Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

doi:10.21203/rs.3.rs-708494/v1

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Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

https://doi.org/10.21203/rs.3.rs-708494/v1

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Background: The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global, so it is of great significance to predict the survival time of GC patients. Ferroptosis and iron-metabolism make a critical impact on tumor development and are closely linked to the treatment of cancer and the prognosis of patients. However, the predictive value of the genes involved in ferroptosis and iron-metabolism in GC and their effects on immune microenvironment remain to be further clarified.

Methods: In this study, the RNA sequence information and general clinical indicators of GC patients were acquired from the public databases. We first systematically screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) based on the LASSO-penalized Cox regression analysis. The 6-gene prognostic risk model was established in the TCGA cohort and the GC patients were separated into the high- and the low-risk groups through the risk score median value. GEO cohort was used for verification. The expression of PRDEGs was verified by quantitative QPCR.

Results: Our study demonstrated that patients in the low-risk group had a higher survival probability compared with those in high-risk group. In addition, univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prediction parameter. The ROC curve analysis and nomogram manifested that the risk model had the high predictive ability and was more sensitive than general clinical features. Furthermore, compared with the high-risk group, the low-risk group had higher TMB and a longer 5-year survival period. In the immune microenvironment of GC, there were also differences in immune function and highly infiltrated immune cells between the two risk groups.

Conclusions: The prognostic risk model based on the six genes associated with ferroptosis and iron-metabolism has a good performance for predicting the prognosis of patients with GC. The treatment of cancer by inducing tumor ferroptosis or mediating tumor iron-metabolism, especially combined with immunotherapy, provides a new possibility for individualized treatment of GC patients.

Cancer Biology

Oncology

Ferroptosis

Gastric cancer

Iron-metabolism

Prognosis

Tumor Immune microenvironment

The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global [1]. Existing treatment methods have improved a lot in the treatment of GC, but the prognosis and 5-year survival period of advanced GC patients are still not ideal[2]. Therefore, early diagnosis is essential to improve the prognosis of GC patients[3]. GC has complex etiology and highly heterogeneous, which make it imminent to screen new biomarkers with high sensitivity and specificity for early GC[4, 5]. In addition, due to the limited treatment of GC and the molecular characteristics and mechanisms involved in tumorigenesis, we also need to develop new models for the diagnosis and prognosis of GC.

Ferroptosis is a kind of programmed cell death, which was first proposed by Dixon in 2012[6]. The interior of the cell is characterized by excessive accumulation of iron ions and accumulation of lipid reactive oxygen species (ROS). Morphologically, the main manifestations of ferroptosis were mitochondrial shrinkage and narrowing of the mitochondrial ridges[6–8]. Iron is an indispensable nutrient element in various biological processes in cells[9]. In recent years, researches have confirmed that ferroptosis is intimately associated with the treatment and prognosis of cancer[10, 11]. Hao's team indicated that not only erastin can directly induce ferroptosis of GC cells, but the highly expressed cysteine dioxygenase type 1 (CDO1) can also lead to ferroptosis in GC cells by inhibiting the expression of glutathioneperoxidase4 (GPX4)[12]. According to reports, the tumor immune microenvironment (TIME) was inseparable from the pathogenesis and progression of GC[13]. Many immune cells in the TIME are inseparable from iron-metabolism and the maintenance of iron homeostasis[14, 15]. Wang et al. revealed that interferon-gamma (IFN-γ) released by CD8 + T cells can down-regulate the expression of glutamate-cystine antiporter system xc-, which in turn inhibits cystine uptake by tumor cells, and finally promotes lipid peroxidation and iron death in tumor cells[16]. Lang and his team further demonstrated that immunotherapy sensitized tumors to radiation therapy by promoting ferroptosis of tumor cells[17]. Thus, ferroptosis of tumor promotes by immune cells may be a novel anti-tumor idea. Targeting tumor ferroptosis mechanisms combined with checkpoint blockade constitutes a therapeutic strategy.

In this study, we screened the genes, involved in iron-metabolism and ferroptosis, which were intimately related to the prognostic survival of GC patients. Next,we built a model to predict the prognostic survival of GC. We also probed the potential mechanism of these genes and the relationship between these genes and the TIME. The findings of our study may be helpful to predict the 5-year survival of GC patients.

1. Data collection

375 stomach adenocarcinoma samples data and 32 normal samples data (including the mRNA expression profiles, clinical parameters and somatic mutation information) were retrieved from The Cancer Genome Atlas portal (TCGA, https://portal.gdc.cancer.gov/), and GSE26253 data set was acquired from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo). The 204 genes that associated with ferroptosis and iron-metabolism were obtained from the KEGG PATHWAY Database (https://www.genome.jp/kegg/pathway.html) , the Reactome Pathway Database (https://reactome.org/) , the AmiGo2 database (http://amigo.geneontology.org/amigo) and the related literatures. 318 tumor-related transcription factors downloaded from the CISTROME database (www.cistrome.org) .

2. Screening the prognostic ferroptosis- and iron metabolism-related differentially expressed genes (PRDEGs) in GC

Employing “limma” R package extracted genes expression involved in ferroptosis and iron-metabolism from TCGA cohort. And differential expression analysis, with a false discovery rate (FDR) < 0.05, was performed to screening the differentially expressed genes (DEGs) between tumor samples and adjacent normal samples. Univariate Cox analysis was applied to assess the survival time and survival status of GC patients to identify the prognosis-related genes (PRGs, filter condition: P value < 0.05). The “venn” R package was performed to take the intersection of DEGs and PRGs to obtain PRDEGs.

