Evidence map›Paper›PMID 40806769›Full record

ArticleInternational journal of molecular sciences2025

A Multi-Omics Integration Framework with Automated Machine Learning Identifies Peripheral Immune-Coagulation Biomarkers for Schizophrenia Risk Stratification.

Feitong Hong, Qiuming Chen, Xinwei Luo, Sijia Xie, Yijie Wei, Xiaolong Li, Kexin Li, Benjamin Lebeau, Crystal Ling, Fuying Dao and 4 more

Abstract read
In one paragraph

Article in International journal of molecular sciences, 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

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

1 citing paper in PubMed.

  1. Review
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

14 authors.

Feitong HongThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID 0009-0004-0064-0961
Qiuming ChenThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Xinwei LuoThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Sijia XieThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Yijie WeiThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Xiaolong LiThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Kexin LiThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Benjamin LebeauSchool of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore.ORCID 0000-0003-1218-2983
Crystal LingSchool of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore.
Fuying DaoSchool of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore.ORCID 0000-0001-5285-6044
Hao LinThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID 0000-0001-6265-2862
Lixia TangThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID 0000-0001-9460-1277
Mi YangThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Hao LvThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID 0000-0002-7580-0155

Funding

China Postdoctoral Science Foundation 2023TQ0047China Postdoctoral Science Foundation GZC20230380Health Commission of Chengdu 2024141Health Commission of Sichuan Province 24CXTD11National Natural Science Foundation of China 62373079National Natural Science Foundation of China 62402089Science and Technology Department of Sichuan Province 2024ZYD0039Science and Technology Department of Sichuan Province 2025ZNSFSC1465Sichuan medical association S23012
6 · The paper itself

Abstract

Schizophrenia (SCZ) is a complex psychiatric disorder with heterogeneous molecular underpinnings that remain poorly resolved by conventional single-omics approaches, limiting biomarker discovery and mechanistic insights. To address this gap, we applied an artificial intelligence (AI)-driven multi-omics framework to an open access dataset comprising plasma proteomics, post-translational modifications (PTMs), and metabolomics to systematically dissect SCZ pathophysiology. In a cohort of 104 individuals, comparative analysis of 17 machine learning models revealed that multi-omics integration significantly enhanced classification performance, reaching a maximum AUC of 0.9727 (95% CI: 0.8889-1.000) using LightGBMXT, compared to 0.9636 (95% CI: 0.8636-1.0000) with CNNBiLSTM for proteomics alone. Interpretable feature prioritization identified carbamylation at immunoglobulin-constant region sites IGKC_K20 and IGHG1_K8, alongside oxidation of coagulation factor F10 at residue M8, as key discriminative molecular events. Functional analyses identified significantly enriched pathways including complement activation, platelet signaling, and gut microbiota-associated metabolism. Protein interaction networks further implicated coagulation factors F2, F10, and PLG, as well as complement regulators CFI and C9, as central molecular hubs. The clustering of these molecules highlights a potential axis linking immune activation, blood coagulation, and tissue homeostasis, biological domains increasingly recognized in psychiatric disorders. These results implicate immune-thrombotic dysregulation as a critical component of SCZ pathology, with PTMs of immune proteins serving as quantifiable disease indicators. Our work delineates a robust computational strategy for multi-omics integration into psychiatric research, offering biomarker candidates that warrant further validation for diagnostic and therapeutic applications.

Indexed as

BiomarkersBlood CoagulationMachine LearningSchizophreniaAdultFemaleHumansMaleMetabolomicsMiddle AgedMultiomicsProtein Processing, Post-TranslationalProteomicsBiomarkersAIimmune–thrombotic dysregulationmolecular biomarkermulti-omicsschizophrenia

Identifiers

PMID40806769
PMCPMC12347864

What Socratic 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 Socratic graph.