ArticleDisease markers2020
Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through Integrative Reanalysis of Transcriptomic Data.
Article in Disease markers, 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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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
8 citing papers in PubMed, 15 citations in OpenAlex.
- Investigating the role of CpG islands and DNA methylation at 3'UTRs in cancer.Life science alliance · 2026Article
- Lessons from the physiological role of guanosine in neurodegeneration and cancer: Toward a multimodal mechanism of action?Purinergic signalling · 2025Review
- Discovery and Characterization of a Chemical Probe for Cyclin-Dependent Kinase-Like 2.ACS medicinal chemistry letters · 2024Article
- Identification of a risk model for prognostic and therapeutic prediction in renal cell carcinoma based on infiltrating M0 cells.Scientific reports · 2024Article
- Discovery and Characterization of a Chemical Probe for Cyclin-Dependent Kinase-Like 2.bioRxiv : the preprint server for biology · 2024Article
- A novel tumor mutational burden-based risk model predicts prognosis and correlates with immune infiltration in ovarian cancer.Frontiers in immunology · 2022Article
- Article
- DecreasedFrontiers in molecular biosciences · 2021Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC).
methodsData from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis.
resultsA total of 5,777 differentially expressed genes were identified from the differential analysis. The Cox analysis showed 1,853 significant genes (
conclusionThe gene expression model consisting of CDKL2, LRFN1, STAT2, and SOWAHB is a promising tool for predicting the prognosis of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC.
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