al. [71] formulated a quadratic optimization problem for an integrated control of longitudinal speed and lateral motion control based on longitudinal progression maximization and lateral path tracking error minimization.


**1. Introduction**

This chapter is a discussion of medical schools' education curricula in preparing medical doctor practitioners to practice medicine in their countries and globally. The discussion will answer the questions about whether the worldwide medical schools' curricula are responding to the communities' needs with consideration of the most recent up-to-date evidence in the medical sciences and the psychosocial sciences combined. It will also answer the question of what kind of theoretical frameworks are being in use, and are these frameworks considering a comprehensive and holistic view of the beneficiaries of health care.

The idea of this discussion has emerged after a long-life career in health care as nurses working closely with doctors in primary, secondary, and tertiary care settings. Our long experience of over 30 years' duration as nurses and faculty members exposed us to practicing doctors as we were student nurses, as graduate nurses practicing nursing in multiple care settings, as academics (faculty members) teaching medical students some of their non-medical courses, and as colleagues working with medical doctors on research projects on different topic areas and research designs. This journey has enabled us to capture some crucial areas of strengths and weaknesses in the preparation of medical doctors that may benefit from using a critical lens that exposes issues of concern for the sake of the wider community and the beneficiaries of medical healthcare services. In this chapter, our aim is to elucidate these points by referring to the historical development of medical practice and education, to medical schools' curricula and mission statements in general without reference to any program or school in particular, and to explain the theoretical models of teaching and practice that have been in place over the last few decades at schools of medicine. This will be a critical eye of how medical education and medical practice have evolved (or not evolved) to meet the psycho-socio-cultural changes that occurred over time. What is presented in the following pages is not a definitive argument of medical education but rather a case that draws heavily on our observations that have long been echoed elsewhere.


3.4.2. Stability Evaluation

Immediately after implantation, the ISQ values in all of the experimental groups were higher than in control group, but differences were not statistically significant. At eight weeks after surgery, ISQ values were higher than at baseline in the experimental groups, whereas in the control group, they were lower or similar than baseline values. ISQ increases were significantly greater in the experimental groups than in the control group (*p* < 0.05) (Table 2). But, no difference was observed between the groups (*p* > 0.05).

**Table 2.** Implant stability quotient (ISQ) values of groups immediately and 8 weeks after implantation.

Notes: At surgery: ISQ value at surgery; At week 8: ISQ value 8 weeks after surgery; a,b: Numbers in the same column with the same superscripts were not significantly different; *p* values were obtained by ANOVA; \**p* < 0.05.


3.2.1. Elevated Plus Maze

The performance of the mice in this test measuring affective function was analyzed and is presented in Figure S1. The GFAP-ApoE3 and GFAP-ApoE4 mice spent the same amount of time in the open arms, however, males spent more time in the open arm than females, supported by a main effect of sex (*p* < 0.001) and no effect of strain (*p* = 0.366). Treatments did not affect performance (all *p* > 0.185), with the exception of Sed-Aox group spending more time than the Sed-Con group in the open arms (*p* = 0.046). Overall, the GFAP-ApoE3 mice were more active than the GFAP-ApoE4 mice, and some

treatments affected the activity of females (all *p* < 0.003). These observations were supported by the significant main effects of strain and treatment and a sex × strain × treatment interaction. (all *p* < 0.01).


**A.1 DNSA reagents for terminating endo-polygalacturonase activity**

**Reagents [17]**

i.Sodium hydroxide – 10 g

ii.Potassium sodium tartrate – 200 g

iii.Phenol – 2 g

iv. 3, 5-Dinitrosalicylic acid – 10 g

v. 5% Sodium sulphite – 10 ml

vi.0.03% Glucose – 10 ml

vii.Distilled water – 1Litre


*4.4. Strategies for the Preservation and Development of the Auspicious Culture of Traditional Shaxi Bai Architecture*

The traditional architecture of the Shaxi Bai has its way of showing that things will go well. In terms of public architecture, the Xingjiao Temple is a Buddhist-dominated public building. Moreover, the Patron God Monastery is a public building dominated by the native culture. Due to dominating culture, they have various auspicious elements. At the same time, these buildings are also icons of Shaxi Bai's traditional settlement. Development and changes are prohibited from perpetuating their dominant cultural status. According to researchers, "repair the old as the old, minimising intervention and maximising preservation" [39]. In terms of residential architecture, a unified conservation standard should be issued. The essential protected dwellings have significant historical and cultural value. They should avoid over-growth and blind development. In contrast, the generally protected dwellings should be built sensibly. This way fits within the rules of conservation. It is to meet the social needs of the tourism industry, which is proliferating. Cultural integration is a general trend. It has led to an increase in auspicious cultural diversity. Building local communities' cultural confidence is the essential solution. Furthermore, the Patron God Monastery is a good example.

#### **5. Conclusions**

Shaxi ancient town is "the only surviving ancient market in the world". Bai culture has been exchanged and absorbed here from ancient times. However, today's rapid tourism development and urbanisation. It has led to a deepening cultural fusion in the town. Traditional architecture is an essential part of traditional villages. Overdevelopment and ethnic and cultural loss challenges are severe threats today. We started with the auspicious culture of Shaxi Bai's traditional architecture. We then analysed the old town's auspicious elements, auspicious cultural expression, and cultural influences. Shaxi Bai's traditional building shows off its lucky cultural traits. The study concludes that Buddhist culture affected Shaxi Bai's traditional architecture. However, the Bai people also keep and spread the local culture. It reflects the coexistence of multiple cultures. Moreover, the Patron God Monastery shows its confidence in the local culture. It is this characteristic that they show when facing cultural integration. The study's results show how the Bai people feel about their culture and how confident they are. It has led to their acceptance of multiculturalism without losing their native culture as well as understanding how to inherit and develop the culture in traditional architecture. The cultural outlook of the Bai people may be a good case to discuss.

**Author Contributions:** Methodology, H.Z.; data curation, H.Z.; writing—original draft preparation, H.Z.; visualization, H.Z.; software, C.D. and Y.R.; validation, Y.R.; investigation, H.Z.; writing—review and editing, Z.H.; supervision, Z.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Nature Science Foundation of China (NSFC) project (Grant numbers 51868008 and 51978187). Funder: National Natural Science Foundation of China.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Informed consent was obtained from all participants in this study.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors are thankful to reviewers and editors for their insightful comments and suggestions to an earlier edition of this article.

**Conflicts of Interest:** The authors declare no conflict of interest.


Perspective Chapter: Edge-Cloud Collaboration for Industrial IoT – An Online Approach

*You Shi, Yuye Yang, Changyan Yi, Bing Chen and Jun Cai*

## **Abstract**

In this chapter, we take the Industrial Internet of Things (IIoT) as the background for studying the energy-saving resource management framework to control the cloud center (CC), edge server (ES), and terminal equipment in a closed loop. In this framework, industrial sensors collect data and transmit it to the ES for aggregation. These data form computing tasks for data analysis. Our goal is to minimize the energy consumption of the whole system while ensuring satisfied data processing accuracy and service delay of all IIoT tasks. We formulate the ES preprocessing mode selection, sensor sampling rate adaptation, and edge cloud computing and communication resource allocation as a joint optimization problem. Due to the random arrival of data and time-varying channel conditions, we introduce an online dynamic algorithm with low complexity, which efficiently solves the problem.

**Keywords:** edge-cloud collaboration, industrial IoT, preprocessing method selection, sampling rate adaption, computing and communication resource allocation

#### **1. Introduction**

Due to the rapid development of 5G and Industry 4.0, IIoT-sensing devices, such as smart manufacturing, smart plants, and smart industrial services, have generated a abundant of data. For traditional cloud computing, it is considerably challenging to process such massive data efficiently. Fortunately, the edge computing can significantly reduce the cloud computing's computing load, and thus has been proposed as supplementary paradigm recently [1]. In industrial area, the edge server (ES) close to the data source can be enabled to process some computing tasks, so as to provide more effective data processing services and lead to less communication overhead.

Although some researchers have proposed edge cloud collaboration to increase the industrial systems' operational and energy efficiencies, there are still some inherent while unaddressed limitations. Particularly, computing and communication resources of the edge-cloud collaboration are relatively limited [2]. Hence, if closed-loop optimization is not considered in the management of cloud, ES, and terminal devices, edge cloud collaboration cannot fully make use of its advantages. Some relevant researchers have studied the resource allocation problem of IIoT's edge cloud

collaboration [3, 4], including delay awareness, price-based service scheduling [2, 3], and energy-aware resource allocation [4, 5].

