Development and Validation of a Seven-Gene Signature for Predicting the Prognosis of Lung Adenocarcinoma.

BioMed Research International
Yingqing ZhangWenyu Chen

Abstract

Prognosis is a main factor affecting the survival of patients with lung adenocarcinoma (LUAD), yet no robust prognostic model of high effectiveness has been developed. This study is aimed at constructing a stable and practicable gene signature-based model via bioinformatics methods for predicting the prognosis of LUAD sufferers. The mRNA expression data were accessed from the TCGA-LUAD dataset, and paired clinical information was collected from the GDC website. R package "edgeR" was employed to select the differentially expressed genes (DEGs), which were then used for the construction of a gene signature-based model via univariate COX, Lasso, and multivariate COX regression analyses. Kaplan-Meier and ROC survival analyses were conducted to comprehensively evaluate the performance of the model in predicting LUAD prognosis, and an independent dataset GSE26939 was accessed for further validation. Totally, 1,655 DEGs were obtained, and a 7-gene signature-based risk score was developed and formulated as risk_score = 0.000245∗NTSR1 + (7.13E - 05)∗RHOV + 0.000505∗KLK8 + (7.01E - 05)∗TNS4 + 0.000288∗C1QTNF6 + 0.00044∗IVL + 0.000161∗B4GALNT2. Kaplan-Meier survival curves revealed that the survival rate of patients in the high-risk group...Continue Reading

References

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Datasets Mentioned

BETA
GSE26939

Methods Mentioned

BETA
chip

Software Mentioned

survivalROC
R package “ edgeR ”
edgeR

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