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

TL;DR

This study integrates GEO-derived transcriptomic data to uncover shared gene regulatory networks between Hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma. By identifying common DEGs, constructing TF-miRNA and PPI networks, and extracting hub genes with Degree and MCC, the authors reveal a convergent regulatory architecture centered on a 10-gene hub set, including JUN and ERBB2. Functional association analyses (GeneMANIA) and TF-gene/miRNA-gene-disease mappings further extend the regulatory context, while DSigDB-enriched drug prediction suggests eight candidate therapeutics targeting these hubs for both diseases. The work advances the notion of shared molecular bases for comorbidity and highlights candidate drugs that could address both cancers, pending experimental validation.

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.

Figures (14)

• Figure 1: Diagram representing the proposed methodology of the current research. For Hypopharyngeal Cancer and EGFR-mutated Lung Adenocarcinoma, two datasets are used. Each dataset has eight samples. Using the R programming language, the DEGs (Differentially Expressed Genes) from those two datasets are retrieved. The VENNY tool is used to find out the common genes between these two diseases. With the aid of these widespread DEGs, GO terms, pathways, PPI networks, TF-miRNA, and Hub genes are identified. Functional association, TF-gene, Gene-miRNA, Gene-disease, and Some therapeutic compounds are anticipated based on the hub genes of individuals with Hypopharyngeal Cancer and EGFR-mutated Lung Adenocarcinoma who have these 2 diseases concurrently.
• Figure 2: Venn Diagram of shared DEGs. 32 common genes were found between HC and EGFR-mutated LC. Common DEGs were 2% among 1667 DEGs.
• Figure 3: Top 10 GO terms concomitant to biological process, molecular function, and cellular component pinpointing entrenched on the combined score (The log of the p-value from the Fisher exact test and multiplying that by the z-score of the deviation from the expected rank.
• Figure 4: Top 10 pathways from (a) Reactome, (b) KEGG, (c) WikiPathways, and (d) BioCarta pinpointing entrenched on the combined score (The log of the p-value from the Fisher exact test and multiplying that by the z-score of the deviation from the expected rank).
• Figure 5: Visualization of TF-miRNA coregulatory network through NetworkAnalyst. Green-black highlighted Nodes indicate seeds, Red Diamond-shaped nodes for TF, and Blue Box Shaped Nodes for miRNA.