However, data collection and data analysis have some special requirements which will be affected by the complex industrial environment, which has been ignored by most studies: (*i*) In IIoT system, industrial equipment needs high-precision adjustment. Any small error may cause industrial equipment to make wrong behavior and cause serious troubles. [5]. Therefore, ensuring the accuracy of data processing in IIoT service is very important. This motivates the investigation and optimization of edge cloud management variables such as processing mode and sampling rate. (*ii*) There are commonly a variety of industrial noises in practical applications, such as electromagnetic noise [6]. Because of these, we cannot analyze the data collected by the sensor directly [7]. Hence, enabling data preprocessing at ESs is necessary (for example, data cleaning [7] and data denoising [6]) before conveying data to the cloud. This necessitates a balance of optimal resource allocations between the cloud and ESs. (*iii*) Since the IIoT system environment is always complex and there are random data arrival and time-varying channels, we are required to carefully manage the computing and communication resources with the guarantee of a long-term performance. Otherwise, the system will soon run out of limited CPU resources and network capacity [8]. As a result, the system efficiency will be seriously affected.

However, solving the aforementioned issues to achieve the closed-loop management is very challenging: (*i*) It is intuitive that the processing accuracy is increasing with the sampling rate. However, increasing the rate is equivalent to the increase of the computing load, and thus will also increase transmission delay and computing energy consumption, leading to the degradation of the system performance [5]. This implies that sampling rate must be carefully chosen for balancing different performance indicators. (*ii*) In practical applications, different preprocessing methods have different computing resource requirements and corresponding processing performance. In addition, data's edge preprocessing will bring extra computing delay and energy consumption. It is difficult to optimize service delay, processing accuracy, and power consumption with mutual trade-offs. (*iii*) In response to the random data's arrival and the time-varying channel, we need to jointly optimize and dynamically adjust the selection of preprocessing methods, sampling rate, and resource allocation. However, it is hard if not impossible to obtain random information of dynamic network in time, which is a necessary condition for long-term optimization of system performance. This will obviously lead to incomplete decision information of IIoT system. Lyapunov optimization method is often used to solve such problems. However, in IIoT applications, decision variables (such as preprocessing method and sampling rate) are often integers, and constraints like processing accuracy and service delay are sometimes nonlinear. This makes the problem much more complex than traditional ones.

#### **2. Chapter contributions and organization**

In this book chapter, we study an IIoT energy resource management framework. This framework is constructed on the basis of edge cloud collaboration, and aims to conduct a closed-loop management on the cloud center (CC), ESs, and terminal devices. To be more specific, in this chapter, we consider to optimize the selection of ESs' preprocessing mode, terminal devices'sampling rates, edge cloud computing and communication resource allocation for jointly to minimize the system's energy consumption. Meanwhile, we ensure service delay and accuracy of data processing in the

long term. In addition, considering the random arrival of data and time-varying channel conditions, we introduce a dynamic online algorithm with a low complexity to solve this problem.

In particular, based on the network state of the current time slot, we decompose the long-term optimization problem into a sequence of deterministic instantaneous subproblems. After that, we define a continuous probability model and take into account the future influences, and by such we use the Markov approximation algorithm to solve these subproblems to near optimal. Finally, we theoretically analyze the system performance in terms of its asymptotic upper bound.

This chapter's main contributions are listed as follows.

This chapter's rest contents are listed below. Section 3 models the IIoT edge-cloud collaboration's system. Section 4 formulates the corresponding joint online optimization problem for the closed-loop resource management. Section 5 introduces a novel algorithm with a low complexity based on the Markov approximation and Lyapunov optimization. Section 6 analyzes theoretical performance. Section 7 demonstrates the simulation outcomes and Section 8 concludes the chapter.


*1.1. Cancers and Infection Related Cancers*

Cancer is a leading cause of death worldwide [1]. In 2018, 609,640 cancer deaths and 1,735,350 new cancer cases were projected to occur in the United States alone [2]. The most deaths are caused by breast, gastric, liver, lung, and colon cancer [1]. Lung cancer is the leading cause of cancer-related death worldwide and in the United States. Lung cancer is also the largest contributor to new cancer diagnoses [3]. Breast cancer is the second most common cancer in women and accounts for 25% of all cancer diagnoses in American women [4]. Gastric cancer is the second most commonly occurring cancer worldwide and the fourth and fifth most common cancer in men and women, respectively [1]. Colon cancer is the third most common cancer worldwide and its likelihood of diagnosis increases progressively from age 40 [5]. Lastly, liver cancer is the fifth most common cancer in the world and has a poor survival rate due to its aggressive nature [6].

Viruses are estimated to cause about 15% of all human cancers worldwide, and most of these tumor viruses are hooked on lipid signaling, synthesis, and metabolism [7]. DNA viruses that contribute to human cancers include human papillomavirus (HPV], Epstein–Barr virus (EBV), Kaposi's sarcoma-associated herpesvirus (KSHV)—also known as human herpesvirus 8 (HHV-8), Merkel cell polyomavirus—a polyomavirus (MCPyV) associated with the development of Merkel cell carcinoma (MCC) and hepatitis B virus [7]. The two RNA viruses that can cause the development of human cancer are hepatitis C and human T lymphotropic virus (HTLV-1] [7]. EBV and KSHV are both herpesviruses with DNA genomes [7]. EBV is associated with Hodgkin's disease, B and T cell lymphomas, post-transplant lymphoproliferative disease [8], nasopharyngeal carcinomas, and leiomyosarcomas [7]. It has been associated with up to 10% of all gastric cancers, and up to 200,000 new malignancies every year worldwide [9,10]. A vaccine to prevent or treat EBV has not yet been licensed [10]. KSHV is similar to EBV in that the B lymphocyte is the predominant infected cell, and it has been estimated to cause 34,000 new cancer cases globally [7,11]. It is the leading cause of AIDS-related malignancy and cancer mortality [12]. Kaposi's sarcoma (KS] is the most common AIDS-defining cancer [13–16]. KS is a serious clinical problem prevailing in up to 50% of HIV+KS+ patients in the United States and 19–61% in Sub-Saharan Africa, who never regain remission even after combination of anti-retroviral therapy (cART] [17–19]. HPV is a DNA tumor virus that causes warts or benign papilloma, and persistent infection is associated with the development of cervical cancer [7]. It infects epithelial cells, integrates into host DNA, produces E6 and E7 oncoproteins, and disrupts tumor suppressor pathways to encourage the proliferation of cervical cancer cells [7]. It also plays a role in cancers of the skin, head, and neck [7]. The HPV vaccine is effective against HPV 16 and 18, but it does not protect against all high-risk HPV types and may not benefit women who are already infected [7].

Hepatitis C virus (HCV] and hepatitis B virus (HBV) together cause 80% of hepatocellular carcinoma cases [7]. Hepatitis C is an RNA virus that can infect liver cells and cause acute and chronic hepatitis [7]. Infection with hepatitis C virus can result in cirrhosis, which can then lead to primary hepatocellular carcinoma [7]. By contrast, hepatitis B is a DNA virus, but it can also cause acute and chronic hepatitis, which can lead to cirrhosis, liver failure, and hepatocellular carcinoma [7]. Both of these viruses could use new methods of treatment. Hepatitis C is not well suited to vaccines, because its genome mutates at a high rate and therefore it is able to escape elimination and immune recognition [7]. Hepatitis B does have a vaccine, but up to 10% are non-responders, and HBV infection still causes an estimated 1 million deaths annually [7,20]. HTLV-1 is an RNA retrovirus that has been linked to adult T-cell leukemia and a variety of chronic inflammatory diseases including [7]. Once tumor formation begins, progression is very rapid [7]. Chemotherapy for HTLV-1 associated adult T-cell leukemia can be at first beneficial but relapse is common and survival is an average of eight months [7].

There is a need for therapies that can block viral replication, and studies show that viral entry and release, and consequently replication, can potentially be blocked if membrane lipid composition is altered [21]. Additionally, modification of lipid metabolism may offer new possibilities for antiviral therapies [21].

