Gene-Knockdown Methods for Silencing Nuclear-Localized Insulin Receptors in Lung Adenocarcinoma Cells: A Bioinformatics Approach

Abstract

Background: Lung adenocarcinoma, the predominant subtype of lung cancer, presents a significant challenge to public health due to its notably low five-year survival rate. Recent epidemiological data highlights a concerning trend: patients with pulmonary adenocarcinoma and comorbid diabetes exhibit substantially elevated mortality rates compared to those without diabetes, suggesting a potential link between hyperinsulinemia in diabetic individuals and accelerated progression of pulmonary adenocarcinoma. Insulin Receptor (IR) is a tyrosine-protein kinase on the cell surface, and its over-expression is considered the pathological hallmark of hyperinsulinemia in various cancer cell types. Research indicates that IR can translocate to the nucleus of lung adenocarcinoma cells to promote their proliferation, but its precise molecular targets remain unclear. This study aims to silence IRs in lung adenocarcinoma cells and identify key genes within the ERK pathway that may serve as potential molecular targets for intervention. Methods: Gene expression data from lung adenocarcinoma and para cancer tissues were retrieved from the Gene Expression Omnibus (GEO) database and assessed through "pheatmap", GO annotation, KEGG analysis, R calculations, Cytoscape mapping, and Hub gene screening. Significant genes were visualized using the ggplot2 tool to compare expression patterns between the two groups. Additionally, survival analysis was performed using the R "survminer" and "survival" packages, along with the R "pathview" package for pathway visualization. Marker genes were identified and linked to relevant signaling pathways. Validation was conducted utilizing real-time quantitative polymerase chain reaction and immunoblotting assays in an A549 lung cancer cell model to determine the roles of these marker genes in associated signaling cascades. Results: The study examined 58 lung adenocarcinoma samples and paired para-neoplastic tissues. Analysis of the GSE32863 dataset from GEO revealed 1040 differentially expressed genes, with 421 up-regulated and 619 down-regulated. Visualization of these differences identified 172 significant alterations, comprising 141 up-regulated and 31 down-regulated genes. Functional enrichment analysis using Gene Ontology (GO) revealed 56 molecular functions, 77 cellular components, and 816 biological processes. KEGG analysis identified 17 strongly enriched functions, including cytokine interactions and tumor necrosis factor signaling. Moreover, the ERK signaling pathway was associated with four Hub genes (FGFR4, ANGPT1, TEK, and IL1B) in cellular biological processes. Further validation demonstrated a positive correlation between IL-1B expression in the ERK signaling pathway and lung cancer through real-time fluorescence quantitative enzyme- linked reaction with immunoblotting assays. Conclusion: In IR-silenced lung adenocarcinoma, the expression of the IL-1B gene exhibited a positive correlation with the ERK signaling pathway.