ArticleComputational and structural biotechnology journal2017
A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer.
Article in Computational and structural biotechnology journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Systematically identification of survival-associated eQTLs in a Japanese kidney cancer cohort.PLoS genetics · 2025Article
- Bioinformatics analysis and validation of CSRNP1 as a key prognostic gene in non-small cell lung cancer.Heliyon · 2024Article
- What can we learn about acid-base transporters in cancer from studying somatic mutations in their genes?Pflugers Archiv : European journal of physiology · 2024Review
- XGBG: A Novel Method for Identifying Ovarian Carcinoma Susceptible Genes Based on Deep Learning.Frontiers in oncology · 2022Article
- Article
- An Ovarian Cancer Susceptible Gene Prediction Method Based on Deep Learning Methods.Frontiers in cell and developmental biology · 2021Article
- Identification of seven-gene signature for prediction of lung squamous cell carcinoma.OncoTargets and therapy · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
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
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Registered trials
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