Identification and Validation of the Prognostic Panel in Clear Cell Renal Cell Carcinoma Based on Resting Mast Cells for Prediction of Distant Metastasis and Immunotherapy Response

Clear cell renal cell carcinoma (ccRCC) has a high metastatic rate, and its incidence and mortality are still rising. The aim of this study was to identify the key tumor-infiltrating immune cells (TIICs) affecting the distant metastasis and prognosis of patients with ccRCC and to construct a relevant prognostic panel to predict immunotherapy response. Based on ccRCC bulk RNA sequencing data, resting mast cells (RMCs) were screened and verified using the CIBERSORT algorithm, survival analysis, and expression analysis. Distant metastasis-associated genes were identified using single-cell RNA sequencing data. Subsequently, a three-gene (CFB, PPP1R18, and TOM1L1) panel with superior distant metastatic and prognostic performance was established and validated, which stratified patients into high- and low-risk groups. The high-risk group exhibited lower infiltration of RMCs, higher tumor mutation burden (TMB), and worse prognosis. Therapeutically, the high-risk group was more sensitive to anti-PD-1 and anti-CTLA-4 immunotherapy, whereas the low-risk group displayed a better response to anti-PD-L1 immunotherapy. Furthermore, two immune clusters revealing distinct immune, clinical, and prognosis heterogeneity were distinguished. Immunohistochemistry of ccRCC samples verified the expression patterns of the three key genes. Collectively, the prognostic panel based on RMCs is able to predict distant metastasis and immunotherapy response in patients with ccRCC, providing new insight for the treatment of advanced ccRCC.


Introduction
Renal cell carcinoma (RCC) accounts for nearly 4% of all newly diagnosed cancers and 2.5% of cancer deaths worldwide [1]. Clear cell renal cell carcinoma (ccRCC) accounts for approximately 80-90% of RCC, leading to death in most patients [2]. Globally, the incidence and mortality of ccRCC have increased in the past few decades, resulting in a heavy burden on the healthcare system [3]. Due to the lack of specific clinical symptoms and diagnostic methods, 20-25% of patients with ccRCC present with metastatic disease at diagnosis, and their 5-year survival rate is less than 10% [4,5]. Traditional radiotherapy and chemotherapy have poor curative effects on ccRCC. Surgery is the most effective treatment

Data Processing and DEG Screening
Transcriptome counts were normalized using the R package "limma" [20], and the gene expression values from TCGA and ICGC datasets were transformed as log2 (x + 1) counts. The R package "DEseq2" [21] was used to screen DEGs between ccRCC and normal kidney samples. The screening criteria for DEGs was set to |log2FoldChange| > 1, with an adjusted p-value < 0.05. Venn diagrams were drawn to obtain the intersection of DEGs from TCGA and ICGC using a web tool (http://bioinformatics.psb.ugent.be/ webtools/Venn/, accessed on 25 March 2022). Volcano plots were acquired based on "ImageGP" and "EnhancedVolcano" packages, and heat maps were generated using the R package "pheatmap".

Acquisition and Analysis of Single-Cell RNA Sequencing (scRNA-seq) Data for Distant Metastatic Sites in ccRCC
The scRNA-seq profiles of 121 ccRCC cells produced using the Illumina HiSeq 2500 platform were obtained from the GSE73121 dataset in the GEO database. Then, 85 tumor cells isolated from patient-derived xenografts of metastatic RCC (PDX-mRCC) and patientderived xenografts of primary RCC (PDX-pRCC) were extracted and processed using the "Seurat" package. First, we filtered cells with fewer than 1000 detected gene numbers and excluded genes with fewer than three detected cells. The PercentageFeatureSet function was used to evaluate the percentage of mitochondrial genes, and cells with a mitochondrial gene content of less than 5% were screened. The correlation between sequencing depth and gene expression counts was calculated using the FeatureScatter function. Subsequently, data normalization was performed using the NormalizeData method, and the top 2000 highly variable genes were screened using the FindVariableFeatures function. After principal component analysis (PCA), significantly available dimensions with an estimated p-value < 0.05 were identified, and the t-distributed stochastic neighbor-embedding (t-SNE) algorithm was employed for dimension optimization. Moreover, the FindAllMarkers function was utilized to identify the significant marker genes in each cell cluster.

