Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma
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RESEARCH
Open Access
Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma Talip Zengin1,2 and Tuğba Önal-Süzek1,3* From The Sixth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2019) Niagara Falls, NY, USA. 07 September 2019
* Correspondence: tugbasuzek@mu. edu.tr 1 Department of Bioinformatics, Muğla Sıtkı Koçman University, Muğla, Turkey 3 Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey Full list of author information is available at the end of the article
Abstract Background: Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patientbased biomarkers which are the candidates of molecular targeting. Results: We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories. We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets. (Continued on next page)
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