A Novel Model of Tumor-Infiltrating B Lymphocyte Specific RNA-Binding Protein-Related Genes With Potential Prognostic Value and Therapeutic Targets in Multiple Myeloma

Background: RNA-binding proteins (RBPs) act as important regulators in the progression of tumors. However, their role in the tumorigenesis and prognostic assessment in multiple myeloma (MM), a B-cell hematological cancer, remains elusive. Thus, the current study was designed to explore a novel prognostic B-cell-specific RBP signature and the underlying molecular mechanisms. Methods: Data used in the current study were obtained from the Gene Expression Omnibus (GEO) database. Significantly upregulated RBPs in B cells were defined as B cell-specific RBPs. The biological functions of B-cell-specific RBPs were analyzed by the cluster Profiler package. Univariate and multivariate regressions were performed to identify robust prognostic B-cell specific RBP signatures, followed by the construction of the risk classification model. Gene set enrichment analysis (GSEA)-identified pathways were enriched in stratified groups. The microenvironment of the low- and high-risk groups was analyzed by single-sample GSEA (ssGSEA). Moreover, the correlations among the risk score and differentially expressed immune checkpoints or differentially distributed immune cells were calculated. The drug sensitivity of the low- and high-risk groups was assessed via Genomics of Drug Sensitivity in Cancer by the pRRophetic algorithm. In addition, we utilized a GEO dataset involving patients with MM receiving bortezomib therapy to estimate the treatment response between different groups. Results: A total of 56 B-cell-specific RBPs were identified, which were mainly enriched in ribonucleoprotein complex biogenesis and the ribosome pathway. ADAR, FASTKD1 and SNRPD3 were identified as prognostic B-cell specific RBP signatures in MM. The risk model was constructed based on ADAR, FASTKD1 and SNRPD3. Receiver operating characteristic (ROC) curves revealed the good predictive capacity of the risk model. A nomogram based on the risk score and other independent prognostic factors exhibited excellent performance in predicting the overall survival of MM patients. GSEA showed enrichment of the Notch signaling pathway and mRNA cis-splicing via spliceosomes in the high-risk group. Moreover, we found that the infiltration of diverse immune cell subtypes and the expression of CD274, CD276, CTLA4 and VTCN1 were significantly different between the two groups. In addition, the IC50 values of 11 drugs were higher in the low-risk group. Patients in the low-risk group exhibited a higher complete response rate to bortezomib therapy. Conclusion: Our study identified novel prognostic B-cell-specific RBP biomarkers in MM and constructed a unique risk model for predicting MM outcomes. Moreover, we explored the immune-related mechanisms of B cell-specific RBPs in regulating MM. Our findings could pave the way for developing novel therapeutic strategies to improve the prognosis of MM patients.