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Back to Journal »International Journal of General Medicine» Volume 14

Constructing iron death-related gene signatures to predict the survival and immune microenvironment of patients with melanoma

Authors: Zeng Nan, Ma Li, Cheng Yi, Xia Q, Li Yi, Chen Yi, Lu Z, Lu Q, Jiang Fei, Luo Dan 

Published on October 5, 2021, Volume 2021: 14 pages, 6423-6438 pages

DOI https://doi.org/10.2147/IJGM.S327348

Single anonymous peer review

Editor who approved for publication: Dr. Scott Fraser

Zeng Ni, 1, 2 Ma Liwen, 3 Cheng Yuxin, 1 Xia Qingyue, January Yueli, 1 Chen Yihe, 1 Lu Zhiyu, 1 Lu Qian, 1 Feng Jiang, 4 Luo Dan 1 1 Nanjing First Affiliated Hospital Skin Department of Dermatology, Nanjing Medical University, 210000; 2Department of Dermatology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000; 3Department of Dermatology, Nanjing Gulou Hospital, Nanjing Medical University, Nanjing 210000; 4Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011 China Electronics Mail [email protected] Feng Jiang Department of Neonatology, Obstetrics and Gynecology Hospital, Fudan University, No. 419 Fangxie Road, Shanghai, 200011, People’s Republic of China Email [email protected] Purpose: In this study, we studied the iron-related deaths Genetically develop prognostic models and find out the association with the tumor immune microenvironment of patients with skin melanoma (SKCM). Method: In order to find the iron death genes related to SKCM, we used Cox regression and LASSO methods on 60 iron death related genes and RNA-seq related to SKCM. Later, gene signatures related to iron death were created. Calculate the ROC curve over time and Kaplan-Meier analysis to determine its predictive power. In addition, multiple assessments were used to assess overall survival (OS) and to create nomograms for clinicopathological factors and the gene signatures related to iron death that we established. We also studied the relationship between the characteristics of iron death-related genes and the three immune checkpoints and immune cell infiltration. Results: Our prognostic model includes two genes (ALOX5, CHAC1). In the TCGA and GEO cohorts, the high-risk category had lower OS. Using our genetic signature, we can reliably predict OS. In addition, our genetic profile can predict immune cell infiltration and SKCM immunotherapy response. Conclusion: Our genetic markers have been proven to be reliable predictors of OS, reflecting the immune microenvironment, and predicting the effectiveness of immunotherapy for SKCM patients. Keywords: skin melanoma, SKCM, iron death, immune microenvironment, immune checkpoint

There are more than 200,000 newly diagnosed cases of skin melanoma (SKCM) every year worldwide, which is a very aggressive disease. 1 The over-survival (OS) of patients who have been alive for 5 years from the date of diagnosis of advanced melanoma is 5 years with an average of 10-29%, and the overall success rate of the chemotherapy form is less than 20%. 1,2 Although great efforts have been made in the management of advanced and metastatic SKCM, its treatment and management are far less effective. 3 Ultraviolet radiation and hereditary congenital are the main risk factors for melanoma. 4 Early detection and management of melanoma can help improve outcomes and provide new treatments for more severe disease stages. The mortality rate of melanoma has actually remained the same, and has hardly improved over time, which emphasizes the importance of continuing to study the molecular mechanisms and clinical goals of melanoma. 5

In the human body, iron is a micronutrient necessary for certain biological processes, such as cell metabolism, cell development and proliferation. 6 The homeostasis of iron is precisely controlled by the body's iron intake, systemic transport and preservation. 7 In tumor cells, changes in iron metabolism may cause two effects: the synthesis and chelation of heme, leading to the accumulation of free iron and the consumption of hemoglobin. 8 Although the iron requirement of most tumor cells is elevated, the iron content in the body is sufficient to promote tumor growth and proliferation. Iron accumulation beyond this range may cause cell death, or cell death may lead to membrane lipid peroxidation. 9 Ferroptosis can be used to treat certain cancers. After its first appearance in 2012, iron death became popular as a potential cancer treatment. 10 Many experiments have concluded that iron death plays a crucial role in cell death and tumor suppression. 11 Other studies have shown the importance of iron death in diagnosis and prognosis. 12 However, in the formation and development of the disease, the key regulatory factors and pathways of iron death are still unclear.

