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Updated: Dec 4, 2025

The 4-vessel Sampling Approach to Integrative Studies of Human Placental Physiology In Vivo
Published on: August 2, 2017
Integrative analysis provides multi-omics evidence for the pathogenesis of placenta percreta
Qingyuan Jiang1,2, Lei Dai3, Na Chen3
1Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University and Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.
Abstract:
Pernicious placenta previa with placenta percreta (PP) is a catastrophic condition during pregnancy. However, the underlying pathogenesis remains unclear. In the present study, the placental tissues of normal cases and PP tissues of pernicious placenta previa cases were collected to determine the expression profile of protein-coding genes, miRNAs, and lncRNAs through sequencing. Weighted gene co-expression network analysis (WGCNA), accompanied by miRNA target prediction and correlation analysis, were employed to select potential hub protein-coding genes and lncRNAs. The expression levels of selected protein-coding genes, Wnt5A and MAPK13, were determined by quantitative PCR and immunohistochemical staining, and lncRNA PTCHD1-AS and PAPPA-AS1 expression levels were determined by quantitative PCR and fluorescence in situ hybridization. The results indicated that 790 protein-coding genes, 382 miRNAs, and 541 lncRNAs were dysregulated in PP tissues, compared with normal tissues. WGCNA identified coding genes in the module (ME) black and ME turquoise modules that may be involved in the pathogenesis of PP. The selected potential hub protein-coding genes, Wnt5A and MAPK13, were down-regulated in PP tissues, and their expression levels were positively correlated with the expression levels of PTCHD1-AS and PAPPA-AS1. Further analysis demonstrated that PTCHD1-AS and PAPPA-AS1 regulated Wnt5A and MAPK13 expression by interacting with specific miRNAs. Collectively, our results provided multi-omics data to better understand the pathogenesis of PP and help identify predictive biomarkers and therapeutic targets for PP.
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