# SLC9A1 Binding mTOR Signaling Pathway-Derived Risk Score Predicting Survival and Immune in Clear Cell Renal Cell Carcinoma

Authors: Dengshuang Wu; Zhipeng Xu; Jianning Wang
Journal: Journal of Oncology
 (2023)

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## Abstract

Objective. Clear cell renal cell carcinoma (ccRCC) is one of the common renal cell carcinomas (RCC) with a high risk of recurrence. Considering that SLC9A1 is involved in various cellular physiological processes and probably mediates the course of mTOR signaling in tumors, this study constructed a risk model for SLC9A1 combined with mTOR signaling in ccRCC, aiming at better predicting the prognosis of patients. Methods. ccRCC expression matrices were downloaded from TCGA and ICGC databases to compare the expression of SLC9A1 in TCGA, and qRT-PCR was adopted to validate the SLC9A1 expression in different RCC cells and normal kidney cells. The CIBERSORT and ESTIMATE algorithms were used to assess samples for immunity. mTOR signaling-associated genes were downloaded from the KEGG website, and then the genes were adopted to screen genes associated with SLC9A1 expression and mTOR signaling pathway colleagues, based on which univariate COX regression and lasso regression Cox analyses were conducted to construct a ccRCC prognostic risk model. ROC curves and nomograms were used to assess the validity of the models. Results. ccRCC tumor samples showed lower SLC9A1 expression than normal samples, as also evidenced by qRT-PCR. The SLC9A1 expression was highly correlated with tumor immunity. Totally, 564 key genes associated with both SLC9A1 expression and mTOR signaling were screened out, and the risk model consisting of 11 gene signatures was constructed in ccRCC based on the 564 genes. Since patients at a high risk had poorer survival outcomes, the high-risk group presented poorer immunotherapy outcomes. Moreover, a higher clinical grade of patients suggested a higher risk score. The risk score can serve as one independent prognostic factor for the prognosis prediction of ccRCC patients. Conclusion. An extremely promising prognostic indicator for ccRCC based on SLCA9A1 and mTOR signaling has been constructed to provide reference for clinical treatment.


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## Body

## 1. Introduction

Renal cell carcinoma (RCC) is one of the most common solid tumors of the adult kidney [1]. Among its subtypes, clear cell renal cell carcinoma (ccRCC) is the major one, with a high rate of occurrence (accounting for 80–90% of all cases) and relapse risk [2, 3]. Moreover, about 30% of patients presented distant metastasis during initial diagnosis [4]. Although the treatment of ccRCC has achieved great progress in recent years, especially immunotherapy, which has been considered an effective therapeutic method for advanced patients [5–7], cancer-specific morbidity and mortality continue to rise, and drug resistance persists unfortunately worldwide [8, 9]. The prognostic staging system currently does not provide adequate guidance for treatment and cannot accurately predict clinical outcomes [10, 11]. Accordingly, it is urgent to identify the efficient prognostic model of ccRCC patients.SLC9A1, also named Na/H exchanger 1 (NHE1) [12], belongs to the NHE exchanger family [13]. As a membrane protein, SLC9A1 exists in many mammalian cell types and is involved in intracellular pH (pHi) regulation [14, 15]. Many physiological processes are dependent on SLC9A1, including cell proliferation, cell volume regulation, cellular immunity, and cell death [16]. Furthermore, prior research has revealed that potential downstream impacts of mTOR on cell growth, survival, and tumorigenesis were under medication by SLC9A1 [17]. Reportedly, in gastric cancer, hepatocellular carcinoma (HCC), ovarian cancer, and gliomas, SLC9A1 favors tumorigenesis and predicts poor prognosis [13, 18, 19]. In breast cancer, SLC9A1 acts as a facilitator in tumor invasiveness [20]. Based on these findings, SLC9A1 protein has emerged as an important marker for tumorigenesis and prognosis, whereas the potential role of SLC9A1 in ccRCC has not been fully understood.This study analyzed the SLC9A1 expression in ccRCC and its association with immunity. A prognostic risk model was established on the basis of SCL9A1 as well as mTOR signaling in ccRCC, with the purpose of finding possible prognostic markers in ccRCC and providing a theoretical basis for prognostic prediction of patients.


## 2. Materials and Methods

### 2.1. Cell Strains and Reagents

Human RCC cell strains (786-O, A498, OS-RC-2, ACHN, 769-P, Caki-1, and Caki-2) and human normal renal cells (293T and HK-2) were provided by American Type Culture Collection (ATCC, Rockville, MD, and the States). A498, ACHN, and HK-2 were incubated in MEM (Invitrogen, 11090-081). 786-O, 769-P, and OS-RC-2 were subjected to incubation in RPMI-1640 (Gibco, 11875500BT). Caki-1 and Caki-2 were subjected to incubation in Mccoy 5A (Gibco, 12330031). 293T was cultured in DMEM (Gibco, 11995500BT). The medium were supplemented with 10% fetal bovine serum (Gibco, the States). All the cell strains were maintained in an incubator (37°C, 5% CO2).