The term "tumor microenvironment" refers to the immune cells that exist inside the tumor. Time regulates iron synthesis and homeostasis in the body. Iron homeostasis is usually maintained by Th1 cells and macrophages. In addition, it was found that immunomodulation and iron death work together at TIME. 13 The activation of cytotoxicity in tumor cells reveals tumor antigens and enhances the immunogenicity of the microenvironment, thereby improving the effectiveness of treatment. Another study found that activating CD8 T cells can enhance TIME’s anti-tumor lipid peroxidation Activity, and the increase in lipid peroxidation of tumor cells contributes to the therapeutic effect of immunotherapy. 14,15

Although there are multiple characteristics of SKCM, iron death studies have not been confirmed. The Ferroptosis model was first constructed based on detailed gene expression, but a model representing the actual physiological state was developed for the SKCM population to predict the microenvironment of patients in a patient cohort. This approach may help make treatment choices in the future.

A total of 460 SKCM RNA-seq data and accompanying clinical details were downloaded from the TCGA database. RNA-seq data and SKCM clinical details of another 213 samples were extracted from the Gene Expression Comprehensive (GSE65904) database. TCGA and GEO data can be accessed free of charge. Therefore, this study was excluded from the ethics committees of their respective jurisdictions. This study follows the TCGA and GEO guidelines.

For differentially expressed iron death-related genes, a protein-protein interaction (PPI) network was discovered using the STRING database. To explore molecular interactions, Cytoscape bioinformatics tools are used.

As part of the univariate Cox analysis of OS, look for genes related to iron death with prognostic significance. By minimizing the chance of overfitting, the LASSO method is used to construct prediction signatures. The R package "glmnet" is implemented for variable selection and shrinking. After that, multiple regression is used to define the model with the lowest standard score, which is a measure of goodness of fit. 16 Then, combine the risk factors and the expression equation (β). Risk score = β1 * Gene 1 expression β2 * Gene 2 expression β3 * Gene 3 expression... βn * Gene expression. Using a risk scoring algorithm, for each patient, a risk score is determined. To classify patients into high-risk and low-risk categories, we use the median risk score level as the cutoff value. In this analysis, the Kaplan-Meier method was used to test the significance of the difference in survival time between the high-risk and low-risk categories. ROC curves (including 1-year, 3-year, and 5-year survival rates) were created using the "survivalROC" R package to represent iron-based death characteristics to show sensitivity and specificity.

We used the "survival" R package to complete univariate and multivariate studies of gene features related to iron death and clinicopathological features in TCGA and GSE65904. In addition, various characteristics were tested to determine whether the genetic characteristics associated with iron death are related to clinicopathological factors.

In order to provide a quantitative method for predicting the survival risk of SKCM patients, the nomogram was developed by the R package "rms" using iron death characteristics and quantitative data. At the same time, draw a calibration curve to provide an accurate estimate of the prediction and test the accuracy of the nomogram.

According to their calculated risk values, the SKCM sample was divided into two groups (high-risk group and low-risk group). We use GSEA to distinguish the two groups in order to discover and study the basic mechanism of the KEGG pathway. 17 The reference gene set is c2.cp.kegg.v6.2.symbols.gmt, which has been annotated.

In these two data sets, CIBERSORT is used to measure the proportion of tumor infiltrating immune cells. Through the linear support vector principle, CIBERSORT is very effective in analyzing the expression matrix of immune cell types. 18 studied the correlation between 22 different types of tumor-infiltrating immune cells. A comprehensive study of Spearman's coefficient and Wilcoxon rank sum was conducted to determine the relationship between 22 tumor-infiltrating immune cells. 19 We assessed the association between the risk score and the expression levels of CTLA4, PD-1, and PD-. L1, the three main immune checkpoint genes.

