Metabolic reprogramming and immune microenvironment profiling in clear cell renal cell carcinoma: implications for prognosis, targeted therapy, and drug resistance

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of kidney cancer, distinguished by intricate interactions between metabolic reprogramming, immune microenvironment dynamics, and genetic mutations. In this detailed investigation, we analyzed the ccRCC cohort from The Cancer Genome Atlas (TCGA) alongside 81 metabolic signaling pathways from the KEGG database. By utilizing Gene Set Variation Analysis (GSVA), we performed hierarchical clustering of patients based on their metabolic pathway activity profiles, identifying three distinct clusters with notable differences in pathway activity and survival outcomes. Cluster 1 displayed high metabolic activity and more favorable survival outcomes, while Cluster 3 was characterized by low metabolic activity and poorer prognosis. Clinical comparisons revealed significant disparities in gender, histological stage, and survival status, with Cluster 3 exhibiting a higher proportion of patients at advanced stages and those who had passed away. Genetically, Cluster 1 showed the highest mutation burden, with prominent mutations in genes such as VHL and PBRM1. Biological process analysis indicated that pathways like organic carboxylic acid metabolism and ATP synthesis were upregulated in Cluster 1 but suppressed in Cluster 3. Machine learning models (GBM, CoxBoost, and LASSO regression) enabled the identification of four pivotal genes-BCAT1, IL4I1, ACADM, and ACADSB-which were subsequently used to construct a multifactorial Cox regression model. This model successfully stratified patients into high- and low-risk groups, correlating with marked differences in immune activities. The high-risk group showed elevated expression of chemokines, TNF, and HLA molecules. Drug sensitivity analysis suggested that AKT inhibitor III was more effective in the low-risk cohort, while Bortezomib might be more beneficial for high-risk patients. Additionally, a clinical prediction model integrating risk scores and clinical factors demonstrated strong predictive power for patient survival. Methylation profiling of the core genes via the UALCAN platform revealed distinct epigenetic signatures in ccRCC, providing deeper insight into the disease's molecular mechanisms. This study contributes to a more comprehensive understanding of ccRCC and proposes valuable directions for personalized treatment strategies and enhanced patient management.