Year: 2026
Type: article
Abstract: OBJECTIVE: The underlying mechanism of recurrent pregnancy loss (RPL) is still not fully understood. We aimed to identify the key genes involved in the process by which obesity influences RPL. METHODS: RPL and obesity data were retrieved from the GEO database. The differentially expressed genes (DEGs) between disease and normal samples among RPL and obesity were selected. Through intersecting the above DEGs, important DEGs were obtained. GO and KEGG analyses were used to analyze the function of these important genes. Two algorithms (LASSO and SVM) and receiver operator characteristic (ROC) analysis were used to optimize the DEGs. Furthermore, the immune infiltration and single gene enrichment analysis were performed to explore the correlations between key biomarkers and immune cells. RESULTS: A total of 1857 RPL-related DEGs and 2880 obesity-related DEGs were selected, respectively. Through intersecting the above two parts of DEGs, 100 important genes were obtained, which were involved in immune response processes such as the EGFR tyrosine kinase inhibitor resistance JAK-STAT signaling pathway, and T cell receptor signaling pathway. Through LASSO, SVM, and ROC analyses, five down-regulated optimal genes in RPL were finally considered as biomarkers in obesity-related RPL: GIPR, KRTAP4-11, NFU1, OPN4, and PRMT7. The five biomarkers showed effective diagnostic ability in RPL, with AUC above 0.8. Furthermore, eosinophils, CD56 bright natural killer cells, and monocytes were significantly correlated with the five biomarkers. CONCLUSION: This study identified five effectively diagnostic genes and explored their correlations with immune cells, providing indications for the following development of diagnostic tools and potential mechanism exploration.
Source: Gynecological Endocrinology
Authors: Haixia Mi, Lingling Zhao, Junhong Guo
Institutions: Wenzhou Medical University, Xinjiang Medical University
Language: English
Subfield: Immunology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7155049540