Abstract 4388: Weighted gene co-expression network analysis identified cell cycle signaling pathway associated hub genes correlated with progression and prognosis of multiple myeloma

Abstract

Abstract There is a gap in understanding the molecular mechanisms behind early relapse / multiple myeloma (MM) progression in the newly diagnosed patient on standard treatment for MM, i.e., lenalidomide, bortezomib, and dexamethasone (RVD). As a result, the relatively high percentage of patients that progress after early treatment with RVD is significant. Therefore, identification of clinically relevant clusters of co-expressed genes or representative biomarkers for MM progression, while on RVD, would help identify new molecular mechanisms and drug targets. The objective of this study is to use weighted gene co-expression network analysis (WGCNA) to identify gene-signaling networks associated with early relapse / MM progression. To this end, we performed a WGCNA to determine module-trait correlations. We next examined the overrepresentation of upstream regulators and signal pathway networks from MM patients in the MMRF CoMMpass dataset (n = 175, gene transcripts= 30,598). WGCNA constructed 48 modules based on the correlations between patients on RVD and death. We are using death as a biomarker for MM progression and two years as cut off for treatment. We identified two modules, Green (p < 4.6 x 10-8), and Pale Turquoise (p < 8.8 x 10-7), that significantly and positively correlated with overall survival of < 2 years following initial treatment with RVD. Further analysis identified HDAC8, PARPBP, HSPG2, MAGE, KIF, and FOXM1 as the top six hub genes having tight variations in positive correlations. The signatures for these cell cycle signaling pathway related hub genes were found to be largely associated with the biological process for proliferation. Future studies will use connectivity mapping to identify drug signatures that can target any of the identified hub genes. The selective targeting of these hub genes is expected to improve response to RVD in MM patients. This transcriptome study is the first using this approach to identify gene signaling networks associated with MM progression. Citation Format: Olayinka O. Adebayo, Tiara Griffen, Corey Young, Eric Dammer, James W. Lillard. Weighted gene co-expression network analysis identified cell cycle signaling pathway associated hub genes correlated with progression and prognosis of multiple myeloma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4388.