3. Building and validating the prognostic risk model

We used LASSO-penalized Cox regression analysis to construct a prognostic risk model that can minimize the risk of overfitting[18,19]. The penalty parameter (λ) of the signature which followed the minimum criteria was defined through tenfold cross-validation. The risk score calculation method is . Based on the risk score’s median value in the TCGA cohort, GC patients were parted into the high-risk group and the low-risk group. Subsequently, we performed principal component analysis (PCA) and T-distribution stochastic neighbor embedding (t-SNE) analysis to reduce the dimensionality of sample expression to determine whether patients of the two risk groups of the signature were well distinguished. Kaplan-Meier (K-M) survival curves were employed to analyze the overall survival (OS) in the two risk groups and the difference between them. Time-dependent receptor operating characteristic (ROC) curves was carried out with R package "survivalROC " to estimate the prognostic value of the risk model.

4. Independence of the risk score of the prognostic risk model

We conducted univariate and multivariate Cox regression analyses to evaluate whether the risk score of the prognostic risk signature was an independent predictor for GC patients’ survival condition. Hazard ratios (HRs) and 95% confidence intervals (CIs) were computed for the risk score and other clinical indices, and P < 0.05 was believed statistically valuable. Nomograms containing the risk score and available clinical variables were generated by “rms” R package. And the calibration curves of the nomograms were carried out to validated if the forecast effect of the risk model was consistent with the clinical reality.

5. Functional enrichment analysis of differentially expressed genes based on high- and low-risk groups

The “limma” R package was ran to obtain differentially expressed genes based on the two risk groups (DEGRGs, |log2FC| ≥ 1, FDR < 0.05). Next, we performed the R packages (“clusterProfiler”, “enrichplot”, “ggplot2”) to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on these differentially expressed genes. What’s more, Gene Set Enrichment Analysis (GSEA) was employed to search more potentially biological effect and related enrichment pathways (NOM. p < 0.05, FDR < 0.25).

6. Analysis of the abundance of infiltrating immune cells

With multiple immune gene sets as the core, the immune-related characteristics of each sample were evaluated comprehensively by using ssGSEA algorithm[20]. Cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was an algorithm for evaluating the degree of infiltration of 22 human immune cell subsets[21]. The infiltration scores of diverse immune cells in the TIME of GC can be calculated by R software package " CIBERSORT ".

7. Cell cultures and QPCR

The human gastric cancer cell lines (HGC-27 and MKN-45) and human gastric mucosal epithelial cell line (GES-1) were purchased from Cell Bank, Institute of Life Sciences, Chinese Academy of Sciences Cell Bank (Shanghai, China). The HGC-27 and GES-1 cells were cultured in DMEM medium (Gibco, Grand Island, America), and MKN-45 cells were cultured in RPMI 1640 medium (Gibco, Grand Island, America) with 10% fetal bovine serum (Gibco, Grand Island, America) under a humidified atmosphere of 37 °C and 5% of CO2. Total cellular RNA was extracted using FastPure cell Total RNA Isolation Kit (Vazyme, Nanjing, China) and quantified by NanoDrop Lite spectrophotometer (Thermo Scientific). The total RNA underwent reverse transcription using HiScript II Q RT SuperMix for qPCR (Vazyme, Nanjing, China) for cDNA synthesis according to the manufacturer’s instruction. The relative RNA expression levels were determined by QPCR in triplicate on the Applied Biosystems StepOnePlus QPCR System (Thermo Fisher Scientific) using the T aq Pro Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China). We used the GAPDH as an internal control. And we used the 2⁻^△△Ct method to calculate the relative expression of each RNA. Primers sequences were listed in Table S1.

8. Statistical analysis

In this study, sample data collation, extraction and ID conversion were run through Perl scripts. All statistical analysis were executed by R software (version 3.6.1). Wilcoxon test and Kruskal-Wallis test were suitable for comparison of data between multiple groups. Qualitative data comparison used Pearsonχ2 test or Fisher's exact test. And K-M curves and log-rank tests explored survival data. If not specified, P <0.05 was considered statistically valuable, with all P values were two tails.

1. Determination the PRDEGs in GC

A total of 134 DEGs associated with ferroptosis and iron metabolism were obtained in this study (Figure1A). The volcano plot displayed that 97 genes were the high expression in tumor tissues, and the remaining 37 were the opposite (Figure1B). Univariate Cox regression analysis was applied to filter out 13 ferroptosis- and iron metabolism-related genes with prognostic values (PRGs) in GC patients (Figure1C).

Combined with the previous analysis of PRGs expression and the data in ImmPort database, we screened a total of 70 differential transcription factors based on GC (GCTFs, Figure1D). The volcano plot displayed 52 GCTFs were up-regulated and 18 GCTFs were down-regulated (Figure1E). Moreover, correlation analysis revealed the regulatory network between GCTFs-PRGs. As presented in Figure1F, there were 37 pairs of GCTFs-PRGs regulatory relationships, of which 26 pairs were positive and 11 pairs were negative.

