Molecular subtypes and prognostic models for predicting prognosis of lung adenocarcinoma based on miRNA-related genes

Abstract

Background: MicroRNAs (miRNAs) are crucial in cancer development and progression, and therapies targeting miRNAs demonstrate great therapeutic promise. Aim: We sought to predict the prognosis and therapeutic response of lung adenocarcinoma (LUAD) by classifying molecular subtypes and constructing a prognostic model based on miRNA-related genes. Method: This study was based on miRNA-mRNA action pairs and ceRNA networks in the Cancer Genome Atlas (TCGA) database. Three molecular subtypes were determined based on 64 miRNA-associated target genes identified in the ceRNA network. The S3 subtype had the best prognosis, and the S2 subtype had the worst prognosis. The S2 subtype had a higher tumor mutational load (TMB) and a lower immune score. The S2 subtype was more suitable for immunotherapy and sensitive to chemotherapy. The least absolute shrinkage and selection operator (LASSO) algorithm was performed to determine eight miRNA-associated target genes for the construction of prognostic models. Result: High-risk patients had a poorer prognosis, lower immune score, and lower response to immunotherapy. Robustness was confirmed in the Gene-Expression Omnibus (GEO) database cohort (GSE31210, GSE50081, and GSE37745 datasets). Overall, our study deepened the understanding of the mechanism of miRNA-related target genes in LUAD and provided new ideas for classification. Conclusion: Such miRNA-associated target gene characterization could be useful for prognostic prediction and contribute to therapeutic decision-making in LUAD.