Identification and validation of a glycosyltransferase gene signature as a novel prognostic model for lung adenocarcinoma

Abstract

Background: The role of glycosyltransferase (GT) genes in lung adenocarcinoma (LUAD) needs further elucidation. Thus, our study aims to identify the prognostic gene signature of LUAD and explore its molecular functions. Methods: We initially extracted GT gene sets from the database, and obtained mRNA expression levels and clinical data from The Cancer Genome Atlas (TCGA) database. For constructing a prognostic model for GT genes, we utilized univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Using the model, patients were categorized into high- and low-risk groups. Additionally, we evaluated differences in tumor immune infiltration between these groups and identified potential therapeutic drugs. Finally, we experimentally validated the expression levels of these crucial prognostic genes. Results: We developed a risk score comprising nine GT genes (C1GALT1, FUT1, GALNT2, PLOD2, POMK, PYGB, ST3GAL6, UGT2B11, UGT3A1). Patients were then categorized into low- and high-risk groups based on this score. The low-risk group showed superior overall survival (OS) compared to the high-risk group. There were significantly distinct tumor immune microenvironment statuses observed between the two groups. We identified potential therapeutic drugs, including the MEK inhibitor (PD-184352). Finally, we verified the expression of these nine GT genes through immunohistochemistry (IHC) staining and quantitative real-time PCR (qPCR). Conclusion: We identified a distinct LUAD GT gene signature, and these differentially expressed mRNAs could serve as valuable prognostic biomarkers and therapeutic targets. Furthermore, we experimentally validated their expression levels and identified potential therapeutic agents.

Keywords

• Lung adenocarcinoma
• Glycosyltransferase
• Bioinformatics
• Biomarker