# Screening of Differentially Expressed Iron Death-Related Genes and the Construction of Prognosis Model in Patients with Renal Clear Cell Carcinoma

Authors: Ding Wu; Zhenyu Xu; Zhan Shi; Ping Li; Huichen lv; Jie Huang; Dian Fu
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

Objective. In this study, we used the TCGA database and ICGC database to establish a prognostic model of iron death associated with renal cell carcinoma, which can provide predictive value for the identification of iron death-related genes and clinical treatment of renal clear cell carcinoma. Methods. The gene expression profiles and clinical data of renal clear cell carcinoma and normal tissues were obtained in the TCGA database and ICGC database, and the differential genes related to iron death were screened out. The differential genes were screened out by single and multifactor Cox risk regression model. R software, “edge” package (version 4.0), was used to identify the DELs of 551 transcriptional gene samples and 522 clinical samples. The risk prediction model with genes was established to analyze the correlation between the genes in the established model and clinical characteristics, Through the final screening of iron death related genes, it can be used to predict the prognosis of renal clear cell carcinoma and provide advice for clinical targeted therapy. Results. Seven iron death differential genes (CLS2, FANCD2, PHKG2, ACSL3, ATP5MC3, CISD1, PEBP1) associated with renal clear cell carcinoma were finally screened and were refer to previous relevant studies. These genes are closely related to iron death and have great value for the prognosis of renal clear cell carcinoma. Conclusion. Seven iron death genes can accurately predict the survival of patients with renal clear cell carcinoma.


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## Body

## 1. Background

According to the latest statistical data obtained by GLOBALCAN in 2020, renal cancer is still the main cause of death among cancer affected people [1]. There are about 2.2 million more cases of renal cancer in the world, of which 1.8 million cases of cancer patients die due to renal cancer, which is one of the malignant tumors with high incidence rate and mortality in the world. In recent years, the incidence rate and mortality of renal cancer have shown an obvious upward trend. Early renal cancer basically has no obvious symptoms. Clinically, most patients are diagnosed as advanced when symptoms appear, and the 5-year survival rate of patients with advanced renal cell carcinoma is not high. Renal clear cell carcinoma is a common renal cell carcinoma, which is the most common subtype of renal cell carcinoma in the world [2–4].Although with the development of medical technology, the diagnosis, surgical treatment, radiotherapy, and molecular therapy of renal clear cell carcinoma are gradually progressing, which can enable patients with renal clear cell carcinoma to have a relatively long survival time [5]. However, the 5-year survival rate of patients with renal clear cell carcinoma is still at a low level. In recent years, tumor immunotherapy can kill tumor cells by regulating the immune defense system of the human body. Because of its advantages of small adverse reactions and strong specificity, it has been widely concerned by many scholars [5].With the in-depth progress of research, new targets and technologies for immunotherapy continue to appear. The discovery of iron death makes people have a new understanding of the formation and progress of tumor diseases. Iron death is a nonapoptotic cell death process with iron-dependent nature and characterized by the increase of intracellular reactive oxygen species discovered by Dixon in 2012 [6]. It is different from the conventional cell death mode of apoptosis and necrosis. It is an oxidation and antioxidant mechanism that converges in the mechanism of cell degradation, thus forming an iron death mechanism. The mechanism of iron death will change with the change of the effect of stressors [7]. Iron death is considered to be a cell death mode driven by the imbalance between the oxidative stress system and the antioxidant system. Iron death may have two main pathways, namely, exogenous and endogenous regulatory pathways. For example, the most common endogenous pathway is to induce lipid peroxidation process or mitochondrial dysfunction by regulating glutathione peroxidase 4 (GPX4). However, the occurrence and development of exogenous or endogenous pathways are closely related to various metabolic pathways and subcellular organelles (such as endoplasmic reticulum, mitochondria, golgi apparatus, lysosome, nucleus, and peroxisome). In the current research on drugs for the treatment of tumors through the iron death pathway, iron chelators antioxidants and other drugs can induce the iron death process of tumor cells to inhibit the proliferation and metastasis of tumor cells. Therefore, the discovery of iron death-related genes may become a new target for the treatment of related tumors in the future [8–10].In this study, based on the gene expression profile of renal clear cell carcinoma in the TCGA public database and clinical-related data, the differentially expressed genes related to iron death were screened byR language software, and the prognostic risk prediction model was constructed by single factor and multifactor Cox analysis. The relationship between the relevant genes in the model and clinical pathological features was further evaluated, and the model was applied to the prognosis prediction of renal clear cell carcinoma.


