Evidence map›Paper›PMID 41827864›Full record

ArticleCells2026

Machine Learning-Driven Multi-Omics Analysis Identifies CHP2 as a Key PANoptosis-Related Dual-Function Biomarker in Colorectal Cancer.

Zetian Zhang, Xingyu Jiang, Xin Zhang, Fan Li

Abstract read
In one paragraph

Article in Cells, 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

4 authors.

Zetian ZhangThe Key Laboratory of Zoonosis, Department of Pathogen Biology, Chinese Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun 130000, China.ORCID 0000-0001-6326-8205
Xingyu JiangThe Key Laboratory of Zoonosis, Department of Pathogen Biology, Chinese Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun 130000, China.
Xin ZhangThe Key Laboratory of Zoonosis, Department of Pathogen Biology, Chinese Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun 130000, China.
Fan LiThe Key Laboratory of Zoonosis, Department of Pathogen Biology, Chinese Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun 130000, China.ORCID 0000-0002-7998-0093

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The heterogeneity of colorectal cancer (CRC) represents a great challenge in therapy. We integrated multiomics and machine learning, interpreted by SHAP models to provide a clinical rationale, to identify Calcineurin B Homologous Protein 2 (CHP2) as a core candidate, which was further validated via in vitro and zebrafish models. The expression of CHP2 are decreased in CRC, which is associated with a poor prognosis and an immune suppressed "cold" TIME. Functionally, CHP2 overexpression inhibits cell growth and invasion by inducing PANoptosis. Clinically, specific CHP2 expression profiles discriminate patients at high risk that are resistant to standard chemotherapy (e.g., 5-FU) but sensitive to targeted inhibitors. CHP2 is a powerful dual-function biomarker-prognostic for survival and predictive for the response to therapy-that could lead to a personalized approach in treating drug-resistant CRC.

Indexed as

Biomarkers, TumorColorectal NeoplasmsMachine LearningAnimalsCell Line, TumorCell ProliferationGene Expression Regulation, NeoplasticHumansMultiomicsPrognosisZebrafishBiomarkers, Tumorcalcineurin B homologous protein 2colorectal cancerdual-function biomarkermachine learning-driven multiomics analysisPANoptosistumor immune microenvironment

Identifiers

PMID41827864
PMCPMC12985049

What OpenQuestion holds

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LicenceCC BY
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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.