Single-nucleotide variants as potential prognostic biomarkers in newly diagnosed multiple myeloma patients

Microarrays enable high-throughput detection of single-nucleotide variants, making them valuable tools in genetic research. The use of this technology in multiple myeloma, a genetically complex malignancy with highly variable outcomes, may facilitate the identification of novel prognostic biomarkers.

To identify single-nucleotide variants with prognostic value in newly diagnosed multiple myeloma and to evaluate the ability of microarray technology to distinguish multiple myeloma from monoclonal gammopathy of undetermined significance.

A total of 56 newly diagnosed multiple myeloma and 14 monoclonal gammopathy of undetermined significance patients were retrospectively analyzed using the Infinium Global Screening Array-24 v3.0. Binary discriminant and principal component analyses were employed to identify single-nucleotide variants associated with post-induction response. Kaplan-Meier curves and log-rank tests were used to evaluate overall survival and progression-free survival.

A total of 692 single-nucleotide variants were associated with post-induction response, of which 42 (t-score >4) were the most discriminant. Variants in the PTPRD, NOTCH4, SH3RF3, DCC, and CSMD1 genes were linked to poorer treatment responses: carriers of alternative alleles showed higher partial remission rates (p-value = 0.005) and early relapse (p-value = 0.021). These patients also showed a reduced 5-year overall survival (p-value = 0.008) and shorter progression-free survival (p-value = 0.017). The current cohort exhibited higher minor allele frequencies for SH3RF3, PTPRD, and CSMD1 relative to broader Latin American datasets. Additionally, 13 single-nucleotide variants were multiple myeloma-specific and eight were specific for monoclonal gammopathy of undetermined significance.

Single-nucleotide variants of the PTPRD, NOTCH4, SH3RF3, DCC, and CSMD1 genes emerge as promising prognostic biomarkers in newly diagnosed multiple myeloma. Microarray-based single-nucleotide variants profiling shows potential for personalized risk stratification, warranting further validation and functional characterization.