文献类型：期刊文献

英文题名：Identification of immune- and autophagy-related genes and effective diagnostic biomarkers in endometriosis: a bioinformatics analysis

作者：Ji, Xiujia[1];Huang, Cancan[1];Mao, Haiyan[2];Zhang, Zuoliang[1];Zhang, Xiaohua[1];Yue, Bin[1];Li, Xinyue[1];Wu, Quansheng[1]

第一作者：吉秀家

通信作者：Wu, QS[1]

机构：[1]Gansu Univ Chinese Med, Sch Chinese Clin Med, Lanzhou 730000, Peoples R China;[2]Gansu Prov Hosp, Tradit Med Diag & Treatment Ctr, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Sch Chinese Clin Med, Lanzhou 730000, Peoples R China.|[10735]甘肃中医药大学;

年份：2022

卷号：10

期号：24

外文期刊名：ANNALS OF TRANSLATIONAL MEDICINE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000912066700103)】；

基金：This work was supported by the National Natural Science Foundation of China (No. 82260949), the Scientific Research and Innovation Fund Project of Gansu University of Traditional Chinese Medicine (No. 2022KCZD-5), and the Gansu Provincial University Innovation Fund Project (No. 2021A-088).

语种：英文

外文关键词：Endometriosis; autophagy-related genes (ARGs); immune-related genes (IRGs); biomarker

摘要：Background: To identify autophagy- and immune-related hub genes affecting the diagnosis and treatment of endometriosis. Methods: Gene expression data were downloaded from the Gene Expression Omnibus (GEO) (GSE11691 and GSE120103 for training, and GSE7305 for validation). By overlapping the differentially expressed genes (DEGs), Weighted gene co-expression network analysis (WGCNA) module genes, and autophagy-related genes (ARGs), and immune-related genes (IRGs) separately, hub genes were identified using the least absolute shrinkage and selection operator (LASSO)and support vector machine recursive feature elimination (SVM-RFE). The hub genes were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A hub gene-prediction model was constructed and assessed using five-fold cross-validation via five supervised machine-learning algorithms: random forest, the sequential minimal optimization (SMO), K-nearest neighbours (IBK), C4.5 decision tree (J48), and logistics regression. The area under the receiver operating characteristic curve (AUC) was adopted to assess the identification ability of characteristic genes. Results: 1,116 DEGs were obtained from the training cohort, and 22 endometriosis-related IRGs were identified by overlapping the 1,116 DEGs, 3,222 module genes, and 1,793 IRGs. Meanwhile, 45 endometriosis-related ARGs were obtained (1,928 ARGs). Subsequently, nine IRG hub genes (BST2, CCL13, CD86, CSF1, FAM3C, GREM1, ISG20, PSMB8, and S100A11) and nine ARG hub genes (GSK3A, HTR2B, RAB3GAP1, ARFIP2, BNIP3, CSF1, MAOA, PPP1R13L, and SH3GLB2) were obtained by LASSO and SVM-RFE. GO analysis indicated that the ARG hub genes responded to the regulation of autophagy and mitochondrial outer membrane permeabilization, and KEGG enrichment analysis involved serotonergic and dopaminergic synapses. GO analysis also indicated that the IRG hub genes responded to the regulation of leukocyte proliferation and mononuclear cell migration, and KEGG analysis showed enrichment involved in viral protein interaction with cytokines and cytokine receptors. The AUC of the random-forest algorithm of ARGs was 0.975 in the training cohort and 0.940 in the validation cohort, and the AUC of the SMO algorithm of IRGs was 0.907 in the training cohort and 0.8 in the validation cohort. Conclusions: Seventeen hub genes are closely associated with endometriosis. These genes are potential autophagy- and immune-related biomarkers for diagnosis and treatment of endometriosis.