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| Title |
Integrin and gene network analysis reveals that ITGA5 and ITGB1 are prognostic in non-small-cell lung cancer
|
|---|---|
| Published in |
OncoTargets and therapy, April 2016
|
| DOI | 10.2147/ott.s91796 |
| Pubmed ID | |
| Authors |
Weiqi Zheng, Caihui Jiang, Ruifeng Li |
| Abstract |
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. 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. 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. 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. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Unknown | 41 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 17% |
| Student > Bachelor | 6 | 14% |
| Researcher | 5 | 12% |
| Student > Doctoral Student | 3 | 7% |
| Unspecified | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 15 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 17% |
| Medicine and Dentistry | 5 | 12% |
| Agricultural and Biological Sciences | 4 | 10% |
| Unspecified | 2 | 5% |
| Engineering | 2 | 5% |
| Other | 5 | 12% |
| Unknown | 17 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 May 2016.
All research outputs
#22,759,452
of 25,374,647 outputs
Outputs from OncoTargets and therapy
#2,078
of 3,016 outputs
Outputs of similar age
#271,857
of 314,725 outputs
Outputs of similar age from OncoTargets and therapy
#86
of 128 outputs
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We're also able to compare this research output to 128 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.