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| Bioinformatics Screening of Autophagy Differentially Expressed Genes Related to Early Diagnosis of Endometriosis |
| SHEN Zhan-jun, ZHANG Ling, FU Kai-li, et al |
| The Maternal and Child Health and Family Planning Service Center of Liling City, Zhuzhou Hunan 412200 |
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Abstract 【Objective】To explore the bioinformatics screening of autophagy differentially expressed genes (ATDEGs) related to the early diagnosis of Endometriosis (EMT).【Methods】We used the Limma package to obtain differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) database for EMT and healthy control (HC), and conducted GSEA enrichment analysis. The functional modules related to EMT were obtained through the weighted Gene co-expression network analysis, and the genes in the functional modules were enriched with GO and KEGG. ATDEGs had been downloaded from the Genecards database. Taking the intersection of three parts, we obtained ATDEGs related to EMT and constructed a protein interaction network diagram; The LASSO algorithm was used to screen diagnostic genes and evaluate their diagnostic ability through the receiver operating characteristic (ROC) curve.【Results】This study obtained 62 ATDEGs and identified the most significant module using Cytoscape's MCODE plugin, which includes 17 hub genes; Further LASSO analysis identified 7 diagnostic genes, which were validated in external datasets, indicating their good diagnostic ability.【Conclusion】 The results of this study contribute to the early diagnosis and intervention of EMT, and provide a theoretical basis for further clinical work and mechanism research.
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Received: 09 May 2023
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2023, Vol. 40 
