Gene Expression Profiling to Predict Prognostic Biomarkers for Relapse in Multiple Myeloma

Abstract

Background: Multiple myeloma (MM), an incurable malignancy of plasma cells (PCs), is the second most common hematological cancer caused primarily by structural variations (chromosomal aberrations and somatic mutations). The majority of the patients acquired resistance against standard therapeutic approaches for MM and experienced relapse despite the continuous advances in MM therapies.Methods: We performed Differential Gene Expression (DGE), literature mining and SNV analysis of Next Generation Sequencing (NGS) data of Newly Diagnosed MM (NDMM) and Relapsed/Refractory MM (RRMM). The selected Differentially Expressed Genes (DEGs) were subjected to functional enrichment and pathway analysis. Immune cells infiltration analysis was also performed to estimate immune cells variations in the Tumor Microenvironment (TME) of RRMM. Result: CSF1R, VCAN, NRP1, COL22A1, BPI, BIRC5, MNX1, FAT1, ERG, TCL1A, AFF3 were selected after DGE, literature mining and SNV analysis. The functional enrichment of these DEGs showed significant enrichment for positive regulation of cell population proliferation, serene/threonine kinase activity, endothelial cell proliferation, cytokine binding, G protein activity and GDP binding, whereas KEGG pathway analysis revealed vital role of PI3K-Akt signaling pathway along with various cancer pathways. The immune cells infiltration analysis revealed the higher count of neutrophils and lesser level of T cells (CD8+) in TME of RRMM. Conclusion: Our study suggests that neutrophils play an important role in modulation of TME in RRMM. The selected DEGs have previously been identified in progression, drug resistance and relapse of various cancers. The role of these biomarkers in RRMM has not been explored yet. Therefore, neutrophils, selected DEGs and PI3K-Akt signaling pathway are potential targets to investigate in RRMM.Keywords: Multiple Myeloma; Relapse; Differential Expression; Immune Cell Infiltration; Literature Mining