Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes

Abstract

BackgroundIdentification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally.ResultsWe provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. The network measures under consideration are based on appropriate formulations of various centrality measures: betweenness, clustering coefficients, degree centrality, random walk distances, graph-theoretical distances, and Jaccard index centrality.ConclusionsAmong all the studied centrality-based graph-theoretical properties, we show that a betweenness-based measure differentiates BRCA genes across all normal versus tumor network pairs, than the rest of the popular centrality-based measures. The AUROC and AUPR values of the gene lists ordered with respect to the measures under study as compared to NCBI BioSystems pathway and the COSMIC database of cancer genes are the largest with the betweenness-based differentiation, followed by the measure based on degree centrality. In order to test the robustness of the suggested measures in prioritizing cancer genes, we further tested the two most promising measures, those based on betweenness and degree centralities, on randomly rewired networks. We show that both measures are quite resilient to noise in the input interaction network. We also compared the same measures against a state-of-the-art alternative disease gene prioritization method, MUFFFINN. We show that both our graph-theoretical measures outperform MUFFINN prioritizations in terms of ROC and precions/recall analysis. Finally, we filter the ordered list of the best measure, the betweenness-based differentiation, via a maximum-weight independent set formulation and investigate the top 50 genes in regards to literature verification. We show that almost all genes in the list are verified by the breast cancer literature and three genes are presented as novel genes that may potentialy be BRCA-related but missing in literature.

Network‐based integration method for potential breast cancer gene identification

“…The top 50 candidate genes from paired or all samples of the TCGA breast cancer data achieved the best classification performance for most expression datasets, indicating the effectiveness of our prioritization algorithm. With few genes overlapping between 20 potential breast cancer genes and the top 50 genes from Dopazo and Erten (2017) or from Lopez‐Cortes et al (2018), good classification performance demonstrated that these methods identified breast cancer‐associated genes effectively from different aspects.…”

Information Thermodynamics of the Cell Signal Transduction as a Szilard Engine

“…Recently, many theories for computing protein–protein networks and gene expression networks have been developed [ 21 , 22 ]. Signaling entropy was recently investigated from the viewpoint of genome informatics [ 23 , 24 ], and its availability was confirmed [ 25 , 26 ]. In these studies, signaling entropy was defined using the transient probability obtained from each node in a network graph of the transcriptome profile of a single cell in order to quantify the gene activation levels of its molecular pathways.…”

Ranking Cancer Drivers via Betweenness-based Outlier Detection and Random Walks

“…A key current challenge in cancer genomics is to distinguish driver mutations that are causal for cancer progression from passenger mutations that do not confer any selective advantage. Consequently, several computational methods have been proposed for the identification of cancer driver genes or driver modules of genes by integrating mutations data with various other types of genetic data [3,4,5,6,7,8,9,10]; see [11,12,13,14] for recent comprehensive evaluations and surveys on the topic.…”

“…Masica and Karchin present one of the early models based on such a strategy by employing statistical methods for setting up the correlation between mutated genes and the differentialy expressed genes to identify candidate drivers [1]. Many different models follow a similar trail by further incorporating biological pathway/network information for setting up such a correlation [6,19,20,21,22,23]. DriverNet is among the notable approaches employing mutations data in addition to gene expression and biological network data [19].…”