Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma

• URL: http://arxiv.org/abs/2402.17807v1
• Date: Tue, 27 Feb 2024 11:29:36 GMT
• Title: Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma
• Authors: Abanti Bhattacharjya, Md Manowarul Islam, Md Ashraf Uddin, Md. Alamin Talukder, AKM Azad, Sunil Aryal, Bikash Kumar Paul, Wahia Tasnim, Muhammad Ali Abdulllah Almoyad, Mohammad Ali Moni
• Abstract summary: In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the metastases in hypopharyngeal cancer. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes.
• Score: 5.178086150698542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common 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). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously.

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