Identification of Genes Involved in Breast Cancer Metastasis by Integrating Protein–Protein Interaction Information with Expression Data

Using hidden Markov model to predict recurrence of breast cancer based on sequential patterns in gene expression profiles

A new approach is presented to predict breast cancer recurrence through gene expression profiles using hidden Markov models (HMM). In this regard, 322 genes were selected from 44 published gene lists related to breast cancer prognosis. Afterwards, using gene set enrichment analysis, 922 gene sets were found from subsets of genes with the same biological meaning. In order to extract the sequential patterns from gene expression data, we ranked the gene sets using appropriate criteria and used HMM in which the ranked gene sets considered as observation sequences and hidden states represented priority of gene sets for discriminating between expression profiles. In this experiment, seven publicly available microarray datasets, including 1271 breast tumor samples, were used to classify cancer patients into two groups according to risk of recurrence. Our experiments indicated the greater performance and more robustness of the proposed model compared with other widely used classification methods.

Unveiling network-based functional features through integration of gene expression into protein networks

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.