Integrative analysis provides multi-omics evidence for the pathogenesis of placenta percreta.
Qingyuan JiangLei DaiNa ChenJunshu LiYan GaoJing ZhaoLi DingChengbin XieXiaolian YiHongxin DengXiaodong WangPublished in: Journal of cellular and molecular medicine (2020)
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.
Keyphrases
- network analysis
- poor prognosis
- genome wide identification
- binding protein
- genome wide
- bioinformatics analysis
- gene expression
- genome wide analysis
- stem cells
- signaling pathway
- cell proliferation
- oxidative stress
- transcription factor
- magnetic resonance imaging
- risk assessment
- high resolution
- machine learning
- copy number
- quantum dots
- artificial intelligence
- big data
- human health
- single molecule