Molecular Identification of Immunity- and Ferroptosis-related Gene Signature in Non-small Cell Lung Cancer

doi:10.21203/rs.3.rs-390478/v1

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Molecular Identification of Immunity- and Ferroptosis-related Gene Signature in Non-small Cell Lung Cancer

https://doi.org/10.21203/rs.3.rs-390478/v1

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Background: Lung cancer is one of the dominant causes of cancer-related deaths worldwide. Ferroptosis, an iron-dependent regulated cell death, plays an important role in the cancer immunotherapy. However, the role of immunity- and ferroptosis-related gene signature in non-small cell lung cancer (NSCLC) remains unknown.

Method: The RNA sequencing (RNA-seq) expression data and clinical information of NSCLC were downloaded from The Cancer Genome Atlas (TCGA) database and performed differential analysis. Univariate and multivariate cox regressions were used to identify the ferroptosis-related gene, and receiver operating characteristic (ROC) model was established using the independent risk factors. GO and KEGG enrichment analyses were performed to investigate the biological functions of differential genes.

Results: A 5-gene signature was constructed to stratify patients into high- and low-risk groups. Compared with patients in the low-risk group, patients in the high-risk group showed significantly poor overall survival (P < 0.001 in the TCGA cohort and P = 0.001 in the GSE13213 cohort). The risk score was an independent predictor for overall survival in multivariate Cox regression analyses (HR > 1, P < 0.01). The 1 year-, 2 year- and 3 year-ROCs were 0.792, 0.644 and 0.641 in TCGA and 0.623, 0.636 and 0.631 in GSE13213, respectively. Functional analysis revealed that immune-related pathways were enriched, and immune status were different between two risk groups.

Conclusions: We identified differently expressed immunity- and ferroptosis-related genes that may involve in NSCLC. These genes may predict the overall survival in NSCLC and targeting ferroptosis may be an alternative for clinical therapy.

Cancer Biology

lung cancer

ferroptosis

immunity

overall survival

gene signature

Lung cancer has become the leading deadly malignancy worldwide (1), among which non-small cell lung cancer (NSCLC) accounts for more than 85% of all cases (2). Despite extensive research on molecular targeted therapies and checkpoint inhibitors, more than 50% of patients died within one year after diagnosis of NSCLC, and the 5-year overall survival rate is less than 18% (3). These data indicate that there is an urgent need for novel therapeutic drug research of NSCLC, and analyses of the underlying mechanism of NSCLC from a genetic level may provide clues for finding new therapeutic targets.

Ferroptosis is a type of programmed cell death associated with the imbalance of redox homeostasis (4, 5). The process of ferroptosis is usually accompanied by a large amount of iron accumulation and lipid peroxidation (6). Emerging evidence shows that ferroptosis is closely related to the development of human diseases, especially cancer (7-11) . Ferroptosis has been identified to be a promising trigger option for cancer cell death, especially for malignant tumors that are resistant to traditional therapies (12-14). Moreover, ferroptotic cancer cells might release signals like oxidized lipid mediators to affect anti-tumor immunity, or a small proportion of cells undergoing ferroptosis might suppress the immune system and allow tumor growth (15). The induction of ferroptosis leads to the anti-tumor efficacy of immunotherapy, suggesting that the immune system might, at least in part, function through ferroptosis (16). However, the relationship between immunity- and ferroptosis-related genes and NSCLC patient prognosis is still unknown, making it still a challenge for developing ferroptosis therapy for NSCLC.

In this study, we collected the RNA‐Seq data and clinical information of NSCLC in The Cancer Genome Atlas (TCGA) dataset. We identified five immunity- and ferroptosis‐related differential genes to establish a prognostic risk model. Meanwhile, patients with NSCLC in the Gene Expression Omnibus (GEO) database were selected as a validation cohort. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to explore the functions and pathways enriched between high‐risk and low‐risk subgroups. Furthermore, a nomogram model were developed based on risk scores and clinical features to assess prognosis. Our results demonstrated that the immunity- and ferroptosis‐related risk model was a potential gene signature and therapeutic target for NSCLC.

