Background: Rates of renal cell carcinoma (RCC) occurrence and mortality are steadily rising. In an effort to address this issue, the present bioinformatics study was developed with the goal of identifying major lipid metabolism biomarkers and immune infiltration characteristics associated with RCC cases.
Methods: The Cancer Genome Atlas (TCGA) and E-MTAB-1980 were used to obtain matched clinical and RNA expression data from patients diagnosed with RCC. A LASSO algorithm and multivariate Cox regression analyses were employed to design a prognostic risk model for these patients. The tumor immune microenvironment (TIME) in RCC patients was further interrogated through ESTIMATE, TIMER, and single-cell gene set enrichment analysis (ssGSEA) analyses. Gene Ontology (GO), KEGG, and GSEA enrichment approaches were further employed to gauge the mechanistic basis for the observed results. Differences in gene expression and associated functional changes were then validated through appropriate molecular biology assays.
Results: Through the approach detailed above, a risk model based on 8 genes associated with RCC patient overall survival and lipid metabolism was ultimately identified that was capable of aiding in the diagnosis of this cancer type. Poorer prognostic outcomes in the analyzed RCC patients were associated with higher immune scores, lower levels of tumor purity, greater immune cell infiltration, and higher relative immune status. In GO and KEGG enrichment analyses, genes that were differentially expressed between risk groups were primarily related to the immune response and substance metabolism. GSEA analyses additionally revealed that the most enriched factors in the high-risk group included the stable internal environment, peroxisomes, and fatty acid metabolism. Subsequent experimental validation in vitro and in vivo revealed that the most significantly differentially expressed gene identified herein, ALOX5, was capable of suppressing RCC tumor cell proliferation, invasivity, and migration.
Conclusion: In summary, a risk model was successfully established that was significantly related to RCC patient prognosis and TIME composition, offering a robust foundation for the development of novel targeted therapeutic agents and individualized treatment regimens. In both immunoassays and functional analyses, dysregulated lipid metabolism was associated with aberrant immunological activity and the reprogramming of fatty acid metabolic activity, contributing to poorer outcomes.
Keywords: biomarker; immune infiltration; lipid metabolism; renal cell carcinoma; risk model.
Copyright © 2023 Zhang, Lin, Chen, Ye, Huang, Chen and Li.