ArticleInternational journal of molecular sciences2016
Prioritization of Susceptibility Genes for Ectopic Pregnancy by Gene Network Analysis.
Article in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Genome-wide association study meta-analysis supports association between MUC1 and ectopic pregnancy.Human reproduction (Oxford, England) · 2023Pooled it
- Exploration of the core protein network under endometriosis symptomatology using a computational approach.Frontiers in endocrinology · 2022Pooled it
- Evaluation of novel biomarkers for early pregnancy outcome prediction†.Biology of reproduction · 2024Article
- Candidate gene prioritization for chronic obstructive pulmonary disease using expression information in protein-protein interaction networks.BMC pulmonary medicine · 2021Article
- The role of microRNAs in ectopic pregnancy: A concise review.Non-coding RNA research · 2020Review
- Pristimerin Suppresses Trophoblast Cell Epithelial-Mesenchymal Transition via miR-542-5p/EGFR Axis.Drug design, development and therapy · 2020Article
- Bioinformatic analysis of the molecular mechanism underlying bronchial pulmonary dysplasia using a text mining approach.Medicine · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ectopic pregnancy is a very dangerous complication of pregnancy, affecting 1%-2% of all reported pregnancies. Due to ethical constraints on human biopsies and the lack of suitable animal models, there has been little success in identifying functionally important genes in the pathogenesis of ectopic pregnancy. In the present study, we developed a random walk-based computational method named TM-rank to prioritize ectopic pregnancy-related genes based on text mining data and gene network information. Using a defined threshold value, we identified five top-ranked genes: VEGFA (vascular endothelial growth factor A), IL8 (interleukin 8), IL6 (interleukin 6), ESR1 (estrogen receptor 1) and EGFR (epidermal growth factor receptor). These genes are promising candidate genes that can serve as useful diagnostic biomarkers and therapeutic targets. Our approach represents a novel strategy for prioritizing disease susceptibility genes.
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Registered trials
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