Identification of Biomarkers in Key Gene Prediction in Lung Carcinoma

Venkataramanan Swaminathan; Tamilambikai Parandaman; Kavitha Kannan; Norfatiha Binti Bawahi; K. M. Kumar

doi:10.2991/978-94-6463-164-7_19

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Identification of Biomarkers in Key Gene Prediction in Lung Carcinoma

Authors

Venkataramanan Swaminathan¹^{, *}, Tamilambikai Parandaman¹, Kavitha Kannan², Norfatiha Binti Bawahi¹, K. M. Kumar³

¹Department Diagnostic and Allied Health Science, Faculty of Health Sciences, Management and Science University (MSU), Shah Alam, Malaysia

²School of Bioscience and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, India

³School of Life Sciences, Department of Bioinformatics, Pondicherry University, Kalapet, Pondicherry, India

^*Corresponding author. Email: s_venkataramanan@msu.edu.my

Corresponding Author

Venkataramanan Swaminathan

Available Online 5 June 2023.

DOI: 10.2991/978-94-6463-164-7_19 How to use a DOI?
Keywords: Lung Cancer; PPI network; DEGs; Hub genes; STRING; Cytoscape; GEPIA2
Abstract: Lung adenocarcinoma is an imminent principal cancer that causes a huge number of mortality for both men and women because of the respiratory epithelium. The survival rate analysis of patients improved year by year by using bioinformatics tools. The purpose of this research aimed to investigate the PPI network of lung cancer and identify the three gene ontology (GO) of gene expression. The discovery of hub gene biomarkers, on the other hand, aids in the investigation of overall survival and the occurrence of lung cancer expression. The GSE176348 and GSE85841 were obtained from GEO Databank, and by analyzing GEO2R, both data classify to the upregulated and downregulated.The regulated data was analyzed by DAVID server. By using server STRING, Cytoscape and CytoHubba, PPI network and hub genes of the upregulated and downregulated were constructed. The OS and expression level were identified by entering both genes to the KM plotter server and GEPIA2. 2 DEGs dataset was get from analyzed logFC value using GEO2R. Results obtained 2 DEGs dataset by GEO2R based on logFC value and the DEGs will present pathway enrichment analysis. The PPI network and hub genes identification results shows by cut-off > 0.9 value of ADH1B, CAV1, GSTA1, ADH1C ADH1A, CXCL12, FGF1, PPARG, FGF2, and IL1A. The analyzed result from OS showed hazard ratio but expression level presented the different gene of both LUSC or LUAD and normal tissues. Useful for understanding biomarker hub genes of the disease and providing bioinformatics tools for prognosis prediction analysis.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the Joint 3rd International Conference on Bioinformatics and Data Science (ICBDS 2022)
Series: Advances in Health Sciences Research
Publication Date: 5 June 2023
ISBN: 10.2991/978-94-6463-164-7_19
ISSN: 2468-5739
DOI: 10.2991/978-94-6463-164-7_19 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Venkataramanan Swaminathan
AU  - Tamilambikai Parandaman
AU  - Kavitha Kannan
AU  - Norfatiha Binti Bawahi
AU  - K. M. Kumar
PY  - 2023
DA  - 2023/06/05
TI  - Identification of Biomarkers in Key Gene Prediction in Lung Carcinoma
BT  - Proceedings of the Joint 3rd International Conference on Bioinformatics and Data Science (ICBDS 2022)
PB  - Atlantis Press
SP  - 270
EP  - 293
SN  - 2468-5739
UR  - https://doi.org/10.2991/978-94-6463-164-7_19
DO  - 10.2991/978-94-6463-164-7_19
ID  - Swaminathan2023
ER  -

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