Evidence map›Paper›PMID 42375537›Full record

ArticleiScience2026

Machine learning algorithms develop a tumor-educated platelets-related gene signature to predict colorectal cancer prognosis and therapy response.

Yueqiong Lao, Jie Yang, Minli Hu, Jing Li, Weikang Xu, Jiahui Xu, Huan Wang, Hechenhao Jiang, Zhihao Pei, Xinyi Qiu and 3 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

13 authors.

Yueqiong LaoDepartment of Gastroenterology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Jie YangDepartment of Gastroenterology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Minli HuGuangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Jing LiGuangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Weikang XuGuangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Jiahui XuGuangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Huan WangGuangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Hechenhao JiangGuangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Zhihao PeiGuangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Xinyi QiuDepartment of Gastroenterology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Kunyuan WangGuangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Xuan LiGuangzhou Key Laboratory for Research and Development of Nano-Biomedical Technology for Diagnosis and Therapy&Guangdong Provincial Education Department Key Laboratory of Nano-Immunoregulation Tumour Microenvironment, Department of Oncology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.
Hui YangGuangzhou Institute of Cardiovascular Disease, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510245, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor-educated platelets (TEPs) have recently emerged as an important component of liquid biopsy, yet the clinical relevance in colorectal cancer (CRC) remains unclear. Here, we employed 10 machine learning algorithms to develop a stable, accurate TEP-related gene signature (TEPGS) to explore its links to tumor-associated macrophages (TAMs) and spatial platelet abundance. TEPGS correlated strongly with poor prognosis and outperformed 71 published gene signatures in predicting CRC overall survival. Multi-omics analysis displayed that high TEPGs were marked by increased TP53 mutations, copy number alterations, diminished immune features, enrichment of pro-tumor SPP1

Indexed as

CancerComputing methodologyImmunologyMachine learning

Identifiers

PMID42375537
PMCPMC13311190

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.