Assessment of TIICs Using CIBERSORT
The "CIBERSORT" R package [15], an algorithm used to characterize the immune cell composition of complex samples based on a particular gene expression profile, was utilized to estimate the infiltrating fraction of TIICs in ccRCC samples obtained from TCGA. LM22, a leukocyte gene matrix containing 547 specific genes that distinguish 22 human infiltrating immune cell subpopulations, including seven T-cell types, naive and memory B cells, plasma cells, natural killer (NK) cells, and myeloid subsets, served as a reference expression signature.

Weighted Gene Coexpression Network Analysis (WGCNA)
The R package "WGCNA" was used to employ WGCNA, a comprehensive algorithm that utilizes a gene expression matrix to construct a scale-free network [22]. First, a sample tree was built to check all samples for outliers using the goodSamplesGenes function. The best soft threshold parameter was then screened to generate an adjacency matrix by calculating a group of soft threshold powers (range: [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Subsequently, a topological overlap measure was performed to ensure the non-scaling of the network. In the scale-free network, genes with high correlations were classified into the same module to construct a cluster dendrogram. The parameters were set as follows: TOMType = unsigned, minMod-uleSize = 50, deepSplit = 2, and mergeCut Height = 0.15. Finally, we linked the coexpressed modules with key TIICs using Pearson's correlation method. The module with the highest absolute correlation coefficient value was filtered for subsequent analysis.

Gene Functional Enrichment Analysis
The R package "clusterProfiler" [23] was used to perform gene ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The top 20 terms were filtered according to an adjusted p-value < 0.05. Metascape (http:// pantherdb.org, accessed on 23 June 2022), a comprehensive database that plays an important role in the annotation and visualization of the gene list, was used for pathway analysis and protein-protein interaction (PPI) analysis [24].

Correlation Analysis between Mast Cell Markers and Distant Metastasis-Associated Resting Mast-Cell-Sensitive Genes (DMA-RMSGs)
We compared the same elements in the key resting mast cell (RMC)-related module and distant metastasis-associated marker genes (DMAGs) to obtain DMA-RMSGs. Marker genes of mast cells were downloaded from the Panglao database (https://panglaodb.se/, accessed on 15 July 2022). The search criteria were initially limited to human donor tissue. Pearson's algorithm was used to examine the association between DMA-RMSGs and mast cell markers. Furthermore, correlation analysis was performed using the LabeledHeatmap function in the R package WGCNA.

Key DMA-RMSG Screening and Prognostic Model Establishment
DMA-RMSGs with prognostic potential were first screened using univariate Cox regression analysis (p < 0.05). Then, Kaplan-Meier analysis was utilized to validate the prognostic performance of DMA-RMSGs based on the "survival" package. To find the hub prognostic DMA-RMSGs, a least absolute shrinkage and selection operator (LASSO) regression analysis was conducted using the R package "glmnet". Moreover, the Akaike information criterion (AIC), which reflects the goodness of fit among different prognostic factors, was calculated and compared to enhance the prediction accuracy of the model. Furthermore, multivariate Cox regression analysis was used to identify the independent prognostic DMA-RMSGs (p < 0.05). Then, the differences in the expression levels of key DMA-RMSGs between distant metastasis and other important clinical phenotypes were illustrated using the R package "ggpubr". Based on the screened independent prognostic factors and the regression coefficient (β) obtained from the multivariate Cox regression analysis, we constructed a risk score model as follows: Additionally, the median risk score was calculated to classify patients with ccRCC into low-and high-risk groups.