#### *1.2. Diet and Obesity in Cancer*

Nutrition, diet, obesity, hyperlipidemia, hyperglycemia, and other modifiable risk factors such as lack of exercise, hypertension, and insulin resistance have been linked to type 2 diabetes, cardiovascular disease, hypertension, and several cancers such as breast, endometrial, pancreatic, kidney, gallbladder, colorectal, and ovarian cancers [22,23]. Obesity is a risk factor for breast cancer [23,24]. Overweight and obese breast cancer patients have an increased risk of lymph node metastasis, large tumors, and mortality [24]. There are several hypotheses as to why obesity is correlated with breast cancer. (A) The first is that the increased amount of adipose tissue in obese women means more peripheral aromatization of androgens, which causes higher levels of circulating estrogens (Figure 1) [24]. (B) Another hypothesis (Figure 1) is that obesity leads to higher levels of circulating insulin and insulin-like growth factor (IGF), which act as mitogens [24]. Extra adipose tissue releases additional non-esterified fatty acids, which leads to the development of insulin resistance [24]. Tissues are not able to efficiently absorb glucose and so the pancreas increases insulin secretion in both fasted and fed

states [24]. Insulin is necessary for cell growth and it promotes DNA synthesis [24]. It also increases levels of insulin-like growth factor 1 (IGF-1), which can target breast epithelial cell receptors and induce anti-apoptotic and mitogenic pathways [24]. Half of the breast tumors have been shown to overexpress the IGF-1 receptor, and inactivation of the receptor leads to diminished mammary tumor growth [24]. Insulin and IGF-1 promote angiogenesis, increase cell proliferation, and inhibit apoptosis [25].

**Figure 1.** Pathways that link breast cancer with obesity: Several consequences of obesity, such as insulin resistance, higher levels of circulating estrogen, and secreted cytokines play a role in the development of breast cancer. Insulin and IGF-1 promote proliferation and angiogenesis by activating the PI3K/Akt and Ras/Raf/MAPK pathways. Insulin also inhibits sex-hormone-binding globulin (SHBG), which binds testosterone and estradiol so there is increased free estradiol. Circulating estrogens promote growth of breast epithelial cells and lead to more proliferation and angiogenesis as well. Adipocytes can secrete pro-inflammatory cytokines which stimulate more lipolysis and further release of free fatty acids to promote cancer cell survival. T-bars in red denote inhibition.

Estrogen and the insulin/IGF-1 pathway work together (Figure 1) in breast epithelial cells to increase transcriptional activation of estrogen receptor (ER-α) and induce mitogenic responses [24]. Estrogen does this by stimulating resting breast epithelial cells in G0/G1 to re-enter the cell cycle and go through cell division [24]. This is mediated by c-Myc, a transcription factor that is induced with estrogen stimulation [24]. With higher levels of insulin and IGF-1, concentrations of sex-hormone binding globulin (SHBG) are reduced [24]. SHBG binds estradiol and testosterone, so a decrease in its levels leads to an increase in circulating estradiol [24]. SHBG binds to breast cancer cells to inhibit estradiol-induced cell proliferation, and incubation of breast cancer cells with SHBG before treatment with estradiol cancels out the anti-apoptotic effect of estradiol [24]. It has been shown that breast cancer risk is inversely correlated with blood levels of SHBG (Figure 1) [24]. (C) The third hypothesis is that adipocytes are like endocrine cells that secrete hormone-like molecules and cytokines [24]. When invasive tumors penetrate the basement membrane and tissue barriers, the adipocytes and breast cancer cells can simply participate in paracrine interactions [24]. Studies have shown that tumor growth can be directly influenced by adipose tissue; mice injected with adipose tissue and mammary carcinoma cell line SP1 developed tumors, but no tumor growth was observed with injection of SP1 far from any fat [24]. Furthermore, breast cancer cells that were treated with adipocyte-conditioned media upregulated proliferation and metastasis while also downregulating BRCA1-associated RING domain protein 1 (BARD1), a tumor suppressor, and p18, a cell-cycle checkpoint inhibitor [24].

Adipocytes secrete tumor necrosis factor-alpha (TNFα), an inflammatory cytokine [24]. Its expression is increased in obese rodent models and it inhibits the insulin receptor signaling pathway, thus assisting in the development of insulin resistance [24] (Figure 1). Adipocytes also secrete IL-6, high levels of which are associated with poor prognosis in breast cancer [24]. IL-6 production is also associated with the signals from prostaglandin PGE2, which induces DNA transcription for IL-6 synthesis [26–28] (Figure 1). IL-6 activates the mitogen-activated protein kinase (MAPK) pathway which promotes cell migration, and it inhibits the activation of proteases that are involved in apoptosis [24]. IL-6 also inhibits cell differentiation and promotes osteoclast formation and therefore promotes metastatic growth [24].  We found that the SUM1315MO2 and SUM149PT spheres were larger with differences in morphology, composition and their microenvironment [29]. We performed cytokine profiling of HMEC, SUM1315MO2 and SUM149PT conditioned media, which showed an abundance of inflammatory cytokines and chemokines such as interleukins IL-6, IL-8, and IL-17. Levels of survival kinases such as AKT, p44/42 MAPK, p65, and GSK3β were also, higher in breast cancer SUM1315MO2 and SUM149PT spheres when compared to HMEC spheres [29]. Our study [29] for the first time, revealed that osteoprotegerin (OPG) is secreted and expressed at very high levels from the SUM1315MO2 invasive breast cancer cell line, as well as the SUM149PT and SUM190PT inflammatory breast cancer cell lines when compared to healthy HMECs. Our study [29] demonstrated specific OPG staining in inflammatory breast cancer patient tumor sections. Interestingly, immunoprecipitation of breast cancer cell extracts by OPG antibody revealed lipid metabolic enzyme, fatty acid synthase (FASN), which is a key enzyme of the fatty acid biosynthetic pathway [30,31]. FASN controls the process of producing de novo fatty acids from carbohydrate and amino acid-derived carbon sources [32]. Adult body mass index (BMI) is a reflection of the accumulation of adipose tissue [33]. Obesity has also been linked with gastric cancer and its complications including gastroesophageal reflux, insulin resistance, high adiponectin, leptin, and an abnormally high blood level of IGF [34] (Figure 1).

#### **2. Lipid Synthesis in Human Cancers and Viral Infection Linked Cancers**

#### *2.1. Regulation of Lipids in Membrane Microdomains*

The goal of lipid synthesis is to convert carbons derived from nutrients into fatty acids, cholesterol, phosphoglycerides, eicosanoids, and sphingolipids [35,36]. Cancerous cells show an increased rate of lipid synthesis, which has several important functions [35,36]. Lipid (fatty acids) compositional complexity, versatility, repertoire, fluidity, and lipid asymmetry is very essential to determine the characteristics of the membrane, rafts or even cell per se [35–37]. The membranes include the organelles such as the mitochondria, Golgi and the endoplasmic reticulum [35–37]. Therefore, changing lipid properties can drastically affect biomembranes, their topology, spatial organization and overall cellular machinery [35,37]. Higher levels of lipid saturation in the cell membrane protect cancer cells from oxidative damage by reducing oxidative degradation of lipids and may inhibit chemotherapeutic drug uptake [35,36,38]. Breast cancer cells have less membrane fluidity because of the increased levels of lipids, and inhibition of their synthesis is associated with apoptosis and cell cycle inhibition [35–38].

Lipids also function as signaling molecules in cancer [35]. Phosphoinositides are lipid second messengers that relay signals to the cellular machinery from activated growth factor receptors [35,39]. Lysophosphatidic acid (LPA) is another lipid second messenger that binds to G-protein-coupled receptors and activates cell migration, proliferation, and survival [35,40]. Ceramides, which are involved in inducing apoptosis and arresting cell growth, are downregulated in cancer cells [35,41,42]. Conversely, sphingosine-1-phosphate (S1P), which promotes angiogenesis and cell growth, is upregulated in cancer cells [35,43–46]. Eicosanoids regulate inflammation and thus assist in tumor progression [35,47,48]. Lipids function in protein regulation as well [35]. Prenylation facilitates

the activity and localization of several signaling proteins [35,49,50]. Glycosylphosphatidylinositol (GPI) targets proteins to the outer layer of the plasma membrane [35]. Protein trafficking and localization requires different kinds of lipid anchors [35]. Association with membrane rafts is promoted by protein modification with saturated acyl chains [35]. In contrast, unsaturated fatty acids keep proteins out of cholesterol-rich membrane rafts. Regulation of growth factors is also associated with protein acylation [35,51]. Lastly, lipids are associated with autophagy—a self-destructive mechanism required under nutrient-poor conditions to remove dysfunctional components [35,52–54]. This allows for cancer cells to conserve their energy during nutrient limitation and therefore promotes cell survival [35]. The overexpression of lipid metabolism-related genes such as ATP-binding cassette transporter (*ABCA1*), acyl-coA synthetase long-chain family member 1 (*ACSL1*), 1-acylglycerol-3-phosphate *O*-acyltransferase 1 (*AGPAT1*) and stearoyl-CoA desaturase (Δ-9-desaturase) (*SCD*) has been proposed as a prognostic marker of stage II colorectal cancer (CRC) and is also called a ColoLipidGene signature [55]. Rectal adenocarcinoma (RAC), a common malignant tumor of the digestive tract is also linked to lipid peroxidation related oxidative stress, and plasmalogen alterations [56]. Pancreatic ductal adenocarcinoma (PDAC), a devastating disease is related to the intake of total fat, but especially of saturated and mono-unsaturated fatty acids (MUFAs) [57]. Given the functions of lipids in membrane structure, cell signaling, and post-translational modification of proteins, it is clear that lipids have vital roles that regulate the survival and proliferation of cancer cells [35,58]. We will focus primarily on aberrant cholesterol and fatty acid synthesis.

