ArticleFrontiers in oncology2026
A serum metabolite-based machine learning model predicts response to neoadjuvant immunotherapy in mismatch repair-deficient colorectal cancer.
Article in Frontiers in oncology, 2026. 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.
- Bridging Ancestry-Stratified Bias in Pharmacogenomics AI: Toward Metabolomics-Inclusive Multi-Omics Precision Medicine.Journal of personalized medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colorectal cancer (CRC) with microsatellite instability-high (MSI-H) or mismatch repair-deficient (dMMR) shows significant sensitivity to immune checkpoint inhibitors (ICIs). However, a considerable proportion of patients still exhibit primary or acquired resistance to ICIs. Until now, efficient and non-invasive biomarkers for accurately predicting immunotherapy efficacy remain unavailable. Methods: In this multicenter study, we employed liquid chromatography-mass spectrometry (LC-MS) and enzyme-linked immunosorbent assay (ELISA) to identify and validate serum metabolites associated with response to immunotherapy. Using machine learning algorithms, we constructed a random forest predictive model based on a panel of five metabolites. This model, termed the 5-Metabolite Predictive Model (5-MPM), incorporates prostaglandin E2 (PGE2), tryptophan, arginine, citrulline, and histidine. Results: The 5-MPM model demonstrated robust predictive performance in both training cohort and external validation cohort, with AUC values of 0.85 and 0.88, respectively. The SHAP analysis elucidated the contribution of each metabolite to model predictions. Integrating above five metabolites with metastasis stage did not further improve the predictive performance of this model. Discussion: This study provides the first systematic characterization of metabolic reprogramming in dMMR colorectal cancer with different response to immunotherapy, and establishes a non-invasive, high-precision predictive tool that offers a new basis for individualized 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.