The Kaplan-Meier study was conducted using the R packages "survival" and "survminer". The "Survival" package is used to analyze Cox research. For ROC research, the R package "survivalROC" is used. A p-value <0.05 indicates statistical significance.

A total of 460 melanoma samples were obtained from the TCGA database. There are 213 individual samples in the validation data set. Table S1 includes all basic demographic information. Figure 1 shows the design of this analysis. Figure 1 Flow chart of the analysis plan of this study.

Figure 1 Flow chart of the analysis plan of this study.

The study included a total of 60 iron death-related genes (Table S2). A PPI network was established to clarify the interrelationships between these genes (Figure S1). We developed a Kaplan-Meier curve from the TCGA database of iron death-related genes to study OS. Among the 60 genes, 10 genes are closely related to the patient results in the log-rank test (p <0.05) (Figure 2A). In order to construct an iron death-related model, LASSO was used to select the best model (Figure 2B and C). 8 genes were discovered using the LASSO algorithm. Finally, multivariate Cox regression analysis is used to generate a risk model. The genes ALOX5 and CHAC1 have been found to be highly predictive (Figure 2D). The calculated expression equation (β) The following equation is used to calculate the risk value of the signature: risk score = (-0.3258) x expression (ALOX5) (0.1597) x expression (CHAC1). Among them, the conclusion is that ALOX5 has a protective effect, and the coefficient associated with long OS is <0 (Figure S2A). CHAC1 is associated with short OS and a coefficient> 0, which is considered a high risk factor (Figure S2B). Each patient in the TCGA and GEO cohorts received a risk score and divided them into low-risk and high-risk categories. Figure 2 The TCGA cohort was used to identify potential iron death-related genes. (A) The univariate Cox regression study identified prognostic factors. (B) LASSO coefficient distribution of 10 iron death-related prognostic genes. (C) The coefficient distribution graph generated by the logarithm (lambda) series of the parameter selection (lambda). (D) The multivariate Cox study was used to construct iron death-related gene signatures in the TCGA cohort.

Figure 2 The TCGA cohort was used to identify potential iron death-related genes. (A) The univariate Cox regression study identified prognostic factors. (B) LASSO coefficient distribution of 10 iron death-related prognostic genes. (C) The coefficient distribution graph generated by the logarithm (lambda) series of the parameter selection (lambda). (D) The multivariate Cox study was used to construct iron death-related gene signatures in the TCGA cohort.

Measure the risk score and classify patients into high-risk and low-risk categories based on the median level. (Figure 3A). In TCGA and GSE65904, the expression of ALOX5 increased with low risk, as shown in the heat map (Figure 3B). The expression of CHAC1 increases with the high risk in the robot data set. Patients in the TCGA population get weaker OS as the risk level increases (Figure 3C). According to our findings (Figure 3D and E), the mortality rate in the high-risk group is also higher. In addition, the ROC study was conducted for the prognostic classification of risk scores. We looked at the prognostic prediction classification efficiency for 1, 3, and 5 years. For the 1, 3, and 5-year survival rates in the TCGA cohort, the AUC values ​​for prognostic characteristics were 0.651, 0.638, and 0.622, respectively. At 1, 3, and 5 years, the AUC values ​​in the GEO data set were 0.560, 0.636, and 0.557 (Figure 3F). In addition, the ROC study was conducted for the prognostic classification of risk scores. We looked at the prognostic prediction classification efficiency for 1, 3, and 5 years. For the 1, 3, and 5-year survival rates in the TCGA cohort, the AUC values ​​for prognostic characteristics were 0.651, 0.638, and 0.622, respectively. At 1, 3, and 5 years, the AUC values ​​in the GEO data set were 0.560, 0.636, and 0.557 (Figure 3F). These results indicate that the developed prognostic tool has good sensitivity and specificity for estimating SKCM patients. The expression of the two identified genes in the signature (Figure 4). Figure 3 The characteristics of Ferroptosis-related genes have prognostic significance in SKCM in the TCGA and GEO data sets. (A) Distribution of mRNA risk levels; (B) Heat maps of two iron death-related genes in the two groups from the TCGA and GEO cohorts; (C) Kaplan- Meier study; (D) The distribution of the survival status of the two groups of patients. This point reflects the patient's condition and is assessed as the risk score increases. (E) Mortality in the two groups; (F) ROC curve regression in the TCGA and GEO cohorts. Figure 4 Use the HPA database to test the expression of hub ferroptosis-related genes in SKCM and normal tissues. (A) ALOX5 (B) CHAC1.

