Involvement of Small Non-Coding RNA and Cell Antigens in Pathogenesis of Extramedullary Multiple Myeloma

Abstract

Extramedullary multiple myeloma (EMD) is an aggressive disease; malignant plasma cells lose their dependence in the bone marrow microenvironment and migrate into tissues. EMD is a negative prognostic factor of survival. Using flow cytometry and next-generation sequencing, we aimed to identify antigens and microRNAs (miRNAs) involved in EMD pathogenesis. Flow cytometry analysis revealed significant differences in the level of clonal plasma cells between MM and EMD patients, while the expression of CD markers was comparable between these two groups. Further, miR-26a-5p and miR-30e-5p were found to be significantly down-regulated in EMD compared to MM. Based on the expression of miR-26a-5p, we were able to distinguish these two groups of patients with high sensitivity and specificity. In addition, the involvement of deregulated miRNAs in cell cycle regulation, ubiquitin-mediated proteolysis and signaling pathways associated with infections or neurological disorders was observed using GO and KEGG pathways enrichment analysis. Subsequently, a correlation between the expression of analyzed miRNAs and the levels of CD molecules was observed. Finally, clinicopathological characteristics as well as CD antigens associated with the prognosis of MM and EMD patients were identified. Altogether, we identified several molecules possibly involved in the transformation of MM into EMD.

Keywords: NGS; bioinformatics; immunophenotyping; microRNA; multiple myeloma.

Figure 1

Heat map. A total of 43 miRNAs with significantly deregulated expression in patients with extramedullary disease (EMD, yellow) compared to multiple myeloma (MM) patients (blue) (adjusted p < 0.025).

Figure 2

RT-qPCR validation and ROC analysis of selected microRNAs. (A) MiR-30e-5p was significantly downregulated in EMD vs. MM patients (p = 0.009). (B) MiR-26a-5p was significantly downregulated in EMD vs. MM patients (p = 0.003). (C) MiR-30e-5p enabled discrimination between MM and EMD patients (sensitivity 80.0%, specificity 64.1%; AUC = 0.71, cut-off = 8.8054). (D) MiR-26a-5p enabled discrimination between MM and EMD patients (sensitivity 80.0%, specificity 61.5%; AUC = 0.74, cut-off = 37.6610).

Figure 3

Target prediction and protein association network analysis. (A) In total, six thousand and thirty-seven different genes were predicted as potential targets of five significantly deregulated miRNAs (miR-18a-3p, miR-18a-5p, miR-26a-5p, miR-30e-5p and miR-92a) using miRNet online tool. (B) Protein–protein interaction network of twenty-six hub genes regulated by identified miRNAs (STRING) together with two other genes (with asterisk) significantly involved in the network. Light blue line—from curated databases, pink line—experimentally determined, yellow line—textmining, black line– co-expression, light purple line—protein homology, red line—gene fusions, dark blue line—gene co-occurrence.

Figure 4

Functional annotation and pathway enrichment analysis. (A) Ten most enriched biological processes based on the GO classification. (B) Ten most enriched signaling pathways based on the KEGG analysis. (C) Signaling pathways interaction network based on the 20 most significant signaling pathways defined by KEGG. ALS—Amyotrophic lateral sclerosis, SP—signaling pathway, EB—Epstein–Barr virus, HTLV-1—Human T-cell leukemia virus 1, ER—endoplasmic reticulum, CML—chronic myeloid leukemia.

Figure 5

Overall survival and progression-free survival of multiple myeloma (MM) and extramedullary myeloma (EMD) patients. (A) MM patients had significantly longer overall survival compared to EMD patients (p < 0.001). (B) MM patients had significantly longer progression-free survival compared to EMD patients (p = 0.051).