Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0593
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-30T06:09:14.325020Z
status: published
lens: g_space
sector: health
headline: Identification of Methionine Metabolism-Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment
dek: Aim Tumor heterogeneity, driven by metabolic reprogramming, challenges colorectal cancer (CRC) treatment. Methionine metabolism is crucial for tumor progression, but its role in CRC heterogeneity and the tumor immune microenvironment (TIME) requires systematic investigation. Methods A systematic evaluation of 101 combinations of machine learning and statistical algorithms was conducted within a 10-fold cross-validation framework to develop and validate the optimal model, termed the methionine metabolism-related…
gain_title: A machine learning-derived methionine metabolism-related risk score stratified colorectal cancer patients into high- and low-risk groups with significantly different overall survival in training and external validation cohorts.
problem_title: (none)
trace_subject: (none)
gain_reading: A machine learning-derived methionine metabolism-related risk score stratified colorectal cancer patients into high- and low-risk groups with significantly different overall survival in training and external validation cohorts.
gain_evidence: which effectively stratified patients into high- and low-risk groups with significantly different survival across all cohorts
problem_reading: (none)
problem_evidence: (none)
quick_read: By July 28, 2026, researchers reported a systematic machine learning analysis of methionine metabolism in colorectal cancer, using TCGA-COAD for training and two GEO cohorts for external validation. Unsupervised clustering defined metabolism-high and metabolism-low subtypes, and a StepCox plus plsRcox model termed MMRS was built from 41 subtype-associated prognostic genes to stratify patients by risk.

The work matters because it links metabolic heterogeneity to prognosis and immune microenvironment features, offering a tool that separated high- and low-risk groups with significantly different survival across cohorts. What remains uncertain is whether the signature can guide treatment selection, as the authors note prospective validation is still required.
limitation: The MMRS prognostic value was demonstrated retrospectively, but its utility as a predictive biomarker for treatment selection has not been prospectively validated.
tag: Evidence-backed gain
key_points: Unsupervised clustering defined methionine metabolism-high (MMH) and metabolism-low (MML) subtypes, with MMH showing poorer overall survival and immunosuppressive TIME. | 101 combinations of machine learning and statistical algorithms were tested in 10-fold cross-validation using TCGA-COAD as training and GSE39582 and GSE17536 as external validation. | Immune infiltration was assessed with MCP-counter and model discrimination with time-dependent ROC analysis at 1, 3, and 5 years. | 41 prognostic genes from subtype-associated DEGs were used to build the optimal StepCox[both] + plsRcox model termed MMRS.
rundown: Researchers systematically evaluated 101 machine learning and statistical algorithm combinations within 10-fold cross-validation to develop the methionine metabolism-related risk score, training on TCGA-COAD and validating on two independent GEO cohorts GSE39582 and GSE17536.

From differentially expressed genes between MMH and MML subtypes, 41 prognostic genes were identified and used in the StepCox[both] + plsRcox model, with immune context assessed by MCP-counter and discrimination measured by AUC at 1, 3, and 5 years.
sources:
- peer_reviewed | Asia-Pacific Journal of Clinical Oncology | https://doi.org/10.1111/ajco.70147 | 2026-07-28
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

8a9a81e5669626ec3505f27e416ca9fa7ba34977cebb378578cb56ce8d335b60

previous

0000000000000000000000000000000000000000000000000000000000000000