### 2.2. RNA Isolation and Quantitative Real-Time PCR (qRT-PCR)

Total RNA was extracted from cells through TRIzol reagent (Invitrogen). First-strand cDNA was generated from 1μg total RNA using Hifair® II 1st Strand cDNA Synthesis Kit (11119ES60) from Yeasen (Shanghai, CN). qRT-PCR was conducted three times with a SYBR Green premix qPCR kit (Accurate Biotechnology, Changsha, Hunan, CN, AG11701). Sequences of primers for qRT-PCR were provided as follows: SLC9A: forward: 5′-ACC​ACG​AGA​ACG​CTC​GAT​TG-3′, reverse: 5′-ACG​TGT​GTG​TAG​TCG​ATG​CC-3′. GAPDH: forward: 5′- GGA​GCG​AGA​TCC​CTC​CAA​AAT-3′, reverse: 5′-GGC​TGT​TGT​CAT​ACT​TCT​CAT​GG-3′. The specific experimental procedures were carried out in strict accordance with the kit instructions. Gene expression was measured and normalized relative to the GAPDH level using the 2−ΔΔCt method.


### 2.3. Data Collection and Processing

Expression matrices for 526 ccRCC samples and 72 normal ones were acquired from The Cancer Genome Atlas (TCGA,https://portal.gdc.cancer.gov/repository) database and filtered for samples with missing clinical and survival information, with gene expression as the mean value. Data about mutation data and copy number variation for ccRCC were also obtained from TCGA. Additionally, ninety-one primary renal cell cancer samples with complete prognostic and clinical information in the International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/) were screened, and their expression profiles were downloaded.


### 2.4. Tumor Immunity

The relative proportions of the 22 immune cell compositions in the expression matrix were assessed using CIBERSORT, andp<0.05 was used for subsequent comparisons. In addition, immune scores in tumor samples were calculated using the ESTIMATE algorithm, outputting scores for immune, stromal, and ESTIMATES.


### 2.5. Screening for SLC9A1 and mTOR-Related Genes and Gene Sets

The mTOR pathway-related genes were acquired from the official website of the Kyoto Encyclopedia of Genes and Genomes (KEGG,https://www.genome.jp/kegg/), and sample mTOR pathway scores were calculated using the ssGSEA algorithm. The correlation between gene sets and genes was calculated, respectively, on the basis of the Hmisc package rcorr function of the R language, and the correlated gene sets were screened by |cor| > 0.25 and p<0.001.


### 2.6. Enrichment Analysis

Gene ontology (GO) enrichment analysis was conducted to explore the possible biological functions of the relevant genes in biological processes, cellular components, and molecular functions through the cluster Profiler package in R. A separate KEGG pathway enrichment analysis was performed to search for potential mechanisms.


### 2.7. Construction and Validation of the Prognostic Risk Model

Prognostic significance of genes was calculated by single and multifactor Cox analyses performed with the survival package.p<0.05 was the selection criterion for screening for subsequent analyses. Candidate genes were subjected to lasso regression analysis using the R package glmnet for screening for prognosis-associated gene signatures, and models were constructed by 10-fold cross-validation.(1)Riskscorepatient=∑i=1nexpressiongenei×cofficientGenei.The median risk score was defined with the R package survminer, and samples were classified according to the risk level. Kaplan–Meier (K–M) curves were drawn through the R package time ROC to predict the prognostic classification efficiency of the risk score.Module genes were extracted from published articles [21–23], and the c-index of these models was calculated and compared by the survcomp package.


### 2.8. Immunotherapy

The online software Tumor Immune Dysfunction and Exclusion (TIDE,https://tide.dfci.harvard.edu/) is used to assess the potential clinical effects of samples in immunotherapy.


### 2.9. Nomogram

The rms package was used along with the risk score and clinical characteristics to create nomogram that quantify the prognostic risk and likelihood of survival for patients at 1, 3, and 5 years. Moreover, usefulness of the model is also assessed by performing a decision curve analysis (DCA).


### 2.10. Statistical Analyses

With R software (3.6.1) and GraphPad Prism (9), statistical analyses were conducted, and the ggplot2 package was adopted for visualization. The correlation between gene expression and the pathway score was determined through Spearman correlation analysis and compared between the groups using the Wilcox test,p<0.05.


## 2.1. Cell Strains and Reagents

Human RCC cell strains (786-O, A498, OS-RC-2, ACHN, 769-P, Caki-1, and Caki-2) and human normal renal cells (293T and HK-2) were provided by American Type Culture Collection (ATCC, Rockville, MD, and the States). A498, ACHN, and HK-2 were incubated in MEM (Invitrogen, 11090-081). 786-O, 769-P, and OS-RC-2 were subjected to incubation in RPMI-1640 (Gibco, 11875500BT). Caki-1 and Caki-2 were subjected to incubation in Mccoy 5A (Gibco, 12330031). 293T was cultured in DMEM (Gibco, 11995500BT). The medium were supplemented with 10% fetal bovine serum (Gibco, the States). All the cell strains were maintained in an incubator (37°C, 5% CO2).