Figure 3 The characteristics of Ferroptosis-related genes have prognostic significance in SKCM in the TCGA and GEO data sets. (A) Distribution of mRNA risk levels; (B) Heat maps of two iron death-related genes in the two groups from the TCGA and GEO cohorts; (C) Kaplan- Meier study; (D) The distribution of the survival status of the two groups of patients. This point reflects the patient's condition and is assessed as the risk score increases. (E) Mortality in the two groups; (F) ROC curve regression in the TCGA and GEO cohorts.

Figure 4 Use the HPA database to test the expression of hub ferroptosis-related genes in SKCM and normal tissues. (A) ALOX5 (B) CHAC1.

The independence of the feature model was determined using univariate and multivariate Cox regression analysis in the clinical application of the TCGA (Figure 5A) and GEO (Figure 5B) data sets. By using the univariate Cox study, the risk score was positively correlated with the prognosis; however, by using the multivariate Cox study, it showed that this feature is an independent prognostic risk factor. Our results show that the dual gene feature is effective in clinical practice. Figure 5 In the TCGA and GEO cohorts, univariate and multivariate studies were used to find independent prognostic factors for SKCM OS. (AB) TCGA queue (CD) GEO queue.

Figure 5 In the TCGA and GEO cohorts, univariate and multivariate studies were used to find independent prognostic factors for SKCM OS. (AB) TCGA queue (CD) GEO queue.

We generated a nomogram that combined features related to iron death and typical clinicopathological factors centered on the TCGA cohort (Figure 6A) and GEO cohort (Figure 6E) to use a quantitative process to estimate the OS risk of SKCM patients. According to the calibration chart (Figure 6F-H), the nomogram has reasonable accuracy as the best model for the TCGA data set (Figure 6B-D) and GEO data set. Figure 6 (A) In the TCGA cohort, the nomogram shows that the OS of SKCM patients can be predicted based on age, stage, and risk score. (BD) TCGA cohort calibration curve after 1, 3 and 5 years. (E) In the GEO cohort, a nomogram dependent on stage and risk score is shown to estimate the 1, 3, and 5-year OS of SKCM patients. (F–H) GEO queue 1, 3, and 5-year calibration curve.

Figure 6 (A) In the TCGA cohort, the nomogram shows that the OS of SKCM patients can be predicted based on age, stage, and risk score. (BD) TCGA cohort calibration curve after 1, 3 and 5 years. (E) In the GEO cohort, a nomogram dependent on stage and risk score is shown to estimate the 1, 3, and 5-year OS of SKCM patients. (F–H) GEO queue 1, 3, and 5-year calibration curve.

We use GSEA to determine the high-risk group and the low-risk group in terms of biological pathways. In the TCGA and GEO cohorts, the GSEA study showed that the genome is greatly enriched in RNA polymerase and aminoacyl tRNA biosynthesis. Oxidative phosphorylation is also shown to be abundant in the TCGA data set, as is the base excision repair in the GEO data set (Figure 7). Figure 7 GSEA enrichment between low-risk and high-risk groups.

Figure 7 GSEA enrichment between low-risk and high-risk groups.