## 2. Data and Methods

### 2.1. Data Source

In this study, samples of renal clear cell carcinoma (CCRC) were obtained from the Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov) database. 522 iron death gene transcripts and clinical data of patients with renal clear cell carcinoma (CCRC) were downloaded, and the known iron death-related genes were listed from 2018 to 2022. The intersection genes were extracted and verified by data downloaded from the ICGC database (http://icgc.org/).Take p>0.05 as the statistical standard and divide the samples into high-risk and low-risk groups according to the model. According to the experiment and expression data analysis, the primary tumor samples were selected to extract the relevant expression volume. We performed survival analysis, risk analysis, and independent prognostic analysis to validate the prognostic model of renal clear cell carcinoma (CCRC). Finally, we performed univariate and multivariate Cox regression analysis to determine the correlation between iron death-related differential genes and independent risk factors. The feasibility of the prediction model was evaluated by subject operating characteristic analysis.


### 2.2. Data Acquisition and Processing

In this study, samples of renal clear cell carcinoma (CCRC) were obtained from the TCGA database. These data include 551 iron death-related transcriptional gene samples and the expression data of renal clear cell carcinoma and normal renal tissue. The iron death sequencing data and relevant clinical information of patients with renal clear cell carcinoma were obtained from TCGA. In addition, we screened the clinical data of 522 patients with renal clear cell carcinoma from TCGA. Extract relevant clinical information, including survival time, survival status, age, gender, clinical stage, and TNM stage, using the list of known iron death-related genes, the generic domain name format. According to the corresponding gene sequence numbers, the two sets of data were combined to screen 49 overlapping iron death-related mRNA for further analysis. Among the 49 mRNA genes, 42 iron death genes related to the prognosis of renal clear cell carcinoma were identified.


### 2.3. Differential Expression Analysis

The known iron death-related genes and clinical data were used to analyze the related data, and the expression of iron death-related genes was obtained. Finally, 49 iron death-related genes related to renal clear cell carcinoma were obtained. According to the screening criteria of∣logFC∣>0 and p<0.05, 42 differentially expressed genes between renal clear cell carcinoma and normal renal tissue were obtained by Wilcox test analysis. According to the relevant literature, the differential genes of iron death were further determined. In R software, “edge” package (version 4.0, URL: http://bioinf.wehi.edu.au/edgeRhttps://bioconductor.Org/packages/edge) was used to identify the DELs of 551 transcriptional gene samples and 522 clinical samples. These samples were adjusted according to ∣logFC∣>2 and p<0.01. In the analysis of survival time, it was found that there were significant differences in 7 mRNA-related to prognosis.


### 2.4. Clinical Pathological Correlation Analysis

By using the “survival” package inR software and combining the clinical and pathological characteristics (age, TNM stage, and survival time) of the patients, the expression of related genes, survival time, and survival status in the model was further analyzed. By performing univariate Cox regression analysis on the expression genes related to iron death (p<0.05), the iron death prognosis genes related to renal clear cell carcinoma were obtained. Analyze the correlation between the genes in the model and the clinicopathological features.


### 2.5. Establishment of Prognostic Risk Survival Analysis Model

By analyzing the intersection of prognosis-related genes and differential genes, the iron death differential genes related to the prognosis of renal clear cell carcinoma were finally obtained. Thep<0.05 standard was considered as the difference was statistically significant and included in the multifactor Cox regression, which was used to establish the prognostic risk prediction model and calculate the risk scores of patients with different renal clear cell carcinoma. Divide the patients into high-risk and low-risk groups according to the different scores and carry out relevant tests.


### 2.6. Statistical Analysis

Continuous normally distributed data are expressed as themeans±SDs. All statistical calculations were carried out using SPSS statistical software (SPSS Inc., USA). The p<0.05 standard was considered as the difference.


## 2.1. Data Source

In this study, samples of renal clear cell carcinoma (CCRC) were obtained from the Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov) database. 522 iron death gene transcripts and clinical data of patients with renal clear cell carcinoma (CCRC) were downloaded, and the known iron death-related genes were listed from 2018 to 2022. The intersection genes were extracted and verified by data downloaded from the ICGC database (http://icgc.org/).Take p>0.05 as the statistical standard and divide the samples into high-risk and low-risk groups according to the model. According to the experiment and expression data analysis, the primary tumor samples were selected to extract the relevant expression volume. We performed survival analysis, risk analysis, and independent prognostic analysis to validate the prognostic model of renal clear cell carcinoma (CCRC). Finally, we performed univariate and multivariate Cox regression analysis to determine the correlation between iron death-related differential genes and independent risk factors. The feasibility of the prediction model was evaluated by subject operating characteristic analysis.


