Abstract
Breast cancer encompasses a group of heterogeneous diseases, each associated with distinct clinical implications. Dozens of molecular biomarkers capable of categorizing tumors into clinically relevant subgroups have been proposed which, though considerably contribute in precision medicine, complicate our understandings toward breast cancer subtyping and its clinical translation. To decipher the networking of markers with diagnostic roles on breast carcinomas, we constructed the diagnostic networks by incorporating 6 publically available gene expression datasets with protein interaction data retrieved from BioGRID on previously identified 1015 genes with breast cancer subtyping roles. The Greedy algorithm and mutual information were used to construct the integrated diagnostic network, resulting in 37 genes enclosing 43 interactions. Four genes, FAM134B, KIF2C, ALCAM, KIF1A, were identified having comparable subtyping efficacies with the initial 1015 genes evaluated by hierarchical clustering and cross validations that deploy support vector machine and k nearest neighbor algorithms. Pathway, Gene Ontology, and proliferation marker enrichment analyses collectively suggest 5 primary cancer hallmarks driving breast cancer differentiation, with those contributing to uncontrolled proliferation being the most prominent. Our results propose a 37-gene integrated diagnostic network implicating 5 cancer hallmarks that drives breast cancer heterogeneity and, in particular, a 4-gene panel with clinical diagnostic translation potential.
Highlights
Despite the considerable contributions of traditional diagnostic and treatment modalities made in the battle against breast cancer, it still remains as the leading cause of women death worldwide[1, 2]
These networks were named by concatenating the gene expression dataset with ‘protein network. Protein interactions (PPI)’, which represents protein interactions retrieved from BioGRID, by ‘&’
Using GSE24450 as the discovery set for finalizing the pivotal gene panel, we selected the 10-fold cross validation approach to assess the trajectory of the prediction power of the gene panels where one gene from the integrated diagnostic network was added at one time
Summary
Despite the considerable contributions of traditional diagnostic and treatment modalities made in the battle against breast cancer, it still remains as the leading cause of women death worldwide[1, 2]. Cancer hallmark network has opened a novel window for predicting patient clinical outcome from a myriad of phenotypic complexities governed by a limited set of organizing principles[30] Under this framework, a set of mutations and copy number variations were reported effective in predicting subtype-specific drug targets in breast cancer[31]; and cancer hallmark-based gene signature sets were identified beneficial in predicting the recurrence and chemotherapy response of stage II colorectal cancer patients[32]. A set of mutations and copy number variations were reported effective in predicting subtype-specific drug targets in breast cancer[31]; and cancer hallmark-based gene signature sets were identified beneficial in predicting the recurrence and chemotherapy response of stage II colorectal cancer patients[32] Inspired by these previous efforts we, in this paper, focus on identifying genes and hallmarks governing the heterogeneity of breast cancer from the network point of view. These update our knowledge toward breast cancer complexity and, more importantly, provide practical insights and tools for breast cancer control
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