Perform CIBERSORT to better understand how the dual gene signature and immune microenvironment interact and create detailed comparisons with risk scores. Figure 8A shows the relative content distribution of 22 immune cells in the TCGA cohort. Figure 8B shows that in the high-risk population, the concentration of macrophages M0 and mast cells is higher than that of other groups. In the high-risk population, T cell CD4 memory is resting, T cell CD8, T cell CD4 memory activation, and macrophage M1 is lower than the other group. As shown in Figure 9, tumor-infiltrating immune cells are an independent predictor of cancer survival. Therefore, we assessed whether ALOX5 expression is related to the amount of immune infiltration in SKCM. We examined the correlation between genetic characteristics (ALOX5 and CHAC1) and 24 immune cell subsets in SKCM and found that ALOX5 is strongly positively correlated with B cell memory, B cell naivety, plasma cells, CD8 T cells and T cell regulation ; However, ALOX5 is strongly negatively correlated with macrophages M2, eosinophils, mast cell quiescence, and NK cell quiescence. Another analysis showed that CHAC1 expression and activated NK cell infiltration level (R = 0.15, p = 0.007), T cell regulation (R = 0.11, p = 0.022) and eosinophils (R = 0.11, p = 0.022) )closely related. ), but has nothing to do with the level of T cell memory infiltration (R = -0.21, p = 0.0001). Figure 8 Immune cell infiltration in patients with SKCM: distribution and visualization (A) Description of the calculated composition of 22 immune cell subtypes in TCGA. (B) In TCGA, 22 immune cell subtypes between the two groups were compared. Figure 9 Correlation between ALOX5, CHAC1 and infiltrating immune cells in SKCM patients.

Figure 8 Immune cell infiltration in patients with SKCM: distribution and visualization (A) Description of the calculated composition of 22 immune cell subtypes in TCGA. (B) In TCGA, 22 immune cell subtypes between the two groups were compared.

Figure 9 Correlation between ALOX5, CHAC1 and infiltrating immune cells in SKCM patients.

The relationship between three immune checkpoint genes and risk scores was studied in the TCGA and GEO cohorts.​​​ As shown in Figure 10A, low levels of PD-1, PD-L1 and CTLA4 showed a weaker survival rate. In the TCGA and GEO data sets, we found that the levels of PD-L1, PD-1, and CTLA4 in low-risk groups were higher than those in high-risk groups. This risk score was comparable to CTLA4, PD-L1, and PD-1 (Figure 10B-D), indicating The low-risk group is more likely to have an immune response to immunotherapy. Figure 10 The risk score is associated with PD-1, PD-L1, and CTLA4 levels in the TCGA and GEO cohorts. (A) Kaplan-Meier study of SKCM patients classified as high-risk or low-risk based on PD-1, PD-L1 and CTLA4 expression; (B) The correlation between the two groups of PD-1 expression and PD-1 level and risk score (C) the correlation between the two groups of PD-L1 expression and PD-L1 level and the risk score; (D) the two groups of CTLA4 expression and the correlation between CTLA4 and the risk score.

Figure 10 The risk score is associated with PD-1, PD-L1, and CTLA4 levels in the TCGA and GEO cohorts. (A) Kaplan-Meier study of SKCM patients classified as high-risk or low-risk based on PD-1, PD-L1 and CTLA4 expression; (B) The correlation between the two groups of PD-1 expression and PD-1 level and risk score (C) the correlation between the two groups of PD-L1 expression and PD-L1 level and the risk score; (D) the two groups of CTLA4 expression and the correlation between CTLA4 and the risk score.

Melanoma is the most aggressive type of skin cancer, and its prevalence is on the rise worldwide. 20 Although intense sporadic sun exposure is the most important risk factor for melanoma, other factors such as family background, genetic sensitivity, environmental factors, and immunosuppression often play a role. 21 Since SKCM is a molecularly heterogeneous cancer, its molecular characteristics are related to biological processes such as cell proliferation, microvascular infiltration, and distant metastasis. They play an important role in the prognosis of SKCM. 22 As Therefore, it is important to identify important molecular markers that affect the prognosis of SKCM patients in order to better early diagnosis and treatment to improve the clinical outcome of SKCM.

The improvement of the developed high-throughput technology has opened up the possibility of discovering new genes related to the occurrence and evolution of SKCM. Iron death requires iron-dependent oxidation, which is a process of autophagy-mediated cell death. 23 Excessive iron storage in cells is caused by iron metabolism disorders, which may lead to iron death. 24 There are several genes that affect iron death. Previous studies have shown that iron death is an important method to kill SKCM cells, but the exact molecular modification and mechanism of action are still unknown.

