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. 2016 May 24;7(21):30561-74.
doi: 10.18632/oncotarget.8723.

Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis

Vienna Ludovini  1 , Fortunato Bianconi  2 , Annamaria Siggillino  1 , Danilo Piobbico  2 , Jacopo Vannucci  3 , Giulio Metro  1 , Rita Chiari  1 , Guido Bellezza  4 , Francesco Puma  3 , Maria Agnese Della Fazia  2 , Giuseppe Servillo  2 , Lucio Crinò  1
Affiliations

Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis

Vienna Ludovini et al. Oncotarget. .

Abstract

Risk assessment and treatment choice remains a challenge in early non-small-cell lung cancer (NSCLC). The aim of this study was to identify novel genes involved in the risk of early relapse (ER) compared to no relapse (NR) in resected lung adenocarcinoma (AD) patients using a combination of high throughput technology and computational analysis. We identified 18 patients (n.13 NR and n.5 ER) with stage I AD. Frozen samples of patients in ER, NR and corresponding normal lung (NL) were subjected to Microarray technology and quantitative-PCR (Q-PCR). A gene network computational analysis was performed to select predictive genes. An independent set of 79 ADs stage I samples was used to validate selected genes by Q-PCR.From microarray analysis we selected 50 genes, using the fold change ratio of ER versus NR. They were validated both in pool and individually in patient samples (ER and NR) by Q-PCR. Fourteen increased and 25 decreased genes showed a concordance between two methods. They were used to perform a computational gene network analysis that identified 4 increased (HOXA10, CLCA2, AKR1B10, FABP3) and 6 decreased (SCGB1A1, PGC, TFF1, PSCA, SPRR1B and PRSS1) genes. Moreover, in an independent dataset of ADs samples, we showed that both high FABP3 expression and low SCGB1A1 expression was associated with a worse disease-free survival (DFS).Our results indicate that it is possible to define, through gene expression and computational analysis, a characteristic gene profiling of patients with an increased risk of relapse that may become a tool for patient selection for adjuvant therapy.

Keywords: cancer systems biology; computational biology; gene expression profiling; gene networks; lung adenocarcinoma.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Microarray results
A. Heat map plot for gene expression ratio- the (log10) of ER vs Normal, NR vs Normal and ER vs NR. B. Heat map plot for the selected genes: lower (0.10) and upper (0.90) quartiles of the distribution of the logarithm of the fold of ER versus NR.
Figure 2
Figure 2. Validation experiments for the increased and decreased selected genes (orange diamond), Q-PCR pool (cyan square) and Q-PCR patients (violet triangle)
A. Logarithm of the ratio ER vs NR for the increased genes. B. Logarithm of the ratio ER vs NR for the decreased genes.
Figure 3
Figure 3. Computational analysis for increased and decreased genes
A. GeneMANIA networks of validated genes B. Community Landscape Analysis obtained with ModuLand plug-in. The four modules were plotted in the graph using a different color for each groupof nodes in the same module. C. Key nodes that predict the function of all four modules. D. GeneMANIA networks of validated genes E. Community Landscape Analysis obtained with ModuLand plug-in. The five modules were plotted in the graph using a different color for each groupof nodes in the same module. F. Key nodes that predict the function of all five modules.
Figure 4
Figure 4. Box plots and Kaplan-Meier estimates for disease-free survival (DFS) for an independent patient population
A. Box plot for the logarithm of FABP3 gene expression for NR and ER patients. B. Box plot for the logarithm of SCGB1A1 gene expression for NR and ER patients. C. Kaplan-Meier estimates for disease-free survival (DFS) according to lowand high FABP3 expression with respect to the mean of this gene expression in the study population. D. Kaplan-Meier estimates fordisease-free survival (DFS) according to low and high SCGB1A1 expression with respect to the mean of this gene expression in the studypopulation.
Figure 5
Figure 5. Study flowchart
See this image and copyright information in PMC

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