We intersected 134 DEGs and 13 PRGs to determine 6 PRDEGs (GCTFs, Figure1G). The differential expression of six PRDEGs between normal and tumor tissues was represented in Figure 1 H. Univariate Cox regression analysis clarified that the six PRDEGs were linked to the prognosis of GC patients, among which NOX4 and ZFP36 were high risk genes, while GLS2, MTF1, SLC1A5 and SP1 were low risk genes (Figure1I). The correlation network showed the co-expression relationship among these PRDEGs (Figure1J).

2. Creation and verification of the prognostic risk model of GC

Based on the PRDEGs expression profile and LASSO-penalized Cox regression analysis, our study finally identified the 6-gene signature in accordance with the optimal value of λ (Figure2A-B). Six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) were further confirmed to be closely correlated to prognosis of GC patients. The risk score= (-0.341* expression level of GLS2) + (-0.359* expression level of MTF1) + (-0.104* expression level of SLC1A5) + (-0.158* expression level of SP1) + (0.489* expression level of NOX4) + (0.154* expression level of ZFP36).

For the TCGA cohort, 350 GC patients were separated into the high-risk group (n=175) and the low-risk group (n=175) according to the risk score median value (Figure2C). PCA and t-SNE analysis confirm that preciseness and distinction of grouping in the prognostic risk signature (Figure2D-E). Figure 2F revealed that with the risk score raised, the distribution of living patients decreased, indicating that there was a correlation between this risk score and the OS of patients. Next，K-M survival curve revealed the low-risk group patients had a higher survival probability compared with the high-risk group (Figure2G, P< 0.001). The area under the curves (AUCs) of the time-dependent ROC reached 0.636 at 1 year, 0.639 at 2 years, and 0.654 at 3 years, suggesting the prognostic risk signature having good sensitivity in predicting survival of GC patients (Figure2H).

The above analysis results were verified in the GEO cohort. We divided 431 patients with GC from the GEO cohort into two groups in accordance with the median value calculated from the TCGA cohort: 13 patients in the high-risk group and 418 patients in the low-risk group (Figure2I). Likewise, PCA and t-SNE analysis confirm the two groups’ patients were disseminated in opposite directions (Figure2J-K). Consistent with the TCGA cohort results, the high-risk group’s patients have the higher death rate and shorter OS compared with the low-risk group’s patients (Figure2L-M, P< 0.001). Last, the AUCs for the GC patients’ OS was 0.579 at 1 year, 0.593 at 2 years, and 0.600 at 3 (Figure2N).

3. Independent prognostic performance of the prognostic risk model

Univariate and multivariate Cox regression analyses were employed to examine whether the prognostication effect of the risk score for GC patient's prognosis was independent of other available clinical indicators (including age, gender, histological grade and TNM stage). Univariate Cox regression analyses demonstrated that the risk scores of the prognostic risk signature and prognosis of patients were highly correlation in both the two cohorts (TCGA cohort: P<0.001, HR =2.932, 95% CI=1.847-4.655; GEO cohort: P<0.001, HR=1.609, 95% CI =1.271-2.035) (Figure3A-B). Furthermore, multivariate analysis once again verifies that risk score was an independent parameter for predicting the patients’ prognosis (TCGA cohort: P<0.001, HR =3.195, 95% CI=2.012-5.072; GEO cohort: P<0.001, HR=1.592, 95% CI =1.250-2.028) (Figure3C-D).

To appraise the accuracy of the risk score compared with other clinical indicators, we calculated the AUCs of the ROC curves in two cohorts. The results disclosed that the risk score’s AUCs in TCGA and GEO cohorts were 0.642 and 0.579, respectively (Figure3E-F). From this, we concluded that this prognostic risk model could precisely predict the GC patients’ OS. Subsequently, the predictive nomograms were built to estimate possible survival time of individual GC patient at 1, 2 and 3 years (Figure3G-H). what’s more, the calibration curves of the nomograms based on the prognostic risk signature exhibited that the predicted survival probability and actual OS had good consistency at 3-year (Figure3I-J).

Finally, our study investigated the correlation between the prognostic risk model (including the risk score and the genes) and clinical indicators in TCGA cohort. With the enhance of the risk score and NOX4, the histological grade of patients with GC enhanced, but the result of SLC1A5 was on the contrary (Figure3K-L). In terms of pathological stage, with the increased expression of NOX4 and SP1, the stage also increased (Figure3M-N). These results demonstrated that the abnormal expression of genes in the prognostic risk signature were intimately connected with the GC’s progression.

4. Analysis of association between risk score and TMB of GC

The occurrence of tumors is caused by the accumulation of somatic mutations in the genes of diseased cells[22].Here, we first analyzed the difference in tumor mutation burden (TMB) of GC patients in the high-risk group (Figure 4A) and the low-risk group (Figure 4B). Figure4 C showed that the number of TMB was higher in the low-risk group than in the high-risk group. At the same time, patients in the low-risk group with the high TMB had a higher 5-year survival rate than those in the high-risk group with the low TMB (Figure 4D). TMB has become a biomarker to identify cancer patients who can benefit from immunotherapy and to predict the efficacy of immunoassay inhibitors [22].

Moreover, the fractions of T cells CD4 memory activated, NK cells resting, macrophages M1, and neutrophils were higher in the high TMB group, while the T cells regulatory (Tregs) and mast cells resting were just the opposite (P < 0.05, Figure 4E).

To illuminate the underlying mechanism of the protective effects of the prognostic risk model and Immune microenvironment of GC, we further investigated the effect of somatic cell copy number (CNA) of the 6-gene signature on different kinds of immune cell infiltration. CNA of the 6-gene signature significantly affected the fraction of B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells (DCs) in GC (Figure 4F-K). Above the results manifested that the prognostic risk model plays a critical role in regulating the tumor immune microenvironment of GC patients.

