Development of a ferroptosis-based model to predict prognosis, tumor microenvironment, and drug response for lung adenocarcinoma with weighted genes co-expression network analysis

• Published in: Frontiers in pharmacology Vol. 13; p. 1072589
• Main Authors: Cheng, Tao, Shan, Guangyao, Yang, Huiqin, Gu, Jie, Lu, Chunlai, Xu, Fengkai, Ge, Di
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 17.11.2022
• Subjects: bioinformatics analysis; ferroptosis; immune microenvironment; lung adenocarcinoma; Pharmacology; prognosis; WGCNA
• ISSN: 1663-9812; 1663-9812
• DOI: 10.3389/fphar.2022.1072589

Objective: The goal of this study was to create a risk model based on the ferroptosis gene set that affects lung adenocarcinoma (LUAD) patients’ prognosis and to investigate the potential underlying mechanisms. Material and Methods: A cohort of 482 LUAD patients from the TCGA database was used to develop the prognostic model. We picked the module genes from the ferroptosis gene set using weighted genes co-expression network analysis (WGCNA). The least absolute shrinkage and selection operator (LASSO) and univariate cox regression were used to screen the hub genes. Finally, the multivariate Cox analysis constructed a risk prediction score model. Three other cohorts of LUAD patients from the GEO database were included to validate the prediction ability of our model. Furthermore, the differentially expressed genes (DEG), immune infiltration, and drug sensitivity were analyzed. Results: An eight-gene-based prognostic model, including PIR, PEBP1, PPP1R13L, CA9, GLS2, DECR1, OTUB1, and YWHAE, was built. The patients from the TCGA database were classified into the high-RS and low-RS groups. The high-RS group was characterized by poor overall survival (OS) and less immune infiltration. Based on clinical traits, we separated the patients into various subgroups, and RS had remarkable prediction performance in each subgroup. The RS distribution analysis demonstrated that the RS was significantly associated with the stage of the LUAD patients. According to the study of immune cell infiltration in both groups, patients in the high-RS group had a lower abundance of immune cells, and less infiltration was associated with worse survival. Besides, we discovered that the high-RS group might not respond well to immune checkpoint inhibitors when we analyzed the gene expression of immune checkpoints. However, drug sensitivity analysis suggested that high-RS groups were more sensitive to common LUAD agents such as Afatinib, Erlotinib, Gefitinib, and Osimertinib. Conclusion: We constructed a novel and reliable ferroptosis-related model for LUAD patients, which was associated with prognosis, immune cell infiltration, and drug sensitivity, aiming to shed new light on the cancer biology and precision medicine.