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3. Additional file 3 of Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

Author

Maharjan, Mona, Raihanul Bari Tanvir, Chowdhury, Kamal, Wenrui Duan, and Mondal, Ananda Mohan

Abstract

Additional file 3. Enriched GO Terms. The top 10 significant GO terms in 3 categories – Biological Process, Cellular Component, and Molecular Function – enriched with non-treatment and treatment biomarkers.

Published
2020
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9. Determination of Biomarkers for Diagnosis of Lung Cancer Using Cytoscape-based GO and Pathway Analysis.

Author

Maharjan, Mona, Tanvir, Raihanul Bari, Chowdhury, Kamal, and Mondal, Ananda Mohan

Subjects
*LUNG cancer diagnosis, *GENE ontology, *GENE expression, *BIOLOGICAL tags, *DATA analysis
Abstract

Lung cancer is the second most common cancer in the world. The aim of this study is to identify biomarkers for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles from GEO database were analyzed by GEO2R to identify Differentially Expressed Genes (DEGs) and further analyzed using Cytoscape. The data was divided into two categories: non-treatment and treatment groups. A total of 407 DEGs (254 upregulated and 153 downregulated) and 259 DEGs (124 upregulated and 135 downregulated) were isolated for non-treatment and treatment studies respectively. The significant Gene Ontologies and pathways enriched with DEGS were identified using Cytoscape apps, BiNGO and ReactomeFIPlugIn, respectively. Hub genes based on network parameters - Degree, Closeness and Betweenness - were isolated using CytoHubba. In conclusion, DEGs identified in this study may play an important role in early diagnosis or as biomarkers of lung cancer. [ABSTRACT FROM AUTHOR]

Published
2019

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