## 2.2. Data Acquisition and Processing

In this study, samples of renal clear cell carcinoma (CCRC) were obtained from the TCGA database. These data include 551 iron death-related transcriptional gene samples and the expression data of renal clear cell carcinoma and normal renal tissue. The iron death sequencing data and relevant clinical information of patients with renal clear cell carcinoma were obtained from TCGA. In addition, we screened the clinical data of 522 patients with renal clear cell carcinoma from TCGA. Extract relevant clinical information, including survival time, survival status, age, gender, clinical stage, and TNM stage, using the list of known iron death-related genes, the generic domain name format. According to the corresponding gene sequence numbers, the two sets of data were combined to screen 49 overlapping iron death-related mRNA for further analysis. Among the 49 mRNA genes, 42 iron death genes related to the prognosis of renal clear cell carcinoma were identified.


## 2.3. Differential Expression Analysis

The known iron death-related genes and clinical data were used to analyze the related data, and the expression of iron death-related genes was obtained. Finally, 49 iron death-related genes related to renal clear cell carcinoma were obtained. According to the screening criteria of∣logFC∣>0 and p<0.05, 42 differentially expressed genes between renal clear cell carcinoma and normal renal tissue were obtained by Wilcox test analysis. According to the relevant literature, the differential genes of iron death were further determined. In R software, “edge” package (version 4.0, URL: http://bioinf.wehi.edu.au/edgeRhttps://bioconductor.Org/packages/edge) was used to identify the DELs of 551 transcriptional gene samples and 522 clinical samples. These samples were adjusted according to ∣logFC∣>2 and p<0.01. In the analysis of survival time, it was found that there were significant differences in 7 mRNA-related to prognosis.


## 2.4. Clinical Pathological Correlation Analysis

By using the “survival” package inR software and combining the clinical and pathological characteristics (age, TNM stage, and survival time) of the patients, the expression of related genes, survival time, and survival status in the model was further analyzed. By performing univariate Cox regression analysis on the expression genes related to iron death (p<0.05), the iron death prognosis genes related to renal clear cell carcinoma were obtained. Analyze the correlation between the genes in the model and the clinicopathological features.


## 2.5. Establishment of Prognostic Risk Survival Analysis Model

By analyzing the intersection of prognosis-related genes and differential genes, the iron death differential genes related to the prognosis of renal clear cell carcinoma were finally obtained. Thep<0.05 standard was considered as the difference was statistically significant and included in the multifactor Cox regression, which was used to establish the prognostic risk prediction model and calculate the risk scores of patients with different renal clear cell carcinoma. Divide the patients into high-risk and low-risk groups according to the different scores and carry out relevant tests.


## 2.6. Statistical Analysis

Continuous normally distributed data are expressed as themeans±SDs. All statistical calculations were carried out using SPSS statistical software (SPSS Inc., USA). The p<0.05 standard was considered as the difference.


## 3. Results

### 3.1. Acquisition of Iron Death-Related Differentially Expressed Genes

By analyzing the gene expression information and clinical expression data of 522 transcriptional samples downloaded from the TGCA database, 49 differentially expressed genes were obtained, including 28 upregulated genes and 14 downregulated genes. Seven iron death-related genes were differentially expressed between tumor and normal tissues. Finally, seven prognostic genes related to iron death in renal clear cell carcinoma were identified (Figure1).Figure 1
Venn illustrates the intersection of differential genes and iron death-related genes in renal clear cell carcinoma.


### 3.2. Screening of Differential Genes and Iron Death-Related Genes in Renal Clear Cell Carcinoma

After obtaining seven iron death prognosis differential genes associated with renal clear cell carcinoma, we usedR package “pheatmap” to map differential genes based on the intersection differential genes and differential gene expression. The abscissa represents the sample, and the ordinate represents the genes related to prognosis. It can be seen that CLS2, FANCD2, PHKG2, ACSL3, ATP5MC3, and CISD1 genes are upregulated, and PEBP1 gene is downregulated in the tumor group (Figure 2). In previous studies, scholars have found that iron inhibin-1 can combine with 15LOX/PEBP1 complex, inhibit the production of peroxidized ETE-PE, and prevent iron death.Figure 2
Heat map shows the expression of 7 iron death-related genes between high-risk and low-risk patients.


