HanYu ZHANG, ChengLei GU, XiuFeng XIE, LuYang ZHAO, Yan TU, YuanGuang MENG. Identification of lesion-specific genes and their diagnostic potential in endometriosis via RNA sequencingJ. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL. DOI: 10.12435/j.issn.2095-5227.25091803
Citation: HanYu ZHANG, ChengLei GU, XiuFeng XIE, LuYang ZHAO, Yan TU, YuanGuang MENG. Identification of lesion-specific genes and their diagnostic potential in endometriosis via RNA sequencingJ. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL. DOI: 10.12435/j.issn.2095-5227.25091803

Identification of lesion-specific genes and their diagnostic potential in endometriosis via RNA sequencing

  • Background The pathogenesis of endometriosis (EMs) remains incompletely elucidated, and its diagnosis continues to depend on laparoscopic exploration combined with histopathological examination, resulting in diagnostic delays and the absence of reliable early diagnostic biomarkers. Objective To investigate the differentially expressed genes (DEGs) between ectopic endometrium and eutopic endometrium from patients with EMs and normal endometrium from non-EMs patients. The study aims to analyze the key genes and signaling pathways involved, and to explore their diagnostic value for endometriosis.Methods  Paired samples of ectopic and eutopic endometrium were collected from 7 patients with EMs, along with normal endometrium from 5 non-EMs for RNA sequencing. DEGs were found by bioinformatics technology, and Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction network. The expression levels of partial DEGs were verified in paired clinical specimens via Quantitative real time polymerase chain reaction (RT-qPCR). Receiver Operating Characteristic (ROC) curves from the Gene Expression Omnibus (GEO) databases were compared to evaluate the diagnostic values of each gene. Results We screened 98 significant DEGs, of which 31 were up-regulated and 67 were downregulated. KEGG enrichment analysis showed that PI3K-Akt signal pathway might be involved in the pathogenesis of endometriosis. In both clinical samples and public databases, the genes C7, ITGA11, JCHAIN, LTF, NR4A3, and NTRK2 were significantly upregulated in EMs (P<0.01). Among them, C7, ITGA11 and NTRK2 demonstrated significant potential as diagnostic biomarkers, with Area Under the Curve (AUC) values all exceeding 0.90.Conclusion In conclusion, this study for the first time reveals that ITGA11 is specifically overexpressed in endometriotic lesions and may promote disease progression by activating the PI3K-Akt signaling pathway. These findings highlight its potential as a novel diagnostic biomarker, warranting further validation in non-invasive samples such as menstrual blood to facilitate clinical translation.
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