Integrated Multi-Omics Analysis Unveils Distinct Molecular Subtypes and a Robust Immune–Metabolic Prognostic Model in Clear Cell Renal Cell Carcinoma

作者：Yilin Zhu, Shihui Yu, Dan Yang, Yu Tian, Yi Liu, Wenlong Du · 发表于：International Journal of Molecular Sciences · 年份：2025 · DOI：10.3390/ijms26073125 · 被引用次数：5 · 研究领域：Renal cell carcinoma treatment、Ferroptosis and cancer prognosis、Epigenetics and DNA Methylation

Clear cell renal cell carcinoma (ccRCC) is characterized by significant clinical and molecular heterogeneity, with immune and metabolic processes playing crucial roles in tumor progression and influencing patient outcomes. This study aims to elucidate the molecular subtypes of ccRCC by employing non-negative matrix factorization (NMF) clustering on differentially expressed genes (DEGs), thereby identifying distinct transcriptional profiles, immune cell infiltration patterns, and subsequent survival outcomes. Utilizing NMF clustering, we identified two molecular subtypes of ccRCC. We developed a prognostic model using LASSO-Cox regression, validated with multiple datasets and quantitative reverse transcription polymerase chain reaction (qRT-PCR), incorporating ten immunity- and metabolism-related genes (IMRGs) for overall survival (OS) prediction. Immune cell infiltration and tumor mutational burden (TMB) analyses were performed to explore differences between high- and low-risk groups, while Gene Set Enrichment Analysis (GSEA) provided insights into relevant biological pathways. The findings revealed that subtype C1, characterized by a "cold" tumor microenvironment, correlates with better prognostic outcomes compared to subtype C2, which exhibits an immunologically active environment and worse survival prospects. High-risk patients demonstrated poorer OS associated with alterations in immune and metabolic pathways. Immune checkpoint analysis indicated the upregulation of CTLA4...