5. Functional enrichment analysis

We performed GO and KEGG analysis on the DEGRGs. In terms of GO, DEGRGs were mainly correlated to extracellular matrix organization, extracellular structure organization, and glycosaminoglycan binding in both the TCGA and GEO cohort (P < 0.05, Figure 5A, E). KEGG analyses elucidated that DEGRGs were enriched in ECM-receptor interaction, PI3K-AKT signaling pathways and protein digestion and absorption in both cohorts (P < 0.05, Figure 5B, F). Subsequently, we performed GSEA analysis on GO and KEGG in the two cohorts to find more special biological processes and molecular signaling pathways. The results demonstrated the DEGRGs from the two cohorts were primarily associated with metabolic- related biological processes (Figure 5C, G). Related enrichment pathways analyses elucidated that DEGRGs were mainly concentrated on lysine degradation, cysteine and methionine metabolism, glutathione metabolism, and TGF beta signaling pathway (Figure 5D, H). These pathways involved in ferroptosis-related biological processes and immune-related molecular functions.

6. The connection between the different risk groups and TIME of GC

Considering the relationship between prognosis and the TIME in patients with GC, we further evaluated the enrichment scores of diverse kinds of immune cell subsets and immune-associated effects in two risk groups from the TCGA and GEO cohorts using ssGSEA.

For the TCGA cohort, B cells, macrophages, and plasmacytoid dendritic cells (pDCs) had higher infiltration in high-risk group. In particularly， immune-related functions, such as type II IFN response, APC co-stimulation，and Chemokine Receptor (CCR) were also more active in the high-risk group (Figure 6A, B). In terms of the GEO cohort, the scores of T follicular helper cells (Tfh), T helper type 2 (Th2) cells, inflammation-promoting, MHC class I, and type II IFN response had statistical difference between the high-and low-risk groups (Figure 6C, D)

Figure 6E-J exhibited the relevance among the prognostic risk score and diverse immune cells. The association between the six prognostic risk genes’ expression and the immune infiltrate was shown in Figure 6K-P.

7. Validation the expression of PRDEGs in cell lines

The QPCR was used to detect the expression level of PRDEGs in different GC cell lines. The QPCR results showed that the expression of GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36 was upregulated in GC cell lines (HGC-27) vs human gastric mucosal epithelial cell line (GES-1). The expression of MTF1 was upregulated while SP1, NOX4, ZFP36 was downregulated in GC cell lines (MKN-45) vs GES-1(Figure 7A-F). The QPCR data results were roughly the same as our bioinformatics analysis results (Figure 7G).

Ferroptosis is a novel way to regulate cell survive through iron-dependence and lipid peroxidation, and it is also a link between various metabolic processes and human health. In recent years, with in-depth research on ferroptosis, it has been confirmed that ferroptosis was intimately linked to the pathophysiological process of numerous illnesses, especially in the field of oncology, which has attracted widespread attention[23–27]. Emerging evidences for the treatment of cancer by inducing iron death, especially treatment combined with immunotherapy, undoubtedly provide a promising treatment strategy for carcinoma patients[16, 17].

In this work, we first systematically to screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) through the intersection of DEGs and PRGs. Subsequently, we built a prognostic risk model according to the six PRDEGs. Through the verification of different cohorts, we found that this prognostic risk model has strong specificity and sensitivity in predicting the GC patients’ prognostication. For details,GLS2 is an enzyme that catalyzes the conversion of glutamine to glutamate, which is one of the 16 essential metabolic genes for tumorigenesis in functional genomics[28, 29]. Recent researches revealed that GLS2 was a tumor suppressor gene in glioblastoma and hepatocellular carcinoma[30].Niu et al.’s study clarified that Physcion 8-O-β- Glucopyranoside promotes hypertrophy and inhibits tumorigenesis by inhibiting the expression of miR-103a-3p/GLS2[31]. These research’s results were consistent with the results of our study. MTF1 is a transcription factor that regulates the iron level[32]. Nuclear translocation of MTF1 protects cells from ferroptosis by reducing intercellular iron level[33]. Chen et al. found that genetic depletion of MTF1 eliminated ataxia-telangiectasia mutated 's regulation of iron-regulating elements and re-sensitized cells to ferroptosis[34]. Liang and his colleagues demonstrated that over-expressed MTF1 can promote EMT to promote ovarian tumor metastasis, a new biomarker for early diagnosis of the ovary[35]. Our study discovered that low expression of MTF1 in patients with GC indicated a good prognosis. SLC1A5, a cell surface transport protein, can mediate cell uptake of glutamine(Gln)that is tightly linked to ferroptosis[36]. The experiment had found that miR-137 inhibited melanoma cells' ferroptosis by inhibiting SLC1A5, leading to a decrease in Gln uptake and malondialdehyde (MDA) accumulation[26]. Since SLC1A5-regulated Gln uptake plays aSLC1A5n important role in tumor cell metabolism and ferroptosis, targeting Gln transport through regulation of SLC1A5 can be used as a potential therapy for cancers. Sp1 belongs to the Sp/Kruppel-like family transcription factors, which can regulate the occurrence and development of tumors[37–40]. Previous research has suggested that ROS induced triterpenoids to inhibit rhabdomyosarcoma cells and tumor growth by targeting Sp transcription factors(Sp1, Sp3, Sp4)[41]. Yao et al. found that the SP1’s expression was associated with the survival time of advanced GC patients [42]. Our study further showed that low expression of SP1 in GC patients has a longer survival time. NOXs are a kind of membrane-binding enzyme complexes whose main function is to produce superoxide or hydrogen peroxide. ROS in cancer cells mainly comes from NOXs[43]. The study has shown that inhibiting the expression of NOX4 could prevent ferroptosis of cells[44]. Also, the increase of iron-activated Nox4 led to excessive production of hydrogen peroxide and lipid peroxides, which in turn induced ferroptosis in tumor cells[45]. ZFP36, a member of the tandem CCCH zinc finger proteins, has recently been identified as a new type of ferroptosis post-transcriptional regulator[46–49]. Studies by Zhang et al. illustrated that the transcription of ZFP36 was reduced in ferroptosis cells. Furthermore, knockdown of ZFP36 induced ferroptosis, whereas over-expression of ZFP36 resisted ferroptosis[46]. ZFP36 RNA-binding proteins (RBPs) ,are important cancer-related immunomodulators, play a major role in inhibiting T cell expansion and effector function, which can be used as a strategy of immunotherapy for cancer patients[50]. In summary, the six genes in the prognostic risk model are closely linked to in iron-metabolism, lipid metabolism, oxidative stress, and immune function. In this study, these genes (GLS2, MTF1, SLC1A5, SP1, and NOX4) were highly expressed in GC tumor tissues, while ZFP36 was on the contrary.