### 3.3. Prognosis Evaluation of Intersection Genes

In this study, we further used the intersection genes, single factor analysis result files, and “survival” package to construct the forest map. The first column is the name of the prognosis difference gene.p<0.05 represents that the gene is related to prognosis; HR>1 indicates that the gene is a high-risk gene; HR<1 indicates that the gene is a low-risk gene. We visualized the gene to obtain the forest map. Through the forest map, we can see that ATP5MC3, CISD1, FANCD, and ACSL3 are a high-risk gene in renal clear cell carcinoma, and the rest are low-risk genes (Figure 3).Figure 3
Single factor Cox regression analysis.


### 3.4. ICGC Database Validation Analysis

Taking the data of Japanese samples as an example, the samples were classified into high-risk and low-risk groups according to the model withp>0.05 as the statistical standard. According to the experiment and expression data analysis, the primary tumor samples were selected, and the relevant expression quantities were extracted,


### 3.5. Survival Analysis

Using the primary tumor data samples extracted by ICGC, according to the survival time and survival status of the patient samples, the blank ones are deleted to obtain the text related to the single factor significant gene expression in the tumor data samples and the samples of iron death difference genes related to the prognosis extracted from the TCGA database. TheR language “limma” package is used to read the files and delete the normal samples. Extract survival data.


### 3.6. Construction of Prognosis Model

According to the obtained prognosis-related differential genes, a single factor Cox regression analysis was performed to construct a prognosis model. According to the formula of the prognosis model, the risk value of each patient in the TCGA database was calculated. According to the median value of risk value, the patients were divided into high-risk and low-risk groups. According to the obtained single factor significant gene expression files, ICGC expression and survival data, and iron death prognosis-related differential genes, the model was constructed by using the “glmnet” package and “survival” package inR language. If the s value (the minimum value of crossvalidation error) coefficient was 0, it was deleted to obtain the relevant gene coefficient. Get the model formula, get the risk value of the train group, extract the gene expression amount whose gene coefficient is not 0 in the train group model, the location function: add the gene expression amount ∗ coefficient, use the obtained formula to get the risk value of each patient, and divide the patients into high-risk and low-risk groups according to the median value of the risk value. According to the survival analysis of the ICGC database, the survival time is /365, the gene expression amount is treated with log2, the gene expression amount is extracted, and the risk score is obtained according to the formula.


### 3.7. Survival Curve

Based on the obtained risk file, using the “survival” and “survivminer” packages inR language, and using the survival function, the significance p value of the difference between high and low risk values is obtained, which is displayed in the form of scientific counting method. The survival curves of TCGA and ICGC were drawn. The Kaplan–Meier curve showed that high-risk patients had significantly worse OS; so, they were more likely to die early than low-risk patients (p<0.001) (Figure 4).Figure 4
Kaplan–Meier survival curve.


### 3.8. ROC Curve

A good survival model can predict the patient’s survival gene and verify the accuracy of the survival gene. It can be realized through the ROC curve, and the ROC curve can be obtained by using theR language “timeROC” package. TCGA time-related ROC curve and area under curve (AUC) show that the score is 0.695 in 1 year, 0.678 in 2 years, and 0.674 in 3 years. Time-related ROC curve and area under curve (AUC) of TCGA and ICGC show that the score is 0.695 in one year, 0.678 in two years, and 0.674 in three years (Figure 5).Figure 5
ROC curve analysis of risk score and prognosis of clinicopathological parameters.
(a)(b)


### 3.9. Risk Curve

The relationship between risk value and survival status is observed through the risk curves of the two databases. The patients are sorted by risk score. The patients are divided into high- and low-risk groups according to the median value of risk value. It can be seen that the risk value is related to the patients. With the increase of risk value, the number of dead patients increases (Figure6).Figure 6
Distribution of risk scores of high-risk and low-risk renal clear cell carcinoma patients with prognostic characteristics of iron death related genes.