Data Acquisition

The RNA sequencing (RNA-seq) data (n = 594) and corresponding clinical information of NSCLC were obtained from TCGA dataset (https://tcga-data.nci.nih.gov/tcga/). To validate the findings from TCGA dataset, we will also employ an independent cohort GSE13213 which contained 117 NSCLC cases from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). The RNA-seq data and clinical information from two datasets were reviewed by two authors independently (TX-L and JY-Z) to avoid potential mistakes.

Identification of differential expression gene

The DEGs between tumor and normal tissues were identified using the “limma” R package (3.6.2 version, https://cran.r-project.org/) with the Wilcoxon test. The cut-off values were determined according to the parameters with a false discovery rate (FDR) < 0.05 and a |log2FoldChange| > 1. Univariate and multivariate cox regressions were used to evaluate the relationships between the DEGs and OS. Patients were stratified into high- and low-risk groups according to the risk score. The risk score was calculated as follow: riskscore=∑^j_(n=1)Coef_j*X_j, where Coef _j represents the coefficient and X_j represents the relative expression levels of each DEG standardized by z-score.

Development of ROC curves

The prognostic DEGs with clinical information was firstly analysed using the univariate cox regression. Significant prognostic factors (P < 0.05) identified by univariate cox regression were then enrolled into multivariate cox regression to identify the independent prognostic risk factors. The receiver operating characteristic (ROC) analysis was used to determine the sensitivity and specificity of the prognostic model in predicting overall survival.

PCA and t-SNE analysis

PCA and t-SNE were used for dimensionality reduction analysis, which was applied to the gene signature using the ‘prcomp’ function in the R ‘stats’ package and the ‘Rtsne’ package, respectively. Based on the expression of genes in the signature, PCA was carried out with the "prcomp" function of the "stats" R package. Besides, t-SNE were performed to explore the distribution of different groups using the "Rtsne" R package.

Interaction network and enrichment analysis

The interaction network of DEGs was performed using the STRING database (http://string-db.org/cgi/input.pl). Moreover, GO and KEGG analysis were applied to analyze the functional enrichment of DEGs using R software (3.6.2 version, https://cran.r-project.org/).

Immune cells and ferroptosis

The infiltrating scores of 16 kinds of immune cells and the activities of 13 immune-related pathways with the ssGSEA were further evaluated using the "gsva" R package (3.6.2 version, https://cran.r-project.org/).

Statistical analysis

Statistical analysis was performed using the R software (3.6.2 version, https://cran.r-project.org/). Student's t-test was used to evaluate the difference between different groups. The ssGSEA scores between high and low-risk groups were compared by Mann–Whitney test. The Kaplan–Meier method was applied to perform OS analysis and the difference was assessed by two-sided log-rank test. Two-sided P < 0.05 indicated a statistically significant difference.

Identification of prognostic ferroptosis-related DEGs

The workflow of our study was shown in Fig. 1. A total of 594 NSCLC patients from the TCGA-LUAD cohort and another 176 NSCLC patients from the GEO (GSE13213) dataset were finally enrolled. To identify ferroptosis-related differentially expressed genes (DEGs), all these samples were included for analysis while 85 samples without follow-up data were excluded for identifying ferroptosis-related genes associated with prognosis. The detailed clinical characteristics of these patients are summarized in Table S1. In total, we identified 16 differentially expressed genes (26.7%) between tumor and adjacent normal tissues and 14 (23.3%) prognostic genes in tumor samples (Fig. 2A). Notably, 5 of them (ALOX5, DPP4, FANCD2, GCLC and SLC7A11) were both differentially expressed and correlated with overall survival (OS) in the univariate Cox regression analysis (Fig. 2B and C). Interaction network analysis showed that SLC7A11, GCLC, HMOX1, GCLM, G6PD, NQO1 and NOX1 were the hub genes (Fig. 2D and E), indicating that these genes may be the main components responsible for regulating ferroptosis in NSCLC.

