An Integrative Computational Approach Based on Expression Similarity Signatures to Identify Protein–Protein Interaction Networks in Female-Specific Cancers

Breast, ovarian and endometrial cancers have a major impact on mortality in women. These tumors share hormone-dependent mechanisms involved in female-specific cancers which support tumor growth in different manners. Integrated computational approaches may allow to better detect genomic similarities between these different female-specific cancers, helping to deliver more sophisticated diagnosis, and precise treatments. Recently, several TCGA initiatives have encouraged integrated analyses of multiple cancers rather than individual tumors. These studies, revealed common genetic alterations (driver genes) even in clinically distinct entities such as breast, ovarian, and endometrial cancers. In this study, we aimed to identify expression similarity signatures by extracting common genes among TCGA Breast (BRCA), Ovarian (OV), and Uterine Corpus Endometrial Carcinoma (UCEC) cohorts and infer co-regulatory protein-protein interaction networks that might have a relationship with estrogen signaling pathway. Thereby, we carried out an unsupervised PCA-based computational approach, using RNA sequencing data of 2015 female cancer and 148 normal samples, in order to simultaneously capture the inter–tumors data heterogeneity. Firstly, we identified tumor-associated genes from gene expression profiles. Secondly, we investigated the signaling pathways and co-regulatory protein-protein interaction networks underlying these three cancers by leveraging the Ingenuity Pathway Analysis software. In details, we discovered 1643 expression similarity signatures (638 down–regulated and 1005 up–regulated genes, with respect to normal phenotype), denoted as tumor-associated genes. Through functional genomic analyses, we assessed that these genes were involved in the regulation of cell-cycle dependent mechanisms, including metaphase kinetochore formation and estrogen-dependent S-phase entry. Furthermore, we generated putative co-regulatory protein-protein interaction networks, based on upstream regulators such as ERBB2/HER2 gene. Moreover, we provided an ad hoc bioinformatic workflow with a manageable list of inter-tumor expression similarity signatures for the three female-specific cancers. The expression similarity signatures identified in this study might uncover potential estrogen-dependent molecular mechanisms promoting carcinogenesis.