Identification and verification of feature biomarkers associated with immune cells in recurrent pregnancy loss

The aim of this study was to investigate the causes, diagnostic markers, and treatment methods for recurrent pregnancy loss (RPL) using bioinformatics approaches.

Bioinformatics methods were utilized to analyze gene expression databases to identify key genes and modules associated with RPL. Weighted gene co-expression network analysis (WGCNA) was employed to identify gene sets related to maternal-fetal immunity. Gene set variation analysis (GSVA) and protein-protein interaction networks were used to explore signaling pathways and molecular interactions in RPL. Immune cell infiltration was assessed using single-sample gene set enrichment analysis (ssGSEA).

Thirteen genes were identified as potential diagnostic markers, some of which were involved in placental amino acid transport, glucose absorption, and reactive oxygen species production. Several gene sets related to protein transport, steroid synthesis, and glycosaminoglycan degradation were found to be associated with RPL. Immune cell infiltration analysis found that CD56bright NK cells and monocytes showed significantly increased infiltration in RPL and were associated with key hub genes. The validation of hub genes, including PCSK5, CCND2, SLC5A3, RASAL1, MYZAP, MFAP4, and P2RY14, as potential diagnostic markers, showed promising value.

This study contributes to a better understanding of the etiology of RPL and potential diagnostic markers. The identified immune-related gene sets, signaling pathways, and immune cell infiltrations provide valuable insights for future research and therapeutic advancements in RPL.