Construction of a prognostic model in TCGA cohort

Based on the 5 ferroptosis-related genes (ALOX5, DPP4, FANCD2, GCLC and SLC7A11) identified above, we constructed a prognostic model using LASSO Cox regression analysis. The patients were stratified into high-risk (n = 297) and low-risk group (n = 297) groups by the median cut-off value (Fig. 3A). The results of principal components analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analysis indicated that our prognostic model could effectively stratified patients into different directions (Fig. 3B and C). Moreover, patients in the high-risk group had a higher probability of earlier death than those in low-risk group (Fig. 3D). The Kaplan-Meier curves showed that patients in the high-risk group had a significantly worse OS than their low-risk counterparts (Fig. 3E). Furthermore, univariate (hazard ratio [HR]: 2.97; 95% CI: 1.74–5.06; P < 0.001) and multivariate Cox regression analysis (HR: 2.70; 95% CI: 1.57–4.64; P < 0.001) also revealed that patients with high-risk achieved significantly worse OS than those with low-risk (Table S2). Receiver operating curve (ROC) analysis indicated that the area under the curve (AUC) of this model reached 0.792 at 1 year, 0.644 at 2 years, and 0.641 at 3 years (Fig. 3F). Taken together, these results suggested that the prognostic model based on the five genes had strong prognostic power.

Validation of the 5-gene signature in GSE13213 dataset

Next, we validated the prognostic model in the GSE13213 dataset. Similarly, the 107 patients were stratified into high-risk and low-risk groups by the median risk score value (Fig. 4A). PCA and t-SNE analysis suggested that patients with different risks were well separated into two different groups (Fig. 4B and C). Also, patients in the high-risk group had a higher probability of earlier death (Fig. 4D) and achieved significantly worse OS than those in low-risk group (Fig. 4E). The predictive power of our prognostic model was satisfactory (Fig. 4F). Furthermore, the risk score was identified as an independent predictor of OS by both univariate analysis (HR: 5.18; 95% CI: 1.8–14.92; P < 0.01) and multivariate Cox regression analysis (HR: 5.59; 95% CI: 1.79–17.44; P < 0.001; Table S2). Collectively, these findings suggested that our prognostic model also had strong power in GSE13213 dataset.

Functional enrichment analysis

GO enrichment and KEGG pathway analysis were performed to explore the functional roles of the DEGs in the TGCA LUAD cohort and GSE13213 dataset. GO analysis showed that the DEGs were mostly enriched in several immunity- and ferroptosis-related biological processes and molecular functions (P < 0.05; Fig. 5A and B). KEGG analysis showed that DEGs were mostly enriched in the ferroptosis pathway and immune-related pathways such as human T-cell leukemia virus 1 (HTLV-1) infection pathway (P < 0.05; Fig. 5C and D). These results suggested that there may be a crosslinking between ferroptosis and tumor immune in NSCLC.

To further uncover the correlation between the risk score and immune status, the enrichment scores of diverse immune cell subpopulations and related functions or pathways with single-sample gene set enrichment analysis (ssGSEA) were further quantified. Interestingly, the antigen presentation process, including the score of aDCs, iDCs, APC co-stimulation and HLA were significantly different between the low risk and high risk group in the TCGA cohort (P < 0.05; Fig. 6A and B). APC co-inhibition and HLA class were significantly lower in high-risk patients than that in high-risk patients (P < 0.05; Fig. 6B). Comparisons in the GSE13213 dataset identified the differences of HLA class, Type I -IFN response and Type II -IFN response (P < 0.05; Fig. 6C and D). In particular, the scores of macrophages and mast cells were the most statistically different between the two risk groups in both the TCGA cohort and the GSE13213 dataset, which was consistent with the findings in the GO enrichment analysis.

