Identification of pathway-based prognostic gene signatures in patients with multiple myeloma.

Molecular profiling is used to extract prognostic gene signatures in different cancers such as multiple myeloma (MM), which is the second most common hematological malignancy. In this study, we utilized gene expression profiles to find biological pathways that could efficiently predict survival time in patients with MM. Four data sets-namely GSE2658 (559 samples), GSE9782 (264 samples), GSE6477 (147 samples), and GSE57317 (55 samples)-were employed. GSE2658 was used as a training data set and the others as validation data sets. The genes significantly associated with survival were identified using the univariate Cox proportional hazards analysis, and their roles in the biological pathways were explored using the Gene-Set Enrichment Analysis (GSEA) in the training data set. Next, the significant genes and their corresponding pathways were used to reconstruct pathway-based prognostic signatures. Thereafter, the significant gene signatures were externally validated in 3 independent cohorts-namely GSE9782, GSE6477, and GSE57317. Our results revealed that 9 pathway-based prognostic signatures were able to efficiently predict survival time in the training data set (Ps < 0.01). The testing of these signatures in the validation data sets demonstrated that 3 signatures-namely MYC targets, spliceosome, and metabolism of RNA-were able to strongly predict the clinical outcome in the 3 cohorts at P values < 0.01. In addition, in the multivariate Cox analysis, the 3 gene signatures remained as independent prognostic factors compared with the routine prognostic variables in MM-namely serum albumin, serum β2-microglobulin, and age. These signatures were by far the most powerful independent prognostic factors (MYC targets: P = 0.009, spliceosome: P = 0.024, and metabolism of RNA: P < 0.001).