Integrated Bioinformatics Analysis to Screen Hub Gene Signatures for Fetal Growth Restriction. (30th March 2023)

• Record Type: Journal Article
• Authors: Yang, Jingjin; Liu, Yuxin; Dong, Minyue
• Other Names: Xu Ying-Kun Academic Editor.
• Is Part Of: Genetical research. Volume 2023(2023)
• Publication Date: 2023-03-30
• Subjects: Genetics -- Periodicals; Heredity -- Periodicals; 576.5
• DOI: 10.1155/2023/3367406
• Languages: English
• ISSNs: 0016-6723

Abstract

Abstract : Background . Fetal growth restriction (FGR) is the impairment of the biological growth potential of the fetus and often leads to adverse pregnancy outcomes. The molecular mechanisms for the development of FGR, however, are still unclear. The purpose of this study is to identify critical genes associated with FGR through an integrated bioinformatics approach and explore the potential pathogenesis of FGR. Methods . We downloaded FGR-related gene microarray data, used weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs), and protein-protein interaction (PPI) networks to screen hub genes. The GSE24129 gene set was used for validation of critical gene expression levels and diagnostic capabilities. Results . A weighted gene co-expression network was constructed, and 5000 genes were divided into 12 modules. Of these modules, the blue module showed the closest relationship with FGR. Taking the intersection of the DEGs and genes in the blue module as pivotal genes, 277 genes were identified, and 20 crucial genes were screened from the PPI network. The GSE24129 gene set verified the expression of 20 genes, and CXCL9, CXCR3, and ITGAX genes were identified as actual pivotal genes. The expression levels of CXCL9, CXCR3, and ITGAX were increased in both the training and validation sets, and ROC curve validation revealed that these three pivotal genes had a significant diagnostic ability for FGR. Single-gene GSEA results showed that all three core genes activated "hematopoietic cell lineage" and "cell adhesion molecules" and inhibited the "cGMP-PKG signaling pathway" in the development of FGR. CXCL9, CXCR3, and ITGAX may therefore be closely associated with the development of FGR and may serve as potential biomarkers for the diagnosis and treatment of FGR.