Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

Autor: Wenrui Duan, Raihanul Bari Tanvir, Kamal Chowdhury, Ananda Mohan Mondal, Mona Maharjan
Rok vydání: 2020
Předmět:
Lung Neoplasms
Bioinformatics
DNA repair
Computational identification of biomarker
Biology
lcsh:Computer applications to medicine. Medical informatics
Biochemistry
03 medical and health sciences
0302 clinical medicine
Structural Biology
Gene expression
Biomarkers
Tumor
medicine
Humans
Gene Regulatory Networks
Protein Interaction Maps
Treatment studies
Lung cancer
lcsh:QH301-705.5
Molecular Biology
Gene
Lung cancer biomarkers
Survival analysis
030304 developmental biology
0303 health sciences
Research
Gene Expression Profiling
Applied Mathematics
Computational Biology
Cancer
Prognosis
medicine.disease
Survival Analysis
Computer Science Applications
Gene Expression Regulation
Neoplastic
Gene Ontology
lcsh:Biology (General)
030220 oncology & carcinogenesis
Non-treatment studies
Cancer research
lcsh:R858-859.7
Biomarker (medicine)
DNA microarray
Signal Transduction
Zdroj: BMC Bioinformatics
BMC Bioinformatics, Vol 21, Iss S9, Pp 1-19 (2020)
ISSN: 1471-2105
Popis: 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 non-treatment 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. Conclusion A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.
Databáze: OpenAIRE
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