The purpose of this study is to use Cox proportional hazard regression and LASSO method to analyze SKCM-related RNA sequences obtained from high-throughput array technology to identify iron death-related genes related to the prognosis of SKCM. Previous studies have identified several genes, lncRNA and miRNA as promising therapeutic biomarkers in SKCM. 25-27 However, the characteristics of differential expression explored between normal and tumor samples, between primary and metastatic tissues, and between molecules related to cancer progression have not been taken into consideration. Our model is based on the construction of genes related to iron death. We also compared our model with other researchers. For example, Shou et al. built a hypoxia gene-based model, but it did not work well in the validation set and did not have complete 1, 3, and 5-year prediction capabilities. 28 Wu et al. constructed a predictive model for SKCM, but the sample size of the validation set was too small to represent the accuracy of the model. 29 Two genes (ALOX5, CHAC1) collaborated to create a prognostic model that accurately estimates the prognosis of SKCM patients, based on our findings. In addition, the difference in the underlying diseases of SKCM has no effect on the expression characteristics of these two genes, which means that prognostic models should be used to determine the prognosis of a wide range of SKCM patients. ALOX5 is a member of the family of pro-inflammatory lipid mediators derived from arachidonic acid. ALOX5 also plays an important role in lipid peroxidation mediation. 30 It was recently discovered that ALOX5 plays a key role in cell death processes such as apoptosis and iron death. 31 CHAC1 is a protein belonging to the glutamyl cyclotransferase family. The deglycation of Notch receptor can prevent receptor maturation and reduce Notch signal transduction, and it has been shown to promote neuronal differentiation through the encoded protein. 32 This protein can also participate in unfolded protein reactions, glutathione control, and cellular oxidation balance. 33 CHAC1 was found to digest glutathione, convert it into 5-oxoproline and cysteine-glycedipeptide, and reduce intracellular GSH levels. 34 Increased expression of CHAC1 in breast and ovarian cancer patients may mean a higher risk of cancer recurrence. 35 So far, the mechanism of ALOX5 and CHAC1 in SKCM is still a mystery.

Since immune cell penetration is essential in tumors, CIBERSORT was also performed in each SKCM specimen to measure the proportion of 22 different types of immune cells. 36 According to some data, the interaction between the tumor and the microenvironment is important during development. SKCM and the possibility of responding to immunotherapy. 37 Therefore, we investigated whether gene signatures related to iron death can be used to detect immune cell infiltration. According to our findings, the proportion of T cell CD4 memory resting, T cell CD8 and macrophage M1 and T cell CD4 memory activation in the low-risk group is greater than the other groups' contribution to the immune response.

Immunotherapy has brought new light to the management of SKCM, and immune checkpoint inhibitors (ICI) have become theoretically successful treatment options. 38 Anti-tumor immunity can be enhanced by targeting immune checkpoint molecules. 39,40 Correlation characteristics between iron death-related genes and ICI responsiveness are used to predict ICI responsiveness. The expression levels of PD-L1, PD-1 and CTLA4 in low-risk groups are higher than those in high-risk groups. In SKCM patients, low PD-L1, PD-1 and CTLA4 expressions are associated with poor prognosis, indicating that iron death-related gene features have the ability to identify immunogenic and ICI-responsive SKCM patients. The treatment selection of ICIs in clinical practice is theoretically based on the predictive power of iron death-related gene characteristics. It is hoped that this predictive method can help accelerate the development of personalized cancer immunotherapy.

