A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer
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Updated: Feb 18, 2026
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Mary Qu Yang1,2, Dan Li1,2, William Yang3
1Joint Bioinformatics Graduate Program, Department of Information Science, George W. Donaghey College of Engineering and Information Technology, University of Arkansas at Little Rock, USA.
Abstract:
Clear cell renal cell carcinoma (ccRCC) is the most common and most aggressive form of renal cell cancer (RCC). The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1, as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways.
Insights
This study identifies key mutated genes driving pathway dysregulation in clear cell renal cell carcinoma (ccRCC), a common kidney cancer. The findings offer new insights into ccRCC molecular mechanisms and potential therapeutic targets.
Area of Science:
- Oncology
- Genomics
- Systems Biology
Background:
- Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive kidney cancer subtype.
- Increasing incidence and poorly understood pathogenesis highlight the need for novel molecular insights.
- Identifying driver genes and dysregulated pathways is crucial for improving ccRCC patient outcomes.
Purpose of the Study:
- To develop and apply a systems biology approach for prioritizing somatic mutated genes in ccRCC.
- To identify causative mutations and key disease genes driving pathway alterations in ccRCC.
- To provide a new perspective on the molecular mechanisms underlying ccRCC.
Main Methods:
- Integrated multi-layer information including transcriptome and protein-protein interaction data.
- Identified differential gene modules associated with ccRCC using gene expression and network analysis.
- Performed gene module-based eQTL analysis to pinpoint somatic mutated driver genes.
Main Results:
- Prioritized a list of candidate ccRCC driver genes, including known genes (BAP1, PBRM1) and novel candidates (NOD2, RRM1, CSRNP1, SLC4A2, TTLL1, CNTN1).
- Identified differential gene modules and their associated driver genes, offering insights into ccRCC molecular mechanisms.
- Validated findings in independent ccRCC patient datasets.
Conclusions:
- The study presents a novel systems biology method for prioritizing disease-associated genes and pathways in ccRCC.
- The identified gene modules and driver genes enhance understanding of ccRCC pathogenesis.
- This approach can aid in discovering new therapeutic targets for ccRCC.
