Integrin and gene network analysis reveals that ITGA5 and ITGB1 are prognostic in non-small-cell lung cancer
W Zheng, C Jiang, R Li - OncoTargets and therapy, 2016 - Taylor & Francis
W Zheng, C Jiang, R Li
OncoTargets and therapy, 2016•Taylor & FrancisBackground Integrin expression has been identified as a prognostic factor in non-small-cell
lung cancer (NSCLC). This study was aimed at determining the predictive ability of integrins
and associated genes identified within the molecular network. Patients and methods A total
of 959 patients with NSCLC from The Cancer Genome Atlas cohorts were enrolled in this
study. The expression profile of integrins and related genes were obtained from The Cancer
Genome Atlas RNAseq database. Clinicopathological characteristics, including age, sex …
lung cancer (NSCLC). This study was aimed at determining the predictive ability of integrins
and associated genes identified within the molecular network. Patients and methods A total
of 959 patients with NSCLC from The Cancer Genome Atlas cohorts were enrolled in this
study. The expression profile of integrins and related genes were obtained from The Cancer
Genome Atlas RNAseq database. Clinicopathological characteristics, including age, sex …
Background
Integrin expression has been identified as a prognostic factor in non-small-cell lung cancer (NSCLC). This study was aimed at determining the predictive ability of integrins and associated genes identified within the molecular network.
Patients and methods
A total of 959 patients with NSCLC from The Cancer Genome Atlas cohorts were enrolled in this study. The expression profile of integrins and related genes were obtained from The Cancer Genome Atlas RNAseq database. Clinicopathological characteristics, including age, sex, smoking history, stage, histological subtype, neoadjuvant therapy, radiation therapy, and overall survival (OS), were collected. Cox proportional hazards regression models as well as Kaplan–Meier curves were used to assess the relative factors.
Results
In the univariate Cox regression model, ITGA1, ITGA5, ITGA6, ITGB1, ITGB4, and ITGA11 were predictive of NSCLC prognosis. After adjusting for clinical factors, ITGA5 (odds ratio =1.17, 95% confidence interval: 1.05–1.31) and ITGB1 (odds ratio =1.31, 95% confidence interval: 1.10–1.55) remained statistically significant. In the gene cluster network analysis, PLAUR, ILK, SPP1, PXN, and CD9, all associated with ITGA5 and ITGB1, were identified as independent predictive factors of OS in NSCLC.
Conclusion
A set of genes was identified as independent prognostic factors of OS in NSCLC through gene cluster analysis. This method may act as a tool to reveal more prognostic-associated genes in NSCLC.
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