## 2.2. RNA Isolation and Quantitative Real-Time PCR (qRT-PCR)

Total RNA was extracted from cells through TRIzol reagent (Invitrogen). First-strand cDNA was generated from 1μg total RNA using Hifair® II 1st Strand cDNA Synthesis Kit (11119ES60) from Yeasen (Shanghai, CN). qRT-PCR was conducted three times with a SYBR Green premix qPCR kit (Accurate Biotechnology, Changsha, Hunan, CN, AG11701). Sequences of primers for qRT-PCR were provided as follows: SLC9A: forward: 5′-ACC​ACG​AGA​ACG​CTC​GAT​TG-3′, reverse: 5′-ACG​TGT​GTG​TAG​TCG​ATG​CC-3′. GAPDH: forward: 5′- GGA​GCG​AGA​TCC​CTC​CAA​AAT-3′, reverse: 5′-GGC​TGT​TGT​CAT​ACT​TCT​CAT​GG-3′. The specific experimental procedures were carried out in strict accordance with the kit instructions. Gene expression was measured and normalized relative to the GAPDH level using the 2−ΔΔCt method.


## 2.3. Data Collection and Processing

Expression matrices for 526 ccRCC samples and 72 normal ones were acquired from The Cancer Genome Atlas (TCGA,https://portal.gdc.cancer.gov/repository) database and filtered for samples with missing clinical and survival information, with gene expression as the mean value. Data about mutation data and copy number variation for ccRCC were also obtained from TCGA. Additionally, ninety-one primary renal cell cancer samples with complete prognostic and clinical information in the International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/) were screened, and their expression profiles were downloaded.


## 2.4. Tumor Immunity

The relative proportions of the 22 immune cell compositions in the expression matrix were assessed using CIBERSORT, andp<0.05 was used for subsequent comparisons. In addition, immune scores in tumor samples were calculated using the ESTIMATE algorithm, outputting scores for immune, stromal, and ESTIMATES.


## 2.5. Screening for SLC9A1 and mTOR-Related Genes and Gene Sets

The mTOR pathway-related genes were acquired from the official website of the Kyoto Encyclopedia of Genes and Genomes (KEGG,https://www.genome.jp/kegg/), and sample mTOR pathway scores were calculated using the ssGSEA algorithm. The correlation between gene sets and genes was calculated, respectively, on the basis of the Hmisc package rcorr function of the R language, and the correlated gene sets were screened by |cor| > 0.25 and p<0.001.


## 2.6. Enrichment Analysis

Gene ontology (GO) enrichment analysis was conducted to explore the possible biological functions of the relevant genes in biological processes, cellular components, and molecular functions through the cluster Profiler package in R. A separate KEGG pathway enrichment analysis was performed to search for potential mechanisms.


## 2.7. Construction and Validation of the Prognostic Risk Model

Prognostic significance of genes was calculated by single and multifactor Cox analyses performed with the survival package.p<0.05 was the selection criterion for screening for subsequent analyses. Candidate genes were subjected to lasso regression analysis using the R package glmnet for screening for prognosis-associated gene signatures, and models were constructed by 10-fold cross-validation.(1)Riskscorepatient=∑i=1nexpressiongenei×cofficientGenei.The median risk score was defined with the R package survminer, and samples were classified according to the risk level. Kaplan–Meier (K–M) curves were drawn through the R package time ROC to predict the prognostic classification efficiency of the risk score.Module genes were extracted from published articles [21–23], and the c-index of these models was calculated and compared by the survcomp package.


## 2.8. Immunotherapy

The online software Tumor Immune Dysfunction and Exclusion (TIDE,https://tide.dfci.harvard.edu/) is used to assess the potential clinical effects of samples in immunotherapy.


## 2.9. Nomogram

The rms package was used along with the risk score and clinical characteristics to create nomogram that quantify the prognostic risk and likelihood of survival for patients at 1, 3, and 5 years. Moreover, usefulness of the model is also assessed by performing a decision curve analysis (DCA).


## 2.10. Statistical Analyses

With R software (3.6.1) and GraphPad Prism (9), statistical analyses were conducted, and the ggplot2 package was adopted for visualization. The correlation between gene expression and the pathway score was determined through Spearman correlation analysis and compared between the groups using the Wilcox test,p<0.05.


## 3. Results

### 3.1. SLC9A1 Is Lowly Expressed in ccRCC

The overall flowchart is shown in Supplementary Figure1. Comparison of TCGA expression profiles revealed lowly expressed SLC9A1 in ccRCC tumor tissues as compared with normal paracancerous tissues (Figure 1(a)). Moreover, qRT-PCR results revealed downregulated SLC9A1 in RCC cell strains as compared with normal kidney cell strains (Figure 1(b)). Next, we sorted out the mutation information of SLC9A1 through the SNV data of TCGA, and 2 samples had mutations in SLC9A1 gene, while 524 samples did not have mutations. As shown in Figure 1(c), gene expression in samples without SLC9A1 mutation was higher than that of the sample with SLC9A1 mutation, but there was no significant difference (maybe because of the too small sample size). Then, based on the CNV data of TCGA, the samples were divided into amplification, deletion, and diploid groups based on the CNV mutation of SLC9A1. We found that the samples without CNV mutation in SLC9A1 were significantly higher (Figure 1(d)).Figure 1
SLC9A1 is lowly expressed in ccRCC samples. (a) Expression of SLC9A1 in ccRCC cancer tissues and normal tissues in TCGA database. (b) qRT-PCR analysis of SLC9A1 in RCC cells vs. human normal renal cells (293T and HK-2). (c) Comparison of SLC9A1 expression between samples with or without SNV mutation. (d) Comparison of SLC9A1 expression between samples with or without CNV mutation. Note: SNV: single nucleotide variants; CNV: copy number variation. ∗p<0.05, ∗∗p<0.01, ∗∗∗p<0.001, and ∗∗∗∗p<0.0001.
(a)(b)(c)(d)


