基于机器学习的整合鉴定出特发性肺纤维化中与浆细胞相关的基因特征ST6GAL1。

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

作者信息

Lin Fanjie, Lin Ken, Li Donglei, Kong Weiguo, Zhuang Jiayu, He Wei, Wei Xinguang, Xiao Tianchi, Zu Hao, Zhang Zili, Lu Wenju

机构信息

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.

Guangzhou Medical University, Guangzhou, Guangdong, P.R. China.

出版信息

BMC Pulm Med. 2025 Jul 2;25(1):295. doi: 10.1186/s12890-025-03696-9.

DOI:10.1186/s12890-025-03696-9

PMID:40604645

Abstract

BACKGROUND

Idiopathic 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.

METHODS

The 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.

RESULTS

The 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.

CONCLUSIONS

PCRGS 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.

摘要

背景

特发性肺纤维化（IPF）是一种罕见的、进行性的纤维化疾病，预后较差且缺乏治疗选择。作为肺适应性免疫系统的主要组成部分，浆细胞在纤维化过程中发挥着关键的调节作用。本研究的目的是系统地探索与IPF患者预后相关的浆细胞相关基因。

方法

通过单细胞RNA测序（scRNA-seq）分析提取浆细胞的标记基因。通过加权基因共表达网络分析（WGCNA）筛选出与IPF状态和浆细胞浸润水平最相关的枢纽基因。此外，基于批量RNA测序和微阵列数据获得差异表达基因（DEG）。另外，开发了一种基于机器学习的综合程序来构建一致性浆细胞相关基因特征（PCRGS）。通过实验进一步鉴定和验证了PCRGS中的一个核心基因。最后，对核心基因进行了网络药理学分析。

结果

基于七个基因（SLAMF7、JCHAIN、PNOC、POU2AF1、MEI1、ST6GAL1和VOPP1）建立的PCRGS被确定为总生存的独立预后因素。与传统临床特征和22个已发表的特征相比，它还表现出良好的稳健性。最终，选择ST6GAL1作为核心基因，并通过实验验证了其在浆细胞中的定位以及在博莱霉素损伤小鼠肺中的过表达。小分子药物预测和对接分析表明槲皮素是靶向ST6GAL1的最佳配体，它可能与其形成稳定的结合构象。