ArticleTranslational andrology and urology2020
Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis.
Article in Translational andrology and urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Integrative Meta-Analysis and WGCNA Reveal Candidate Diagnostic Hub Genes in Clear Cell Carcinoma.International journal of cell biology · 2026Article
- Diagnostic and prognostic relevance ofLeukemia research reports · 2025Article
- Angiopoietin-like protein 4 dysregulation in kidney diseases: a promising biomarker and therapeutic target.Frontiers in pharmacology · 2024Review
- Identification and Validation of Cytotoxicity-Related Features to Predict Prognostic and Immunotherapy Response in Patients with Clear Cell Renal Cell Carcinoma.Genetics research · 2024Article
- Identification of key genes and signalling pathways in clear cell renal cell carcinoma: An integrated bioinformatics approach.Cancer biomarkers : section A of Disease markers · 2024Article
- An Early Neoplasia Index (ENI10), Based on Molecular Identity of CD10 Cells and Associated Stemness Biomarkers, is a Predictor of Patient Outcome in Many Cancers.Cancer research communications · 2023Article
- Expression ofFrontiers in genetics · 2021Article
- The Choice of Candidates in Survival Markers Based on Coordinated Gene Expression in Renal Cancer.Frontiers in oncology · 2021Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClear cell renal cell carcinoma (ccRCC) is the most common malignant tumor of kidney with high mortality. The pathogenesis of ccRCC is complicated and effective prognostic predictors for clinical practice are still limited. This study aimed to identify significant genes with prognostic influence in ccRCC via bioinformatics analysis.
methodsFour gene expression profiles were acquired from the Gene Expression Omnibus (GEO) database, including 168 ccRCC tissues and 143 normal tissues. Common differentially expressed genes (DEGs) between ccRCC tissues and normal kidney tissues were screened out. Then gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were investigated. Protein-protein interaction (PPI) network of the common DEGs was diagrammed and analyzed. Kaplan-Meier analysis was conducted to identify genes with prognostic influence in ccRCC. Gene Expression Profiling Interactive Analysis (GEPIA) was finally applied to validating differential expression of genes.
resultsNinety-nine common DEGs between ccRCC tissues and normal kidney tissues were eventually screened out (P<0.05, |log FC| >2). GO functional analysis showed that the down-regulated genes were enriched in excretion, negative regulation of cell proliferation, heparin binding and cellular response to BMP stimulus, etc. KEGG pathway analysis indicated that the common DEGs were particularly enriched in HIF-1 signaling pathway and aldosterone-regulated sodium reabsorption. Seven core DEGs were distinguished through PPI network analysis, of which 6 core genes
conclusionsOur study shows that high expression of
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