A Network Analysis of Multiple Myeloma Related Gene Signatures.
Bayesian network
gene signature
multiple myeloma
prognostic
treatment response
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
27 Sep 2019
27 Sep 2019
Historique:
received:
31
07
2019
revised:
20
09
2019
accepted:
20
09
2019
entrez:
2
10
2019
pubmed:
2
10
2019
medline:
2
10
2019
Statut:
epublish
Résumé
Multiple myeloma (MM) is the second most prevalent hematological cancer. MM is a complex and heterogeneous disease, and thus, it is essential to leverage omics data from large MM cohorts to understand the molecular mechanisms underlying MM tumorigenesis, progression, and drug responses, which may aid in the development of better treatments. In this study, we analyzed gene expression, copy number variation, and clinical data from the Multiple Myeloma Research Consortium (MMRC) dataset and constructed a multiple myeloma molecular causal network (M3CN). The M3CN was used to unify eight prognostic gene signatures in the literature that shared very few genes between them, resulting in a prognostic subnetwork of the M3CN, consisting of 178 genes that were enriched for genes involved in cell cycle (fold enrichment = 8.4,
Identifiants
pubmed: 31569720
pii: cancers11101452
doi: 10.3390/cancers11101452
pmc: PMC6827160
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : NIH HHS
ID : U01 HG008451
Pays : United States
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