Identifying diagnostic gene biomarkers of moderate or severe endometriosis

doi:10.3969/j.issn.1006-7795.2024.02.021

Abstract

Abstract: Objective To identify potential diagnostic markers for moderate or severe endometriosis（EM）.Methods Two publicly available gene expression profiles (GSE51981 and GSE7305 datasets) from human EM and control samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 48 moderate or severe EM and 71 control samples. The Least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were performed to identify candidate biomarkers. The area under the receiver operating characteristic (AUC) curve value was obtained and used to evaluate discriminatory ability. The expression level and diagnostic value of the biomarkers in EM were further validated in the GSE7305 dataset. The compositional patterns of the 22 types of immune cell fraction in EM were estimated based on the merged cohorts by using CIBERSORT. Results A total of 73 DEGs were identified between samples with moderate-to-severe EM and normal controls. These DEGs were significantly enriched in malignant tumors and immune-related pathways. Thirteen candidate gene biomarkers were further screened by using two machine learning methods, LASSO regression model and SVM-RFE analysis. Among them, the ADAT1 gene showed high diagnostic value for moderate-to-severe EM, which was validated in the validation dataset. Immune infiltration analysis showed that the levels of plasma cells and T cells follicular helper were significantly higher in moderate-to-severe EM than those in the normal group. The diagnostic marker gene ADAT1 was positively correlated with activated dendritic cells, T cells gamma delta, T cells CD4 memory activated, eosinophils, neutrophils, and B cells naive. In contrast, ADAT1 was negatively correlated with plasma cells, T cells CD8, T cells regulatory and monocytes.Conclusion ADAT1 may be a diagnostic biomarker for moderate-to-severe EM, providing new insights into the occurrence, progression, and molecular mechanisms of EM.

Key words: endometriosis, bioinformatics, machine learning, immune infiltration, ADAT1

CLC Number:

R737.33

iang Yan, Zhao Xuanyu, Sui Feng. Identifying diagnostic gene biomarkers of moderate or severe endometriosis[J]. Journal of Capital Medical University, 2024, 45(2): 322-332.

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URL: https://journal03.magtech.org.cn/Jweb_sdykdxxb/EN/10.3969/j.issn.1006-7795.2024.02.021

https://journal03.magtech.org.cn/Jweb_sdykdxxb/EN/Y2024/V45/I2/322

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