Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies
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RESEARCH
Open Access
Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies Mona Maharjan1, Raihanul Bari Tanvir1, Kamal Chowdhury2, Wenrui Duan3 and Ananda Mohan Mondal1* From The 20th International Conference on Bioinformatics & Computational Biology (BIOCOMP 2019) Las Vegas, NV, USA. 29 July-01 August 2019
* Correspondence: amondal@fiu. edu 1 School of Computing and Information Sciences, Florida International University, Miami, FL, USA Full list of author information is available at the end of the article
Abstract Background: Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. Results: The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from nontreatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. (Continued on next page)
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