# Integrative Meta‐Analysis and WGCNA Reveal Candidate Diagnostic Hub Genes in Clear Cell Carcinoma

## TL;DR

This study identifies potential diagnostic genes for clear cell kidney cancer by combining genetic data and analyzing gene networks.

## Contribution

The study introduces a novel integrative approach combining meta-analysis and WGCNA to identify diagnostic hub genes in ccRCC.

## Key findings

- 4651 differentially expressed genes were identified, enriched in metabolic pathways and immune response.
- 21 hub genes were identified through WGCNA, with 17 showing diagnostic potential (AUC > 0.7).
- Nine of the hub genes were found to have significant prognostic relevance.

## Abstract

Clear cell renal cell carcinoma (ccRCC) remains poorly understood at the genetic level. In this study, 12 GEO microarray datasets were combined, and meta‐analysis and multiparameter gene prioritization were used to reveal candidate diagnostic biomarkers. Improved preprocessing and analysis were used to identify differentially expressed genes (DEGs), which led to 4651 DEGs enriched in metabolic pathways, immune response, and kidney development. Key transcription factors (TFs), including ZNF692, ZNF395, and ZNF582, were identified. Utilizing WGCNA, 21 hub genes were identified, and diagnostic potential was determined via ROC analysis. Seventeen of the genes with AUC > 0.7 were validated as diagnostic hub genes, including ALDH2, ACADM, KIF11, and PTPRC. Nine of these genes showed significant prognostic relevance. The current work identifies key biomarkers that can enhance ccRCC diagnosis and guide future therapy.