Correlating Transcriptional Networks to Papillary Renal Cell Carcinoma Survival: A Large-Scale Coexpression Analysis and Clinical Validation.

Abstract

We aimed to investigate the potential mechanisms of progression and identify novel prognosis-related biomarkers for papillary renal cell carcinoma (PRCC) patients. The related data were derived from The Cancer Genome Atlas (TCGA) and then analyzed by weighted gene coexpression network analysis (WGCNA). The correlation between each module and the clinical traits were analyzed by Pearson’s correlation analysis. Pathway analysis was conducted to reveal potential mechanisms. Hub genes within each module were screened by intramodule analysis, and visualized by Cytoscape software. Furthermore, important hub genes were validated in an external dataset and clinical samples. A total of 5,839 differentially expressed genes were identified. By using WGCNA, we identified 21 coregulatory gene clusters based on 289 PRCC samples. We found many modules were significantly associated with clinicopathological characteristics. The gray, pink, light yellow, and salmon modules served as prognosis indicators for PRCC patients. Pathway enrichment analyses found that the hub genes were significantly enriched in the cancer-related pathways. With the external Gene Expression Omnibus (GEO) validation dataset, we found that PCDH12, GPR4, and KIF18A in the pink and yellow modules were continually associated with the survival status of PRCC, and their expressions were positively correlated with pathological grade. Notably, we randomly chose PCDH12 for validation, and the results suggested that the PRCC patients with higher pathological grades (II + III) mostly had higher PCDH12 protein expression levels compared with those patients in grade I. These validated hub genes play critical roles in the prognosis prediction of PRCC and serve as potential biomarkers for future personalized treatment.