Identification of macrophage-related immune characteristic genes in recurrent miscarriage through bioinformatics approaches
10.3760/cma.j.cn101441-20231214-00362
- VernacularTitle:基于生物信息学方法筛选复发性流产中巨噬细胞相关的免疫特征基因
- Author:
Yifen GUO
1
;
Shuyue REN
;
Zhixian GAO
;
Yan GU
Author Information
1. 天津医科大学第二医院计划生育科，天津 300211
- Publication Type:Journal Article
- Keywords:
Machine learning;
Recurrent miscarriage;
Bioinformatics;
Immune infiltration;
Weighted gene co-expression network analysis
- From:
Chinese Journal of Reproduction and Contraception
2024;44(6):617-627
- CountryChina
- Language:Chinese
-
Abstract:
Objectives:To screen out genes potentially involved in the dysregulation of immune microhomeostasis at the maternal-fetal interface of recurrent miscarriage (RM) patients, and to identify novel biomarkers of RM by bioinformatic analysis.Methods:The dataset GSE165004 of endometrial tissues from RM patients ( n=24) and normal women as the control ( n=24) was downloaded from the GEO database, and differentially expressed genes (DEGs) and immune-related modules were analyzed by using the R language's Limma package, along with CIBERSORT immune infiltration and Weighted Gene Co-expression Network Analysis (WGCNA). The functional associations of these core genes were evaluated through Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA). Finally, we used the decidual tissue dataset GSE161969 to further validate the diagnostic value of these key genes. Results:Differential analysis identified 580 DEGs, and 3 271 immune-related modular genes were selected by WGCNA analysis. FGF2, ANO1, and LAPTM5 were subsequently identified as key genes through machine learning techniques. GSVA analysis further revealed critical roles of FGF2, ANO1 and LAPTM5 in immune infiltration and macrophage pathways. Conclusion:FGF2, ANO1 and LAPTM5 might participate in the immuno-related pathogenesis of RM, and present potential biomarkers for the early diagnosis and treatment of RM.
