Co-expression network analysis identified cell cycle signaling hub genes associated with progression and prognosis of multiple myeloma

Abstract

Abstract There is a gap in understanding the molecular mechanisms behind early multiple myeloma (MM) progression in the newly diagnosed patient on standard treatment, 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 related 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 MM progression. Therefore, we performed a novel WGCNA to determine module-trait correlations. We next examined the overrepresentation of upregulators and signal pathway networks from MM patients in the MMRF CoMMpass dataset (n = 175). WGCNA constructed 45 modules based on the correlations between patients on RVD and death using 30,598 gene transcripts. We are using death as a biomarker for MM progression and two years as cut off for treatment. One of the highest modules identified; Pink module has HDAC8, HSPG2, LILRB4, CDC and FOXM1 genes and other 595 genes that are co-expressed together, that are significantly and positively correlated (p < 3.6 × 10-8, R2 =0.38) with overall survival of < 2 years following initial treatment with RVD. These genes are associated with cell cycle signaling, and Jak-Stat signaling pathways and their biological process is largely significant for proliferation and tumorigenesis. In future, connectivity mapping will be used to identify drug signatures that can target any of the hub genes. This study is expected to improve MM’s relapse.