Development and validation of a combined ferroptosis and immune prognostic signature for lung adenocarcinoma

Abstract

Background: Studies have shown that the regulation of ferroptosis could be a new approach to cancer treatment and abnormal ferroptosis is closely associated with a dysregulated immune response. However, a combined signature with ferroptosis-related genes (FRGs) and immune-related genes (IRGs) is necessary to be constructed for predicting prognoses and guiding individualized precision therapy of lung adenocarcinoma (LUAD) patients.

Methods: In this study, based on the Cancer Genome Atlas (TCGA) cohort, prognosis-related FRGs and IRGs were first identified and incorporated into the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression model to generate a combined signature of ferroptosis- and immune-related genes (CSFI) values to predict the overall survivals (OSs) of LUAD patients. And patients with LUAD from the Gene Expression Omnibus (GEO) database were applied for the validation set. Nomogram was constructed based on multivariate Cox regression analysis. Subsequently, ferroptosis, immunity, and gene mutation status of patients between the CSFI-high and -low groups were compared. Additionally, the enrichment pathways in CSFI-high and -low groups were explored by Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia (KEGG) and Gene Set Enrichment Analysis (GSEA) analyses.

Results: As a result, the CSFI-low group showed a good prognosis instead of the CSFI-high group. CSFI was identified to be an independent prognosis factor for LUAD. In general, there were ferroptosis- and immune-suppressive states in CSFI-high patients. Notably, the mutation frequencies of TP53 were higher in CSFI-high patients.

Conclusions: In LUAD, CSFI which served as a novel classifier was offered for predicting the prognoses of patients and contributing to guiding personalized targeted therapy of patients. Therefore, based on these findings, it was believed that a synergistic treatment of ferroptosis and immunity would be more effective on LUAD patients with low CSFI values.

Keywords: Ferroptosis; immune; lung adenocarcinoma (LUAD); personalized therapy; prognosis.

Figure 1

Flow diagram of the study design and analysis. LUAD, lung adenocarcinoma; TCGA, the Cancer Genome Atlas; FRGs, ferroptosis-related genes; DEGs, differentially expressed genes; IRGs, immune-related genes; CSFI, combined signature of ferroptosis- and immune-related genes; GEO, Gene Expression Omnibus.

Figure 2

Identification of prognostic genes in LUAD. (A) A volcano plot presenting DEGs between normal and LUAD samples. (B) A Venn diagram indicating differentially expressed IRGs. (C) A Venn diagram indicating differentially expressed FRGs. (D) A forest plot of prognosis-related FRGs identified by univariate Cox regression. (E) A forest plot of prognosis-related IRGs identified by univariate Cox regression. (F) A heatmap showing expressions of the prognostic genes of FRGs and IRGs in tumor samples and normal samples. DEGs, differentially expressed genes; IRGs, immune-related genes; FRGs, ferroptosis-related genes; LUAD, lung adenocarcinoma.

Figure 3

Construction and validation of CSFI. (A) A PPI network manifesting the relationship between prognosis-related IRGs and FRGs. (B,C) The selection of parameter of the LASSO regression model based on the 43 prognosis-related genes. (D) A PCA plot in different CSFI groups of training cohort. (E) A plot suggesting the distribution of CSFI values, OSs, and expressions of model genes in the TCGA cohort. (F) KM curves of different CSFI groups in TCGA cohort. (G) ROC curves of different CSFI groups in TCGA cohort. (H) A PCA plot in different CSFI groups of the validation cohort. (I) KM curves of different CSFI groups in the validation cohort. (J) ROC curves of different CSFI groups in the validation cohort. CSFI, combined signature of ferroptosis- and immune-related genes; OS, overall survival; AUC, area under curve; CI, confidence interval; PPI, protein-protein interaction; IRGs, immune-related genes; FRGs, ferroptosis-related genes; LASSO, Least Absolute Shrinkage and Selection Operator; PCA, principal component analysis; TCGA, the Cancer Genome Atlas; KM, Kaplan-Meier; ROC, receiver operating characteristic.

Figure 4

Ferroptosis profiles and enriched pathways in the different CSFI groups of the TCGA cohort. (A) Comparison of the expressions of 10 DOFs between CSFI-high and CSFI-low groups. (B) Comparison of the expressions of 10 SOFs between CSFI-high and CSFI-low groups. *, P<0.05; **, P<0.01; ***, P<0.001. (C) A bubble diagram showing GO enrichment (the bigger bubble means the more genes enriched, and the increasing depth of blue means the differences were more obvious). (D) A loop graph indicating KEGG enrichment. (E) GSEA of different CSFI groups. CSFI, combined signature of ferroptosis- and immune-related genes; TCGA, the Cancer Genome Atlas; DOFs, drivers of ferroptosis; SOFs, suppressors of ferroptosis; GO, Gene Ontology; KEGG, Kyoto Gene and Genome Encyclopedia; GSEA, Gene Set Enrichment Analysis.

Figure 5

Gene mutation and immune profiles in TCGA cohort. (A) Oncoplot of the mutated genes in TCGA cohort. (B) A heatmap manifesting expressions of 6 ICPGs in CSFI-high and -low groups. (C) Box plots suggesting scores of immune cells in two CSFI groups. (D) Box plots suggesting scores of immune-related pathways in two CSFI groups. *, P < 0.05; **, P < 0.01; ***, P < 0.001. CSFI, combined signature of ferroptosis- and immune-related genes; TCGA, the Cancer Genome Atlas; ICPGs, immune checkpoint genes.

Figure 6

Identification of independent prognostic value of the CSFI. (A,B) Forest plots from the univariate and multivariate Cox regression analyses in the training cohort. (C,D) Forest plots from the univariate and multivariate Cox regression analyses in the validation cohort. (E) Nomogram for predicting 1–3 years OS rate of training cohort. (F) Calibration plots of the nomogram. *, P<0.05; ***, P<0.001. CI, confidence interval; CSFI, combined signature of ferroptosis- and immune-related genes; OS, overall survival.