ArticleOncology letters2016
Identification of genes associated with renal cell carcinoma using gene expression profiling analysis.
Article in Oncology letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Genome-wide meta-analysis identifies new candidate genes for sickle cell disease nephropathy.Blood advances · 2023Pooled it
- Exploring the Potential of Malvidin and Echiodinin as Probable Antileishmanial Agents Through In Silico Analysis and In Vitro Efficacy.Molecules (Basel, Switzerland) · 2025Article
- Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications.Scientific reports · 2024Article
- Integrated mendelian randomization analyses highlight AFF3 as a novel eQTL-mediated susceptibility gene in renal cancer and its potential mechanisms.BMC cancer · 2024Article
- Article
- System biology approaches identified novel biomarkers and their signaling pathways involved in renal cell carcinoma with different human diseases.Bioscience reports · 2022Article
- Identification of a neural development gene expression signature in colon cancer stem cells reveals a role foriScience · 2022Article
- Network-based transcriptomic analysis identifies the genetic effect of COVID-19 to chronic kidney disease patients: A bioinformatics approach.Saudi journal of biological sciences · 2021Article
- Therapeutically Targeting Cancers That Overexpress FOXC1: A Transcriptional Driver of Cell Plasticity, Partial EMT, and Cancer Metastasis.Frontiers in oncology · 2021Review
- PATZ1 Is Overexpressed in Pediatric Glial Tumors and Correlates with Worse Event-Free Survival in High-grade Gliomas.Cancers · 2019Article
- The Clinical Value of PELP1 for Breast Cancer: A Comparison with Multiple Cancers and Analysis in Breast Cancer Subtypes.Cancer research and treatment · 2019Article
- Genetic alteration of histone lysine methyltransferases and their significance in renal cell carcinoma.PeerJ · 2019Article
- The transformics assay: first steps for the development of an integrated approach to investigate the malignant cell transformation in vitro.Carcinogenesis · 2018Article
- FOXC1 in cancer development and therapy: deciphering its emerging and divergent roles.Therapeutic advances in medical oncology · 2017Review
- The POZ/BTB and AT-Hook Containing Zinc Finger 1 (PATZ1) Transcription Regulator: Physiological Functions and Disease Involvement.International journal of molecular sciences · 2017Review
- FOXC1: an emerging marker and therapeutic target for cancer.Oncogene · 2017Review
- RegulatorTrail: a web service for the identification of key transcriptional regulators.Nucleic acids research · 2017Article
- Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults and accounts for ~80% of all kidney cancer cases. However, the pathogenesis of RCC has not yet been fully elucidated. To interpret the pathogenesis of RCC at the molecular level, gene expression data and bio-informatics methods were used to identify RCC associated genes. Gene expression data was downloaded from Gene Expression Omnibus (GEO) database and identified differentially coexpressed genes (DCGs) and dysfunctional pathways in RCC patients compared with controls. In addition, a regulatory network was constructed using the known regulatory data between transcription factors (TFs) and target genes in the University of California Santa Cruz (UCSC) Genome Browser (http://genome.ucsc.edu) and the regulatory impact factor of each TF was calculated. A total of 258,0427 pairs of DCGs were identified. The regulatory network contained 1,525 pairs of regulatory associations between 126 TFs and 1,259 target genes and these genes were mainly enriched in cancer pathways, ErbB and MAPK. In the regulatory network, the 10 most strongly associated TFs were FOXC1, GATA3, ESR1, FOXL1, PATZ1, MYB, STAT5A, EGR2, EGR3 and PELP1. GATA3, ERG and MYB serve important roles in RCC while FOXC1, ESR1, FOXL1, PATZ1, STAT5A and PELP1 may be potential genes associated with RCC. In conclusion, the present study constructed a regulatory network and screened out several TFs that may be used as molecular biomarkers of RCC. However, future studies are needed to confirm the findings of the present study.
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