ArticleOncology letters2018
Bioinformatics and functional analyses of key genes and pathways in human clear cell renal cell carcinoma.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- A systematic investigation of clear cell renal cell carcinoma using meta-analysis and systems biology approaches.Molecular genetics and genomics : MGG · 2024Pooled it
- Integrative Meta-Analysis and WGCNA Reveal Candidate Diagnostic Hub Genes in Clear Cell Carcinoma.International journal of cell biology · 2026Article
- PROM1 and EFTUD2 Expression in High-Grade Clear Cell Renal Cell Carcinoma as a Molecular Marker for Survival Rate.International journal of molecular sciences · 2025Article
- Screening of common genomic biomarkers to explore common drugs for the treatment of pancreatic and kidney cancers with type-2 diabetes through bioinformatics analysis.Scientific reports · 2025Article
- Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications.Scientific reports · 2024Article
- Article
- Article
- Article
- Identification of CXCL10 as a Prognostic Biomarker for Clear Cell Renal Cell Carcinoma.Frontiers in oncology · 2022Article
- The Role of CXCL12 in Kidney Diseases: A Friend or Foe?Kidney diseases (Basel, Switzerland) · 2021Review
- The Outer Retinal Membrane Protein 1 Could Inhibit Lung Cancer Progression as a Tumor Suppressor.Computational and mathematical methods in medicine · 2021Article
- Identification of Seven Cell Cycle-Related Genes with Unfavorable Prognosis and Construction of their TF-miRNA-mRNA regulatory network in Breast Cancer.Journal of Cancer · 2021Article
- Expression and Prognostic Significance of Cadherin 4 (CDH4) in Renal Cell Carcinoma.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Cell Adhesion-Related Molecules Play a Key Role in Renal Cancer Progression by Multinetwork Analysis.BioMed research international · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. The present study was conducted to explore the mechanisms and identify the potential target genes for ccRCC using bioinformatics analysis. The microarray data of GSE15641 were screened on Gene-Cloud of Biotechnology Information (GCBI). A total of 32 ccRCC samples and 23 normal kidney samples were used to identify differentially expressed genes (DEGs) between them. Subsequently, the clustering analysis and functional enrichment analysis of these DEGs were performed, followed by protein-protein interaction (PPI) network, and pathway relation network. Additionally, the most significant module based on PPI network was selected, and the genes in the module were identified as hub genes. Furthermore, transcriptional level, translational level and survival analyses of hub genes were performed to verify the results. A total of 805 genes, 403 upregulated and 402 downregulated, were differentially expressed in ccRCC samples compared with normal controls. The subsequent bioinformatics analysis indicated that the small molecule metabolic process and the metabolic pathway were significantly enriched. A total of 7 genes, including membrane metallo-endopeptidase (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.