Lipids play an important role in viral infection, as they are the structural elements of cellular and viral membranes [21]. Viruses target lipid synthesis and signaling to remodel their host cells and generate lipids for the viral envelope [59]. Lipid interactions such as membrane fusion, envelopment, and remodeling are vital for viral replication, and compounds that affect lipids such as cholesterol and sphingolipids interfere with viral replication [21]. Viruses replicate inside the host cell, so they have to cross the host cell membrane for entry and exit [21]. Lipids have several roles in viral entry. They can function as direct and indirect viral receptors, as entry cofactors, and fusion cofactors [59]. Lipids are involved in viral replication in several ways. They have a role in phosphoinositide signaling to reorganize the membrane or bind viral proteins [59]. Viruses may also generate lipids at sites of replication by promoting lipid biosynthesis [59]. By inducing lipid metabolism, viruses exploit the energy in lipid stores during their replication [59]. Viruses can induce autophagy to degrade lipid droplets and release lipids, which are oxidized in mitochondria to generate ATP [59]. In addition to providing energy, lipid droplets can aid in viral assembly and budding [59]. Lipids may also facilitate viral exit by use of the VLDL secretion machinery [59]. Knockdown of apolipoproteins ApoE and ApoB decreased the amount of secreted infectious virus [59].

Hepatitis C affects lipid metabolism and uses it to its advantage throughout the infectious cycle [60]. An increase in lipid droplets has been found in liver biopsies of infected patients [60]. EBV-encoded latent membrane protein 1 (LMP1) has been shown to promote cell proliferation and progression of nasopharyngeal carcinoma via activation of SREBP1-mediated lipogenesis [61]. Short-chain fatty acids (SCFA) stimulate the two related human gamma-herpesviruses to enter the lytic cycle through different pathways of chromatin remodeling [62]. EBV LMP1 has been shown to reorganize membrane lipid rafts and cytoskeleton microdomains to modulate phosphatidylinositol 3-kinase (PI3K) and its downstream target, Akt signal transduction [63].

In patients infected with human T lymphotropic virus (HTLV-1), significantly higher levels of VLDL and triglycerides were detected [64,65].It has also been found that disruption of lipid rafts can lead to a decrease in infection by HTLV-1 [66]. HTLV-1 encoded Tax1 protein has been associated with the accumulation of cellular alterations that promote leukemia in infected HTLV-1-infected individuals [66]. The cytoplasmic Tax1 protein persistently resides in the Golgi-associated lipid raft microdomains and Tax1 directs lipid raft translocation of IKK through selective interaction with IKKγ [67–69]. Depletion of IKKγ impairs Tax1-directed lipid raft recruitment of IKKα and IKKβ suggesting that Tax1 actively recruits IKK to the lipid raft microdomains for continuous/sustained NF-κB activation and contributes to HTLV-1 infection linked tumorigenesis [67–69].

#### *2.2. Association of Lipid Pathways with Glycolysis, Fatty Acid Synthesis, and Glutaminolysis*

High-throughput RNA sequencing demonstrated significant changes in genes involved in overlapping lipid-related functions and/or glucose metabolism disorder in KS lesions [70]. KSHV infection has been shown to induce glycolysis, glutaminolysis, and fatty acid synthesis pathways, for the survival of latently infected endothelial cells [71]. KSHV infection of primary endothelial cells utilizes the host lipid raft-dependent macropinocytosis pathway and endosomal sorting complexes required for transport (ESCRT)-0 proteins for entry [72,73]. KSHV ORF45, a viral protein in the tegument layer, connecting capsid and envelope, associates with lipid rafts of host cellular membrane triggering KSHV budding for final envelopment and virion maturation [74–76]. Lipid metabolism plays a vital part in KSHV infection and therefore may be used as a drug target [12,76]. KSHV can manipulate lipid biosynthesis in a host cell to promote viral infection and tumorigenesis in several ways [12,76]. Lipids play a role in the initial infection, survival and proliferation, reactivation, and angiogenesis of KSHV infected cells [12,76]. Studies have shown that lipids are important for the survival of KSHV-infected cells and that they have higher rates of fatty acid synthesis and aerobic glycolysis than primary B cells [12,76]. Reprogramming of cholesteryl ester metabolism has been demonstrated to be involved in regulating neo-angiogenesis and metastasis in KSHV infected endothelial cells [77]. KSHV stabilized hypoxia-inducible factors (HIFs) has been reported to play a critical role in KSHV latency, reactivation and metabolic reprogramming (carbohydrate, lipid, and amino acids) [78].

#### **3. Cholesterol Synthesis in Human Cancers and Viral Infection Linked Cancers**

Cholesterol has vital physiological roles such as controlling membrane fluidity and using cell signaling to regulate cell growth, proliferation, and migration [79]. It is also a precursor for steroid hormones which activate nuclear receptors to control inflammation and immune functions [79]. Cholesterol is transported from the liver to cells through the bloodstream in a low-density lipoprotein (LDL) bound form [80]. Cells take-up the LDL using clathrin mediated endocytosis and the endocytic pathway transports it to lysosomes where it is hydrolyzed to free cholesterol molecules [80]. The cholesterol molecules are then taken to the membrane-bound organelles and the cell membrane [80].

Cholesterol levels are tightly regulated in the body [79]. The key regulators are sterol regulatory element-binding protein transcription factor 2 (SREBF2) and liver x receptors (LXR; LXRα and LXRβ) [80]. Levels of endoplasmic reticulum (ER) cholesterol are used to sense for intracellular cholesterol homeostasis [80]. If there is a decrease in ER cholesterol, SREBF2 is translocated from the ER to Golgi to the nucleus to activate gene transcription for cholesterol synthesis [80]. Conversely, if there is an increase in cholesterol levels, its synthesis is shut down and its export is facilitated by activation of LXR receptors [80]. LXRs are sterol-sensitive transcription factors of the nuclear receptor superfamily. LXRs regulate the expression of several genes involved in the uptake, transport, efflux, and excretion of cholesterol in a tissue-dependent manner. These are also crucial regulators of the reverse cholesterol transport pathway and subsequently whole-body cholesterol content [81].

There are several signaling pathways that activate cholesterol synthesis in cancer cells [80]. Intracellular cholesterol levels are promoted by the activation of PI3K/AKT signaling, which induces cholesterol synthesis by activating the SREBP transcription factor, the regulator of cholesterol synthesis encoding genes [80] (Figure 2). SREBP is activated by inhibiting mTORC1 dependent ABCA1 mediated cholesterol export and activating LDL receptor-mediated cholesterol import pathway [80]. Promotion of cholesterol synthesis by the AKT/mTORC1/SREBP pathway contributes to cell growth, bone metastases, and cancer aggressiveness [80] (Figure 2).

**Figure 2.** Pathways that lead to an accumulation of cholesterol: Activation of SREBP transcription factor induces cholesterol. This is induced by activation of PI3K/Akt/mTOR signaling, cancer gene RAS, and dysregulation by TP53. The accumulation of cholesterol in the cell promotes survival and inhibits apoptosis. T-bar in red denotes inhibition.

Cholesterol synthesis is also activated through TP53, a frequently mutated gene in cancer [80]. In breast cancer, the cholesterol synthesis pathway is unregulated by the loss of TP53 function [80]. This disrupts the breast tissue architecture and induces proliferation [80]. In studies where the mutant TP53 was knocked down, the morphology of the breast cancer cells changed from the disorganized back to a normal phenotype [80] (Figure 2).

