Exploration of the molecular characteristics and potential clinical significance of shared immune-related genes between preterm preeclampsia and term preeclampsia

• Published in: BMC pregnancy and childbirth Vol. 24; no. 1; pp. 543 - 17
• Main Authors: Huang, Zhengrui, Sun, Lu, Gao, Yudie, Shi, Meiting, Zhang, Ping, Ding, Yuzhen, Wang, Jian, Wei, Jiachun, Yang, Xiuli, Li, Ruiman
• Format: Journal Article
• Language: English
• Published: England BioMed Central 15.08.2024; BMC
• Subjects: Adult; Aspirin; Clinical Relevance; Datasets; Disease prevention; Female; Fetuses; Gene Expression Profiling; Gene Regulatory Networks; Genes; Gestational age; Humans; Hypertension; Immune signature; Immune system; Immunology; Immunotherapy; Nomogram; Placenta; Pre-Eclampsia - genetics; Pre-Eclampsia - immunology; Preeclampsia; Pregnancy; Premature birth; Premature Birth - genetics; Premature Birth - immunology; Preterm birth; Preterm preeclampsia; Regression analysis; Sample size; Software; Statistical analysis; Term preeclampsia; Womens health; Term preeclampsia; Preterm birth; Immune signature; Immunotherapy; Nomogram; Preterm preeclampsia
• DOI: 10.1186/s12884-024-06526-8
• ISSN: 1471-2393

Preeclampsia is a severe obstetric disorder that significantly affects the maternal and neonatal peri-partum safety and long-term quality of life. However, there is limited research exploring the common mechanisms and potential clinical significance between early-onset preeclampsia and full-term preeclampsia from an immunological perspective. In this study, data analysis was conducted. Initially, immune-related co-expressed genes involving both subtypes of preeclampsia were identified through Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were further employed to investigate the shared pathways regulated by immune-related genes. Binary logistic regression identified co-expressed genes with diagnostic value for preeclampsia, and a diagnostic model was constructed. Gene Set Enrichment Analysis (GSEA) predicted the potential biological functions of the selected genes. Lasso and Cox regression analyses identified genes closely associated with gestational duration, and a risk score model was established. A 4-gene feature, immune-related gene model for predicting the risk of preterm birth in preeclamptic pregnant women, was developed and validated through qPCR experiments. Immune cell infiltration analysis determined differences in immune cell infiltration between the two subtypes of preeclampsia. This study identified 4 immune-related co-expressed genes (CXCR6, PIK3CB, IL1RAP, and OSMR). Additionally, diagnostic and preterm birth risk prediction models for preeclampsia were constructed based on these genes. GSEA analysis suggested the involvement of these genes in the regulation of galactose metabolism, notch signaling pathway, and RIG-I like receptor signaling pathway. Immune pathway analysis indicated that the activation of T cell co-inhibition could be a potential intervention target for immunotherapy in early-onset preeclampsia. Our study provides promising insights into immunotherapy and mechanistic research for preeclampsia, discovering novel diagnostic and intervention biomarkers, and offering personalized diagnostic tools for preeclampsia.