ArticleJournal of gastrointestinal oncology2025
Comprehensive analysis of m6A-related lncRNAs as prognosis factors in the immune microenvironment of colorectal cancer.
Article in Journal of gastrointestinal oncology, 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
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
1 citing paper in PubMed.
- Development and validation of a machine learning-based prognostic model using mitochondrial dysfunction-related genes for colorectal cancer patients.Translational cancer research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The prognosis of colorectal cancer (CRC) varies significantly across different immune subtypes. This study aimed to develop a risk prediction model incorporating the tumor immune microenvironment (TIME) to improve prognosis assessment and predict immunotherapy response in CRC patients, given the significant variability in clinical outcomes across different immune subtypes. Methods: CRC transcriptome data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were employed to identify m6A-related long non-coding RNAs (lncRNAs) (mRLs). A risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression and further validated through nomogram analysis, time-dependent receiver operating characteristic (ROC) curves, and Kaplan-Meier survival analysis. Differences in immune infiltration scores and clinical characteristics between low-risk group (LRG) and high-risk group (HRG) were also investigated. Results: An 11-mRL signature model was established based on their expression profiles in CRC and correlation with m6A regulatory factors. This model demonstrated strong predictive performance for OS, as confirmed by Kaplan-Meier analysis, ROC curves, and Cox regression. Notably, the HRG exhibited significantly higher infiltration of specific immune cells and elevated expression of immune checkpoints [programmed cell death protein 1 (PD-1), programmed death-ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA4)] compared to the LRG. Furthermore, the two groups showed distinct responses to immunotherapy, suggesting potential utility in guiding immunosuppressant selection. A nomogram integrating m6A-immune signatures and clinicopathological variables was developed to individualize prognosis prediction. Conclusions: This study constructed an mRLs risk model that effectively predicts CRC prognosis and immune profiles, offering a potential tool for personalized therapeutic decision-making.
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.