ArticleDiscover oncology2025
Constructing a mitochondrial-related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancer.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Review
- Integrating prognosis-related genes with immune-related gene signature for development and validation of a survival stratification model for early-stage colorectal cancer.Translational cancer research · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The role of mitochondria in tumorigenesis and progression is has been increasingly demonstrated by numerous studies, but its prognostic value in colorectal cancer (CRC) remains unclear. To address this, we developed a mitochondrial-related gene prognostic model using 101 combinations of 10 machine learning algorithms. Patients in the high-risk group exhibited significantly shorter overall survival time. The high-risk group exhibited elevated tumor immune dysfunction and exclusion score, indicating diminished immunotherapy efficacy. To address the suboptimal treatment outcomes in these patients, we identified PYR-41 and pentostatin as potential therapeutic agents, which are anticipated to enhance therapeutic efficacy in the high-risk group. Additionally, four biomarkers (HSPA1A, CHDH, TRAP1, CDC25C) were validated by quantitative real-time PCR, with significant expression differences between normal intestinal epithelial cells and colon cancer cells. Our prognostic model provides accurate CRC outcome prediction and guides personalized therapeutic strategies.
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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.