We carried out functional enrichment analysis and GSEA analysis of DEGRGs to probe the potential mechanism of ferroptosis and iron-metabolism in GC. The results suggested that ferroptosis in GC might be related to the metabolic process of cysteine and glutathione, along with immune-related signal pathway. Next, to clarify the internal mechanism between ferroptosis and iron-metabolism in GC with tumor immunity and the connection between the prognosis and TIME in GC patients, we evaluated the differences of immune cell subsets and immune-related functions in the two groups. In this study, the high-risk group had not only many highly infiltrating immune-promoting cells (such as B cells, macrophages, pDCs, Tfh cells and Th2 cells) but also high activity of the type II IFN response (IFN – γ). Some scholars believed that the higher risk score was linkted to the impairment of anti-tumor immunity, therefore, the decline of immune function of anti-tumor for high-risk patients may be one of the reasons for their poor prognosis[51]. According to reports, high infiltration of macrophages in the TME indicates a worse prognosis for patients[52, 53]. The tumor-bearing mice experiments showed that B cells accumulated in melanoma area could induce tumor progression, and the anti-tumor effect of Cytotoxic T lymphocytes (CTLs) and the effect of cancer vaccines could be significantly improved if B cells were depleted[54, 55].Cumulative studies have shown that pDCs have a weak ability to produce IFN-I in the TME, but it have a strong ability to induce Treg cell differentiation [56–58]. In particular,the pDCs in the TME not only have immunosuppressive properties, but also produce abnormal IFN-I [59]. IFN-γ, a major cytokine in the TME, has an irreplaceable role in promoting immune response[60]. However, IFN- γ not only leads to feedback suppression in the process of regulating immune response, but also up-regulates and induces the expression of some immunosuppressive molecules (such as IDO1, MHCII), thus weakening the immune effect of anti-tumor[61–65]. These may be the reasons for the worse prognosis for high-risk patients.

In recent years, more and more clinical trials and basic experiments have confirmed that the immune checkpoints’ expression can be used as biomarkers for cancer patients to receive immunotherapy, and the immune effect of anti-tumor can be stimulated by removing the related immunosuppression. Therefore, the use of immune checkpoint inhibitors can fight tumors[66, 67]. It is reported that patients with high TMB have good clinical efficacy in the treatment of immune checkpoint inhibitors [68]. Here, our work expounded that the low-risk and high TMB group had a better survival period, suggesting that these patients may benefit from immune checkpoint inhibitors therapy. Furthermore, we also manifested that the high TMB group had a higher proportion of cells that promote immune function. CNA of the 6-gene signature significantly affected the fraction of B cells, T cells, neutrophils, macrophages and DCs in GC. These confirmed that ferroptosis and iron-metabolism can regulate the immune microenvironment of GC.

In summary, the interaction of ferroptosis, iron-metabolism and immune microenvironment supplies a novel research idea in the field of oncology. The study of the hub genes associated with ferroptosis and iron-metabolism could disclose tumor antigens and improve the immunogenicity of TME, which has great clinical significance. Our study identified a 6-gene signature with independent prognostic value of GC, this prognostic risk model has a good prognostic performance, opening a new direction for predicting the prognosis of GC. However, how the six genes are associated with the immune microenvironment needs to be further studied.

