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

Abstract

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.

Keywords: WGCNA; bioinformatics analysis; drugs sensitivity; ferroptosis; immune microenvironment; lung adenocarcinoma; prognosis.

FIGURE 1

(A) Clustering of samples and removal of outliers. (B) Analysis of network topology for various soft-thresholding powers. (C) The cluster dendrogram of genes of LUAD patients. Each branch in the figure represents one gene, and every color below represents one co-expression module. (D) Correlation between the gene module and clinical characteristics, including stromal score, immune score, ESTIMATE score, and tumor purity. (E) The ferroptosis-related score difference between the two groups.

FIGURE 2

(A–H) Kaplan–Meier curves of high- and low-expression of the hub genes of LUAD patients in the TCGA.

FIGURE 3

(A–D) Kaplan–Meier curves of high- and low-RS LUAD patients in the TCGA, GSE8894, GSE50081, and GSE68465 cohort. (E–H) ROC curves of one-, two-, and 3-year OS for LUAD patients based on the RS in the TCGA, GSE8894, GSE50081, and GSE68465 cohort.

FIGURE 4

(A–E) Distribution of the RS separated by the clinical-pathological features among the LUAD patients in the TCGA cohort. (F–O) Subgroup analysis of prognostic value of the ferroptosis-prognostic model for LUAD patients by Kaplan–Meier curves according to clinicopathologic characteristics.

FIGURE 5

(A) Uni- and multivariate Cox regression analysis of the associations between survival outcomes and age, gender, stage, and risk score of LUAD patients. (B) ROC curves of one-, two-, and 3-year OS for LUAD patients based on the RS and TNM stage. (C) The comparison of the prediction ability between the two model. (D) The nomogram of the overall survival prediction model. (E–G) Calibration plots for the nomogram: 1-year (E); 3-year (F); 5-year (G) nomogram. (H) Volcano plot of differentially expressed genes between the two groups.

FIGURE 6

(A–C) Top three clusters in the protein-to-protein interaction (PPI) network. (D) Enrichment analysis of the top three clusters. (E) Competing endogenous RNA (ceRNA) network of RS-related DEGs - differentially expressed miRNAs—differentially expressed lncRNAs.

FIGURE 7

(A) Comparisons of infiltration levels of immune cells between high- and low-RS groups with the ssGSEA algorithm. (B) The correlation of the tumor-infiltrated immune cells. (C–E) Forest plot of the tumor-infiltrated immune cells with ssGSEA (C), xCell database (D), and EPIC database (E).

FIGURE 8

(A) Comparisons of the expression of immune checkpoints between high- and low-RS groups. (B–D) Comparisons of TIDE score (B), tumor purity (C), stromal score, immune score, and ESTIMATE score (D) between the high- and low-RS groups. (E) Comparisons of the response to drugs between high- and low-RS groups by GDSC database.