### 3.2. Relationship between the Expression of SLC9A1 and Immunity

We found that some immune cells’ scores expressed differently depending on the SLC9A1’s expression level (Figure2(a)). The immune scores of the tumor samples showed that the highly expressed group of SLC9A1 had higher immune infiltration than the lowly expressed one (Figure 2(b)). Correlation analysis revealed a positive association between SLC9A1 expression and the immune score (Figure 2(c)). On the other hand, we also examined the correlation of the SLC9A1 expression with 22 types of immune cell scores. Among them, there was a positive correlation between SLC9A1 expression and score about Macrophages M0, T regulatory cells (Tregs), etc., and a negative correlation between it and cells, scores such as NK cells activated (Figure 2(d)). We extracted the genes of immune-related pathways, and the expression of some genes (e.g., IL6 and MMP9) in immune-related pathways also increased with the increased expression of SLC9A1 (Figure 2(e)).Figure 2
Expression of SLC9A1 correlates with the immune score and promotes immune cell infiltration. (a) Comparison of 22 types of immune cell scores with high and low expression of SLC9A1. (b) Comparison of immune cell infiltration between high and low expression of SLC9A1. (c) Correlation between the expression of SLC9A1 and the immune score. (d) Correlation between the expression of SLC9A1 and 22 immune cell scores. (e) A heatmap of the correlation between SLC9A1 and the expression of immune-related pathway genes.∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.
(a)(b)(c)(d)(e)


### 3.3. Relationship between the SLC9A1 and mTOR Pathway and Gene Set Enrichment Analysis

The Spearman correlation analysis revealed positive association of SLC9A1 expression with the mTOR signaling pathway scores of the patients calculated by the ssGSEA method (Figure3(a)). Further analysis screened 1502 genes positively associated with SLC9A1 expression, 1077 ones negatively associated with it, 3061 genes with positive association with the mTOR signaling pathway score, and 1611 ones with negative association with the score. By overlapping analysis, 564 genes were found to be associated with both SLC9A1 and mTOR (Figure 3(b)). Meanwhile, enrichment analysis of 564 related genes was conducted and the genes were found to be associated with actin filament-related biological processes, phagocytosis, and the cGMP-PKG signaling pathway (Figure 3(c)).Figure 3
The expression of SLC9A1 combined with the expression of the mTOR signal pathway score. (a) Scatter plot of correlation between the expression of SLC9A1 and the mTOR signaling pathway score. (b) The Venn diagram shows 564 genes from both TCGA and ICGC gene sets. (c) Go and KEGG enrichment analysis of the 564 genes from both TCGA and ICGC gene sets. Note: GO: gene ontology; BP: biological process: CC: cellular components; MF: molecular functions.
(a)(b)(c)


### 3.4. Construction and Validation of the ccRCC Prognostic Risk Model

We identified 39 genes mostly associated with the prognosis in both TCGA and ICGA datasets of ccRCC through univariate Cox regression analysis. Supplementary Table1 presents the results of the one-way Cox analysis for the 564 genes in both TCGA and the ICGC datasets. Then, Lasso regression was performed to further reduce model genes for model optimization. The change trajectory of every independent variable was analyzed as shown in Figures 4(a) and 4(b). According to the results, with the gradual increase of lambda, the number of independent variable coefficients close to 0 also increased gradually. We used 10-fold cross-validation for model establishment. The confidence interval under every lambda was analyzed, and when lambda = 0.0344, the model is optimal. For this reason, we chose eleven genes with lambda = 0.0344 as the target genes. The risk score was calculated using the following formula:(2)Riskscore=0.158∗COL6A2−0.218∗EXTL3+0.357∗HEATR6−0.276∗HSPG2−0.015∗MAML3+0.389∗PPP1R18+0.316∗RCC2+0.173∗SEMA7A+0.022∗SERPINH1−0.484∗TLN1+0.177∗TM9SF4.Figure 4
Construction of the prognostic risk model in TCGA cohort and ICGC cohort. (a) The trajectory of each gene coefficient with lambda. (b) 10-fold cross-validation was used to find the best values for the penalty parameter. (c) The Kaplan–Meier (K–M) survival curve showed patients in the high- and low-risk group in TCGA dataset. (d) The prognostic signature’s ROC curve at 1, 2, 3, 4, and 5 years in TCGA dataset. (e) The K–M survival curve showed that patients in the high- and low-risk group in the ICGC dataset. (f) The prognostic signature’s ROC curve at 1, 2, 3, 4, and 5 years in the ICGC dataset.
(a)(b)(c)(d)(e)(f)The expression of these 11 genes in cancer and normal paracancerous tissues is described in detail in Supplementary Figure2. Next, we used TCGA dataset as the training dataset and calculated the risk score of every sample through the expression of 11 genes. The ROC curve assisted in evaluating the accuracy of OS estimates derived from the prognostic risk model. The classification efficiency of 1–5 year prognosis prediction was analyzed, and the area under curve (AUC) in 1–5 years reached above 0.7. In addition, the patients were assigned to a high- or low- risk group in the light of the mean value of the risk score. The K–M survival curve showed a notably worse overall survival (OS) in the high-risk group than that in the other group (Figures 4(c) and 4(d)). The same method was used to validate the ICGC dataset, and the similar results were observed (Figures 4(e) and 4(f)). Compared with the previous studies, our model is superior as shown in Supplementary Figure 3.