Consensus Clustering Analysis
Based on prognosis-related DMA-RMSGs, unsupervised clustering analysis was conducted to identify the stable immune subtypes in ccRCC. The number of clusters and their stability were assessed by the consensus clustering method. The clustering sets ranged from 2 to 9, and the optimal stratification was determined by evaluating the consistency array and the accumulative distribution. The R package "ConsensusClusterPlus" was utilized to conduct 1000 repetitions to ensure accuracy of the divided subtypes. Additionally, PCA and t-SNE algorithms were implemented to validate the distinct distribution of the DMA-RMSG clusters.

Evaluation and Verification of the Prognostic Model
To assess the predictive accuracy of the risk score model, a time-dependent receiver operating characteristic (ROC) curve analysis was performed. The area under the curve (AUC) at different cutoff times was calculated using the "survival ROC" package. Furthermore, decision curve analysis (DCA) was conducted using the R package "ggDCA" to verify the practicality and reliability of the signature. The "scatterplot3d" package was utilized to conduct PCA between different risk groups. Then, we drew a heat map to illustrate the relationship between the risk score and clinical traits using the "ComplexHeatmap" package. Validation of the expression patterns and disease-free survival (DFS) of key DMA-RMSGs in the prognostic panel was determined using several ccRCC databases, such as GEO (GSE53757, GSE66272, and GSE126964), ICGC, and GEPIA (http://gepia.cancer-pku.cn/, accessed on 25 July 2022). The prognostic performance of the key DMA-MRSGs was validated using the Kaplan-Meier plotter database (http://kmplot.com/analysis/, accessed on 27 July 2022). Furthermore, the TIMER2.0 database (http://timer.comp-genomics.org/, Cells 2023, 12, 180 6 of 30 accessed on 2 August 2022) was used to conduct a pan-cancer analysis based on the key DMA-RMSGs [25].

Gene Set Enrichment Analysis (GSEA)
To explore the potential signaling pathways in the high-and low-risk groups, GSEA (http://software.broadinstitute.org/gsea/index.jsp, accessed on 15 July 2022) was conducted based on the TCGA ccRCC cohort. The annotated gene set list "c2.cp.kegg.v7.5.symbols.gmt" was used as a reference gene set. After performing 1000 permutations, a p-value < 0.05 and a false-discovery rate < 0.25 were regarded as statistically significant. We then used the En-richmentMap package [26] in Cytoscape3.7.1 to visualize the enriched signaling pathways.

Evaluation of ICI Immunotherapy Efficacy
The Cancer Immunome Atlas (TICA; https://tcia.at, accessed on 17 July 2022) is a database that characterizes intratumoral immune landscapes and cancer antigenomes from 20 solid cancers. The immuno scores of patients with ccRCC who received anticytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) treatments were downloaded from the TICA database. Moreover, the transcriptomic information and matching clinical data of patients who received anti-PD-L1 treatment were acquired from IMvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies, accessed on 28 July 2022).

Nomogram Construction and Evaluation
After performing univariate and multivariate Cox regression analyses of the risk score and various clinicopathological factors (distant metastasis, age, sex, race, stage, grade, and N stage), the independent prognostic factors were identified. Subsequently, to evaluate the prognosis of patients with ccRCC, the "rms" R package was used to build a nomogram based on the selected independent prognostic factors. The prognostic performance of the nomogram was evaluated based on the time-dependent ROC curves using the "timeROC" R package. Additionally, calibration curves were generated for different years to measure the accuracy of the nomogram.

Correlation Analysis between the Risk Score Model and TMB
TMB refers to the total number of somatic gene-coding errors, base substitutions, insertions, and deletions detected per million bases. Gene mutation data of patients with ccRCC were obtained from the TCGA database. TMB was calculated as the number of variants/length of the exons (38 MB) using the "maftools" package [27]. Survival analysis was performed to detect the relationship between TMB alone or in combination with the risk model and overall survival (OS) of patients with ccRCC using the "survminer" R package.

Autophagy-Related Genes (ATGs) Associated with the DMA-RMSG-Based Signature
A total of 222 ATGs were collected from the human autophagy database (HADb, http://www.autophagy.lu/, accessed on 9 August 2022), which collects autophagy-related information from papers in PubMed and other biological databases [28]. We utilized correlation analysis to screen the underlying ATGs by which the prognostic panel was potentially regulated, and the ATG with the highest correlation coefficient was identified as the key ATG.

