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.

Bioinformatics Analysis of Pivotal Module and Biomarkers Related to the Prognosis of Breast Cancer Based on Single-cell Transcriptome Data

“…In recent reports, the high expression of PCDH12was found to be associated with the high pathological grade of papillary renal cell carcinoma [32]. As a new type of tumor suppressor gene, SLIT3 has been reported to play a role in breast, liver, lung, and colon cancer, and the promoter methylation of SLIT3 has been reported to be associated with tumor occurrence and progression [33,34].…”

Metabolite Sensing GPCRs: Promising Therapeutic Targets for Cancer Treatment?

“…GPR4 appeared up-regulated in cholangiocarcinoma, down-regulated in cervical and lung cancers, and increased or decreased in kidney tumors depending on the kind of cancer [150]. Analysis centered in particular cancers revealed the up-regulated expression of GPR4 in head and neck squamous cell carcinoma [151], in renal cell carcinoma [152], in colorectal cancer [153] and in hepatocellular carcinoma [154] and, in most of these studies, its high expression correlated with late stage tumors and poor overall survival [152][153][154][155].…”

Identification of hub genes in bladder cancer based on weighted gene co‐expression network analysis from TCGA database

“…11 WGCNA is used by studying the relationship between tissue microarray data and clinical features to identify possible biomarkers for predicting relevant cancers and comparing differentially expressed genes and studying the interactions between genes in different modules. 12 In our study, the RNA sequencing (RNA-seq) profile data of MIBC was downloaded from the Cancer Genome Atlas (TCGA) database.…”

“…Weighted gene co‐expression network analysis (WGCNA) 7 is a systems biology tool for characterizing gene expression patterns in samples and has been widely used in the analysis of various cancers, 8 such as colorectal cancer, 9 non‐small‐cell lung cancer (NSCLC), 10 and breast cancer 11 . WGCNA is used by studying the relationship between tissue microarray data and clinical features to identify possible biomarkers for predicting relevant cancers and comparing differentially expressed genes and studying the interactions between genes in different modules 12 …”