### 3.10. PCA Analysis and T-SNE Analysis

By reducing the dimension of gene expression data and visualizing the selected 7 genes, it can be seen that high-risk and low-risk patients are separated. It can be seen that high-risk and low-risk patients can be distinguished by the model gene expression. Some low-risk patients are in the high-risk group. It can be seen that this kind of patients cannot be distinguished by the model gene expression. It can be seen that our model can divide patients into high- and low-risk groups (Figure7).Figure 7
Analysis of gene differences between PCA and T-SNE and iron death.


### 3.11. Independent Prognostic Analysis of TCGA and ICGC

To evaluate whether the model can be used as an independent prognostic factor independent of other clinical characteristics, univariate independent prognostic risk analysis. By comparing univariate with survival time and survival status, ifp<0.05, it indicates that univariate is related to survival prognosis. In the model, only stage and risk value p are less than 0.05, indicating that these two factors are related to prognosis. If HR value is greater than 1, it indicates that this factor is a high-risk factor. That is to say, the greater the value, the higher the risk of the patient. It can be seen from the model that it is a high-risk factor. The greater the risk, the higher the patient risk. Gender is a low-risk factor, male is 1, and female is 0, indicating that the lower the value, the greater the patient risk, because male is 1 and female is 0, indicating that the risk of female is higher than that of male. The risk of stages III and IV is greater than that of stages I and II. The higher the risk value, the greater the patient risk. The independent prognostic risk of multiple factors is to conduct an independent prognostic analysis of multiple factors for single factor significant clinical traits and compare the multiple factors with survival time and survival status, If p<0.05, it means that this factor can be used as an independent prognostic factor independent of other factors. From the model, it can be seen that stage and risk score p<0.05 indicate that risk score can be used as an independent prognostic factor independent of stage. If the results are <0.05 in univariate and multivariate analysis, it indicates that our model can be used as an independent prognostic factor independent of other clinical traits. Methods were as follows: by analyzing the survival time, survival status, risk value of the risk file, and the age, sex, and stage of the patient’s clinical information, to verify whether our model can be independent of these clinical traits as an independent factor, find the intersection sample. Then, merge to get the p value, HR value, and fluctuation range of HR value of each factor. Univariate independent prognostic analysis was obtained, and the univariate results were filtered. The univariate information with p<0.05 was extracted, and the multivariate prognostic risk was compared with survival time and survival status. Forest map is obtained by forest map function.


### 3.12. Univariate and Multivariate Cox Regression Analysis

To determine whether the risk score is an independent prognostic factor for OS, we realized that in the univariate Cox regression analysis of TCGA, risk score (HR=3.861; 95%CI=2.338–6.376; p<0.001), stage (HR=2.884; 95%CI=1.972–4.216; p<0.001), T (HR=1.597; 95%CI=1.283–1.986; p<0.001), and N (HR=1.769; 95%CI=1.440–2.174; p<0.001) were significantly correlated with OS, and other confounding factors were corrected in the multivariate Cox regression analysis. Then, the risk score proved to be an independent predictor of OS (HR=2.904; 95%CI=1.713−4.922; p<0.001) (Figure 8).Figure 8
Univariate independent prognostic analysis and multivariate prognostic analysis of TCGA.In the univariate Cox regression analysis of ICGC, the risk score was significantly correlated with OS (HR=4.401; 95%CI=2.072–9.384; p<0.001). Other confounding factors were corrected in multivariate Cox regression analysis, and the risk score proved to be an independent predictor of OS (HR=2.904; 95%CI=1.713–4.922; p<0.001) (Figure 9).Figure 9
Univariate independent prognostic analysis and multivariate prognostic analysis of ICGC.


## 3.1. Acquisition of Iron Death-Related Differentially Expressed Genes

By analyzing the gene expression information and clinical expression data of 522 transcriptional samples downloaded from the TGCA database, 49 differentially expressed genes were obtained, including 28 upregulated genes and 14 downregulated genes. Seven iron death-related genes were differentially expressed between tumor and normal tissues. Finally, seven prognostic genes related to iron death in renal clear cell carcinoma were identified (Figure1).Figure 1
Venn illustrates the intersection of differential genes and iron death-related genes in renal clear cell carcinoma.


