Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma.

Autor: Bhattacharjya A; Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh., Islam MM; Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh., Uddin MA; School of Information Technology, Deakin University, Geelong, Australia., Talukder MA; Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh., Azad A; Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia., Aryal S; School of Information Technology, Deakin University, Geelong, Australia., Paul BK; Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.; Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh., Tasnim W; Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh., Almoyad MAA; Department of Basic Medical Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia., Moni MA; Artificial Intelligence & Data Science, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, Australia.; AI & Digital Health Technology, Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, Australia.; Rural Health Research Institute, Charles Sturt University, Orange, Australia.
Jazyk: angličtina
Zdroj: FEBS open bio [FEBS Open Bio] 2024 Jul; Vol. 14 (7), pp. 1166-1191. Date of Electronic Publication: 2024 May 23.
DOI: 10.1002/2211-5463.13807
Abstrakt: Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.
(© 2024 The Authors. FEBS Open Bio published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)
Databáze: MEDLINE
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