ArticleFrontiers in nephrology2024
Bioinformatics-driven identification of prognostic biomarkers in kidney renal clear cell carcinoma.
Article in Frontiers in nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- ERMP1 Exerts Tumor-Suppressive Functions in KIRC by Inhibiting PI3K/AKT Signaling and Remodeling the Immune Microenvironment: A Pan-Cancer Analysis.Human mutation · 2026Article
- Exploring the prognostic role and expression patterns of FAM3A family genes in kidney renal clear cell carcinoma.Scientific reports · 2025Article
- Biomarkers in advanced renal cell carcinoma: current practice and future directions.Current opinion in oncology · 2025Review
- Prediction of Major Adverse Cardiovascular Events in Patients with Peripheral Artery Disease Using Circulating Immunomodulatory Proteins.Biomedicines · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Renal cell carcinoma (RCC), particularly the clear cell subtype (ccRCC), poses a significant global health concern due to its increasing prevalence and resistance to conventional therapies. Early detection of ccRCC remains challenging, resulting in poor patient survival rates. In this study, we employed a bioinformatic approach to identify potential prognostic biomarkers for kidney renal clear cell carcinoma (KIRC). By analyzing RNA sequencing data from the TCGA-KIRC project, differentially expressed genes (DEGs) associated with ccRCC were identified. Pathway analysis utilizing the Qiagen Ingenuity Pathway Analysis (IPA) tool elucidated key pathways and genes involved in ccRCC dysregulation. Prognostic value assessment was conducted through survival analysis, including Cox univariate proportional hazards (PH) modeling and Kaplan-Meier plotting. This analysis unveiled several promising biomarkers, such as
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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 Socratic graph.