Tissue Specimen Collection
Resection and biopsy were performed to collect ccRCC and adjacent tissue specimens at the Renji Hospital (Shanghai, China). The inclusion criteria for tissue samples are as follows: (1) pathological diagnosis of ccRCC, (2) no other malignancy except ccRCC, (3) conformation with indications for radical or partial nephrectomy, and (4) no chemotherapy or radiotherapy before surgery. Two senior pathologists from our institution confirmed the pathological diagnosis of ccRCC after surgery. All participants provided written informed consent, and the protocol was approved by the Ethics Committee of Human Research of the Hospital.

Statistical Analysis
Data analysis and visualization were performed using R software (version 4.2.1, https://www.rstudio.com/, accessed on 3 January 2022) and GraphPad 9.0 (GraphPad Software, San Diego, CA, USA). The significance of differences in the expression of genes among pathological stages or histological grades was evaluated using one-way ANOVA. Student's t-test was used to compare two variables, such as expression data from distant metastasis tissues and nondistant metastasis tissues or high-and low-risk patients. Survival analysis was conducted using the R package "survival". Differences were considered statistically significant at p < 0.05.

Screening for Key TIICs
We evaluated the landscape of 22 immune cell infiltrations in ccRCC tissues. The infiltrating cell proportions of each ccRCC sample are shown in Figure 2A. After ranking the infiltration levels of the 22 TIICs ( Figure 2B), the top ten TIICs were filtered for the next analysis. Subsequently, we investigated the infiltration of the top ten TIICs between distant and nondistant metastasis samples in ccRCC ( Figure 2C). Compared with nondistant metastasis tissues, RMCs and resting NK cells showed significantly lower infiltration in distant metastasis tissues, whereas CD8 T cells showed significantly higher infiltration. To explore the prognostic potential of the ten TIICs, an OS analysis was performed in the TCGA dataset ( Figure 2D, Supplementary Figure S1). The results indicated that RMCs were significantly related to favorable OS in patients with ccRCC (p < 0.001), whereas plasma cells were significantly associated with poor OS (p = 0.019). Additionally, patients with higher infiltration of RMCs also presented better progression-free survival (PFS) (p = 0.005, Figure 2E). Taken together, RMCs were identified as the key TIICs. Moreover, compared with normal kidney tissues, ccRCC tissues contained fewer RMCs ( Figure 2F). Furthermore, the infiltration of RMCs significantly decreased with increasing T stage, pathological stage, and histological grade (p < 0.001, Figure 2G-I). Furthermore, the infiltration of RMCs significantly decreased with increasing T stage, pathological stage, and histological grade (p < 0.001, Figure 2G-I). * p < 0.05, ** p < 0.01; ns, no significance.

Identification of DEGs
Through differential expression analysis between ccRCC samples and normal adjacent samples available in the TCGA and ICGC datasets, we separately identified 11,912 (4029 downregulated and 7883 upregulated) and 10,574 (4281 downregulated and 6293 upregulated) DEGs. Volcano plots were constructed to describe the distribution of each DEG in TCGA and ICGC ( Figure 3A,B). After comparing these DEGs between TCGA and ICGC, 6550 identical DEGs were screened, including 2590 downregulated and 3960 upregulated transcripts ( Figure 3C,D, Supplementary Table S1). The expression patterns of DEGs among various clinical traits are shown using a heat map in Figure 3E.

Identification of DEGs
Through differential expression analysis between ccRCC samples and normal adjacent samples available in the TCGA and ICGC datasets, we separately identified 11,912 (4029 downregulated and 7883 upregulated) and 10,574 (4281 downregulated and 6293 upregulated) DEGs. Volcano plots were constructed to describe the distribution of each DEG in TCGA and ICGC ( Figure 3A,B). After comparing these DEGs between TCGA and ICGC, 6550 identical DEGs were screened, including 2590 downregulated and 3960 upregulated transcripts ( Figure 3C,D, Supplementary Table S1). The expression patterns of DEGs among various clinical traits are shown using a heat map in Figure 3E.

