Metabolic heterogeneity and survival outcomes in papillary renal cell carcinoma: insights from multi-datasets and machine learning analyses.

ABSTRACT:

Background

Renal cell carcinoma is characterized by immune and metabolic alterations. These metabolic reprogramming processes enhance tumor cell proliferation and infiltration. The purpose of this study was to investigate the characteristics of metabolism-related molecules and to identify potential prognostic biomarkers in kidney renal papillary renal cell carcinoma (KIRP).

Methods

We conducted a comprehensive analysis of metabolism-related genes using weighted gene co-expression network analysis and differential expression analysis. Subsequently, we constructed a metabolism-related signature (MRS) by integrating 90 machine learning algorithms. Based on Cox regression analyses, we developed a predictive nomogram. Functional enrichment analysis, genomic variant analysis, chem

SUBMITTER: Hu J 

PROVIDER: S-EPMC12465287 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature