Machine Learning-Assisted Plasma Metabolomics Identifies a Five-Metabolite Panel for Colorectal Cancer Detection.

Jun-Kai Wong, Chung-Hsien Lin, Hsin-Yi Wu, Yen-Ping Lin, Chiau-Jun Chu, Pang-Hung Hsu, Chung-Fa Chang

Abstract read

In one paragraph

Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Jun-Kai WongDepartment of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan.

Chung-Hsien LinDepartment of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan.

Hsin-Yi WuInstrumentation Center, National Taiwan University, Taipei 106, Taiwan.ORCID https://orcid.org/0000-0003-0722-0201

Yen-Ping LinPublic Health Bureau, Tainan City Government, Tainan 701, Taiwan.

Chiau-Jun ChuPublic Health Bureau, Tainan City Government, Tainan 701, Taiwan.

Pang-Hung HsuDepartment of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung 202, Taiwan.ORCID https://orcid.org/0000-0001-6873-6434

Chung-Fa ChangDepartment of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan.ORCID https://orcid.org/0000-0003-2404-1248

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, and improved noninvasive strategies for early detection are urgently needed to complement current screening approaches. We applied an integrated plasma metabolomics workflow that combines untargeted and targeted liquid chromatography-mass spectrometry with machine-learning-assisted feature prioritization. Plasma samples from a discovery cohort of 172 CRC patients and 115 healthy controls were analyzed, followed by validation in an independent cohort of 47 CRC patients and 47 healthy controls. From 146,880 spectral features, random forest analysis was used to robustly rank features, followed by ROC-based prioritization and metabolite annotation. Diagnostic performance was evaluated by using a logistic regression model. Phenotypic assays were conducted to assess the biological activity of selected metabolites in CRC cell models. Five metabolites,

Identifiers

PMID42077901

DOI10.1021/acsomega.6c00857

PMCPMC13129852

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.