### 3.5. Association of the Prognostic Risk Model with Clinicopathological Characteristics

The differences in risk scores were compared for different clinicopathological characteristics, as shown in Figures5(a)–5(f). The higher risk scores were significantly associated with higher TNM, histological grade, and advanced clinical staging. The risk score increases with T, M, and N stage, clinical stage, and histopathological grade.Figure 5
Comparison of the risk score for different clinicopathological characteristics. (a) T stage. (b) M stage. (c) N stage. (d) Clinical stage. (e) Histopathological grade. (f) Age.∗p<0.05, ∗∗p<0.01, ∗∗∗p<0.001, and ∗∗∗∗p<0.0001.
(a)(b)(c)(d)(e)(f)


### 3.6. Relationship between the Prognostic Risk Model and Immune Infiltration

We calculated scores for TCGA cohort samples at different risk levels in terms of 22 immune cells (Figure6(a)), and the higher risk group had higher immune cell scores in some cells including CD8 T cells. Additionally, we also found higher immune scores in the high-risk group (Figure 6(b)). Immune checkpoint inhibitor therapy has gradually become the dominant method of systemic ccRCC treatment options [6]. Hence, the immune checkpoint genes in the high- and low-risk groups were analyzed, and the result showed significant differences in some immune checkpoint genes (Figure 6(c)). Next, as shown in Figure 6(d), the high-risk group got a higher TIDE score than the low-risk group, which indicated that the high-risk group was more likely to have immune escape and less likely to benefit from immunotherapy.Figure 6
Comparison of immunological characteristics of different risk groups and differences of 22 immune cell scores (a), stromal, immune, and ESTIMATE scores (b) of the high- and low-risk groups in TCGA cohort. (c) Immune checkpoint genes differentially expressed in different risk groups in TCGA cohort. (d) The TIDE analysis results of different risk groups in TCGA cohort.∗p<0.05, ∗∗p<0.01, ∗∗∗p<0.001, and ∗∗∗∗p<0.0001.
(a)(b)(c)(d)


### 3.7. Establishment and Validation of a Nomogram for Prediction of OS

Univariate and multivariate Cox regression analyses of the risk score and clinicopathological characteristics revealed that M stage, age, and the risk score were significant prognostic factors (Figures7(a) and 7(b)). For quantifying the risk assessment and survival probability of ccRCC patients, we combined the risk score with other clinicopathological features to establish a nomogram (Figure 7(c)). Moreover, the results suggest that the risk score had the strongest influence on survival prediction. Furthermore, the calibration curve was adopted to evaluate the prediction accuracy of the model. It can be observed that the predicted calibration curves of the three calibration points at 1, 3, and 5 years were nearly coincident with the standard curve, which suggests the good performance of the nomogram (Figure 7(d)). From the results of decision curve analysis (DCA), the risk score and nomogram provided notably higher benefits than the extreme curves. In contrast to other clinicopathological characteristics, both the nomogram and the risk score exhibited the most powerful survival prediction ability (Figures 7(e) and 7(f)). These data demonstrate that our prognostic risk model is reliable and effective at predicting the prognosis of ccRCC patients.Figure 7
Construction of a nomogram and verification that the risk score has a powerful survival prediction ability. Univariate (a) and multivariate (b) Cox regression analysis of the risk score and clinicopathological characteristics in ccRCC patients. (c) The development of a nomogram based on the risk score and clinicopathological characteristics. (d) The calibration curve of the nomogram in 1, 3, and 5 years. (e) Decision curve of the nomogram. (f) Compared with other clinicopathological features, the nomogram and the risk score exhibited the more powerful capacity for survival prediction.
(a)(b)(c)(d)(e)(f)


## 3.1. SLC9A1 Is Lowly Expressed in ccRCC

The overall flowchart is shown in Supplementary Figure1. Comparison of TCGA expression profiles revealed lowly expressed SLC9A1 in ccRCC tumor tissues as compared with normal paracancerous tissues (Figure 1(a)). Moreover, qRT-PCR results revealed downregulated SLC9A1 in RCC cell strains as compared with normal kidney cell strains (Figure 1(b)). Next, we sorted out the mutation information of SLC9A1 through the SNV data of TCGA, and 2 samples had mutations in SLC9A1 gene, while 524 samples did not have mutations. As shown in Figure 1(c), gene expression in samples without SLC9A1 mutation was higher than that of the sample with SLC9A1 mutation, but there was no significant difference (maybe because of the too small sample size). Then, based on the CNV data of TCGA, the samples were divided into amplification, deletion, and diploid groups based on the CNV mutation of SLC9A1. We found that the samples without CNV mutation in SLC9A1 were significantly higher (Figure 1(d)).Figure 1
SLC9A1 is lowly expressed in ccRCC samples. (a) Expression of SLC9A1 in ccRCC cancer tissues and normal tissues in TCGA database. (b) qRT-PCR analysis of SLC9A1 in RCC cells vs. human normal renal cells (293T and HK-2). (c) Comparison of SLC9A1 expression between samples with or without SNV mutation. (d) Comparison of SLC9A1 expression between samples with or without CNV mutation. Note: SNV: single nucleotide variants; CNV: copy number variation. ∗p<0.05, ∗∗p<0.01, ∗∗∗p<0.001, and ∗∗∗∗p<0.0001.
(a)(b)(c)(d)