In addition, in order to evaluate the prognostic significance of the new risk model, we performed Log rank test and ROC curve analysis to study the relationship between the model and clinical parameters. In order to improve the accuracy of prognosis prediction, we created and verified a nomogram to help predict the clinical outcome of SKCM patients by combining risk score, age and level. Using the AUC curve, we next asked whether the iron death-related gene pattern can be used as an early predictor of the incidence of SKCM. Our model shows AUCs of 0.651, 0.638, and 0.622 in the 1, 3, and 5-year TCGA, respectively. More specifically, these modern prognostic methods have the potential not only to improve the accuracy of prognostic prediction, but also to estimate the actual risk of death of a particular patient, which is crucial in clinical practice. Combining our prognostic model with clinicopathological indications improves the predictive sensitivity and specificity of 1-year, 3-year, and 3-year OS, thereby achieving better drug treatment. In summary, our findings indicate that the dual-gene prognostic model is a reliable tool for predicting the overall survival rate of SKCM; it may help guide treatment strategies to improve the clinical outcome of patients with melanoma. This research has many advantages. First of all, this signature has been thoroughly tested and analyzed through various databases, proving its robustness and durability. Secondly, extensive and in-depth research on various topics, including the discussion of the relationship between iron death-related gene characteristics and immune cells and immune checkpoints. Third, a nomogram for quantitative measurement has been created, which is helpful for clinical promotion and implementation. Nevertheless, our research still has some flaws. Therefore, further SKCM patients and validation are needed to verify this feature in prospective studies. However, this study has several limitations. First, it is based entirely on the TCGA and GEO databases; therefore, a large number of clinical samples must be used to verify this finding. In addition, since this study is based on retrospective analysis, prospective studies should be conducted to confirm the model. Third, more research is needed on the process of ALOX5 and CHAC1 in SKCM.

Finally, we developed a gene signature based on iron death that is closely related to the immune microenvironment, which can better predict survival and represent the immunotherapy effect of SKCM patients. In the era of precision medicine, gene markers related to iron death may provide an important method to meet the treatment standards of SKCM to a certain extent. Peroxide causes it. 41 Excessive or insufficient iron death is related to more and more physiological and pathological processes and dysregulated immune responses. 42 Although in vitro mechanically revealed 43,44 more and more data indicate that iron death may involve many pathogenic factors45 However, the role of iron death in T cell immunity and cancer immunotherapy is still uncertain.

Immune checkpoint blocking drugs are a new type of immunotherapy that can selectively activate the innate ability of T cells to attack tumors. 46 The important function of iron in tumor development is related to its potential to regulate innate and acquired immune responses, especially in T cells and macrophages. Our results reveal a strong positive correlation between genetic characteristics (ALOX5 and CHAC1) and the 24 immune cell subgroups in SKCM. ALOX5 is associated with B cell memory, B cell naivety, plasma cells, CD8 T cells and T cell regulation. Strong positive correlation. ; However, ALOX5 is strongly negatively correlated with macrophage M2, eosinophils, mast cell quiescence, and NK cell quiescence. Another study found that CHAC1 expression was significantly correlated with the level of infiltration of activated NK cells, T cell regulatory cells, and eosinophils, but not with the level of infiltration of T cell memory cells.

According to a recent study, the specific composition of the lymphatic environment may inhibit iron death of melanoma cells, thereby promoting metastasis. 47 The interaction between the immune system and iron death remains unknown. Macrophages play a key role in the regulation of iron metabolism. 48 It was found that ALOX5 is involved in the formation of leukotriene B4 (LTB4), which is a pro-inflammatory lipid mediator that acts as a chemotactic agent for phagocytes in previous studies. 49,50 Researchers also proposed that the iron dead cells of melanoma release lipid mediators such as LTB4 through ALOX5 to recruit macrophages to the iron dead cells. Previous studies have shown that CD8 T cells activated by immunotherapy make tumors more prone to iron death, thereby improving the efficacy of immunotherapy in patients with melanoma. 51 The era of immunity and iron in cancer treatment has arrived. One potential cancer treatment is iron death-driven nanotherapy combined with immune regulation. 52 The combination of immunotherapy and radiotherapy has been shown to trigger iron death and T cell immunity in tumors. Therefore, tumor iron death promoted by T cells is a new anti-tumor mechanism. Targeting tumor iron death pathways combined with checkpoint blockade constitutes a treatment approach.

In summary, we found two genes related to iron death in SKCM's OS, which have strong predictive power. The prognostic model based on these two genes works well. The genetic characteristics related to iron death may also reflect the immune microenvironment of patients with SKCM and the efficacy of immunotherapy.

The authors report no conflicts of interest in this work.

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