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.