## 3.2. Screening of Differential Genes and Iron Death-Related Genes in Renal Clear Cell Carcinoma

After obtaining seven iron death prognosis differential genes associated with renal clear cell carcinoma, we usedR package “pheatmap” to map differential genes based on the intersection differential genes and differential gene expression. The abscissa represents the sample, and the ordinate represents the genes related to prognosis. It can be seen that CLS2, FANCD2, PHKG2, ACSL3, ATP5MC3, and CISD1 genes are upregulated, and PEBP1 gene is downregulated in the tumor group (Figure 2). In previous studies, scholars have found that iron inhibin-1 can combine with 15LOX/PEBP1 complex, inhibit the production of peroxidized ETE-PE, and prevent iron death.Figure 2
Heat map shows the expression of 7 iron death-related genes between high-risk and low-risk patients.


## 3.3. Prognosis Evaluation of Intersection Genes

In this study, we further used the intersection genes, single factor analysis result files, and “survival” package to construct the forest map. The first column is the name of the prognosis difference gene.p<0.05 represents that the gene is related to prognosis; HR>1 indicates that the gene is a high-risk gene; HR<1 indicates that the gene is a low-risk gene. We visualized the gene to obtain the forest map. Through the forest map, we can see that ATP5MC3, CISD1, FANCD, and ACSL3 are a high-risk gene in renal clear cell carcinoma, and the rest are low-risk genes (Figure 3).Figure 3
Single factor Cox regression analysis.


## 3.4. ICGC Database Validation Analysis

Taking the data of Japanese samples as an example, the samples were classified into high-risk and low-risk groups according to the model withp>0.05 as the statistical standard. According to the experiment and expression data analysis, the primary tumor samples were selected, and the relevant expression quantities were extracted,


## 3.5. Survival Analysis

Using the primary tumor data samples extracted by ICGC, according to the survival time and survival status of the patient samples, the blank ones are deleted to obtain the text related to the single factor significant gene expression in the tumor data samples and the samples of iron death difference genes related to the prognosis extracted from the TCGA database. TheR language “limma” package is used to read the files and delete the normal samples. Extract survival data.


## 3.6. Construction of Prognosis Model

According to the obtained prognosis-related differential genes, a single factor Cox regression analysis was performed to construct a prognosis model. According to the formula of the prognosis model, the risk value of each patient in the TCGA database was calculated. According to the median value of risk value, the patients were divided into high-risk and low-risk groups. According to the obtained single factor significant gene expression files, ICGC expression and survival data, and iron death prognosis-related differential genes, the model was constructed by using the “glmnet” package and “survival” package inR language. If the s value (the minimum value of crossvalidation error) coefficient was 0, it was deleted to obtain the relevant gene coefficient. Get the model formula, get the risk value of the train group, extract the gene expression amount whose gene coefficient is not 0 in the train group model, the location function: add the gene expression amount ∗ coefficient, use the obtained formula to get the risk value of each patient, and divide the patients into high-risk and low-risk groups according to the median value of the risk value. According to the survival analysis of the ICGC database, the survival time is /365, the gene expression amount is treated with log2, the gene expression amount is extracted, and the risk score is obtained according to the formula.


## 3.7. Survival Curve

Based on the obtained risk file, using the “survival” and “survivminer” packages inR language, and using the survival function, the significance p value of the difference between high and low risk values is obtained, which is displayed in the form of scientific counting method. The survival curves of TCGA and ICGC were drawn. The Kaplan–Meier curve showed that high-risk patients had significantly worse OS; so, they were more likely to die early than low-risk patients (p<0.001) (Figure 4).Figure 4
Kaplan–Meier survival curve.


## 3.8. ROC Curve

A good survival model can predict the patient’s survival gene and verify the accuracy of the survival gene. It can be realized through the ROC curve, and the ROC curve can be obtained by using theR language “timeROC” package. TCGA time-related ROC curve and area under curve (AUC) show that the score is 0.695 in 1 year, 0.678 in 2 years, and 0.674 in 3 years. Time-related ROC curve and area under curve (AUC) of TCGA and ICGC show that the score is 0.695 in one year, 0.678 in two years, and 0.674 in three years (Figure 5).Figure 5
ROC curve analysis of risk score and prognosis of clinicopathological parameters.
(a)(b)


## 3.9. Risk Curve

The relationship between risk value and survival status is observed through the risk curves of the two databases. The patients are sorted by risk score. The patients are divided into high- and low-risk groups according to the median value of risk value. It can be seen that the risk value is related to the patients. With the increase of risk value, the number of dead patients increases (Figure6).Figure 6
Distribution of risk scores of high-risk and low-risk renal clear cell carcinoma patients with prognostic characteristics of iron death related genes.


