ArticleCancer cell international2018
Identification of biomarkers of chromophobe renal cell carcinoma by weighted gene co-expression network analysis.
Article in Cancer cell international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 29 citations in OpenAlex.
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- Prognostic Values of E2F1/2 Transcriptional Expressions in Chromophobe Renal Cell Carcinoma Patients: Evidence from Bioinformatics Analysis.International journal of general medicine · 2021Article
- A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer.Frontiers in oncology · 2021Article
- Development of a novel gene signature to predict prognosis and response to PD-1 blockade in clear cell renal cell carcinoma.Oncoimmunology · 2021Article
- Weighted gene co-expression network analysis identifiesAnnals of translational medicine · 2020Article
- Article
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- Exceptional Response of Metastatic Chromophobe Renal Cell Carcinoma to Vascular Endothelial Growth Factor (VEGF) Inhibitors: Should Increased VEGF-C Expression Be Used to Guide Treatment?Case reports in urology · 2019Article
- Clinical and Expression Significance of AKT1 by Co-expression Network Analysis in Endometrial Cancer.Frontiers in oncology · 2019Article
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundChromophobe renal cell carcinoma (ChRCC) is the second common subtype of non-clear cell renal cell carcinoma (nccRCC), which accounting for 4-5% of renal cell carcinoma (RCC). However, there is no effective bio-marker to predict clinical outcomes of this malignant disease. Bioinformatic methods may provide a feasible potential to solve this problem.
methodsIn this study, differentially expressed genes (DEGs) of ChRCC samples on The Cancer Genome Atlas database were filtered out to construct co-expression modules by weighted gene co-expression network analysis and the key module were identified by calculating module-trait correlations. Functional analysis was performed on the key module and candidate hub genes were screened out by co-expression and MCODE analysis. Afterwards, real hub genes were filter out in an independent dataset GSE15641 and validated by survival analysis.
resultsOverall 2215 DEGs were screened out to construct eight co-expression modules. Brown module was identified as the key module for the highest correlations with pathologic stage, neoplasm status and survival status. 29 candidate hub genes were identified. GO and KEGG analysis demonstrated most candidate genes were enriched in mitotic cell cycle. Three real hub genes (SKA1, ERCC6L, GTSE-1) were selected out after mapping candidate genes to GSE15641 and two of them (SKA1, ERCC6L) were significantly related to overall survivals of ChRCC patients.
conclusionsIn summary, our findings identified molecular markers correlated with progression and prognosis of ChRCC, which might provide new implications for improving risk evaluation, therapeutic intervention, and prognosis prediction in ChRCC patients.
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