Evidence map›Paper›PMID 41566314›Full record

ArticleBMC medical informatics and decision making2026

Integrated multi-omics and machine learning identify an interaction between SLC39A11 and phosphoinositide metabolism in deep vein thrombosis.

Bao-Ze Pan, Ming-Jun Jiang, Jie Chen, Dan Ning, Jing Liang, Zhi-He Deng, Dong-Yang Luo, Yang-Yi-Jing Wang, Yao-Yang Zhong, Xian-Peng Dai and 3 more

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Article in BMC medical informatics and decision making, 2026. 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

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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. 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

13 authors.

Bao-Ze Pan *Department of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Ming-Jun Jiang *Department of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Jie ChenDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Dan NingDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Jing LiangDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Zhi-He DengDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Dong-Yang LuoDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Yang-Yi-Jing WangDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Yao-Yang ZhongDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Xian-Peng DaiDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Li-Ming DengDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China. dlmwz2019@126.com.
Guo-Zuo XiongDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China. 55752528@qq.com.
Guo-Shan BiDepartment of Vascular Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China. doctorbi2020@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDeep vein thrombosis (DVT) is a complex thrombotic disorder with multiple environmental and genetic determinants. Nevertheless, the intricacies of DVT regulatory mechanisms have thus far precluded comprehensive research on its multi-omics characteristics.

methodsIn this study, non-targeted metabolomics (n = 62) and transcriptomics (n = 17) were utilised to comprehensively analyse the peripheral blood changes of DVT patients and healthy subjects. The hub differentially expressed metabolites (DEMs) and differentially expressed genes (DEGs) involved in the pathophysiological process of DVT were screened based on two-way orthogonal partial least squares (O2PLS) and machine learning (ML). The predictive performance of transcriptomic features was evaluated by constructing a nomogram and validated using calibration curves, receiver operating characteristic (ROC) curves and validation sets (n = 18). Furthermore, single-cell RNA sequencing (scRNA-seq, n = 6) facilitates the revelation of intrinsic regulatory relationships between DEMs and DEGs through correlation analysis and inductively coupled plasma-mass spectrometry (ICP-MS).

resultsThe metabolic and transcriptional profiles of DVT patients were found to be significantly different from those of healthy subjects, and a total of 85 DEMs and 193 DEGs were identified by difference analysis. Following an exploration of the relevant biological pathways via Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the top 25 DEMs and DEGs were identified by O2PLS. Following rigorous verification by the correlation network model and ML algorithm, it was determined that phosphatidylinositol (PI) and SLC39A11 exhibited significant characteristics. The nomogram demonstrated that SLC39A11 exhibited excellent prediction performance, with the area under the curve (AUC) value of 1.000. Moreover, SLC39A11 was found to be significantly up-regulated in the validation set (p < 0.01). The SLC39A11 was localized to M1-like macrophages by scRNA-seq, and it was found that it was significantly related to PI metabolism in DVT by correlation analysis and ICP-MS (p < 0.05).

conclusionsThis study demonstrated that the metabolic dysregulation of DVT was predominantly concentrated in lipid metabolism, as indicated by PI, and the gene dysregulation was represented by SLC39A11. SLC39A11 exerts an influence on the regulatory process of DVT through its interaction with zinc ion transport and PI metabolism.

Indexed as

Cation Transport ProteinsMachine LearningPhosphatidylinositolsVenous ThrombosisFemaleHumansMaleMetabolomicsMultiomicsTranscriptomeCation Transport ProteinsPhosphatidylinositolsDeep vein thrombosisMachine learningMetabolomicsMulti-omics analysisSingle-cell RNA sequencingTranscriptomics

Identifiers

PMID41566314
PMCPMC12911184

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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.