## 3.10. PCA Analysis and T-SNE Analysis

By reducing the dimension of gene expression data and visualizing the selected 7 genes, it can be seen that high-risk and low-risk patients are separated. It can be seen that high-risk and low-risk patients can be distinguished by the model gene expression. Some low-risk patients are in the high-risk group. It can be seen that this kind of patients cannot be distinguished by the model gene expression. It can be seen that our model can divide patients into high- and low-risk groups (Figure7).Figure 7
Analysis of gene differences between PCA and T-SNE and iron death.


## 3.11. Independent Prognostic Analysis of TCGA and ICGC

To evaluate whether the model can be used as an independent prognostic factor independent of other clinical characteristics, univariate independent prognostic risk analysis. By comparing univariate with survival time and survival status, ifp<0.05, it indicates that univariate is related to survival prognosis. In the model, only stage and risk value p are less than 0.05, indicating that these two factors are related to prognosis. If HR value is greater than 1, it indicates that this factor is a high-risk factor. That is to say, the greater the value, the higher the risk of the patient. It can be seen from the model that it is a high-risk factor. The greater the risk, the higher the patient risk. Gender is a low-risk factor, male is 1, and female is 0, indicating that the lower the value, the greater the patient risk, because male is 1 and female is 0, indicating that the risk of female is higher than that of male. The risk of stages III and IV is greater than that of stages I and II. The higher the risk value, the greater the patient risk. The independent prognostic risk of multiple factors is to conduct an independent prognostic analysis of multiple factors for single factor significant clinical traits and compare the multiple factors with survival time and survival status, If p<0.05, it means that this factor can be used as an independent prognostic factor independent of other factors. From the model, it can be seen that stage and risk score p<0.05 indicate that risk score can be used as an independent prognostic factor independent of stage. If the results are <0.05 in univariate and multivariate analysis, it indicates that our model can be used as an independent prognostic factor independent of other clinical traits. Methods were as follows: by analyzing the survival time, survival status, risk value of the risk file, and the age, sex, and stage of the patient’s clinical information, to verify whether our model can be independent of these clinical traits as an independent factor, find the intersection sample. Then, merge to get the p value, HR value, and fluctuation range of HR value of each factor. Univariate independent prognostic analysis was obtained, and the univariate results were filtered. The univariate information with p<0.05 was extracted, and the multivariate prognostic risk was compared with survival time and survival status. Forest map is obtained by forest map function.


## 3.12. Univariate and Multivariate Cox Regression Analysis

To determine whether the risk score is an independent prognostic factor for OS, we realized that in the univariate Cox regression analysis of TCGA, risk score (HR=3.861; 95%CI=2.338–6.376; p<0.001), stage (HR=2.884; 95%CI=1.972–4.216; p<0.001), T (HR=1.597; 95%CI=1.283–1.986; p<0.001), and N (HR=1.769; 95%CI=1.440–2.174; p<0.001) were significantly correlated with OS, and other confounding factors were corrected in the multivariate Cox regression analysis. Then, the risk score proved to be an independent predictor of OS (HR=2.904; 95%CI=1.713−4.922; p<0.001) (Figure 8).Figure 8
Univariate independent prognostic analysis and multivariate prognostic analysis of TCGA.In the univariate Cox regression analysis of ICGC, the risk score was significantly correlated with OS (HR=4.401; 95%CI=2.072–9.384; p<0.001). Other confounding factors were corrected in multivariate Cox regression analysis, and the risk score proved to be an independent predictor of OS (HR=2.904; 95%CI=1.713–4.922; p<0.001) (Figure 9).Figure 9
Univariate independent prognostic analysis and multivariate prognostic analysis of ICGC.


