Preeclampsia: a Bioinformatics Approach Through Protein-protein Interaction Networks Analysis

Overview

Journal BMC Syst Biol

Publisher Biomed Central

Specialty Biology

Date 2012 Aug 10

PMID 22873350

Citations 15

Authors

Eduardo Tejera

Joao Bernardes

Irene Rebelo

Affiliations

Soon will be listed here.

Abstract

Background: In this study we explored preeclampsia through a bioinformatics approach. We create a comprehensive genes/proteins dataset by the analysis of both public proteomic data and text mining of public scientific literature. From this dataset the associated protein-protein interaction network has been obtained. Several indexes of centrality have been explored for hubs detection as well as the enrichment statistical analysis of metabolic pathway and disease.

Results: We confirmed the well known relationship between preeclampsia and cardiovascular diseases but also identified statistically significant relationships with respect to cancer and aging. Moreover, significant metabolic pathways such as apoptosis, cancer and cytokine-cytokine receptor interaction have also been identified by enrichment analysis. We obtained FLT1, VEGFA, FN1, F2 and PGF genes with the highest scores by hubs analysis; however, we also found other genes as PDIA3, LYN, SH2B2 and NDRG1 with high scores.

Conclusions: The applied methodology not only led to the identification of well known genes related to preeclampsia but also to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which eventually need to be validated experimentally. Moreover, new possible connections were detected between preeclampsia and other diseases that could open new areas of research. More must be done in this area to resolve the identification of unknown interactions of proteins/genes and also for a better integration of metabolic pathways and diseases.

Citing Articles

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Islam M, Islam M, Rahman M, Islam M, Al Amin M, Ahmed K Pharmaceutics. 2022; 14(8).

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Identifying preeclampsia-associated genes using a control theory method.

Li X, Liu L, Whitehead C, Li J, Thierry B, Le T Brief Funct Genomics. 2022; 21(4):296-309.

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Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers.

Islam M, Rahman M, Islam M, Islam M, Mamun M, Azad A Heliyon. 2022; 8(2):e08892.

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A Multi-Objective Approach for Drug Repurposing in Preeclampsia.

Tejera E, Perez-Castillo Y, Chamorro A, Cabrera-Andrade A, Sanchez M Molecules. 2021; 26(4).

PMID: 33546161 PMC: 7913128. DOI: 10.3390/molecules26040777.

Comprehensive Analysis of Differently Expressed and Methylated Genes in Preeclampsia.

Xu W, Ru P, Gu Z, Zhang R, Pang X, Huang Y Comput Math Methods Med. 2020; 2020:2139270.

PMID: 33204297 PMC: 7652635. DOI: 10.1155/2020/2139270.

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