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A novel 4-gene signature for overall survival prediction in lung adenocarcinoma patients with lymph node metastasis.
Cancer Cell Int. 19:100 (2019)
Background: Lung adenocarcinoma (LUAD) patients experiencing lymph node metastasis (LNM) always exhibit poor clinical outcomes. A biomarker or gene signature that could predict survival in these patients would have a substantial clinical impact, allowing for earlier detection of mortality risk and for individualized therapy.Methods: With the aim to identify a novel mRNA signature associated with overall survival, we analysed LUAD patients with LNM extracted from The Cancer Genome Atlas (TCGA). LASSO Cox regression was applied to build the prediction model. An external cohort was applied to validate the prediction model.Results: We identified a 4-gene signature that could effectively stratify a high-risk subset of these patients, and time-dependent receiver operating characteristic (tROC) analysis indicated that the signature had a powerful predictive ability. Gene Set Enrichment Analysis (GSEA) showed that the high-risk subset was mainly associated with important cancer-related hallmarks. Moreover, a predictive nomogram was established based on the signature integrated with clinicopathological features. Lastly, the signature was validated by an external cohort from Gene Expression Omnibus (GEO).Conclusion: In summary, we developed a robust mRNA signature as an independent factor to effectively classify LUAD patients with LNM into low- and high-risk groups, which might provide a basis for personalized treatments for these patients.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Transcriptome ; Lung Adenocarcinoma (luad) ; Lymph Node Metastasis (lnm) ; Mrna Signature ; Weighted Gene Co-expression Network Analysis (wgcna) ; Overall Survival (os); Cancer; Expression; Validation; Prognosis
ISSN (print) / ISBN
1475-2867
e-ISSN
1475-2867
Journal
Cancer cell international
Quellenangaben
Volume: 19,
Issue: 1,
Article Number: 100
Publisher
BioMed Central
Publishing Place
London
Non-patent literature
Publications
Reviewing status
Peer reviewed