Systemically identifying and prioritizing risk lncRNAs through integration of
pan-cancer phenotype associations
#MMPMID28076842
Xu C
; Qi R
; Ping Y
; Li J
; Zhao H
; Wang L
; Du MY
; Xiao Y
; Li X
Oncotarget
2017[Feb]; 8
(7
): 12041-12051
PMID28076842
show ga
LncRNAs have emerged as a major class of regulatory molecules involved in normal
cellular physiology and disease, our knowledge of lncRNAs is very limited and it
has become a major research challenge in discovering novel disease-related
lncRNAs in cancers. Based on the assumption that diverse diseases with similar
phenotype associations show similar molecular mechanisms, we presented a
pan-cancer network-based prioritization approach to systematically identify
disease-specific risk lncRNAs by integrating disease phenotype associations. We
applied this strategy to approximately 2800 tumor samples from 14 cancer types
for prioritizing disease risk lncRNAs. Our approach yielded an average area under
the ROC curve (AUC) of 80.66%, with the highest AUC (98.14%) for medulloblastoma.
When evaluated using leave-one-out cross-validation (LOOCV) for prioritization of
disease candidate genes, the average AUC score of 97.16% was achieved. Moreover,
we demonstrated the robustness as well as the integrative importance of this
approach, including disease phenotype associations, known disease genes and the
numbers of cancer types. Taking glioblastoma multiforme as a case study, we
identified a candidate lncRNA gene SNHG1 as a novel disease risk factor for
disease diagnosis and prognosis. In summary, we provided a novel lncRNA
prioritization approach by integrating pan-cancer phenotype associations that
could help researchers better understand the important roles of lncRNAs in human
cancers.
|*Gene Expression Regulation, Neoplastic
[MESH]
|*Gene Regulatory Networks
[MESH]
|Gene Expression Profiling/methods
[MESH]
|Genetic Predisposition to Disease/*genetics
[MESH]
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