ArticleDiscover oncology2025
A risk signature constructed by Tregs-related genes predict the clinical outcomes and immune therapeutic response in kidney cancer.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Regulatory T cells (Tregs) have been found to be related to immune therapeutic resistance in kidney cancer. However, the potential Tregs-related genes still need to be explored. Our study found that patients with high Tregs activity show poor prognosis. Through co-expression and differential expression analysis, we screened several Tregs-related genes (KTRGs) in kidney renal clear cell carcinoma. We further conducted the univariate Cox regression analysis and determined the prognosis-related KTRGs. Through the machine learning algorithm-Boruta, the potentially important KTRGs were screened further and submitted to construct a risk model. The risk model could predict the prognosis of RCC patients well, high risk patients show a poorer outcomes than low risk patients. Multivariate Cox regression analysis reveals that risk score is an independent prognostic factor. Then, the nomogram model based on KTRG risk score and other clinical variables was further established, which shows a high predicted accuracy and clinical benefit based on model validation methods. In addition, we found EMT, JAK/STAT3, and immune-related pathways highly enriched in high risk groups, while metabolism-related pathways show a low enrichment. Through analyzing two other external immune therapeutic datasets, we found that the risk score could predict the patient's immune therapeutic response. High-risk groups represent a worse therapeutic response than low-risk groups. In summary, we identified several Tregs-related genes and constructed a risk model to predict prognosis and immune therapeutic response. We hope these organized data can provide a theoretical basis for exploring potential Tregs' targets to synergize the immune therapy for RCC patients.
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.