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通过机器学习和分子亚型分析鉴定特发性肺纤维化的 PANoptosis 相关基因。

Identification of PANoptosis-related genes for idiopathic pulmonary fibrosis by machine learning and molecular subtype analysis.

机构信息

Department of Anesthesiology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China.

School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.

出版信息

Sci Rep. 2024 Oct 14;14(1):24068. doi: 10.1038/s41598-024-76263-7.

DOI:10.1038/s41598-024-76263-7
PMID:39402203
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11473738/
Abstract

Idiopathic pulmonary fibrosis (IPF) is a severe interstitial lung disease characterized by a grim prognosis, in which various forms of cell death are significant contributors to its development. The objective of this study is to explore diagnostic biomarkers and molecular subtypes associated with PANoptosis in IPF, and to develop reliable diagnostic models based on PANoptosis-related mechanisms. The peripheral blood transcriptomic data of IPF from the Gene Expression Omnibus (GEO) database and PANoptosis-related genes from the GeneCards database were utilized to conduct differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), identifying PANoptosis-related differentially expressed genes (PDEGs). We yielded 9 PDEGs and employed machine learning algorithms to identify 3 key diagnostic biomarkers for PANoptosis in IPF: MMP9, FCMR, NIBAN3. Consensus clustering algorithm was applied to recognize two PANoptosis-related subtypes. Cluster 1 exhibited higher abundance of adaptive immune response cells and significant enrichment in DNA damage and repair-related pathways. Cluster 2 exhibited greater prevalence of innate immune response cells and predominant enhancement in pathways related to lipid cholesterol metabolism and vascular remodeling. Diagnostic models were developed with the aid of clinical decision-making and a novel approach to the diagnosis and treatment for IPF.

摘要

特发性肺纤维化(IPF)是一种严重的间质性肺疾病,其预后较差,多种形式的细胞死亡是其发展的重要因素。本研究旨在探索与 IPF 中的 PANoptosis 相关的诊断生物标志物和分子亚型,并基于 PANoptosis 相关机制开发可靠的诊断模型。本研究利用基因表达综合数据库(GEO)中的 IPF 外周血转录组数据和基因卡片数据库中的 PANoptosis 相关基因,进行差异基因表达分析和加权基因共表达网络分析(WGCNA),确定与 PANoptosis 相关的差异表达基因(PDEGs)。我们得到了 9 个 PDEGs,并采用机器学习算法识别出 IPF 中与 PANoptosis 相关的 3 个关键诊断生物标志物:MMP9、FCMR 和 NIBAN3。采用共识聚类算法识别出两个与 PANoptosis 相关的亚型。簇 1 表现出更高丰度的适应性免疫反应细胞,并且在与 DNA 损伤和修复相关的途径中显著富集。簇 2 表现出更高丰度的固有免疫反应细胞,并且在与脂质胆固醇代谢和血管重塑相关的途径中占主导地位。借助临床决策和一种新颖的 IPF 诊断和治疗方法,开发了诊断模型。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5a0/11473738/ae2f491dc586/41598_2024_76263_Fig9_HTML.jpg

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