GC	gastric cancer
ROS	reactive oxygen species
GPX4	glutathioneperoxidase4
CDO1	cysteine dioxygenase type 1
TIME	tumor immune microenvironment
TME	tumor microenvironment
IFN-γ	interferon-gamma
TCGA	The Cancer Genome Atlas
Gene Expression Omnibus	GEO
PRDEGs	prognostic ferroptosis- and iron metabolism-related differentially expressed genes
FDR	false discovery rate
DEGs	differentially expressed genes
PRGs	prognosis-related genes
PCA	principal component analysis
t-SNE	T-distribution stochastic neighbor embedding
ROC	receptor operating characteristic
HRs	hazard ratios
CIs	confidence intervals
GO	Gene Ontology
KEGG	Kyoto Encyclopedia of Genes and Genomes
GSEA	Gene Set Enrichment Analysis
CIBERSORT	Cell type identification by estimating relative subsets of RNA transcripts
GCTFs	differential transcription factors based on GC
AUCs	area under the curves
OS	overall survival
TMB	tumor mutation burden
CNA	cell copy number
DCs	dendritic cells
Tfh	T follicular helper cells
Th2	T helper type 2
CCR	Chemokine Receptor
Gln	glutamine
RBPs	RNA-binding proteins

Ethics approval and consent to participate

Not applicable.

Consent for publication

Consent for publication in this magazine.

Competing interests

The authors have declared that no competing interest exists.

Funding

This study was supported by Pilot gastric cancer project of clinical cooperation of traditional Chinese and western medicine for major and difficult diseases, The Open Projects of the Discipline of Chinese Medicine of Nanjing University of Chinese Medicine Supported by the Subject of Academic priority discipline of Jiangsu Higher Education Institutions [No.ZYX03KF020], and National Administration of Traditional Chinese Medicine : 2019 Project of building evidence based practice capacity for TCM[No.2019XZZX-ZL003].

Authors’ contributions

Fang Wen and Xiaoxue Chen conceived the manuscript. Fang Wen wrote the manuscript. Xiaoxue Chen and Wenjie Huang performed the literature search and collected data for the manuscript. Shuai Ruan SuPing Gu and Yulang Wang analyzed the data. Jiatong liu, Jiayu Zhou, and Ye Li generated the figures. SiYuan Song and Peixing Gu revised the manuscript. Shenlin Liu checked the images. Peng Shu directed the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

Availability of data and materials

All data and materials are available.