In cancer cells, high levels of mitochondrial cholesterol lead to resistance to apoptotic signals [80]. Cholesterol import into the mitochondria is regulated by the two proteins steroidogenic acute regulatory (STAR) and STAR-related lipid transfer domain containing 3 (STARD3) [80].  HER2 is a membrane tyrosine kinase and oncogene, when activated it provides the cell with potent proliferative and anti-apoptosis signals, and confers aggressiveness to breast cancers. Higher levels of STARD3 also decrease the adhesiveness of breast cancer cells, which promotes metastases [80]. Another gene that controls cholesterol homeostasis is ABCA1, a cell membrane cholesterol exporter, and it is dysregulated in cancer cells [80]. Lower levels of ABCA1 increase mitochondrial cholesterol levels and promote cancer cell survival [80]. Growing tumors have been found to have 3-fold lower levels of ABCA1 expression as opposed to normal cells [80].

High cholesterol is a risk factor for several pathologies and is associated with the development of cancer [79]. It has been observed that cholesterol promotes cell proliferation and migration [79]. It accelerates the formation of tumors, enhances tumor angiogenesis, and increases their aggressiveness [23]. Cholesterol is also associated with chemotherapy resistance [79]. In breast cancer tumors, higher plasma cholesterol levels are associated with higher expression of cyclin D1, an oncogenic driver [23]. Furthermore, high cholesterol content in lipid rafts is associated with higher rates of cell survival in prostate cancer cells [23]. Lipid rafts are implicated in Akt activation, which then phosphorylates pro-apoptotic proteins and inactivates them [82]. Cholesterol is a major component of cellular and mitochondrial membranes; the inhibition of cholesterol synthesis may inhibit the formation of new membranes demanded by proliferating tumor cells [83]. It was observed that although lipogenesis was upregulated in cancer cells, during tumor development, plasma cholesterol levels were reduced [23]. This suggests that transformed cells may utilize more cholesterol than normal cells; thus regulation of the cholesterol synthesis pathway may limit cellular proliferation [84]. Cholesterol metabolic pathways

are required for the replication, secretion, and entry of HCV and drugs targeting cholesterol metabolic pathways have potential in treating HCV infection [85]. Cholesterol has also been identified as a critical factor for EBV latent membrane protein 2A trafficking and protein stability as it regulates LMP2 phosphorylation and ubiquitination [86]. Treatment of cells with methyl-beta-cyclodextrin (MβCD), which depletes cholesterol from the plasma membrane, increased LMP2A levels, its secretion in exosomes and blocked LMP2A endocytosis resulting in LMP2A abundance in the plasma membrane [86].

The mevalonate (MVA) pathway, which leads to the production of cholesterol, can be dysregulated in tumor cells [87] (Figure 3). Many of the downstream products are required for protein synthesis, membrane integrity, signaling, and cell-cycle progression, and are therefore critical in cell proliferation [84]. Higher levels of enzymes in the MVA pathway are associated with rapid progression and poor prognosis in cancer patients, and treatment with mevalonate promotes proliferation of breast cancer cells and tumor growth [82,87] (Figure 3). This pathway is upregulated by mutated p53, a tumor suppressor protein [84]. The increased proliferation rates are associated with a faster entry of cells through the G1 restriction point and into S phase [88]. These cells have more activating phosphorylation of cyclin-dependent kinase-2 (CDK-2), which controls initiation of DNA synthesis and replication, and decreased inhibitory binding of CDK-2 to p21, a regulator of the G1 restriction point [88]. The disruption of this pathway in malignant cells may result in the inhibition of cell-cycle progression and reduce proliferation and metastasis of cancer cells [84].

**Figure 3.** Mevalonate pathway as an important metabolic pathway: The mevalonate pathway is regulated by HMG CoA reductase (HMGCR), and this enzyme is targeted by statins to decrease plasma cholesterol. Fibrates target the mevalonate pathway by inhibiting acetoacetyl coenzyme A. This reverses the effects of cholesterol to inhibit cell proliferation and trigger apoptotic parameters. Downregulating the pathway also suppresses production of farnesyl pyrophosphate and geranylgeranyl phosphate to inhibit the invasive properties of cancer cells. T-bars in red denote inhibition.

Hepatitis C virus requires geranylgeranylation, a metabolite of the mevalonate pathway, to allow binding of viral protein NS5A to viral cofactor FBL2 [89,90]. In the mevalonate pathway, simvastatin interferes with the activity and localization of EBV latent membrane protein 1 LMP-1 to induce apoptosis [91]. KSHV viral microRNAs (miRNAs) have been shown to target 3-Hydroxy-3 methylglutaryl-coenzyme A (CoA) synthase 1 (HMGCS1), 3-hydroxy-3-methylglutaryl-CoA reductase, enzymes in the mevalonate/cholesterol pathway [89]. Addition of 25-hydroxycholesterol to primary cells inhibited KSHV infection suggesting that KSHV miRNAs decrease the level of 25-hydroxycholesterol and promote viral infection [89].

## **4. Fatty Acid Synthase (FASN) in Cancers and Viral Infection-Associated Cancers**

Besides cholesterol, triacylglycerol can also be synthesized from acetyl-CoA [92] (Figure 4). Malonyl-CoA is produced from acetyl-CoA via acetyl-CoA carboxylase (ACC), which is converted to Palmitate. Palmitate is lengthened to form stearate by enzyme fatty acid elongase (Elovl #1–7) [92] (Figure 4). Fatty acid elongation primarily occurs in the endoplasmic reticulum and uses malonyl-CoA and fatty acyl-CoA as substrates. Another endoplasmic reticulum-bound enzyme is stearoyl-CoA desaturase (SCD), which catalyzes the desaturation of saturated palmitoyl- and stearoyl-CoA, those are converted to palmitoleoyl- and oleoyl-CoA, respectively [92] (Figure 4). Multiple drugs targeting lipid pathways are in clinical trials. Antitumor synergy has been observed in vitro and in vivo combining A939572 with an mTOR inhibitor in ccRCC and A939572 is a small molecule that specifically inhibits SCD1 enzymatic activity [93–95]. Similarly, T-3764518, a novel and orally available small molecule inhibitor of SCD1 showed promising antitumor effects in colorectal cancer HCT-116 cells and their growth and mesothelioma [96,97].

**Figure 4.** Triacylglycerols are synthesized from acetyl-CoA. Acetyl-coA is metabolized to malonyl-CoA via acetyl-CoA carboxylase, which in turn is converted to Palmitate, the principal product of the fatty acid synthase system in animal cells. Palmitate is lengthened to form stearate by enzyme fatty acid elongase. Stearate, a saturated fatty acid is subsequently metabolized by stearoyl-CoA desaturase enzyme, that forms a double bond in stearoyl-CoA, leading to the monounsaturated fatty acid oleic acid. Carnitine palmitoyltransferase (CPT), the enzyme in the outer mitochondrial membrane, converts long-chain acyl-CoA species to their corresponding long-chain acyl-carnitines for transport into the mitochondria. CPT induces mitochondrial β-oxidation, which is a complex pathway involving energy metabolism. FASN inhibits CPT with resultant inhibition of fatty acid oxidation. T-bar in red denotes inhibition.

FASN, the enzyme responsible for de novo fatty acid synthesis, is expressed at higher levels in breast, prostate, colon, and ovarian cancer cells as opposed to normal human mammary epithelial cells (HMEC) [98]. We analyzed the breast tissue sections of healthy subjects and breast cancer patients for the presence of FASN by immunofluorescence staining using anti-FASN antibody (unpublished results). Abundant FASN expression was detected in breast cancer tissue sections (Figure 5A,B,D,E) compared to the normal healthy control tissue sections (Figure 5C,F). We next evaluated the fold change in FASN expression in all 32 sections by densitometry analysis using ImageJ software. A 0–2, 2–4, 4–6-fold induction in FASN expression was observed in 14, 12 and 6 tumor sections, respectively. Collectively, these results highlight the presence of PPARα expression in human breast cancer tissues.