WGCNA Identified RMSGs
To identify the key module associated with RMCs, we performed WGCNA of the screened DEGs. First, by performing a hierarchical clustering analysis of all ccRCC samples in TCGA, three samples with a distance greater than 250 (TCGA-CJ-4642) or less than 50 (TCGA-BP-4994, TCGA-BP-4995) were removed (Supplementary Figure S2A). The soft thresholding value was set as "4," and a topological matrix with non-scale features (scalefree R 2 = 0.88) was obtained (Supplementary Figure S2B,C). After placing DEGs with

WGCNA Identified RMSGs
To identify the key module associated with RMCs, we performed WGCNA of the screened DEGs. First, by performing a hierarchical clustering analysis of all ccRCC samples in TCGA, three samples with a distance greater than 250 (TCGA-CJ-4642) or less than 50 (TCGA-BP-4994, TCGA-BP-4995) were removed (Supplementary Figure S2A). The soft thresholding value was set as "4," and a topological matrix with non-scale features (scalefree R 2 = 0.88) was obtained (Supplementary Figure S2B,C). After placing DEGs with similar expression patterns into the same modules, 12 independent coexpressed modules were acquired ( Figure 4A, Supplementary Table S2). The clustering tree and heat map of the module eigengenes showed that the DEGs in each module were independent of each other ( Figure 4B). Moreover, the topologically overlapping heat map demonstrated a high degree of topological overlap between the coexpressed modules (Supplementary Figure S2D). Finally, we calculated the Pearson's correlation coefficient between the coexpressed modules and ten TIICs ( Figure 4C). Genes in the blue module that had the highest correlation coefficient with the key RMCs (R = −0.43, p = 3 × 10 −25 ) were identified as RMSGs. The scatter plot of module membership vs. gene significance also revealed that RMSGs were highly associated with RMCs ( Figure 4D, cor = 0.75, p = 1.3 × 10 −127 ).
Cells 2023, 12, x FOR PEER REVIEW 10 of 30 similar expression patterns into the same modules, 12 independent coexpressed modules were acquired ( Figure 4A, Supplementary Table S2). The clustering tree and heat map of the module eigengenes showed that the DEGs in each module were independent of each other ( Figure 4B). Moreover, the topologically overlapping heat map demonstrated a high degree of topological overlap between the coexpressed modules (Supplementary Figure  S2D). Finally, we calculated the Pearson's correlation coefficient between the coexpressed modules and ten TIICs ( Figure 4C). Genes in the blue module that had the highest correlation coefficient with the key RMCs (R = −0.43, p = 3 × 10 −25 ) were identified as RMSGs.

Functional Annotation of the RMSGs
Functional enrichment analysis was conducted to explore the potential functions and pathways of RMSGs (Supplementary Table S3). KEGG pathway enrichment analysis revealed that the RMSGs were mainly involved in immune and inflammatory pathways, including PD-L1 expression and the tPD-1 checkpoint pathway in cancer, the T-cell receptor signaling pathway, NK cell-mediated cytotoxicity, cytokine-cytokine receptor interaction, and the chemokine signaling pathway ( Figure 5A). Consistent with the KEGG results, the top 20 enriched GO terms are shown in Figure 5B-D, including activation of immune response, regulation of T-cell activation, and the antigen receptor-mediated signaling pathway in the biological process; the T-cell receptor complex, immunoglobulin complex, and inflammasome complex in the cell component; and immunoglobulin receptor binding, antigen binding, and cytokine activity in molecular function. Furthermore, highly concordant results for diverse immune-related pathways and their interactions were obtained after enrichment analysis using the Metascape algorithm ( Figure 5E). Moreover, PPI analysis revealed the underlying regulatory network among RMSGs ( Figure 5F).

