Cholesterol metabolism-related genes predict immune infiltration and prognosis in gastric cancer patients.

Abstract

Background: Gastric cancer (GC) is one of the most prevalent malignant diseases worldwide. Abnormal metabolic reprogramming, particularly cholesterol metabolism, influences tumor development and treatment outcomes. This study investigates the predictive and functional significance of cholesterol metabolism-related genes in gastric cancer patients. Methods: Clinical and gene expression data related to cholesterol metabolism in gastric cancer were analyzed using datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A predictive signature was developed and validated using LASSO, Cox regression, and the GSE26889 cohort, followed by evaluation with Kaplan-Meier analysis. A nomogram was constructed by integrating the signature with clinical factors and ssGSEA for immunological analysis. The role of NPC2 was investigated using western blot, qPCR, and cellular assays. Results: We conducted a bioinformatics analysis of 50 genes associated with cholesterol metabolism in gastric cancer. Using the GEO and TCGA datasets, we identified 28 genes with differential expression in gastric cancer patients. Subsequent COX univariate and LASSO regression analyses of these 28 DEGs identified five genes (APOA1, APOC3, NPC2, CD36, and ABCA1) as independent prognostic risk factors. We then constructed a risk model for cholesterol metabolism genes, revealing that survival was worse in the high-risk group compared to the low-risk group, with more severe case staging outcomes. We conducted a comparative analysis of immune cells between the high-risk and low-risk groups, revealing distinct variations in immune cell type expression. We then developed a model using a correlation nomogram to illustrate these conclusions. We further examined the biological characteristics of NPC2. Immunohistochemistry and qPCR results showed that NPC2 exhibited significant protein and mRNA expression in gastric cancer tissues. We used siRNA technology to suppress NPC2, resulting in reduced viability, proliferation, and invasion capacity of gastric cancer cells, as determined by CCK-8, colony formation, wound healing, and Transwell assays. Conclusion: A risk signature comprising five cholesterol metabolism-related genes was constructed using bioinformatics to estimate outcomes and therapeutic responses in gastric cancer patients. The results suggest that NPC2 may serve as a novel biomarker for gastric cancer patients.