## 3.2. Relationship between the Expression of SLC9A1 and Immunity

We found that some immune cells’ scores expressed differently depending on the SLC9A1’s expression level (Figure2(a)). The immune scores of the tumor samples showed that the highly expressed group of SLC9A1 had higher immune infiltration than the lowly expressed one (Figure 2(b)). Correlation analysis revealed a positive association between SLC9A1 expression and the immune score (Figure 2(c)). On the other hand, we also examined the correlation of the SLC9A1 expression with 22 types of immune cell scores. Among them, there was a positive correlation between SLC9A1 expression and score about Macrophages M0, T regulatory cells (Tregs), etc., and a negative correlation between it and cells, scores such as NK cells activated (Figure 2(d)). We extracted the genes of immune-related pathways, and the expression of some genes (e.g., IL6 and MMP9) in immune-related pathways also increased with the increased expression of SLC9A1 (Figure 2(e)).Figure 2
Expression of SLC9A1 correlates with the immune score and promotes immune cell infiltration. (a) Comparison of 22 types of immune cell scores with high and low expression of SLC9A1. (b) Comparison of immune cell infiltration between high and low expression of SLC9A1. (c) Correlation between the expression of SLC9A1 and the immune score. (d) Correlation between the expression of SLC9A1 and 22 immune cell scores. (e) A heatmap of the correlation between SLC9A1 and the expression of immune-related pathway genes.∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.
(a)(b)(c)(d)(e)


## 3.3. Relationship between the SLC9A1 and mTOR Pathway and Gene Set Enrichment Analysis

The Spearman correlation analysis revealed positive association of SLC9A1 expression with the mTOR signaling pathway scores of the patients calculated by the ssGSEA method (Figure3(a)). Further analysis screened 1502 genes positively associated with SLC9A1 expression, 1077 ones negatively associated with it, 3061 genes with positive association with the mTOR signaling pathway score, and 1611 ones with negative association with the score. By overlapping analysis, 564 genes were found to be associated with both SLC9A1 and mTOR (Figure 3(b)). Meanwhile, enrichment analysis of 564 related genes was conducted and the genes were found to be associated with actin filament-related biological processes, phagocytosis, and the cGMP-PKG signaling pathway (Figure 3(c)).Figure 3
The expression of SLC9A1 combined with the expression of the mTOR signal pathway score. (a) Scatter plot of correlation between the expression of SLC9A1 and the mTOR signaling pathway score. (b) The Venn diagram shows 564 genes from both TCGA and ICGC gene sets. (c) Go and KEGG enrichment analysis of the 564 genes from both TCGA and ICGC gene sets. Note: GO: gene ontology; BP: biological process: CC: cellular components; MF: molecular functions.
(a)(b)(c)


## 3.4. Construction and Validation of the ccRCC Prognostic Risk Model

We identified 39 genes mostly associated with the prognosis in both TCGA and ICGA datasets of ccRCC through univariate Cox regression analysis. Supplementary Table1 presents the results of the one-way Cox analysis for the 564 genes in both TCGA and the ICGC datasets. Then, Lasso regression was performed to further reduce model genes for model optimization. The change trajectory of every independent variable was analyzed as shown in Figures 4(a) and 4(b). According to the results, with the gradual increase of lambda, the number of independent variable coefficients close to 0 also increased gradually. We used 10-fold cross-validation for model establishment. The confidence interval under every lambda was analyzed, and when lambda = 0.0344, the model is optimal. For this reason, we chose eleven genes with lambda = 0.0344 as the target genes. The risk score was calculated using the following formula:(2)Riskscore=0.158∗COL6A2−0.218∗EXTL3+0.357∗HEATR6−0.276∗HSPG2−0.015∗MAML3+0.389∗PPP1R18+0.316∗RCC2+0.173∗SEMA7A+0.022∗SERPINH1−0.484∗TLN1+0.177∗TM9SF4.Figure 4
Construction of the prognostic risk model in TCGA cohort and ICGC cohort. (a) The trajectory of each gene coefficient with lambda. (b) 10-fold cross-validation was used to find the best values for the penalty parameter. (c) The Kaplan–Meier (K–M) survival curve showed patients in the high- and low-risk group in TCGA dataset. (d) The prognostic signature’s ROC curve at 1, 2, 3, 4, and 5 years in TCGA dataset. (e) The K–M survival curve showed that patients in the high- and low-risk group in the ICGC dataset. (f) The prognostic signature’s ROC curve at 1, 2, 3, 4, and 5 years in the ICGC dataset.
(a)(b)(c)(d)(e)(f)The expression of these 11 genes in cancer and normal paracancerous tissues is described in detail in Supplementary Figure2. Next, we used TCGA dataset as the training dataset and calculated the risk score of every sample through the expression of 11 genes. The ROC curve assisted in evaluating the accuracy of OS estimates derived from the prognostic risk model. The classification efficiency of 1–5 year prognosis prediction was analyzed, and the area under curve (AUC) in 1–5 years reached above 0.7. In addition, the patients were assigned to a high- or low- risk group in the light of the mean value of the risk score. The K–M survival curve showed a notably worse overall survival (OS) in the high-risk group than that in the other group (Figures 4(c) and 4(d)). The same method was used to validate the ICGC dataset, and the similar results were observed (Figures 4(e) and 4(f)). Compared with the previous studies, our model is superior as shown in Supplementary Figure 3.


