摘要：SummaryWe aimed to identify triple-negative breast cancer (TNBC) drivers that regulate survival time as predictive signatures that improve TNBC prognostication. Breast cancer (BrCa) transcriptomic tumor biopsies were analyzed, identifying network communities enriched with TNBC-specific differentially expressed genes (DEGs) and correlated strongly to TNBC status. Two anticorrelated modules correlated strongly to TNBC subtype and survival. Querying module-specific hubs and DEGs revealed transcriptional changes associated with high survival. Transcripts were nominated as biomarkers and tested as combinatoric ratios using receiver operator characteristic (ROC) analysis to assess survival prediction. ROC test rounds integrated genes with established interactions to hubs and DEGs of key modules, improving prediction. Finally, we tested whether integration of literature-derived genes for implicated hallmark cancer processes could improve prediction of survival. Complementary coexpression, differential expression, genetic interaction, and survival stratification integrated by ROC optimization uncovered a panel of “linchpin survival genes” predictive of patient survival, representing gene interactions in hallmark cancer processes.Graphical abstractDisplay OmittedHighlights•WGCNA identifies coexpression modules predicted to drive TNBC patient survival•Module hubs and DEGs reveal transcriptional changes associated with high survival•Nine genes act synergistically to influence TNBC progression, relapse, and survival•These genes' levels represent reversible changes in TNBC hallmark cancer processesBioinformatics; Systems biology; Cancer systems biology
