Multivariant Transcriptome Analysis Identifies Modules and Hub Genes Associated with Poor Outcomes in Newly Diagnosed Multiple Myeloma Patients.

The molecular mechanisms underlying chemoresistance in some newly diagnosed multiple myeloma (MM) patients receiving standard therapies (lenalidomide, bortezomib, and dexamethasone) are poorly understood. Identifying clinically relevant gene networks associated with death due to MM may uncover novel mechanisms, drug targets, and prognostic biomarkers to improve the treatment of the disease. This study used data from the MMRF CoMMpass RNA-seq dataset (N = 270) for weighted gene co-expression network analysis (WGCNA), which identified 21 modules of co-expressed genes.

Multivariate transcriptome analysis identifies networks and key drivers of chronic lymphocytic leukemia relapse risk and patient survival.

Background: Chronic lymphocytic leukemia (CLL) is an indolent heme malignancy characterized by the accumulation of CD5 CD19 B cells and episodes of relapse. The biological signaling that influence episodes of relapse in CLL are not fully described. Here, we identify gene networks associated with CLL relapse and survival risk.

A network approach reveals driver genes associated with survival of patients with triple-negative breast cancer.

We 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.

Transcriptome Network Analysis Identifies CXCL13-CXCR5 Signaling Modules in the Prostate Tumor Immune Microenvironment.

The tumor immune microenvironment (TIME) consists of multiple cell types that contribute to the heterogeneity and complexity of prostate cancer (PCa). In this study, we sought to understand the gene-expression signature of patients with primary prostate tumors by investigating the co-expression profiles of patient samples and their corresponding clinical outcomes, in particular "disease-free months" and "disease reoccurrence". We tested the hypothesis that the CXCL13-CXCR5 axis is co-expressed with factors supporting TIME and PCa progression.