Decoding Genetic Risk Factors in Recurrent Pregnancy Loss: An Integrative Chromosomal and Bioinformatics Approach.

Abstract

Recurrent pregnancy loss (RPL) is characterized by two or more consecutive pregnancy losses, often associated with genetic, immunological, endocrine, and anatomical abnormalities. Among these, chromosomal abnormalities, including aneuploidies and submicroscopic copy number variations (CNVs), play a critical role in adverse pregnancy outcomes. A total of 125 fetal specimens were collected, of which 118 were included after applying predefined exclusion criteria. DNA isolated from products of conception and fetal tissues was subjected to quantitative fluorescent PCR (QF-PCR) for rapid aneuploidy screening. A subset of 30 samples with selected QF-PCR outcomes underwent array comparative genomic hybridization (aCGH). Identified CNVs were interpreted according to ACMG/ClinGen guidelines, followed by bioinformatics analyses using FunRich, WebGestalt, KEGG, and STRING to explore functional annotations and pathway enrichment. Among 118 samples, QF-PCR identified aneuploidy in 36 cases (30.5%), including monosomy (n = 20) and trisomy (n = 16), while 82 cases were reported as normal. Maternal age showed a significant association with chromosomal abnormalities (p < 0.05), and a weak negative correlation was observed between gestational age and aneuploidy risk (r = - 0.238, p = 0.008855). In the aCGH cohort (n = 30), clinically relevant CNVs were identified, including pathogenic and likely pathogenic variants, as well as variants of uncertain significance (VOUS). Notably, genes such as CFHR3, TNFRSF4, UGT2B17, CD24, MSR1, and the PSG gene family were implicated. Functional enrichment analysis revealed involvement in immune-inflammatory pathways, endocrine regulation, lipid metabolism, extracellular matrix remodeling, and placental development. This study demonstrates the enhanced diagnostic utility of combining aCGH with QF-PCR for identifying chromosomal abnormalities in RPL. However, the observed associations between CNVs and biological pathways are exploratory in nature and require validation in larger, well-powered cohorts.