Identification and Validation of a Novel RNA-Binding Protein-Related Gene-Based Prognostic Model for Multiple Myeloma - Wang, Wei; Xu, Shi-wen; Zhu, Xia-yin; Guo, Qun-yi; Zhu, Min; Mao, Xin-li; Chen, Ya-Hong; Li, Shao-wei; Luo, Wen-da (Frontiers in Genetics, 2021)
Backgrounds: Multiple Myeloma(MM) is a malignant disease of the hematopoietic system, for which most patients cannot be cured. RNA binding proteins have been reported to be involved in the occurrence and development of many tumors, but their prognostic significance was less systematically described in Multiple Myeloma. Here, we developed a prognostic signature based on eight RBP-related genes to distinguish Multiple Myeloma cohorts with different prognoses. Method: After screening the differentially expressed RBPs, univariate Cox regression was performed to evaluate each gene's prognostic relevance through the TCGA-MMRF dataset. LASSO COX regression and Stepwise COX regression was used to establish a risk prediction model through the training set and validated in three GEO datasets. An 8-RBP-related gene-based signature able to classify MM patients into high- and low-score groups was developed. The predictive ability of the risk model or nomogram was evaluated by different bioinformatics-methods. GO, KEGG enrichment analysis, and GSEA was performed to analyze potentially significant biological processes in MM. Result: A 8-gene based prognostic signature established within 94 significant survival-related genes from RBP-related gene sets performed well in the TCGA-MMRF dataset. The signature includes 8 hub genes: HNRNPC, RPLP2, SNRPB, EXOSC8, RARS2, MRPS31, ZC3H6, and DROSHA. Kaplan-Meier survival curves showed the prognosis of the risk status owns significant differences. A nomogram was constructed with age, B2M, LDH, ALB, and risk status as prognostic parameters. ROC curve, C-index, Calibration analysis, and Decision Curve Analysis showed the risk module and nomogram performed well in 1-, 3-, 5- and 7-year. Functional analysis suggested that the Spliceosome pathway might be one of the main pathways of RBPS in the development of myeloma. Moreover, our 8 gene-related signature can improve the R-ISS/ISS scoring system to a certain extent (especially for stage II), which may have guiding significance for the future. Conclusion: We constructed and verified the 8-RBPs signature which can effectively predict the prognosis of myeloma patients, and suggested that RBPs is a promising biomarker for MM.