**Figure 5.**  Magnification for the panels is 4×.

Our previous studies show larger numbers of lipid droplets in SUM1315MO2 (invasive breast cancer cell line) and SUM149PT cells (inflammatory breast cancer cell lines) when compared to HMEC using electron microscopy, immunofluorescence, and fluorescence quantitation [30]. SUM1315MO2 and SUM149PT also had higher expression of FASN when compared to HMEC using immunoblotting and immunofluorescence staining [30]. In addition, Western blot analysis showed higher levels of cyclooxygenase-2 (COX2), which is important in cancer progression and inflammation, in SUM1315MO2 and SUM149PT cells [30]. To investigate the importance of FASN and COX2 in cancer cell progression, these two enzymes were blocked with C75 (FASN inhibitor) and/or celecoxib (COX2 inhibitor) and the results showed a reduction in the amount of lipids formed per cell as well as decreased levels of cell survival proteins p-Erk and p-GSK3β [30]. Since high levels of FASN leads to more lipogenesis, which causes more lipids to be integrated into membrane lipid rafts, activating membrane receptor tyrosine kinases and resulting in oncogenic signaling pathways [98]. Therefore, fatty acid synthase is associated with poor prognosis in patients with cancer [99]. It has been associated with clinically more aggressive cancers; in stage I breast cancer, there was a four-fold increase in mortality risk associated with the expression of fatty acid synthase in two studies [100]. FASN is correlated with peritumoral lymphatic vessel invasion and inversely correlated with breast cancer survival [101].

Fatty acid synthase is an attractive therapeutic target because it is expressed at low or undetectable levels in normal tissues because fatty acids are supplied by diet [100]. In contrast, since FASN is overexpressed in malignant cells, a FASN inhibitor would target the cancerous cells while leaving the normal cells unaffected [100]. It is also restricted solely to fatty-acid synthesis, unlike the other lipogenic enzymes. For example, acetyl-CoA carboxylase is the rate-limiting enzyme of fatty-acid synthesis but it is also widely distributed in muscle [100]. In colon cancer cells, inhibition of fatty acid synthase inhibited S-phase progression and DNA replication [100].

Preclinical evaluation of novel FASN inhibitors in primary colorectal cancer cells (CRCs) and a patient-derived xenograft model of colorectal cancer showed that anti-tumor activity was primary due to a significant decrease in the activation of Akt and Erk1/2 oncogenic pathways in CRCs [102]. Treatment with a FASN inhibitor led to the arrest of cell growth and apoptosis of breast tumor cells, further reinforcing the role of FASN in tumorigenesis [101]. FASN inhibition does this by upregulating pro-apoptotic genes BCL2 Interacting Protein 3 (BNIP3) and tumor necrosis factor related apoptosis-inducing ligand (TRAIL) [101]. BNIP3 induces apoptosis by mitochondrial dysfunction [101]. TRAIL has been found to be inversely correlated with FASN expression, which demonstrates that TRAIL is a component of the apoptotic pathway caused by FASN inhibition [101]. FASN can be inhibited using FASN small interfering RNA (siRNA). FASN inhibition by siRNA increases ceramide synthesis, which upregulates BNIP3 and TRAIL [101].

Cerulenin is another inhibitor of fatty acid synthase, and treatment demonstrated a cytotoxic property that was proportional to fatty acid synthase [100]. In other words, Cerulenin was selectively toxic and induced apoptosis in cancer cells but not normal cells in vitro [100]. Cerulenin is, however chemically unstable and therefore a more stable inhibitor of fatty acid synthase is needed as an anticancer agent [100].

Hepatitis B interferes with lipid metabolism as well [103]. Fatty acid synthase is upregulated in HBV infected cells, and many studies have shown that HBV promotes the synthesis of fatty acids and cholesterol [103]. Lipid metabolism is altered during EBV infection as well [8]. Fatty acid synthase expression is increased in infected cells to engage de novo synthesis of palmitate, instead of relying on dietary fatty acids as healthy cells do [8] (Figure 6). This is confirmed by the findings that tumorigenesis in EBV infected cells driven by fatty acid synthase can be blocked by using fatty acid synthase inhibitors [8]. Moreover, treatment of infected cells with a fatty acid synthase inhibitor reduces cell survival [12]. Inhibition of key enzymes such as acetyl-CoA carboxylase also leads to apoptosis in infected cells [12]. EBV immediate-early (IE) protein BRLF1 activates expression of the host fatty acid synthase through a p38 stress mitogen-activated protein kinase and induces a lytic form of EBV replication [104].

**Figure 6.**

KSHV infection also increases peroxisome biogenesis, and the proteins involved in the peroxisomal metabolism of very long chain fatty acids are critical for the survival of infected cells [105]. Reactivation is induced by short chain fatty acids, and angiogenesis is promoted by the vitamin D receptor pathway and increased cholesteryl ester synthesis [12].

#### **5. Arachidonic Acid Pathway Metabolites in Cancers and Viral Infection-Associated Cancers**

Many cancers have been shown to have aberrant metabolism of arachidonic acid [106–110]. Arachidonic acid is a fatty acid found in the cellular membrane [106] (Figure 7). It is metabolized to eicosanoids through 3 pathways: the cytochrome P450 monooxygenase (ω-hydroxylases and epoxygenases), the cyclooxygenases (COX-1 and COX-2), and the lipoxygenase (5-LO, 12-LO, 15-LOa,

15-LOb) pathways [106]. Metabolism by these pathways generates eicosanoids, such as leukotrienes and prostaglandins, which are pro-inflammatory and promote tumor growth [106–110] (Figure 7). Besides these eicosanoids are the epoxyeicosatrienoic acid (EETs), which are generated via the conversion of arachidonic acid by CYP epoxygenases and are mainly metabolized by soluble epoxide hydrolase (sEH). EETs, lipid signaling molecules are autocrine and paracrine mediators of cell proliferation, migration, inflammation, and angiogenesis in several tissues [111,112]. Pro-inflammatory eicosanoids are highly expressed in cancer cells and can promote tumor progression by inducing the secretion of growth factors by epithelial cells, inducing angiogenesis by binding receptors on stromal cells, regulating apoptosis, cell migration and proliferation by activating receptors on tumor epithelial cells and promoting a microenvironment that supports tumor growth [106].

**Figure 7.** Arachidonic acid cascade: Arachidonic acid is a fatty acid that is freed from cellular membranes by phospholipase A2. It can then be metabolized to prostanoids such as thromboxanes (TXA2), prostacyclin (PGI2) and prostaglandins PGD2, PGE2, and PGF2a through the cyclooxygenase (COX) pathway. The lipoxygenase (LO) pathway along with hydrolases converts arachidonic acid into leukotrienes, hydroxyeicosatetraenoic acids (HETEs), and lipoxins (LXA4 and LXB4). Lastly, the cytochrome P450 monooxygenase pathway converts arachidonic acid into epoxyeicosatrienoic acids (EETs).

The 5-LO expression is promoted by pro-inflammatory stimuli, and it is constitutively expressed in cancers including breast, prostate, lung, colon, and esophagus [106] (Figure 7). Inhibition of this pathway may reduce tumor growth and metastases, and have shown this in mouse models of human breast, esophageal, colon, and skin cancer [106–110]. The COX enzyme has two isoforms: COX1, which is ubiquitously expressed, and COX2, which is inducible [113] (Figure 7). COX2 is highly expressed during tumor formation and in areas of inflammation, and increased expression is associated with decreased rates of survival among patients with mesothelioma, breast, prostate, pancreas, liver, stomach, lung, and esophagus cancers [106–110] (Figure 7). Mammary angiogenesis and tumorigenesis is reduced in rodent models when COX2 is knocked out [113].

Both COX enzymes catalyze the ultimate production of prostaglandin E2 (PGE2), which upregulates the production of aromatase in fat cells and therefore more estrogen production, which promotes tumor cell proliferation [106]. PGE2 also upregulates NFkB activity, which is involved in inhibiting apoptosis and upregulates anti-apoptotic protein Bcl2 via Ras-MAPK signaling [106]. PGE2 has immunosuppressive effects as well: it upregulates immunosuppressive TH2 cytokines while downregulating anti-tumor TH1 cytokines [106]. It suppresses natural killer cells, which have anti-tumor activity, by inhibiting CD8+T activity [106]. A negative correlation has been shown between prostaglandin levels in breast tumors and survival [114].

PGE2 and leukotriene B4 (LTB4) promote angiogenesis by inducing vascular endothelial growth factor (VEGF), fibroblast growth factor 2 (FGF2), and chemokines CCL2 and CXCL1 [106]. They also shift the normal tissue microenvironment to one that supports tumor growth by exacerbating inflammation by recruiting more leukocytes into the tissue from the circulation [106].

