Identification of metabolic reprogramming-associated biomarkers in endometriosis through integrated bioinformatics analysis
Jia Zhen, Ziyuan Zhao, Qi Wu, Xinqian Dong, Zilu Wang, Xiaoxue Han, Wei Shi, Li Xu

TL;DR
This study identifies key metabolic genes linked to endometriosis, which could help in diagnosis and understanding the disease's progression.
Contribution
The study introduces a novel integrated bioinformatics approach to identify metabolic reprogramming biomarkers in endometriosis.
Findings
Seven key genes showed high diagnostic value with AUC > 0.8 in endometriosis.
HSP90B1 overexpression increased metabolic markers in Z12 cells.
Immune cell infiltration was associated with key metabolic genes in endometriosis.
Abstract
Endometriosis (EMs), a common gynecological disorder, involves complex molecular mechanisms. Metabolic reprogramming (MR) has been recognized as a hallmark of EMs, contributing to lesion survival and immune microenvironment remodeling. This study aimed to identify MR-associated hub genes and pathways associated with EMs through integrated multi-omics analyses. EMs-related datasets were downloaded from the Gene Expression Omnibus database, including training sets (GSE51981 and GSE7305) and validation sets (GSE25628 and GSE141549). MR related genes were retrieved from the Genecards database. EMs-related differentially expressed genes (DEGs) were identified, and WGCNA was performed to identify module genes. Protein-protein interaction (PPI) networks were constructed. The expression of key genes was validated in an external dataset and clinical samples (immunohi0stochemistry). The…
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Taxonomy
TopicsEndometriosis Research and Treatment · GDF15 and Related Biomarkers · Genetic Syndromes and Imprinting