Quality Control and Normalization of scRNA-seq Profiling
To improve the accuracy of our analysis, quality control and normalization were performed in the scRNA-seq dataset GSE73121. First, we conducted quality control of the 85 tumor cells, including the scale of the detected gene numbers, sequencing counts, and mitochondrial gene sequences ( Figure 6A). A significant positive correlation between nFeature-RNA and nCount-RNA was observed (R = 0.25, Figure 6B). Using variance analysis, 2000 highly variable DEGs across all cells were extracted for further analysis ( Figure 6C).

Quality Control and Normalization of scRNA-seq Profiling
To improve the accuracy of our analysis, quality control and normalization were performed in the scRNA-seq dataset GSE73121. First, we conducted quality control of the 85 tumor cells, including the scale of the detected gene numbers, sequencing counts, and mitochondrial gene sequences ( Figure 6A). A significant positive correlation between nFeature-RNA and nCount-RNA was observed (R = 0.25, Figure 6B). Using variance analysis, 2000 highly variable DEGs across all cells were extracted for further analysis ( Figure  6C).

Screening DMAGs Using Single-Cell Data
The PCA algorithm was used to identify the significantly correlated genes; the top 30 highly related genes in the first four components are displayed using dot plots and heat maps in Supplementary Figure S3A,B. Moreover, two independent distribution patterns of cell subpopulations based on principal component one and principal component two were observed ( Figure 6D). After calculating the estimated p-value of other components, the principal components with a p-value < 0.05 were selected for the next analysis ( Figure 6E). Additionally, after performing t-SNE on all samples, the cells were clustered into two subpopulations: primary tumor cells and distant metastatic tumor cells ( Figure 6F). Subsequently, 583 DMAGs were screened using differential expression analysis between the two subpopulations. The distribution of the top 30 marker genes between the two subgroups is shown in Figure 6G.

Identification and Evaluation of DMA-RMSGs
After considering the intersection of DMAGs and RMSGs, 17 key genes were identified as DMA-RMSGs ( Figure 6H). We then noted the independent expression patterns of DMA-RMSGs between the primary tumor cluster and the distant metastatic tumor cluster using a bubble plot ( Figure 6I). Furthermore, according to the scatter plots of the t-SNE analysis, most DMA-RMSGs exhibited distinct expression patterns between the two clusters (Supplementary Figure S4).

Screening DMA-RMSGs That Potentially Regulate Mast Cells
To further explore DMA-RMSGs that possibly regulate mast cells, a correlation analysis was conducted between the DMA-RMSGs and marker genes of mast cells (Supplementary  Table S4), and an absolute value of the correlation coefficient of 0.5 was set as the cutoff criterion (Supplementary Figure S5). As a result, IFI27 was excluded from the subsequent analysis. Heat maps were plotted to show the correlation among DMA-RMSGs before and after the removal of IFI27 ( Figure 7A,B).

Identification and Evaluation of the Independent Prognostic DMA-RMSGs
After univariate Cox proportional hazard regression analysis, the 12 DMA-RMSGs that presented prognostic performance were filtered ( Figure 7C). Kaplan-Meier survival analysis for OS validated the prognostic value of DMA-RMSGs. The result showed that the 12 selected DMA-RMSGs exhibited a significant prognosis value (p < 0.05), whereas the other four DMA-RMSGs did not (Supplementary Figure S6). Subsequently, we performed LASSO regression analysis on the 12 prognostic DMA-RMSGs, and eight hub DMA-RMSGs were identified ( Figure 7D,E). Moreover, AICs were calculated to evaluate the fit power of the eight hub DMA-RMSGs. The combination of the three key DMA-RMSGs (CFB, PPP1R18, and TOM1L1) had the lowest AIC value (1850. 22) and was included in the next analysis (Supplementary Table S5). Subsequently, multivariate Cox regression analysis revealed that the three key DMA-RMSGs were independent prognostic factors in ccRCC ( Figure 7F). We then evaluated the expression levels of the three prognostic DMA-RMSGs among several important clinical traits. Compared to nondistant metastatic ccRCC tissues and normal tissues, the expression patterns of CFB and PPP1R18 were significantly higher in distant metastatic ccRCC samples and tumor samples, whereas the expression of TOM1L1 was significantly lower (Figure 7G-L). Furthermore, the expression of CFB and PPP1R18 increased with elevated pathological stage and histological grade, whereas the expression of TOM1L1 decreased ( Figure 7M-R). In addition, survival analysis showed that CFB and PPP1R18 were significantly associated with poor DFS, whereas TOM1L1 was significantly associated with favorable DFS (p < 0.01, Figure 7S-U).

