机器学习联合生物信息学筛选与自噬相关的肺纤维化关键基因及实验验证  

Machine learning combined with bioinformatics screening of key genes for pulmonary fibrosis associated with cellular autophagy and experimental validation

作　　者：巩月红[1,2] 王梦君 任航 郑辉 孙佳佳 刘军鹏 张飞 杨建华[1,2] 胡君萍[4] Gong Yuehong;Wang Mengjun;Ren Hang;Zheng Hui;Sun Jiajia;Liu Junpeng;Zhang Fei;Yang Jianhua;Hu Junping(Pharmaceutical Department of the First Affiliated Hospital of Xinjiang Medical University,Urumqi 830011,Xinjiang Uygur Autonomous Region,China;Xinjiang Key Laboratory of Clinical Drug Research,Urumqi 830011,Xinjiang Uygur Autonomous Region,China;School of Clinical Medicine,Urumqi 830017,Xinjiang Uygur Autonomous Region,China;School of Pharmacy,Xinjiang Medical University,Urumqi 830017,Xinjiang Uygur Autonomous Region,China)

机构地区：[1]新疆医科大学第一附属医院药学部,新疆维吾尔自治区乌鲁木齐市830011 [2]新疆药物临床研究重点实验室,新疆维吾尔自治区乌鲁木齐市830011 [3]新疆医科大学临床医学部,新疆维吾尔自治区乌鲁木齐市830017 [4]新疆医科大学药学院,新疆维吾尔自治区乌鲁木齐市830017

出　　处：《中国组织工程研究》2025年第35期7679-7689,共11页Chinese Journal of Tissue Engineering Research

基　　金：新疆维吾尔自治区卫生健康委员会“天山英才”医药卫生高层次人才培养计划项目(TSYC202301B095),项目负责人:巩月红;新疆维吾尔自治区大学生创新训练计划项目(S202310760059),项目负责人:王梦君;新疆维吾尔自治区科学技术厅自然科学基金重点项目(2021D01D11),项目负责人:胡君萍;新疆医科大学第一附属医院创新团队培养项目(党字[2023]52号),项目负责人:杨建华。

摘　　要：背景:肺纤维化的早期诊断是及时开展抗纤维化药物治疗的基础,因此,探索并发现能够有效应用于肺纤维化早期诊断的理想生物标志物对疾病治疗至关重要。目的:通过生物信息学和机器学习技术对肺纤维化过程中涉及的与自噬相关关键基因进行深入分析,探究与自噬相关的肺纤维化核心基因是否可以作为评估肺纤维化进展中可靠的生物标志物。方法:基于GEO数据库(是由美国国家生物技术信息中心开发和维护的一个公共数据库,用于存储和共享生物信息学数据)下载肺纤维化GSE24206和GSE110147两个数据集,利用R软件中的“limma”包将两组基因表达矩阵归一化处理。从GeneCards数据库(由美国国家生物技术信息中心创建,该知识库自动整合了约200个Web来源的以基因为中心的数据,包括基因组、转录组、蛋白质组、遗传、临床和功能信息)提取自噬相关基因集;对肺纤维化数据集进行差异基因分析,将差异基因与自噬基因集交叉对比提取共同基因,识别肺纤维化过程中可能发挥作用的自噬基因。交集基因通过GO、KEGG进行功能富集和细胞免疫浸润分析。通过蛋白质-蛋白质相互作用和机器学习筛选与自噬相关的肺纤维化核心基因,并对核心基因进行集富集分析。将筛选出的核心基因构建诊断模型,用校准曲线来评估线形图模型的预测能力,采用外部数据集GSE21369进行受试者工作特征曲线分析,验证与自噬相关的肺纤维化基因的表达特征,通过Coremine数据库预测与基因IL6、COL1A2相关的中药。培养人胚肺成纤维细胞,通过转化生长因子β1处理造模,利用qRT-PCR技术验证IL6、COL1A2在模型细胞中的相对表达。结果与结论:①获得肺纤维化差异基因51个、与自噬基因交集基因25个,GO分析显示25个交集基因与细胞外基质组织、胶原代谢过程、胶原原纤维组织、生长因子结合等过程有关,KEGGBACKGROUND:Early diagnosis of pulmonary fibrosis is the foundation for timely antifibrotic drug therapy.Therefore,exploring and discovering ideal biomarkers that can be effectively used for the early diagnosis of pulmonary fibrosis is crucial for the treatment of the disease.OBJECTIVE:To conduct an in-depth analysis of key autophagy-related genes involved in the process of pulmonary fibrosis by means of bioinformatics and machine learning techniques,in order to investigate whether autophagy-related core genes of pulmonary fibrosis can be used as reliable biomarkers in the assessment of the progression of pulmonary fibrosis.METHODS:Two datasets of pulmonary fibrosis,GSE24206 and GSE110147,were downloaded from the Gene Expression Omnibus(GEO)database(a public database developed and maintained by the U.S.National Center for Biotechnology Information to store and share bioinformatics data),and the gene expression matrices of these two datasets were normalized by using the“limma”package in R software.The autophagy-related genes were extracted from GeneCards database(a database created by the U.S.National Center for Biotechnology Information,which automatically integrates gene-centric data from about 200 Web sources,including genomic,transcriptomic,proteomic,genetic,clinical,and functional information).Differential gene analysis was performed on the pulmonary fibrosis dataset,and the common genes were extracted by cross-comparing the differential genes with the autophagy genes,so as to identify autophagy genes that may play a role in the process of pulmonary fibrosis.The intersecting genes were analyzed for functional enrichment and cellular immune infiltration by gene ontology and Kyoto Encyclopedia of Genes and Genomes.Core genes of pulmonary fibrosis associated with autophagy were screened by protein-protein interactions and machine learning,and core genes were subjected to the enrichment analysis.Diagnostic models were constructed from the identified core genes.Calibration curves were used to assess the predict

关 键 词：肺纤维化 自噬 机器学习 生物信息学 免疫浸润 最小绝对收缩与选择算子 基因富集分析 工程化组织构建 

分 类 号：R459.9[医药卫生—治疗学] R318[医药卫生—临床医学] Q811.4[生物学—生物工程]