## 4. Discussion

Renal clear cell carcinoma is one of the most common malignant tumors in human beings, and it is also the main cause of cancer death (accounting for 18.4% of the total cancer deaths) [11]. Iron death is a special regulatory cell death process (RCD) regulated by genes, which is closely related to excessive iron load. It is the abnormal lipid oxidation metabolism of cells catalyzed by iron ions or iron-containing enzymes. Recent studies have found that iron death is related to a variety of diseases. The research field is basically neurodegenerative diseases and many cancers [12]. It may play a very important role in antitumor immune mechanism and tumor inhibition. Therefore, biomarkers related to iron death may be potential diagnostic biomarkers and therapeutic targets for patients with renal cancer [13]. As a common subtype of renal cell carcinoma, in recent years, many medical researchers have devoted themselves to studying the occurrence, development, and treatment of renal clear cell carcinoma [14–18]. Many studies have shown that different subtypes of renal cell carcinoma have different clinical characteristics and results. Iron death is closely related to the occurrence and development of cancer cells [19–22]. Therefore, more and more studies have focused on the genetic characteristics related to the occurrence of iron death to predict the survival rate and immune response of patients with renal clear cell carcinoma [23]. Most epidemiological and experimental studies show that there is a close relationship between iron and renal cell carcinoma [24].In our study, bioinformatics and statistical tools were used to systematically analyze the prediction accuracy of iron death-related genes in renal clear cell carcinoma. We used the sample data of renal clear cell carcinoma patients in TCGA and ICGC databases to obtain the expression level of iron death-related genes and established a Cox regression model composed of seven genes. The risk score of each renal clear cell carcinoma patient was calculated according to the expression of these seven genes in the prognostic markers, and the patients were divided into the high-risk group and low-risk group according to the median risk score. The Kaplan-Meier survival curve shows that the survival rates of patients in the high-risk group and low-risk group are different. ROC curve verifies the accuracy of the model, and then principal component analysis (PCA) proves that the high-risk group and low-risk group are two different components. Then, we analyzed the correlation between prognosis and clinical pathological features and found that the prognostic features we constructed can be widely applied to different populations with different clinical features [25]. The calibration curve showed that the actual 1-, 3-, and 5-year survival rates were highly consistent with the predictions. It is suggested that the prognostic characteristics of iron death-related genes can correctly predict the prognosis of patients with renal clear cell carcinoma, which has great clinical application potential, and can provide reference value for clinical workers to make clinical decisions in time. This established a new prognostic model of seven genes associated with iron death. Among the iron death-related genes that have been studied, some genes have been proved to regulate the occurrence and development of various related cancers through the iron death pathway. For example, FANCD2 inhibited the accumulation of Fe 2 and lipid peroxidation process of iron death induced by erastin, while the deletion of FANCD2 significantly inhibited the mRNA expression of FTH11 (which can bind Fe 2) and steam3 (a metal reductase that can convert iron from Fe 3 to Fe 2) induced by erastin [26]. In the existing studies, it was found that the reason why PHKG2 inhibits the lethality of erastin may be the unknown function of PHKG2. There is a certain hypothetical association between PHKG2 and iron metabolism related to p53. Because studies have shown that the reduction of iron level will inhibit cell death, this iron regulatory function of PHKG2 may be the reason for regulating iron sensitivity [27]. Therefore, the silencing of PHKG2 may be an important factor leading to iron consumption. However, how PHKG2 affects the specific pathway of iron death has not been confirmed by relevant studies. Among the iron death differential genes we screened, CISD1 is highly expressed in tumor tissues. According to previous studies, CISD1 can regulate mitochondrial iron uptake and respiratory capacity. The loss of CISD1 can lead to iron accumulation and peroxidation damage in mitochondria. Mitochondria participate in lipid and glucose metabolism. CISD1 can limit iron uptake in mitochondria, thus inhibiting iron death. Therefore, CISD1 can inhibit iron death by protecting mitochondrial lipid peroxidation [28]. ATP5MC3 and ACSL3 have also been associated with iron death. The above studies show the importance of these iron death related differential genes in their prognostic characteristics [29]. Kaplan-Meier survival curve shows that these seven iron death-related genes are significantly related to OS in patients with renal clear cell carcinoma, reflecting great prognostic value. Our research also has some limitations. These results are best to help us understand the biological functions of iron death related genes in its further biochemical experiments [30]. As mentioned above, we constructed a risk model for the prognostic characteristics of iron death. In this model, we identified 7 differential genes related to iron death among 42 differential genes related to renal clear cell carcinoma. These genes can accurately predict the survival outcome of patients with renal clear cell carcinoma.The advantage of this study was to show that seven iron death genes can accurately predict the survival of patients with renal clear cell carcinoma. However, there are also limits of this study. The mechanism was not clarified, and future studies are needed to verify this.


## 5. Conclusion

Therefore, these seven iron death-related genes are promising biomarkers for prognosis, diagnosis, and targeted therapy in patients with renal clear cell carcinoma. However, the way in which these genes affect the occurrence and development of renal clear cell carcinoma needs further research to find and confirm.



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*Source: 4456987-2022-08-30.xml*