Non-steroidal anti-inflammatory drugs (NSAIDs) inhibit COX and therefore have anti-inflammatory effects. These have been reported to reduce the risk of tumors in breast, lung, colon, and prostate cancers [106–110]. However, prolonged use of NSAIDs has cardiovascular and gastrointestinal side effects [106]. One way to avoid these may be to use antagonists of PGE2 receptors to inhibit the growth of tumors, which has been shown to have an effect in colon, lung, esophageal, and breast cancer animal models [106]. The cyclooxygenase-2-prostaglandin E2-eicosanoid receptor inflammatory axis has been demonstrated to play a key role in KSHV associated malignancies [115]. KSHV can activate parts of the lipoxygenase pathway such as leukotrienes and 5-lipoxygenase to aid in initial infection [12,116].

Concurrent inhibition of COX-2 and soluble epoxide hydrolase (sEH) using PTUPB, an orally bioavailable COX-2/sEH dual inhibitor results in antitumor, anti-angiogenic activity and has organ-protective effects [117]. PTUPB has the potential for future combination chemotherapy partner for cisplatin [117].

#### **6. Use of Statins in Cancers, Viral Infections and Associated Cancers**

Statins are competitive inhibitors of 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR) and are referred to as cholesterol-lowering drugs as these regulate the rate-limiting step of the cholesterol synthesis pathway [118–120]. Statins target the MVA pathway by inhibiting HMGCR and reducing the levels of mevalonate and ultimately reducing plasma cholesterol [121] (Figure 3). HMGCR regulates the mevalonate pathway, and when upregulated, promotes tumor growth in breast cancer cells [122]. High levels of HMGCR are correlated with poor survival outcomes [122]. It has also been shown that HMG CoA reductase can induce growth in breast epithelial cells, independent of Anchorage [35]. Statins, therefore, reduce metastatic potentials and improve the survival rates in metastatic breast cancer mouse models [122,123]. Statins induce apoptosis of many cancer cell types and block pathways driving cell division in several types of leukemias and lymphomas [118–120]. Furthermore, an inverse correlation between cancer risk and statin use has been shown [124]. Studies have shown that statins reduce the incidence of breast cancer, colorectal cancer, prostate cancer, and cholangiocarcinoma [79]. Statins have been associated with lower risk of gastric cancer (due to weakening H. pylori infection), reduced hepatocellular carcinoma risk, and lower risk of melanoma [123,125,126]. By suppressing the mevalonate pathway, statins inhibit cell proliferation, trigger apoptotic parameters, and are powerful anti-inflammatory agents [121]. Another effect of blocking the mevalonate pathway is the regulation of the production of geranylgeranyl and farnesyl pyrophosphates, which modify the Rho and Ras -GTPases [127]. Rho proteins are associated with the invasive and proliferative properties of cancer cells [127]. Statins suppress the expression of matrix metalloproteinases (MMPs) and very late antigens (VLAs), and subsequently inhibit tumor growth and spontaneous metastasis [123]. They also induce cell apoptosis by inhibiting PI3K/Akt signaling [127,128] and produce anti-proliferative effects on lung, liver, colorectal, and prostate tumors [124]. Statins also modulate MAPK and CDK2, which reduce the expression of p21 and p27 cyclin kinase inhibitors, which regulate proliferation and apoptosis of tumor cells [127].

Studies in rodents show that lipophilic statins such as simvastatin, lovastatin, and fluvastatin have a protective effect on the growth of several tumor types by decreasing mevalonate synthesis [88]. Lovastatin arrests the cell cycle at the G1 phase by inducing cell cycle inhibitors p21 and p27, and decreases transition to the S and G2/M phases [83]. Lovastatin treated breast cancer cells show higher levels of caspase activity, which is consistent with apoptosis initiation [83]. Furthermore, cytochrome c was released by a reduction in the mitochondrial membrane potential and Bax translocation into the mitochondria [83]. Fluvastatin increases the rate of apoptosis and reduces breast tumor proliferation [129]. Simvastatin in particular has been shown to reduce the risk of several different cancer types, including breast cancer [125]. In mice that were treated with simvastatin, tumor growth and proliferation were inhibited and reduced [127]. Simvastatin is also associated with a reduced risk of breast cancer recurrence [4]. In bile duct cancer cells, simvastatin suppresses cell proliferation by inducing G1 phase cell cycle arrest [130]. It also activates caspase-3, downregulates

Bcl-2 expression, and enhances Bax expression, which consequently induces apoptosis in bile duct cancer cells [130]. In prostate cancer, simvastatin suppresses metastasis by inhibiting TGF-β1, which promotes tumor progression and is associated with poor outcomes [131]. Statins were reported to prime cancer cells for apoptosis and worked in synergy with an inhibitor of BCL2 called venetoclax, and demonstrated better clinical responses compared to venetoclax alone [118–120]. In breast cancer cells, simvastatin affects several signal transduction pathways and changes expression of Akt, NFkB, BclXL, and PTEN [127]. Simvastatin reduces cell viability through reduction of raft formation, which consequently downregulates survival kinase Akt [82]. Akt promotes cell survival and inactivates pro-apoptotic proteins, and is therefore characteristic of malignant tumors [82]. Simvastatin blocks phosphorylation of Akt, which inhibits Akt activation [127]. Simvastatin inhibits NFkB transcriptional activity, which subsequently decreases expression of anti-apoptotic protein BclXL and increases levels of phosphatase and tensin homolog (PTEN), a tumor suppressor protein [127] (Figure 8). Akt regulates many transcription factors, including NFkB, which is constitutively active in breast cancer cells [127]. NFkB induces expression of several anti-apoptotic proteins including BclXL [127]. Breast cancer cells have higher levels of BclXL, and simvastatin inhibits its transcription by targeting NFkB [127]. In breast cancer cells, a mutation or deletion in PTEN increases levels of PI3 kinase product PIP3 [127], which consequently upregulates Akt activity [127]. Activation of phosphatidylinositol 3 kinase (PI3 kinase) and its target Akt kinase is associated with the anti-apoptotic properties of cancer cells [127]. PI3 kinase is negatively regulated by phosphatase and tensin homolog (PTEN), a tumor suppressor protein [127]. PTEN controls the levels of the product of PI3 kinase: PI 3, 4, 5-triphosphate (PIP3), which activates Akt kinase [127]. Simvastatin increases expression of PTEN to reduce activation of Akt by targeting NF-κB to inhibit its repression of PTEN expression and therefore inhibit breast cancer cell proliferation [127]. Simvastatin thus increases transcription of phosphatase and tensin homolog (PTEN), which suppresses the oncogenic phosphatidylinositol-3-kinase (PI3K) pathway and reduces expression of the anti-apoptotic protein bcl-XL [129].

Two other pathways simvastatin uses to suppress cell growth and induce apoptosis are the MAPK/ERK pathway and the JNK/CHOP/DR5 pathway [132]. Simvastatin suppresses tumor growth by suppressing the MAPK/ERK pathway, which promotes cancer cell survival and metastasis [132]. Simvastatin also activates the JNK/CHOP/DR5 pathway, which induces apoptosis in breast cancer cells [129]. Simvastatin has been reported to inhibit the activity of HBV minichromosome maintenance (MCM) 7 complex, an important host factor aiding virus genome replication in host cells by increasing the phosphorylation of eIF2α, which is mediated by the liver kinase B1 (LKB1)-AMP-activated protein kinase (AMPK) signaling pathway [133]. Statin use may have potential protective effects and reduce the risk for hepatocellular carcinoma (HCC) in HBV-infected patients in a dose-dependent manner [134].

Treatment with statins, which downregulate the mevalonate pathway, has been shown to block hepatitis C virus [89]. Statin treatment affected cell cycle, apoptosis and alternative splicing genes between the native (B-lymphocytes) and EBV transformed cells human lymphoblastoid cells (LCLs) [135]. Simvastatin has been shown to delay the development of EBV-lymphomas in severe combined immunodeficiency mice models and could be considered for the treatment of EBV-lymphomas [136]. Anti-HCV effects or reduced HCV replication via Ceestatin, a drug that binds to 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) synthase and irreversibly inhibits HMG-CoA synthase in a dose-dependent manner are reversed by addition of HMG-CoA, mevalonic acid, or geranylgeraniol [137]. Side effects of statins include acute renal failure, myopathy, myoglobinuria, and hepatotoxicity [138]. Also, increased risk of diabetes mellitus and muscle pain has been observed [79]. Recent studies have found an increased risk of cancer after use of pravastatin, which caused an increase of mevalonate synthesis in extrahepatic tissues and thus promoted the growth of breast cancer cells. No such association has been found with use of lipophilic statins such as simvastatin, lovastatin or fluvastatin [88].

