# Shared Genomic Features Between Lung Adenocarcinoma and Type 2 Diabetes: A Bioinformatics Study

## TL;DR

This study finds shared genes and immune pathways between lung adenocarcinoma and type 2 diabetes, offering new insights for treatment strategies.

## Contribution

Identifies ATR, RFC4, and MCM2 as key genes and specific miRNAs linked to both lung adenocarcinoma and type 2 diabetes.

## Key findings

- ATR, RFC4, and MCM2 are pivotal genes in the pathogenesis of both type 2 diabetes and lung adenocarcinoma.
- hsa-mir147a, hsa-mir16-5p, and hsa-mir-1-3p are significantly correlated with the identified hub genes.
- Immune-related pathways, including TGF-β signaling, are enriched in both diseases.

## Abstract

Lung adenocarcinoma, a prevalent histological variant of lung cancer, is experiencing a persistent rise in global incidence. Concurrently, type 2 diabetes presents a significant risk to human health. However, the molecular mechanisms connecting these two diseases remain predominantly unexplored. This study used bioinformatics analysis techniques. Our objective was to identify the potential genes linked to type 2 diabetes and lung adenocarcinoma, as well as to investigate the associated microRNAs (miRNAs) and transcription factor (TF) genes. The research findings indicate that ATR, RFC4, and MCM2 are pivotal genes in the pathogenesis of type 2 diabetes and lung adenocarcinoma. Moreover, hsa-mir147a, hsa-mir16-5p, and hsa-mir-1-3p exhibit the most significant correlation with these hub genes. These findings enhance the comprehension of the pathogenesis of type 2 diabetes and lung adenocarcinoma while offering critical insights for refining management plans and augmenting treatment strategies for both conditions.

Background: Lung adenocarcinoma (LUAD) is a common histopathological variant of non-small cell lung cancer. Individuals with type 2 diabetes (T2DM) face an elevated risk of developing LUAD. We examined the common genomic characteristics between LUAD and T2DM through bioinformatics analysis. Methods: We acquired the GSE40791, GSE25724, GSE10072, and GSE71416 datasets. Differentially expressed genes (DEGs) were identified through R software, particularly its version 4.1.3 and analyzed via gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Subsequently, we analyzed the relationship between immune cell infiltration and DEGs. we constructed a protein–protein interaction network using STRING and visualized it with Cytoscape. Moreover, gene modules were identified utilizing the MCODE plugin, and hub genes were selected through the CytoHubba plugin. Additionally, we evaluated the predictive significance of hub genes using receiver operating characteristic curves and identified the final central hub genes. Finally, we forecasted the regulatory networks of miRNA and transcription factors for the central hub genes. Results: A total of 748 DEGs were identified. Analysis of immune infiltration showed a notable accumulation of effector-memory CD8 T cells, T follicular helper cells, type 1 T helper cells, activated B cells, natural killer cells, macrophages, and neutrophils in both LUAD and T2DM. Moreover, these DEGs were predominantly enriched in immune-related pathways, including the positive regulation of I-κB kinase/NF-κB signaling, positive regulation of immunoglobulin production, cellular response to interleukin-7, and cellular response to interleukin-4. The TGF-β signaling pathway was significantly important among them. Additionally, seven hub genes were identified, including ATR, RFC4, MCM2, NUP155, NUP107, NUP85, and NUP37. Among them, ATR, RFC4, and MCM2 were identified as pivotal hub genes. Additionally, hsa-mir147a, hsa-mir16-5p, and hsa-mir-1-3p were associated with LUAD and T2DM. SP1 (specific protein 1) and KDM5A (lysine-specific demethylase 5A) regulated MCM2, ATR, and RFC4. Conclusions: Our study elucidates the common mechanisms of immune response, TGF-β signaling pathway, and natural killer cells in LUAD and T2DM, and identifies ATR, RFC4, and MCM2 as key potential biomarkers and therapeutic targets for the comorbidity of these two conditions.