Cell death is an important aspect of mammalian development and homeostasis, which is closely associated with the physiological and pathological state of an organism (17). Ferroptosis is an iron-dependent and non-apoptotic cell death characterized by iron-dependent accumulation of lipid peroxides (18). Recent studies have suggested that ferroptosis plays an important role in cancer development and treatment (19,20). However, the immunity- and ferroptosis-related gene signature in lung cancer was still unknown. In this study, we found that 44% of ferroptosis-related genes were differentially expressed between lung tumor tissues and adjacent normal tissues, and that 5 candidate ferroptosis-related genes were significantly associated with OS. A novel prognostic model integrating 5 candidate ferroptosis-related genes was firstly constructed and validated in a GEO dataset. Functional analysis revealed that immune-related pathways were significantly enriched.

The prognostic model proposed in this study was composed of 5 ferroptosis-related genes (FANCD2, GCLC, SLC7A11, ALOX15 and DPP4). FANCD2 is a nuclear protein involved in DNA damage repair, which medicates ferroptosis in colon adenocarcinoma, clear cell renal cell carcinoma and glioma (21-23). GCLC, a glutamate-cysteine ligase catalytic subunit, acts as a glutathione-independent, non-canonical role in the protection against ferroptosis by maintaining glutamate homeostasis under cystine starvation (24-26). Inhibiting SLC7A11-mediated cystine uptake can lead to a lack of intracellular GSH and hence result in ferroptosis-mediated cell death (18,27,28). ALOX15 is closely related with lipid-ROS production in various types of tissues and tumors (29-31). DPP4, a mitochondria-encoded gene, is responsible for ferroptosis induction (26).

Functional analyses indicated that the DEGs in the high-risk and low-risk patients identified in this study were enriched in several immune-related pathways, including HTLV-1 pathway, which has been implicated in many cancers (32-35). HTLV-1 encodes two viral genes, Tax and HTLV-1 bZIP factor (HBZ), which play critical roles in viral transcription and promotion of T-cell proliferation. HBZ, a suppressor of viral transcription, has the potential to change the immunophenotype of infected cells, conferring an effector regulatory T cell (eTreg)-like signature (CD4+ CD25+ CCR4+ TIGIT+ Foxp3+) and enhancing the proliferation of this subset (36-38). Thus, we speculate that ferroptosis may affect prognosis through HTLV-1 pathway, which drive the differentiation of Treg. Our findings also found that tumor-specific cellular immunity was altered in high-risk compared with low-risk patients. The ssGSEA score for HLA class was significantly lower in the high-risk group, indicating involve in the immune suppression.

Overall, our study constructed a novel immunity- and ferroptosis-related gene signature that may involve in the progression and prognosis in lung cancer. A combination of targeting both immune and ferroptosis-related genes may be a potential method for lung cancer therapy. However, the underlying mechanisms involving in tumor immunity need be further explored.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Research Data are available at TCGA.

Competing Interest

The authors declare that they have no competing interests.

Funding

This study was supported by grants from the National Natural Science Foundation of China (82003212), the Guangzhou Key Medical Discipline Construction Project Fund (02-412-B205002-1004042), Guangzhou High Level Clinical Key Specialty Construction Project (2019-2021) and Clinical Key Specialty Construction Project of Guangzhou Medical University (YYPT202017).

Author contributions

JZ and TSL performed all experiments, prepared the figures, and drafted the manuscript. TSL, TX, JYZ, and TX participated in the data analysis and results interpretation. XSH, TXL, TX, JYZ, and TX designed the study and participated in the data analysis. All authors have read and approved the manuscript.

Acknowledgements

None.

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Molecular Identification of Immunity- and Ferroptosis-related Gene Signature in Non-small Cell Lung Cancer

Status:

Version 1

Abstract

Figures

Background

Materials And Methods

Data Acquisition

Identification of differential expression gene

Development of ROC curves

Interaction network and enrichment analysis

Immune cells and ferroptosis

Statistical analysis

Results

Discussion

Conclusions

Declarations

Ethics approval and consent to participate

Consent for publication

Competing Interest

References

Supplementary Files

Status:

Version 1