Evidence map›Paper›PMID 41438741›Full record

ArticleFrontiers in immunology2025

A multi-omic machine learning approach deconstructs the role of amino acid metabolism in the immune microenvironment and prognosis of colon adenocarcinoma.

Hua Zhong, Jiangdong Jin, Yi Zhang, Nuo Chen, Anya Liu, Chenfei Jiang, Wenbing Zhang, Zirui He

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Hua Zhong *Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiangdong Jin *Department of General Surgery, Anqing First People's Hospital of Anhui Medical University, Anqing, China.
Yi Zhang *Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Nuo ChenDepartment of General Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Anya LiuThe First Regiment, School of Basic Medicine, Fourth Military Medical University, Xian, China.
Chenfei JiangDepartment of General Surgery, Haining People's Hospital, Haining, China.
Wenbing ZhangDepartment of General Surgery, Anqing First People's Hospital of Anhui Medical University, Anqing, China.
Zirui HeDepartment of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The clinical heterogeneity of colon adenocarcinoma (COAD) complicates patient prognosis and treatment. While metabolic reprogramming is a key driver of tumor progression, the role of histidine metabolism in the COAD immune microenvironment remains unclear. Methods: Through an integrated analysis of public single-cell and bulk transcriptomic data, we mapped the COAD cellular atlas and assessed histidine metabolism activity. A prognostic Histidine Metabolism-related Model (HRM) was constructed using an ensemble of 101 machine learning algorithms, and its biological underpinnings were explored. The function of the key gene, TRIP6, was validated via Results: Single-cell analysis identified epithelial cells as the hub of histidine metabolism, which remodels TME intercellular communication. The machine learning-derived HRM robustly stratified patient prognosis across a primary and two validation cohorts. High HRM scores correlated with an "infiltrated-exhausted" immune phenotype, characterized by high immune infiltration alongside elevated checkpoint expression. The key signature gene TRIP6 was identified as a driver of poor prognosis and an immunosuppressive state, and its silencing suppressed malignant phenotypes Conclusion: Histidine metabolism is a critical regulator of the COAD immune microenvironment. Our prognostic model, the HRM, provides a clinically relevant tool for risk stratification, while its key mediator, TRIP6, represents a novel therapeutic target linking tumor metabolism to immune evasion.

Indexed as

AdenocarcinomaAmino AcidsColonic NeoplasmsHistidineMachine LearningTumor MicroenvironmentBiomarkers, TumorGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMultiomicsPrognosisAmino AcidsBiomarkers, TumorHistidineamino acid metabolismcolon adenocarcinomaimmune infiltrationmulti-omicTRIP6

Identifiers

PMID41438741
PMCPMC12719496

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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.