ArticleMedicine2025
Construction of a risk model based on m5C-associated lncRNAs to predict the prognosis in renal cell carcinoma.
Article in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Regulatory networks of non-coding RNAs in renal cell carcinogenesis and therapeutic intervention strategies.World journal of urology · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Renal cell carcinoma (RCC) is one of the most common tumors of the urinary system, and its outcomes vary widely among individuals, primarily due to different molecular characteristics. Both 5-methylcytosine (m5C) methylation and long noncoding RNAs (lncRNAs) play crucial roles in the epigenetics of RCC and may serve as biomarkers for predicting prognosis. Clinical information and transcriptome data of patients with RCC were extracted from the The Cancer Genome Atlas database. Using least absolute shrinkage and selection operator analysis and multivariate Cox regression, m5C-related lncRNAs were filtered. We then built a prognostic prediction model based on m5C-related lncRNAs. The model was analyzed for its predictive role in overall survival (OS) and response to targeted and immunotherapeutic treatments. We selected 3 lncRNAs, HM13-IT1, COLCA1, and AC010285.3, to construct a predictive model that categorizes patients into high-risk and low-risk groups. The results indicated that the high-risk group exhibited a significantly poorer OS than the low-risk group, and upon validation, it was identified as an independent risk factor. Through gene ontology enrichment analysis, this model was found to be closely associated with tumor immune function. The high-risk group showed higher tumor mutation burden and tumor immune dysfunction and exclusion scores, suggesting poorer response to immunotherapy. Additionally, the high-risk group exhibited reduced responsiveness to sorafenib. The predictive model for RCC can accurately forecast the prognosis of RCC, offering new tools for personalized diagnosis and treatment of individual patients.
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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.