Unravelling Breast and Prostate Common Gene Signatures by Bayesian Network Learning Breast invasive carcinoma (BRCA) and prostate adenocarcinoma (PRAD) are two of the most common types of cancer in women and men, respectively. As hormonedependent tumours, BRCA and PRAD share considerable underlying biological similarities worth being exploited. The disclosure of gene networks regulating both types of cancers would potentially allow the development of common therapies, greatly contributing to disease management and health economics. A methodology based on Bayesian network learning is proposed to unravel breast and prostate common gene signatures. BRCA and PRAD RNASeq data from The Cancer Genome Atlas (TCGA) measured over (sim 20000) genes were used. A prior dimensionality reduction step based on sparse logistic regression with elastic net penalisation was employed to select a set of relevant genes and provide more interpretable results. The Bayesian networks obtained were validated against information from STRING, a database containing known gene interactions, showing high concordance.

Cancer Biomarker Discovery from Gene Coexpression Networks Using Community Detection Methods Finding the network biomarkers of cancers and the analysis of cancer driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Clusters of genes in coexpression networks are commonly known as functional units. This work is based on the hypothesis that the dense clusters or communities in the gene coexpression networks of cancer patients may represent functional units regarding cancer initiation and progression. In this study, RNAseq gene expression data of three cancers  Breast Invasive Carcinoma (BRCA), Colorectal Adenocarcinoma (COAD) and Glioblastoma Multiforme (GBM)  from The Cancer Genome Atlas (TCGA) are used to construct gene coexpression networks using Pearson Correlation. Six wellknown community detection algorithms are applied on these networks to identify communities with five or more genes. A permutation test is performed to further mine the communities that are conserved in other cancers, thus calling them conserved communities. Then survival analysis is performed on clinical data of three cancers using the conserved community genes as prognostic covariates. The communities that could distinguish the cancer patients between high and lowrisk groups are considered as cancer biomarkers. In the present study, 16 such network biomarkers are discovered.