Multiple Myeloma: Genetic and Epigenetic Biomarkers with Clinical Potential

Abstract

Multiple myeloma (MM) is characterized by the uncontrolled proliferation of monoclonal plasma cells and accounts for approximately 10% of all hematologic malignancies. The clinical outcomes of MM can exhibit considerable variability. Variability in both the genetic and epigenetic characteristics of MM undeniably contributes to tumor dynamics. The aim of the present study was to identify biomarkers with the potential to improve the accuracy of prognosis assessment in MM. Initially, miRNA sequencing was conducted on bone marrow (BM) samples from patients with MM. Subsequently, the expression levels of 27 microRNAs (miRNA) and the gene expression levels of ASF1B, CD82B, CRISP3, FN1, MEF2B, PD-L1, PPARγ, TERT, TIMP1, TOP2A, and TP53 were evaluated via real-time reverse transcription polymerase chain reaction in BM samples from patients with MM exhibiting favorable and unfavorable prognoses. Additionally, the analysis involved the bone marrow samples from patients undergoing examinations for non-cancerous blood diseases (NCBD). The findings indicate a statistically significant increase in the expression levels of miRNA-124, -138, -10a, -126, -143, -146b, -20a, -21, -29b, and let-7a and a decrease in the expression level of miRNA-96 in the MM group compared with NCBD (p < 0.05). No statistically significant differences were detected in the expression levels of the selected miRNAs between the unfavorable and favorable prognoses in MM groups. The expression levels of ASF1B, CD82B, and CRISP3 were significantly decreased, while those of FN1, MEF2B, PDL1, PPARγ, and TERT were significantly increased in the MM group compared to the NCBD group (p < 0.05). The MM group with a favorable prognosis demonstrated a statistically significant decline in TIMP1 expression and a significant increase in CD82B and CRISP3 expression compared to the MM group with an unfavorable prognosis (p < 0.05). From an empirical point of view, we have established that the complex biomarker encompassing the CRISP3/TIMP1 expression ratio holds promise as a prognostic marker in MM. From a fundamental point of view, we have demonstrated that the development of MM is rooted in a cascade of complex molecular pathways, demonstrating the interplay of genetic and epigenetic factors.

Keywords: microRNA; multiple myeloma; oncogenes; prognostic biomarkers; tumor suppressors.

Figure 1

Hierarchical cluster analysis between 16 multiple myeloma (MM) cases and six non-cancerous blood diseases (NCBD) cases for the microRNAs (miRNAs) that were chosen for validation by RT-PCR in the analyzed groups. Each column represents the expression of a miRNA, and each row denotes a nucleic acid sample. Yellow: upregulated miRNA; blue: downregulated miRNA; green: minor changes; red: a graphical representation of a group of samples.

Figure 2

Comparative analysis of gene expression levels between multiple myeloma (MM) (n = 45) and non-cancerous samples (NCBD) (n = 43). The figure presents the median value, upper and lower quartiles, non-outlier range, and outliers appearing as circles.

Figure 3

Comparative analysis of gene expression levels between multiple myeloma samples of patients with favorable (n = 28) and unfavorable (n = 17) prognosis. The figure presents the median value, upper and lower quartiles, non-outlier range, and outliers appearing as circles.

Figure 4

ROC analysis for the (A) CRISP3, (B) TIMP1, and (C) CRISP3/TIMP1 genes. AUC, sensitivity (Sn), and specificity (Sp) values are indicated. Red line is a diagonal support line, blue is a ROC curve.

Figure 4

ROC analysis for the (A) CRISP3, (B) TIMP1, and (C) CRISP3/TIMP1 genes. AUC, sensitivity (Sn), and specificity (Sp) values are indicated. Red line is a diagonal support line, blue is a ROC curve.

Figure 5

Interactions between microRNAs(miRNAs) and their target genes. Blue squares represent miRNAs, and purple circles indicate their target genes.