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.
Wang J, Qu J, Li Z, Che X, Liu J, Teng Y, et al. Pretreatment platelet‐to‐lymphocyte ratio is associated with the response to first‐line chemotherapy and survival in patients with metastatic gastric cancer. J Clin Lab Anal [Internet]. 2017 [cited 2020 May 10];32. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6817026/
Tan YK, Fielding JWL. Early diagnosis of early gastric cancer. European Journal of Gastroenterology & Hepatology. 2006;18:821–9.
Chen S, Li T, Zhao Q, Xiao B, Guo J. Using circular RNA hsa_circ_0000190 as a new biomarker in the diagnosis of gastric cancer. Clinica Chimica Acta. 2017;466:167–71.
Li P, Chen H, Chen S, Mo X, Li T, Xiao B, et al. Circular RNA 0000096 affects cell growth and migration in gastric cancer. Br J Cancer. 2017;116:626–33.
Dixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM, Gleason CE, et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell. 2012;149:1060–72.
Yang WS, Stockwell BR. Synthetic lethal screening identifies compounds activating iron-dependent, nonapoptotic cell death in oncogenic-RAS-harboring cancer cells. Chem Biol. 2008;15:234–45.
Yagoda N, von Rechenberg M, Zaganjor E, Bauer AJ, Yang WS, Fridman DJ, et al. RAS-RAF-MEK-dependent oxidative cell death involving voltage-dependent anion channels. Nature. 2007;447:864–8.
Andrews NC. Disorders of iron metabolism. N Engl J Med. 1999;341:1986–95.
Tang B, Zhu J, Li J, Fan K, Gao Y, Cheng S, et al. The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma. Cell Commun Signal [Internet]. 2020 [cited 2021 Mar 7];18. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592541/
Xu T, Ding W, Ji X, Ao X, Liu Y, Yu W, et al. Molecular mechanisms of ferroptosis and its role in cancer therapy. J Cell Mol Med. 2019;23:4900–12.
Hao S, Yu J, He W, Huang Q, Zhao Y, Liang B, et al. Cysteine Dioxygenase 1 Mediates Erastin-Induced Ferroptosis in Human Gastric Cancer Cells. Neoplasia. 2017;19:1022–32.
Zeng D, Li M, Zhou R, Zhang J, Sun H, Shi M, et al. Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures. Cancer Immunol Res. 2019;7:737–50.
Ganz T, Nemeth E. Iron homeostasis in host defence and inflammation. Nat Rev Immunol. 2015;15:500–10.
Wang Y, Yu L, Ding J, Chen Y. Iron Metabolism in Cancer. Int J Mol Sci. 2018;20.
Wang W, Green M, Choi JE, Gijón M, Kennedy PD, Johnson JK, et al. CD8+ T cells regulate tumor ferroptosis during cancer immunotherapy. Nature. 2019;569:270–4.
Lang X, Green MD, Wang W, Yu J, Choi JE, Jiang L, et al. Radiotherapy and immunotherapy promote tumoral lipid oxidation and ferroptosis via synergistic repression of SLC7A11. Cancer Discov. 2019;9:1673–85.
Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16:385–95.
Simon N, Friedman J, Hastie T, Tibshirani R. Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent. J Stat Softw. 2011;39:1–13.
He Y, Jiang Z, Chen C, Wang X. Classification of triple-negative breast cancers based on Immunogenomic profiling. J Exp Clin Cancer Res. 2018;37:327.
Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nature Methods. Nature Publishing Group; 2015;12:453–7.
Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol. 2019;30:44–56.
Zhang Z, Wu Y, Yuan S, Zhang P, Zhang J, Li H, et al. Glutathione peroxidase 4 participates in secondary brain injury through mediating ferroptosis in a rat model of intracerebral hemorrhage. Brain Res. 2018;1701:112–25.
Xie B-S, Wang Y-Q, Lin Y, Mao Q, Feng J-F, Gao G-Y, et al. Inhibition of ferroptosis attenuates tissue damage and improves long-term outcomes after traumatic brain injury in mice. CNS Neurosci Ther. 2019;25:465–75.
Xie Y, Zhu S, Song X, Sun X, Fan Y, Liu J, et al. The Tumor Suppressor p53 Limits Ferroptosis by Blocking DPP4 Activity. Cell Rep. 2017;20:1692–704.
Luo M, Wu L, Zhang K, Wang H, Zhang T, Gutierrez L, et al. miR-137 regulates ferroptosis by targeting glutamine transporter SLC1A5 in melanoma. Cell Death Differ. 2018;25:1457–72.
Do Van B, Gouel F, Jonneaux A, Timmerman K, Gelé P, Pétrault M, et al. Ferroptosis, a newly characterized form of cell death in Parkinson’s disease that is regulated by PKC. Neurobiol Dis. 2016;94:169–78.
Katt WP, Lukey MJ, Cerione RA. A tale of two glutaminases: homologous enzymes with distinct roles in tumorigenesis. Future Med Chem. 2017;9:223–43.
Possemato R, Marks KM, Shaul YD, Pacold ME, Kim D, Birsoy K, et al. Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature. 2011;476:346–50.
Matés JM, Campos-Sandoval JA, Márquez J. Glutaminase isoenzymes in the metabolic therapy of cancer. Biochim Biophys Acta Rev Cancer. 2018;1870:158–64.
Niu Y, Zhang J, Tong Y, Li J, Liu B. Physcion 8-O-β-glucopyranoside induced ferroptosis via regulating miR-103a-3p/GLS2 axis in gastric cancer. Life Sci. 2019;237:116893.
Rutherford JC, Bird AJ. Metal-responsive transcription factors that regulate iron, zinc, and copper homeostasis in eukaryotic cells. Eukaryot Cell. 2004;3:1–13.
Lu S, Song Y, Luo R, Li S, Li G, Wang K, et al. Ferroportin-Dependent Iron Homeostasis Protects against Oxidative Stress-Induced Nucleus Pulposus Cell Ferroptosis and Ameliorates Intervertebral Disc Degeneration In Vivo. Oxidative Medicine and Cellular Longevity. Hindawi; 2021;2021:e6670497.
Chen P-H, Wu J, Ding C-KC, Lin C-C, Pan S, Bossa N, et al. Kinome screen of ferroptosis reveals a novel role of ATM in regulating iron metabolism. Cell Death Differ. 2020;27:1008–22.
Ji L, Zhao G, Zhang P, Huo W, Dong P, Watari H, et al. Knockout of MTF1 Inhibits the Epithelial to Mesenchymal Transition in Ovarian Cancer Cells. J Cancer. 2018;9:4578–85.
Kanai Y, Clémençon B, Simonin A, Leuenberger M, Lochner M, Weisstanner M, et al. The SLC1 high-affinity glutamate and neutral amino acid transporter family. Mol Aspects Med. 2013;34:108–20.
Yuan P, Wang L, Wei D, Zhang J, Jia Z, Li Q, et al. Therapeutic inhibition of Sp1 expression in growing tumors by mithramycin a correlates directly with potent antiangiogenic effects on human pancreatic cancer. Cancer. 2007;110:2682–90.
