Molecular signatures of ovarian diseases: Insights from network medicine perspective

Medi Kori, Esra Gov, Esra Göv, Gov E, Kazim Yalcin Arga, Kazım Yalçın Arğa
Systems biology in reproductive medicine · 2016 · vol. 62(4) , pp. 266–282 · doi:10.1080/19396368.2016.1197982 · PMID:27341345 · W2472550104
review OA: bronze CC0 ⤵ 3 in-corpus citations
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This study integrated transcriptomic data from ovarian cancer, endometriosis, and PCOS with biological networks to identify common and unique molecular signatures related to signaling, cell cycle, and apoptosis pathways.

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Abstract

Dysfunctions and disorders in the ovary lead to a host of diseases including ovarian cancer, ovarian endometriosis, and polycystic ovarian syndrome (PCOS). Understanding the molecular mechanisms behind ovarian diseases is a great challenge. In the present study, we performed a meta-analysis of transcriptome data for ovarian cancer, ovarian endometriosis, and PCOS, and integrated the information gained from statistical analysis with genome-scale biological networks (protein-protein interaction, transcriptional regulatory, and metabolic). Comparative and integrative analyses yielded reporter biomolecules (genes, proteins, metabolites, transcription factors, and micro-RNAs), and unique or common signatures at protein, metabolism, and transcription regulation levels, which might be beneficial to uncovering the underlying biological mechanisms behind the diseases. These signatures were mostly associated with formation or initiation of cancer development, and pointed out the potential tendency of PCOS and endometriosis to tumorigenesis. Molecules and pathways related to MAPK signaling, cell cycle, and apoptosis were the mutual determinants in the pathogenesis of all three diseases. To our knowledge, this is the first report that screens these diseases from a network medicine perspective. This study provides signatures which could be considered as potential therapeutic targets and/or as medical prognostic biomarkers in further experimental and clinical studies. Abbreviations DAVID: Database for Annotation, Visualization and Integrated Discovery; DEGs: differentially expressed genes; GEO: Gene Expression Omnibus; KEGG: Kyoto Encyclopedia of Genes and Genomes; LIMMA: Linear Models for Microarray Data; MBRole: Metabolite Biological Role; miRNA: micro-RNA; PCOS: polycystic ovarian syndrome; PPI: protein-protein interaction; RMA: Robust Multi-Array Average; TF: transcription factor.

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Condition tags

endometriosis

MeSH descriptors

Ovarian Diseases Transcriptome Biomarkers Biomarkers Female Humans Metabolic Networks and Pathways Molecular Diagnostic Techniques Ovarian Diseases Ovarian Diseases Ovarian Neoplasms Ovarian Neoplasms Ovarian Neoplasms Proteome

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