Discussion
obesity, high-grade prostate cancer, RP, prostate malignancies, cancer, prostate, PCa, high-grade, prostate tumor, hypertriglyceridemia, metabolic syndrome, TG, prostate cancer, dyslipidemia
OBESITY, HYPERLIPIDEMIA, PROLIFERATION, CANCER, DISEASE, PROSTATE CANCER RECURRENT, COMPLICATIONS, PROSTATE, PROSTATE TUMOR, HYPERTRIGLYCERIDEMIA, METABOLIC SYNDROME, PROSTATE CANCER, TUMORIGENESIS, DYSLIPIDEMIA
PCa is a complicated disease characterized by phenotypes, and each patient appears to have an “individual” process. The lifestyle and nutritional preferences of an individual could influence the development of PCa. There was strong evidence that obesity, hyperlipidemia, and other components of metabolic syndrome significantly elevate the risk of developing prostate cancer [Although the relationship between dyslipidemia and PCa has been extensively explored, the underlying mechanism remains unclear. Many hypotheses have been offered to explain the association between lipids and the growth and development of PCa. Activation of the sterol regulatory element-binding protein pathway by androgens promoted the expression of lipogenic genes, which stimulated the accumulation of neutral lipids (triglycerides and cholesteryl esters) and tumorigenesis, thereby stimulating prostate tumor growth [Multiple studies indicated that elevated LDL levels were associated with an increased risk of cancer. LDL promoted the proliferation, migration, and invasion of prostate cancer cells by inducing JAK1/JAK2/STAT3 activation and upregulated the expression of several oncogenic gene products [In contrast to our findings, some studies discovered that lower LDL levels in RP patients were an independent predictor of prostate cancer recurrence [According to our investigation into the relationship between TG and prostate malignancies, we found that elevated levels of serum TG were associated with a higher risk of developing prostate cancer. The metabolism of TG produces vital fatty acids [Additionally, hypertriglyceridemia was characterized by the presence of small dense LDL particles and cholesterol-rich remnant lipoproteins (RLP), which were the products of the hydrolysis of chylomicrons and VLDL [Lipids were routinely checked by clinical patients, and this was a simple procedure that patients readily accept. We found that the ROC curve study revealed that the prediction model 2 was significantly superior to model 1, PCPT-CRC, and two domestic prediction models, suggesting that lipids plus the common clinical indicators (tPSA, f/tPSA, PSAD, DRE, TRUS) were much more predictive of prostate cancer patients than the common clinical indicators alone, which not only improved the biopsy rate of patients with prostate cancer but also reduced the need for unnecessary invasive testing. In consideration of the potential risks associated with false-negative prostate cancer and complications of prostate biopsy, we determined a cut-off value for AUC when the Jorden Index reached its optimal level. The cut-off value of AUC in the training group was determined to be 0.502. The sensitivity, specificity, positive predictive value, negative predictive value, false negative rate, and false positive rate were determined to be 74.03%, 82.76%, 78.3%, 25.97%, and 17.24%, respectively. Through the incorporation of a threshold value of 0.502 in the training group, our prediction model successfully prevented 95.25% of unnecessary prostate biopsies with 38.96% of missed positive cases. Based on our findings, it is recommended to perform a prostate biopsy when the prediction probability exceeds 0.502, while active monitoring may be considered for cases below this threshold.Despite these promising results, our study is not without limitations: (1) this study was retrospective, hence, selection bias was inevitable; (2) The sample size of the single-center study was modest, and a subsequent multicenter cooperative study was required to confirm the conclusion; (3) The prediction model lacked external validation and follow-up external multicenter data were required; (4) A risk calculator developed and validated in this study was only used to predict prostate cancer on initial prostate biopsy, but failed to distinguish between high-grade and low-grade prostate cancer. In our further studies, we would work in predicting the model of high-grade prostate cancer (defined as a Gleason score sum of 7 or higher).In summary, the main objective of our study was to evaluate the diagnostic efficacy of combining blood lipid levels and clinical symptoms in the diagnosis of prostate cancer. We established an individualized nomograph prediction model to help identify patients with prostate cancer at an early stage and provide individualized risk calculation to reduce unnecessary puncture biopsies.