## 3.5. Association of the Prognostic Risk Model with Clinicopathological Characteristics

The differences in risk scores were compared for different clinicopathological characteristics, as shown in Figures5(a)–5(f). The higher risk scores were significantly associated with higher TNM, histological grade, and advanced clinical staging. The risk score increases with T, M, and N stage, clinical stage, and histopathological grade.Figure 5
Comparison of the risk score for different clinicopathological characteristics. (a) T stage. (b) M stage. (c) N stage. (d) Clinical stage. (e) Histopathological grade. (f) Age.∗p<0.05, ∗∗p<0.01, ∗∗∗p<0.001, and ∗∗∗∗p<0.0001.
(a)(b)(c)(d)(e)(f)


## 3.6. Relationship between the Prognostic Risk Model and Immune Infiltration

We calculated scores for TCGA cohort samples at different risk levels in terms of 22 immune cells (Figure6(a)), and the higher risk group had higher immune cell scores in some cells including CD8 T cells. Additionally, we also found higher immune scores in the high-risk group (Figure 6(b)). Immune checkpoint inhibitor therapy has gradually become the dominant method of systemic ccRCC treatment options [6]. Hence, the immune checkpoint genes in the high- and low-risk groups were analyzed, and the result showed significant differences in some immune checkpoint genes (Figure 6(c)). Next, as shown in Figure 6(d), the high-risk group got a higher TIDE score than the low-risk group, which indicated that the high-risk group was more likely to have immune escape and less likely to benefit from immunotherapy.Figure 6
Comparison of immunological characteristics of different risk groups and differences of 22 immune cell scores (a), stromal, immune, and ESTIMATE scores (b) of the high- and low-risk groups in TCGA cohort. (c) Immune checkpoint genes differentially expressed in different risk groups in TCGA cohort. (d) The TIDE analysis results of different risk groups in TCGA cohort.∗p<0.05, ∗∗p<0.01, ∗∗∗p<0.001, and ∗∗∗∗p<0.0001.
(a)(b)(c)(d)


## 3.7. Establishment and Validation of a Nomogram for Prediction of OS

Univariate and multivariate Cox regression analyses of the risk score and clinicopathological characteristics revealed that M stage, age, and the risk score were significant prognostic factors (Figures7(a) and 7(b)). For quantifying the risk assessment and survival probability of ccRCC patients, we combined the risk score with other clinicopathological features to establish a nomogram (Figure 7(c)). Moreover, the results suggest that the risk score had the strongest influence on survival prediction. Furthermore, the calibration curve was adopted to evaluate the prediction accuracy of the model. It can be observed that the predicted calibration curves of the three calibration points at 1, 3, and 5 years were nearly coincident with the standard curve, which suggests the good performance of the nomogram (Figure 7(d)). From the results of decision curve analysis (DCA), the risk score and nomogram provided notably higher benefits than the extreme curves. In contrast to other clinicopathological characteristics, both the nomogram and the risk score exhibited the most powerful survival prediction ability (Figures 7(e) and 7(f)). These data demonstrate that our prognostic risk model is reliable and effective at predicting the prognosis of ccRCC patients.Figure 7
Construction of a nomogram and verification that the risk score has a powerful survival prediction ability. Univariate (a) and multivariate (b) Cox regression analysis of the risk score and clinicopathological characteristics in ccRCC patients. (c) The development of a nomogram based on the risk score and clinicopathological characteristics. (d) The calibration curve of the nomogram in 1, 3, and 5 years. (e) Decision curve of the nomogram. (f) Compared with other clinicopathological features, the nomogram and the risk score exhibited the more powerful capacity for survival prediction.
(a)(b)(c)(d)(e)(f)


