Purpose: Immune disorders lead to placental dysfunction and fetal growth restriction (FGR), but current research on the immune regulation mechanisms of FGR is insufficient. Therefore, this study aimed to construct an immune-related competing endogenous RNA (ceRNA) network to predict FGR onset risk.
Methods: Based on microarray data from the GEO database, CIBERSORT was used to analyze immune cell infiltration. Weighted gene co-expression network analysis (WGCNA) was used to screen immune-related module genes with differential expression (DE) in FGR. A ceRNA network was constructed by integrating long non-coding RNA (lncRNA)-mRNA co-expression relations, lncRNA-microRNA (miRNA) relations, and miRNA-mRNA negative regulatory relations. The diagnostic values of key genes in the network and their relationships with immune cell infiltration were further validated.
Results: By comparing FGR and normal samples, 442 DE mRNAs, 57 DE miRNAs, and 241 DE lncRNAs were obtained. Furthermore, four types of immune cells exhibited significantly different infiltration levels. WGCNA revealed 236 immune-related DE mRNAs that were involved in hormone secretion and immune cell differentiation. We then constructed a ceRNA network comprising 16 lncRNAs, 16 miRNAs, and 21 mRNAs through co-expression analysis and miRNA prediction. Receiver operating characteristic curves confirmed the superior predictive performance of key genes in the network for FGR. The validation dataset confirmed the increased infiltration of M1 macrophages in FGR samples and their positive correlations with NEURL1 and ODF3B expression.
Conclusion: We constructed an immune-related ceRNA regulatory network wherein key genes are positively correlated with M1 macrophage infiltration and can be used for FGR diagnosis.