HRH4 may be a new predictor for preterm birth

Abstract

Abstract Background Preterm birth remains a significant contributor to infant morbidity and mortality rates worldwide. It is the primary cause of neonatal deaths and is second only to pneumonia in causing deaths in children under the age of five. The main objective of this study was to identify potential genes associated with preterm birth, with the aim of gaining insight into its underlying mechanisms. Methods We retrieved the mRNA dataset GSE960863 from the Gene Expression Omnibus (GEO) database, subsequently identified differentially expressed genes (DEGs) between preterm and full-term birth cohorts. We employed least absolute shrinkage and selection operator (LASSO) regression analysis, support vector machine-recursive feature elimination (SVM-RFE), and random forest methodologies to discern the central hub gene. The diagnostic potential of this hub gene was evaluated using receiver operating characteristic (ROC) analysis. Furthermore, we harnessed CIBERSORT to gauge the density of infiltrating immune cells (IICs), probing the relationship between our identified hub genes and IICs. Utilizing the GSE108876 miRNA database, we constructed an miRNA-mRNA regulatory framework on the Cytoscape platform. To validate our results, we assessed the expression levels of the hub genes via qPCR within a cohort of 68 patients from our facility, comprising 31 preterm and 37 full-term pregnancies Result Our analysis identified a total of 329 differentially expressed genes (DEGs). From the interplay of LASSO, SVM-RFE, and random forest methods, HRH4 emerged as the central hub gene. ROC analysis further validated HRH4's efficacy in diagnosing preterm birth, with an AUC of 0.834. Moreover, a differential infiltration of seven immune cell types was observed between the control and preterm birth groups: these included memory B cells, CD8 T cells, naive CD4 T cells, resting memory CD4 T cells, monocytes, activated mast cells, and neutrophils. Pearson's correlation underscored a significant association between HRH4 and both resting memory CD4 T cells and monocytes. Delving deeper, a miRNA-mRNA network was articulated, pinpointing seven miRNAs that exhibited an inverse regulatory pattern with HRH4 gene expression. Furthermore, clinical specimen validation revealed a statistically significant upregulation of HRH4 mRNA expression in the preterm group (p༜0.001). Conclusion HRH4 may serve to predict the PTB development and provide a new therapeutic target for PTB.