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

Mohamad Zamani-Ahmadmahmudi, Shahreyar Dabiri, Nadia Nadimi,

Translational research : the journal of laboratory and clinical medicine, May 27, 2017

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 

Copyright © 2017 Elsevier Inc. All rights reserved.

Pubmed Link: 28549851

DOI: 10.1016/j.trsl.2017.05.001