Multivariant Transcriptome Analysis Identifies Modules and Hub Genes Associated with Poor Outcomes in Newly Diagnosed Multiple Myeloma Patients.
Olayinka O Adebayo, Eric B Dammer, Courtney D Dill, Adeyinka O Adebayo, Saheed O Oseni, Ti'ara L Griffen, Adaugo Q Ohandjo, Fengxia Yan, Sanjay Jain, Benjamin G Barwick, Rajesh Singh, Lawrence H Boise, James W Lillard
Author Information
Olayinka O Adebayo: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA 30310, USA.
Eric B Dammer: Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA 30322, USA. ORCID
Courtney D Dill: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA 30310, USA.
Adeyinka O Adebayo: Georgia Institute of Technology, Atlanta, GA 30332, USA.
Saheed O Oseni: Department of Immunology, Moffitt Cancer Center, Tampa, FL 33612, USA. ORCID
Ti'ara L Griffen: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA 30310, USA. ORCID
Adaugo Q Ohandjo: East-West Collaborative Research, Marietta, GA 30060, USA.
Fengxia Yan: Department of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, GA 30310, USA.
Sanjay Jain: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA 30310, USA.
Benjamin G Barwick: Winship Cancer Institute, 1365 Clifton Road NE, Atlanta, GA 30322, USA. ORCID
Rajesh Singh: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA 30310, USA. ORCID
Lawrence H Boise: Winship Cancer Institute, 1365 Clifton Road NE, Atlanta, GA 30322, USA. ORCID
James W Lillard: Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA 30310, USA.
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. Genes differentially expressed in patients with poor outcomes were assessed using two independent sample -tests (dead and alive MM patients). The clinical performance of biomarker candidates was evaluated using overall survival via a log-rank Kaplan-Meier and ROC test. Four distinct modules (M10, M13, M15, and M20) were significantly correlated with MM vital status and differentially expressed between the dead (poor outcomes) and the alive MM patients within two years. The biological functions of modules positively correlated with death (M10, M13, and M20) were G-protein coupled receptor protein, cell-cell adhesion, cell cycle regulation genes, and cellular membrane fusion genes. In contrast, a negatively correlated module to MM mortality (M15) was the regulation of B-cell activation and lymphocyte differentiation. MM biomarkers , , , , and were co-expressed in positively correlated modules to MM vital status, which was associated with MM's lower overall survival.
