ArticleTechnology in cancer research & treatment
A Prognostic Risk Signature Based on Myeloid-Derived Suppressor Cells and Regulatory T Cells in Clear Cell Renal Cell Carcinoma.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Obesity-driven metabolic reprogramming and immune dysfunction in renal cancer.Frontiers in immunology · 2026Review
- Nanoplatform-Mediated Remodeling of the Immune Microenvironment in Renal Cell Carcinoma.International journal of nanomedicine · 2026Review
- Immunosuppressive cells as barriers to cancer therapy: mechanisms and emerging solutions.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
IntroductionClear cell renal cell carcinoma (ccRCC) is the most prevalent histological subtype of renal carcinoma. To diagnose ccRCC and assess its prognosis more accurately, it is essential to screen novel prognostic biomarkers and construct prognostic signatures.MethodsImmune infiltration analysis of the TCGA cohort was performed via single-sample gene set enrichment analysis (ssGSEA). The ccRCC cohort from the TCGA database was used to identify MDSC/Treg-related genes. Hub genes were selected from the common genes in the MDSC/Treg-related gene list via machine learning approaches. These hub genes were then employed to construct the risk signature through multivariate analysis.The prognostic performance, immune performance, and functional analysis of the signature were comprehensively assessed. Two independent GEO datasets were used to verify the major findings above. Potential drugs were screened to promote clinical transformation via the CellMiner platform. Finally, the expression levels of six markers were validated through RT-qPCR analysis of clinical tissue samples.ResultsSix MDSC/Treg-related DEGs were identified via machine learning approaches based on the Cancer Genome Atlas cohort. A novel signature (risk score = -0.5579*
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