Evidence map›Paper›PMID 40604645›Full record

ArticleBMC pulmonary medicine2025

Machine learning-based integration identifies plasma cells-related gene signature ST6GAL1 in idiopathic pulmonary fibrosis.

Fanjie Lin, Ken Lin, Donglei Li, Weiguo Kong, Jiayu Zhuang, Wei He, Xinguang Wei, Tianchi Xiao, Hao Zu, Zili Zhang and 1 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Genes · 2025
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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Fanjie Lin *State Key Laboratory of Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, P.R. China.
Ken Lin *Guangzhou Medical University, Guangzhou, Guangdong, P.R. China.
Donglei Li *Guangzhou Medical University, Guangzhou, Guangdong, P.R. China.
Weiguo KongState Key Laboratory of Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, P.R. China.
Jiayu ZhuangState Key Laboratory of Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, P.R. China.
Wei HeState Key Laboratory of Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, P.R. China.
Xinguang WeiState Key Laboratory of Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, P.R. China.
Tianchi XiaoGuangzhou Medical University, Guangzhou, Guangdong, P.R. China.
Hao ZuGuangzhou Medical University, Guangzhou, Guangdong, P.R. China.
Zili ZhangState Key Laboratory of Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, P.R. China. zlzhang@gzhmu.edu.cn.
Wenju LuState Key Laboratory of Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, P.R. China. wlu92@qq.com.

Funding

Guangzhou Laboratory GZNL2023A02006Guangzhou Municipal Science and Technology grant 2024B03J1383National Natural Science Foundation of China 82330002Natural Science Foundation of Guangdong Province 2024A1515030257State Key Laboratory of Respiratory Disease SKLRD-OP-202212
6 · The paper itself

Abstract

backgroundIdiopathic pulmonary fibrosis (IPF) is a rare, progressive, and fibrotic disease with poor prognosis that lacks treatment options. As a major component of the lung adaptive immune system, plasma cells play a crucial regulatory role during fibrosis. The aim of this study is to systematically explore plasma cells-related genes associated with prognosis in patients with IPF.

methodsThe marker genes for plasma cells were extracted via single-cell RNA sequencing (scRNA-seq) analysis. Hub genes most relevant to the IPF state and plasma cells infiltration level were screened by weighted gene co-expression network analysis (WGCNA). Moreover, the differentially expressed genes (DEGs) were obtained based on the bulk RNA-seq and microarray data. In addition, a machine learning-based integrative procedure for constructing a concordance plasma cells-related gene signature (PCRGS) was developed. A core gene in the PCRGS was further identified and validated through experiments. Finally, the network pharmacology analysis for the core gene was implemented.

resultsThe established PCRGS, based on the seven genes (SLAMF7, JCHAIN, PNOC, POU2AF1, MEI1, ST6GAL1, and VOPP1), was identified as an independent prognostic factor for overall survival. It also demonstrated well robustness compared to conventional clinical features and 22 published signatures. Eventually, ST6GAL1 was selected as the core gene and its localization in the plasma cells as well as its over-expression in the lungs of bleomycin-injured mice was experimentally validated. The small molecular drugs prediction and docking analysis suggest quercetin as the optimal ligand targeting ST6GAL1 which might form a stable binding conformation with it.

conclusionsPCRGS might be used to evaluate the IPF prognosis, among which ST6GAL1 is a potential therapeutic target. These results provide an important basis for future studies on the relationship between plasma cells-related genes and IPF.

Indexed as

Idiopathic Pulmonary FibrosisMachine LearningPlasma CellsSialyltransferasesAgedAnimalsFemaleGene Expression ProfilingHumansMaleMicePrognosisTranscriptomeSialyltransferasesGene signaturesIPFMachine learningPlasma cellsST6GAL1

Identifiers

PMID40604645
PMCPMC12224576

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