**Figure 8.** Statin therapy and downstream consequences: Statins block transcription of NFkB. This decreases expression of anti-apoptotic BclXL and inhibits cancer cell proliferation. Inhibition of NFkB also increases expression of pro-apoptotic phosphatase and tensin homolog (PTEN). PTEN inhibits PI3 kinase, and therefore also inhibits the target of PI3 kinase, Akt kinase, which are both associated with the anti-apoptotic properties of cancer cells. T-bars in red denote inhibition.

#### **7. Use of Fibrates (PPAR**α **Agonist) in Cancers, Viral Infections and Associated Cancers**

Fibrates are an effective and safe group of hypolipidemic drugs, frequently used to treat patients with atherogenic dyslipidemia and have shown tremendous potential in cardiovascular outcomes [139–141]. Fibrates also have benefits in carbohydrate metabolism, adipokines levels, thrombosis, atherosclerosis (hardening of arteries), heart attack, stroke and inflammation [139–141]. Fibrates such as fenofibrate can inhibit triglyceride (TG) synthesis via reducing the availability of free fatty acids by promoting β-oxidation and also has anticancer agent potential [139–141]. Fibrates are well-known ligands of peroxisome proliferator-activated receptor α (PPARα). PPARs are ligand-activated transcription factors that modulate lipid metabolism and are involved in transcription of genes that regulate cell proliferation [24,98]. This family of nuclear receptors is expressed in tumors cells as well as in tumor endothelium [142]. PPARα is a particularly important transcriptional regulator of inflammatory and metabolic processes, which means its agonists can be used to treat dyslipidemia [98]. Functional activity of PPAR is maintained by histone deacetylase (HDAC), co-repressors/co-activators, retinoid X receptors (RXRs), RNA polymerase II, histone acetyltransferases (HATs), phosphorylation and dephosphorylation [139–141].

PPARα ligands suppress cancer cell growth in several lines, including breast, colon, and skin [142]. They also suppress the metastasis of melanoma cells [142]. PPARα ligands can induce apoptosis of endothelial cells and inhibit proliferation and migration [142]. PPARα ligands produce anti-inflammatory effects by inhibiting NOS, COX-2, and tumor necrosis factor TNFα, and reducing inflammation in tumors may be associated with inhibition of cell growth and improved prognosis [142]. Moreover, PPARα deficiency suppresses angiogenesis by producing excess thrombospondin (TSP-1) and also prevents tumor growth [142]. Given these reasons, PPARα ligands could potentially be tumor-preventing agents. Fibrates in particular target the MVA pathway by inhibiting acetoacetyl coenzyme A [143,144] (Figure 3).

Clofibrate inhibits cell growth by changing the levels of checkpoint kinases, cell cycle inhibitors, and tumor suppressors [98]. In breast cancer cells, higher levels of PPARα are found when compared to HMEC cells [98]. In these cells, Clofibrate reduces the amount of fatty acid synthase, inhibits growth, and reduces survival kinases [98].  Clofibrate, in turn, downregulates COX-2 as well as 5-LO inflammatory pathway components [98]. In rodents, the use of fibrates for prolonged periods of time can cause peroxisome proliferation, which leads to hepatomegaly and tumor formation. As this has not been demonstrated in humans, a low dose of fibrates for a short duration of time can be used [98]. PPAR agonists have also been associated with weight gain [24].

#### **8. Other Lipid Metabolites and Pathways in Cancer and Viral Infections**

Beside the pathways and metabolites mentioned here, there are many other very important lipid pathways mediating enzymes and metabolites. Gangliosides along with other glycosphingolipids, phospholipids, and cholesterol in glycolipid-enriched microdomains present at the outer leaflet of the plasma membrane interact with signaling molecules including receptor tyrosine kinases and signal transducers and contribute to activation of cell signaling, increasing cell proliferation and migration, as well as tumor growth [145].

Phospholipase D (isoenzymes PLD1 and PLD2) catalyzes the hydrolysis of cell membrane phospholipids and plays the role in several cancers and infectious diseases [146]. PLD inhibitors have been proposed as novel compounds for the treatment of cancers, neurodegenerative disorders, and viral infections [147]. PLD activity has been linked with promoting survival and metastatic phenotype of malignant prostate cancer cells [148] and PLD inhibitors reduce human prostate cancer cell proliferation and colony formation [149]. HPV infection, the key risk factor for the development and progression of cervical cancer utilizes PLD and Phosphatidic acid (PA) and regulate the PI3K/AKT/mTOR pathway [150].

ATP citrate lyase (ACLY) is involved in lipid during membrane biogenesis (fatty acid synthesis) linked with aerobic glycolysis. ACLY catalyzes the conversion of citrate to oxaloacetic acid (OAA) and acetyl-CoA [151]. Acetyl-CoA is the acetyl donor for lysine acetylation that links metabolism, signaling, and epigenetics. ACLY overexpression and activation increase metabolic activity in proliferating cells via activation of Akt signaling in glioblastoma, colorectal cancer, breast cancer, non-small cell lung cancer, and hepatocellular carcinoma etc. [152]. There are varying reports on the effect of ACLY depletion on cancer cells. Few suggest that genetic deletion of ACLY does not kill cells but these cells start proliferating at the impaired rate by utilizing exogenous acetate for de novo lipogenesis and histone acetylation [153]. There are studies in breast cancer, which suggest that depletion of ACLY suppressed breast tumor growth and progression [151]. Low molecular weight cleaved form of cyclin E, a powerful independent predictor of survival in women with progressed breast cancer, was shown to interact with ACLY and promote aberrant lipid metabolism pathways in breast cancer tumorigenesis [154]. ACLY has been implicated in integrin signaling in glioblastoma leading to cell adhesion and migration required for its progression and metastasis [155]. ACLY activation during hepatitis B virus (HBV) has been reported to be involved in the disturbed lipid metabolism and proliferation of hepatocytes [156]. ACSS2-mediated acetyl-CoA synthesis from acetate has been shown to take over ACLY functions during HCMV infection, which has been associated with malignancies including colon cancer, malignant glioma, prostate carcinoma, and breast [157–160].

#### **9. Summary**

Diet and obesity play an important role in breast, prostate, gastric, lung and skin cancer development, and increased lipogenesis has been observed in tumor cells of virally infected hosts [23,161]. This may be caused by disruptions in the same signaling pathways responsible for the oncogenic transformation of cancer cells [161]. The challenge, therefore, is to discover novel, non-toxic therapies that target these essential steps in the development of lipid/obesity-associated cancers. One such pathway, the mevalonate pathway, can be dysregulated in cancer cells and lead to poor prognosis in cancer patients, as cholesterol has been shown to accelerate tumor formation [23,87]. Statins target this pathway by inhibiting HMGCR, which reduces plasma cholesterol and has to been shown to ultimately reduce cancer risk [121,124]. Another possible therapeutic target is fatty acid synthase, which is expressed at high levels in cancer cells and leads to more lipogenesis and oncogenic signaling pathways [98]. Treatment with a FASN inhibitor increases apoptosis and decreases cell growth in cancer cells [101]. PPARs are another modulator of lipid metabolism and regulate cell proliferation [24,98]. PPARα ligands, fibrates, suppress breast cancer cell growth [98]. Lastly, abnormal metabolism of arachidonic acid in cancer cells leads to high levels of pro-inflammatory eicosanoids, which promote tumor progression [106]. NSAIDs inhibit the inflammatory COX pathway and have been reported to reduce the risk of cancer [106]. Since viral infection associated cancers also heavily utilize lipid metabolism for their growth and progression, therefore there could be common targets which could be tested to combat viral cancers. Numerous studies discuss and show promising results targets for NSAIDs [116,162–168], mTOR pathway regulators [169–172], and FASN inhibitors [75] in viral infection associated cancers but additional in vivo studies need to be done to fully exploit these pathways. These studies support the further development of statins, FASN inhibitors, fibrates, and NSAIDs as potential anticancer agents.

**Funding:** We are grateful to funding from H. M. Bligh Cancer Research Laboratories and NIH-funded grant R01CA192970 to NSW. The funders had no role in the design, decision to publish, or preparation of the manuscript. All authors have read the journal's policy on conflicts of interest.

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **Abbreviations**