Discussion
hypoxia, tumor, gastric cancer
TUMOR, HYPOXIA, DISEASE, MALIGNANT TUMOR, INFILTRATING, REGRESSION, GASTRIC CANCER
With the application of immunotherapy to GC, the treatment of GC has entered a new era. However, not all patients with GC can benefit from immunotherapy. Several studies have attempted to identify better immune-related characteristic genes that affect the prognosis of patients but have not been very successfulWCGNA analysis was used to identify 14 gene modules. The evaluation of the correlation between each module and the patient's clinical characteristics showed that among all modules, the magenta module had the highest correlation with the subtype. Therefore, 1104 genes in the magenta module were defined as immune subtype-related genes. Subsequently, these 1104 immune subtype-related genes were used for univariate Cox regression analysis, and 52 prognosis-related genes were identified to establish immune-related signatures. In the total cohort, we fitted 98 prognosis prediction signatures. For each signature, we calculated the C-index value in the total, test 1, and test 2 cohorts, and the signature with the highest average C-index value was considered the best signature. Among all the signatures, StepCox [backward] + Enet [alpha = 0.7] showed the highest average C-index value (0.726). This signature, named in this study as IRS, is composed of 24 genes: A large number of prognosis prediction signatures based on machine learning have been reported in recent literature. In terms of research, these signals have diversity, such as pyroptosis, cuproptosis, ferroptosis, EMT conversion, hypoxia, metabolism, aging, and immune responseSubsequently, we processed the tumor site single-cell sequencing data of patients with GC and identified 2634 malignant tumor cells. Upon cell dimension reduction and visualization, we noticed that the number of T cells is the largest among immune cells. Recent studies emphasize that several types of tumor infiltrating lymphocytes (TIL) are associated with better disease outcomes for various human cancersIn our study, we used a combination of various machine learning algorithms to construct immune-related prognostic signatures for gastric cancer, and validated the stability and effectiveness of the immune-related signature (IRS) using multiple datasets. We compared IRS with 93 previously published prognostic signatures, and demonstrated that IRS was the most effective prognostic signature. Through the evaluation of IRS, doctors can better understand the patient's prognosis and consider it in their treatment plan, helping to develop more personalized treatment plans and maximize the patient's survival rate. Additionally, we further discovered the predictive value of IRS for the response to immune checkpoint therapy, which is based on the patient's immune gene expression profile and can predict the patient's response to immunotherapy. In clinical practice, the application of IRS can providing more accurate and personalized guidance for patient treatment and management. Importantly, we further revealed cellular communication between high-risk and low-risk T cells at the single-cell level, which provides important reference value for the study of tumor immune microenvironment in gastric cancer patients. However, our study still has some shortcomings. IRS needs to be re-validated in real-world cohorts, and the combination of IRS with clinical features that affect patient prognosis may further improve accuracy. In summary, IRS is a promising tool for clinical prognosis prediction and immune therapy decision-making for GC patients.