Wang L, Guan X, Zhang J, Jia Z, Wei D, Li Q, et al. Targeted inhibition of Sp1-mediated transcription for antiangiogenic therapy of metastatic human gastric cancer in orthotopic nude mouse models. Int J Oncol. 2008;33:161–7.
Höcker M, Raychowdhury R, Plath T, Wu H, O’Connor DT, Wiedenmann B, et al. Sp1 and CREB mediate gastrin-dependent regulation of chromogranin A promoter activity in gastric carcinoma cells. J Biol Chem. 1998;273:34000–7.
Szpirer J, Szpirer C, Riviere M, Levan G, Marynen P, Cassiman JJ, et al. The Sp1 transcription factor gene (SP1) and the 1,25-dihydroxyvitamin D3 receptor gene (VDR) are colocalized on human chromosome arm 12q and rat chromosome 7. Genomics. 1991;11:168–73.
Kasiappan R, Jutooru I, Mohankumar K, Karki K, Lacey A, Safe S. Reactive Oxygen Species (ROS)-Inducing Triterpenoid Inhibits Rhabdomyosarcoma Cell and Tumor Growth Through Targeting Sp Transcription Factors. Mol Cancer Res. 2019;17:794–805.
Yao JC, Wang L, Wei D, Gong W, Hassan M, Wu T-T, et al. Association between expression of transcription factor Sp1 and increased vascular endothelial growth factor expression, advanced stage, and poor survival in patients with resected gastric cancer. Clin Cancer Res. 2004;10:4109–17.
Kamata T. Roles of Nox1 and other Nox isoforms in cancer development. Cancer Sci. 2009;100:1382–8.
Poursaitidis I, Wang X, Crighton T, Labuschagne C, Mason D, Cramer SL, et al. Oncogene-Selective Sensitivity to Synchronous Cell Death following Modulation of the Amino Acid Nutrient Cystine. Cell Rep. 2017;18:2547–56.
Wang Z, Ding Y, Wang X, Lu S, Wang C, He C, et al. Pseudolaric acid B triggers ferroptosis in glioma cells via activation of Nox4 and inhibition of xCT. Cancer Lett. 2018;428:21–33.
Zhang Z, Guo M, Li Y, Shen M, Kong D, Shao J, et al. RNA-binding protein ZFP36/TTP protects against ferroptosis by regulating autophagy signaling pathway in hepatic stellate cells. Autophagy. Taylor & Francis; 2020;16:1482–505.
Tiedje C, Diaz-Muñoz MD, Trulley P, Ahlfors H, Laaß K, Blackshear PJ, et al. The RNA-binding protein TTP is a global post-transcriptional regulator of feedback control in inflammation. Nucleic Acids Res. 2016;44:7418–40.
Hausburg MA, Doles JD, Clement SL, Cadwallader AB, Hall MN, Blackshear PJ, et al. Post-transcriptional regulation of satellite cell quiescence by TTP-mediated mRNA decay. Elife. 2015;4:e03390.
Masias C, Vasu S, Cataland SR. None of the above: thrombotic microangiopathy beyond TTP and HUS. Blood. 2017;129:2857–63.
Moore MJ, Blachere NE, Fak JJ, Park CY, Sawicka K, Parveen S, et al. ZFP36 RNA-binding proteins restrain T cell activation and anti-viral immunity. Elife. 2018;7.
Liang J-Y, Wang D-S, Lin H-C, Chen X-X, Yang H, Zheng Y, et al. A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. Int J Biol Sci. 2020;16:2430–41.
Hu B, Wang Z, Zeng H, Qi Y, Chen Y, Wang T, et al. Blockade of DC-SIGN+ Tumor-Associated Macrophages Reactivates Antitumor Immunity and Improves Immunotherapy in Muscle-Invasive Bladder Cancer. Cancer Res. 2020;80:1707–19.
Zhang Q, He Y, Luo N, Patel SJ, Han Y, Gao R, et al. Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma. Cell. 2019;179:829-845.e20.
Ganti SN, Albershardt TC, Iritani BM, Ruddell A. Regulatory B cells preferentially accumulate in tumor-draining lymph nodes and promote tumor growth. Sci Rep. 2015;5:12255.
Inoue S, Leitner WW, Golding B, Scott D. Inhibitory effects of B cells on antitumor immunity. Cancer Res. 2006;66:7741–7.
Conrad C, Gregorio J, Wang Y-H, Ito T, Meller S, Hanabuchi S, et al. Plasmacytoid dendritic cells promote immunosuppression in ovarian cancer via ICOS costimulation of Foxp3(+) T-regulatory cells. Cancer Res. 2012;72:5240–9.
Labidi-Galy SI, Sisirak V, Meeus P, Gobert M, Treilleux I, Bajard A, et al. Quantitative and functional alterations of plasmacytoid dendritic cells contribute to immune tolerance in ovarian cancer. Cancer Res. 2011;71:5423–34.
Sisirak V, Faget J, Gobert M, Goutagny N, Vey N, Treilleux I, et al. Impaired IFN-α production by plasmacytoid dendritic cells favors regulatory T-cell expansion that may contribute to breast cancer progression. Cancer Res. 2012;72:5188–97.
Reizis B. Plasmacytoid Dendritic Cells: Development, Regulation, and Function. Immunity. 2019;50:37–50.
Ikeda H, Old LJ, Schreiber RD. The roles of IFN gamma in protection against tumor development and cancer immunoediting. Cytokine Growth Factor Rev. 2002;13:95–109.
Bald T, Landsberg J, Lopez-Ramos D, Renn M, Glodde N, Jansen P, et al. Immune cell-poor melanomas benefit from PD-1 blockade after targeted type I IFN activation. Cancer Discov. 2014;4:674–87.
Spranger S, Spaapen RM, Zha Y, Williams J, Meng Y, Ha TT, et al. Up-regulation of PD-L1, IDO, and T(regs) in the melanoma tumor microenvironment is driven by CD8(+) T cells. Sci Transl Med. 2013;5:200ra116.
Zhao M, Flynt FL, Hong M, Chen H, Gilbert CA, Briley NT, et al. MHC class II transactivator (CIITA) expression is upregulated in multiple myeloma cells by IFN-gamma. Mol Immunol. 2007;44:2923–32.
Tsujisaki M, Igarashi M, Sakaguchi K, Eisinger M, Herlyn M, Ferrone S. Immunochemical and functional analysis of HLA class II antigens induced by recombinant immune interferon on normal epidermal melanocytes. J Immunol. 1987;138:1310–6.
Maruhashi T, Okazaki I-M, Sugiura D, Takahashi S, Maeda TK, Shimizu K, et al. LAG-3 inhibits the activation of CD4+ T cells that recognize stable pMHCII through its conformation-dependent recognition of pMHCII. Nat Immunol. 2018;19:1415–26.
Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12:252–64.
Topalian SL. Targeting Immune Checkpoints in Cancer Therapy. JAMA. 2017;318:1647–8.
Galuppini F, Dal Pozzo CA, Deckert J, Loupakis F, Fassan M, Baffa R. Tumor mutation burden: from comprehensive mutational screening to the clinic. Cancer Cell Int. 2019;19:209.

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Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

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