Literature DB >> 33744280

Integrated analysis of multiple microarray studies to identify potential pathogenic gene modules in preeclampsia.

Heze Xu1, Yin Xie2, Yanan Sun2, Rong Guo3, Dan Lv2, Xuanxuan Li2, Fanfan Li2, Mengzhou He2, Yao Fan2, Dongrui Deng4.   

Abstract

BACKGROUND: Preeclampsia is a life-threatening hypertensive disorder during pregnancy, while underlying pathogenesis and its diagnosis are incomplete.
METHODS: In this study, we utilized the Robust Rank Aggregation method to integrate 6 eligible preeclampsia microarray datasets from Gene Expression Omnibus database. We used linear regression to assess the associations between significant differentially expressed genes (DEGs) and blood pressure. Functional annotation, protein-protein interaction, Gene Set Enrichment Analysis (GSEA) and single sample GSEA were employed for investigating underlying pathogenesis in preeclampsia.
RESULTS: We filtered 52 DEGs and further screened for 5 hub genes (leptin, pappalysin 2, endoglin, fms related receptor tyrosine kinase 1, tripartite motif containing 24) that were positively correlated with both systolic blood pressure and diastolic blood pressure. Receiver operating characteristic indicated that hub genes were potential biomarkers for diagnosis and prognosis in preeclampsia. GSEA for single hub gene revealed that they were all closely related to angiogenesis and estrogen response in preeclampsia. Moreover, single sample GSEA showed that the expression levels of 5 hub genes were correlated with those of immune cells in immunologic microenvironment at maternal-fetal interface.
CONCLUSIONS: These findings provide new insights into underlying pathogenesis in preeclampsia; 5 hub genes were identified as biomarkers for diagnosis and prognosis in preeclampsia.
Copyright © 2021. Published by Elsevier Inc.

Entities:  

Keywords:  Bioinformatics; Biomarkers; Preeclampsia; Robust rank aggregation

Year:  2021        PMID: 33744280     DOI: 10.1016/j.yexmp.2021.104631

Source DB:  PubMed          Journal:  Exp Mol Pathol        ISSN: 0014-4800            Impact factor:   3.362


  6 in total

1.  Bioinformatics methods in biomarkers of preeclampsia and associated potential drug applications.

Authors:  Ying Peng; Hui Hong; Na Gao; An Wan; Yuyan Ma
Journal:  BMC Genomics       Date:  2022-10-19       Impact factor: 4.547

2.  ceRNA Network and Functional Enrichment Analysis of Preeclampsia by Weighted Gene Coexpression Network Analysis.

Authors:  Chenxu Wang; Chaofan Yang; Xinying Wang; Guanlun Zhou; Chao Chen; Guorong Han
Journal:  Comput Math Methods Med       Date:  2022-01-07       Impact factor: 2.238

3.  Gene Expression Network Analysis Identifies Potential Targets for Prevention of Preeclampsia.

Authors:  Yu Xia; Yu-Dong Zhao; Gui-Xiang Sun; Shuai-Shuai Xia; Zheng-Wang Yang
Journal:  Int J Gen Med       Date:  2022-02-02

4.  The Mechanism of Downregulation of Twist1 Inhibiting Trophoblast Invasion and Aggravating the Development of Preeclampsia.

Authors:  Shuangjian Yang; Wenjuan Tong; Yi Li
Journal:  Front Surg       Date:  2022-03-17

5.  Identification and Verification of Potential Hub Genes in Amphetamine-Type Stimulant (ATS) and Opioid Dependence by Bioinformatic Analysis.

Authors:  Wei Zhang; Xiaodong Deng; Huan Liu; Jianlin Ke; Mingliang Xiang; Ying Ma; Lixia Zhang; Ming Yang; Yun Liu; Feijun Huang
Journal:  Front Genet       Date:  2022-03-30       Impact factor: 4.599

6.  Deconvolution of a Large Cohort of Placental Microarray Data Reveals Clinically Distinct Subtypes of Preeclampsia.

Authors:  Tian Yao; Qiming Liu; Weidong Tian
Journal:  Front Bioeng Biotechnol       Date:  2022-07-13
  6 in total

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