## 4. Discussion

SLC9A1 expression and potential function are becoming increasingly important in various cancers. For instance, tumor tissues showed significantly higher SLC9A1 expression than normal tissues in HCC and revealed that SLC9A1 expression can serve as a crucial prognostic factor for immunotherapy against HCC [24]. By contrast, as a novel prognostic biomarker in colorectal cancer, a lower level of SLC9A1 mRNA expression was observed [25]. According to analysis of TCGA data and qRT-PCR analysis in this study, SLC9A1 was downregulated in ccRCC. Prior research has shown that cell proliferation, motility, survival, and metabolism are all under control by the mTOR signaling pathway, and SLC9A1 may contribute to mTOR’s tumor-promoting effects [17, 26, 27]. The mTOR inhibitors have been approved as a therapeutic option for metastatic ccRCC [28]. Given this, this relationship between SLC9A1 and the mTOR pathway in ccRCC is worth investigating. By analyzing genes related to prognosis step by step using Cox regression, Lasso regression, K–M survival analysis, and multi-cox regression, we constructed a prognosis gene panel with eleven genes having a prognostic risk score model (including COL6A2, EXTL3, HEATR6, HSPG2, MAML3, PPP1R18, RCC2, SEMA7A, SERPINH1, TLN1, and TM9SF4). Additionally, the prognostic risk model was used to determine the risk scores for TCGA cohort and ICGC cohort patients, and a high- and low-risk subgroup for ccRCC patients was identified. Based on the K–M survival analysis and ROC curve, the eleven genes exhibited effective and reliable predictive abilities in the training set, with a significantly shorter OS in high-risk patients than that in low-risk ones. Furthermore, the association of the prognostic risk model with clinicopathological characteristics was verified. The results revealed association of the risk score with clinicopathological characteristics. The risk Score and other clinicopathological characteristics were combined to establish a nomogram, and the calibration curve showed the effectiveness of the nomogram. We also used DCA to evaluate the models, and the results showed that the constructed prognostic risk model is strong and generalizable.In liver HCCs, SLC9A1 was strongly related with immune cell infiltration [24]. SLC9A1 blockade boosts immunity to glioma tumors by restoring oxidative stress in myeloid cells [29]. During this research, we found that the highly expressed group of SLC9A1 had higher immune infiltration in tumor samples and was closely associated with multiple immune cell scores. Alternatively, SLC9A1 participated in partial immune-related signaling pathways. Immune cell infiltrating tumors play an important prognostic role [30]. Therefore, we explored the immune signatures of the prognostic risk model. According to the results, the high-risk group presented a stronger immune cell infiltration and got a higher TIDE score, which indicated that the group was less likely to benefit from immunotherapy. These results may spark novel ideas for research, diagnosis, and treatment of ccRCC.Eleven genes, COL6A2, EXTL3, HEATR6, HSPG2, MAML3, PPP1R18, RCC2, SEMA7A, SERPINH1, TLN1, and TM9SF4, were selected as important prognostic markers. COL6A2 is a member of the Collagen VI family and widely expressed in various cancers and promotes cancer progression. COL6A2 was also positively associated with an increased risk in the model of the current study (coefficient > 0). Zhong et al. found an up-regulation of COL6A2, which could be a factor in poor prognosis in metastatic ccRCC [31]. Exostosin-like glycosyltransferase 3 (EXTL3), belonging to the EXT family, takes a crucial part in predicting the prognosis of various cancers and immune deficiencies [32], but its role in ccRCC remains unknown. HEAT repeat containing 6 (HEATR6) is part of a highly expressed breast cancer amplicon. Prior research indicated that endometrial tumors from African-American women express elevated levels of HEATR6 [33]. There are few research studies of HEATR6 in ccRCC. A recent study showed that heparan sulfate proteoglycan 2(HSPG2) participates in tumor and stromal cell binding to the extracellular matrix of ccRCC, and HSPG2 showed the strongest binding to FN1, COL6, and COL12 in all cells [34]. Zhang et al. confirmed that silencing MAML3 suppresses the proliferation of gastric cancer by acting as a transcriptional coactivator in the Notch signaling pathway [35]. Genetic alterations in MAML3 and the Notch pathway in which it resides also appear to give a better prognosis for patients with ccRCC [36]. As a biomarker for immunotherapy, protein phosphatase 1 regulatory subunit 18 (PPP1R18) serves as an oncogenic role in ccRCC and was significantly related with immunity [37]. The literature confirmed that chromosome condensation 2 (RCC2) was an oncogene and took a crucial part in promoting the proliferation of lung adenocarcinoma, esophageal squamous cell carcinoma, and acute myeloid leukemia [38]. The GPI-anchored semaphorin 7A (SEMA7A) affects inflammatory diseases, and Wang et al. found association of a high SEMA7A level with poor outcomes in ccRCC [39]. Serpin peptidase inhibitor clade H member 1 (SERPINH1), also known asHSP47, belongs to the serpin superfamily. The level of SERPINH1 is significantly elevated at the four TNM stages of ccRCC tissues and strongly correlates with unfavorable clinical outcomes [40]. The talin 1 (TLN1) receptor mediates cell adhesion, regulates integrin signaling, and promotes metastasis in various cancers, such as prostate, colon, and oral cancer [41]. According to Guazzi et al., transmembrane 9 superfamily 4 (TM9SF4) is a highly specific cancer biomarker that can be adopted to detect and stage gastrointestinal cancers [42], and its role in ccRCC remains unclear. The results of the present study show that these 11 genes are related with the prognosis of ccRCC patients, but the exact mechanism still involves a huge network of gene regulation, which needs further exploration.Because of the sample specificity of ccRCC, the dataset that could be selected for this study was small. Further validation of how SLC9A1 mediates mTOR signaling in ccRCC through ex vivo experimental data will be the direction of our subsequent studies. We also expect more scholars to explore this direction and more relevant datasets in the future for early validation of this risk model in a clinical cohort.


## 5. Conclusion

In summary, we have constructed a risk model consisting of 11 genes based on SLCA9A1 and mTOR signaling-related genes in ccRCC, which has great potential for prognostic assessment in ccRCC. The model can guide clinical immunotherapy to accurately identify high-risk patients for early clinical intervention.



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