Identification and evaluation of network modules for the prognosis of basal-like breast cancer

Abstract

PurposeBasal-like breast cancer (BLBC) is a molecular subtype of breast cancer associated with poor clinical outcome, although some patients with BLBC experience long-term survival. Apart from nodal status, current clinical/histopathological variables show little capacity to identify BLBC patients at either high- or low-risk of disease recurrence. Accordingly, we sought to develop a network based genomic predictor for predicting the outcome of patients with BLBC.Experimental DesignWe performed network analysis on global gene expression profiling data of BLBCs, and identified BLBC network modules associated with AP-1 transcription, G-protein coupled receptors, and T-, B-, and NK-cells that are significant predictors of BLBC patient survival.ResultsIn gene expression and tissue microarray (TMA) validation cohorts of 210 and 102 BLBC patients, respectively, the identified network modules were robustly associated with patient outcome. In the gene expression validation cohort, the Kaplan-Meier estimate for 10-year survival in the low-risk group was 90%, whereas in the high-risk group it was a 56%. In the TMA cohort, the Kaplan-Meier estimate for 10-year survival in the low-risk group was 98%, whereas in the high-risk group it was 71%.ConclusionsThe capacity to distinguish between patients with BLBC at high- or low-risk of recurrence at the time of diagnosis could permit timely intervention with more aggressive therapeutic regimens in those patients predicted to be high-risk, and to avoid such therapy in low-risk patients.

Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis

“…Microarray analysis has contributed to our understanding of the heterogeneity and complexity of BC 3 , and it has enabled the identification of gene signatures for diagnosis, molecular characterization, prognosis prediction and treatment recommendation 4 – 6 . Networks of topological characteristics can potentially serve as predictive biomarkers through network-based classification 7 , 8 , and the topology of biological networks has increasingly been used to complement studies of individual genes or gene sets 9 , 10 . Several gene network analysis tools based on various methodologies have been developed, including GeneMania 11 , BisoGenet 12 , Cytoscape 13 , and DAVID 14 .…”

“…Gene co-expression network analysis (GCNA) provides insight into novel biological mechanisms and is complementary to standard differential expression (DE) analysis. This method has proven to be an attractive and effective tool for understanding BC 10 , 15 – 17 . However, gene co-expression networks (GCN) from single transcriptomic studies are often less informative and generalizable due to cohort bias and a limited sample size, whereas the use of integrated analysis through the combination of multiple transcriptomic studies provides more accurate and comprehensive results 18 .…”

Discovery and characterization of functional modules associated with body weight in broilers

“…The module-based approach has already been used to cluster genes into functional groups and to predict protein functions 19 . Investigation of functional modules has mainly been focused on human diseases such as obesity 20 , breast cancer 21,22 , coronary artery disease 23 and asthma 24 . Apart from human, functional modules have been identified in other species as well, such as in Mus musculus for discrete and rhythmic forelimb movements in motor cortex 25 and in Gallus gallus for muscle development and intramuscular fat accumulation at different post-hatching ages 26 .…”