Identification and Validation of DMA-RMSG Subtypes
The consensus clustering method was used to stratify patients with ccRCC based on the 12 prognosis-related DMA-RMSGs. After employing a set of k values, we found that a k value of two was the most stable parameter (Figure 8A), and the ccRCC patients were divided into two clusters ( Figure 8B). Then, an independent distribution of the two DMA-RMSG subtypes was validated in both PCA and t-SNE analysis ( Figure 8C,D). Furthermore, survival analysis demonstrated that the subtype-one patients displayed worse OS (p < 0.001, Figure 8E) and PFS (p < 0.001, Figure 8F). Among the 12 prognosis-related DMA-RMSGs, 9 DMA-RMSGs (PPP1R18, EMP3, APOL2, GBP2, TGM2, RPS19, TAPBP, CFB, and BATF3) linked with poor prognosis (HR > 1, p < 0.05) were upregulated in cluster one, whereas the other 3 DMA-RMSGs (TOM1L1, CTH, EPCAM) related to favorable prognosis (HR < 1, p < 0.05) were downregulated in cluster one ( Figure 8G).

Clinical Characteristics and Immune Status between the Two Clusters
To further explore the potential clinical application of the two DMA-RMSG subtypes, we examined the relationship between the subtypes and various of clinical traits. The results showed that patients in cluster one had a higher proportion of distant metastasis, high stage, high grade, high TMB, and male sex compared to patients in cluster two

Clinical Characteristics and Immune Status between the Two Clusters
To further explore the potential clinical application of the two DMA-RMSG subtypes, we examined the relationship between the subtypes and various of clinical traits. The results showed that patients in cluster one had a higher proportion of distant metastasis, high stage, high grade, high TMB, and male sex compared to patients in cluster two ( Figure 8H-L). However, there was no obvious difference in age between the two clusters ( Figure 8M). Subsequently, the distributions of TIICs between the two clusters were also investigated. The patients in cluster two had higher infiltration of most TIICs (p < 0.001), including RMCs, M2 macrophages, monocytes, resting NK cells, and resting CD4 memory T cells, which could lead to a favorable prognosis of patients in cluster two ( Figure 8N). Moreover, the results of TIIC infiltration obtained using algorithms showed a similar trend ( Figure 8O).

Construction and Evaluation of the Risk Score Model Related to Distant Metastasis
According to the results of multivariate Cox regression analysis, three independent prognostic factors were selected to establish a prognostic panel as follows: risk score = (0.4662 × expression level of PPP1R18) + (−0.3206 × expression level of TOM1L1) + (0.1914 × expression level of CFB). The survival rate of low-risk patients was significantly higher than that of high-risk patients ( Figure 9A). The risk signature that predicted OS after one, three, and five years showed AUC values of 0.752, 0.672, and 0.696, respectively, demonstrating the accuracy of our model in predicting ccRCC prognosis ( Figure 9B). In addition, DCA revealed that the risk model had a good net benefit for OS compared with the key DMA-RMSGs alone ( Figure 9C). The distribution plots revealed that high risk scores were associated with more deaths (Figure 9D,E). Furthermore, the risk scores of patients with distant metastasis ccRCC were significantly higher (p < 0.0001), demonstrating the stable power of our model in predicting distant metastasis in ccRCC ( Figure 9F). PCA based on the prognostic panel displayed distinct distribution patterns, revealing remarkably different immune characteristics between the high-and low-risk groups ( Figure 9G). Additionally, a heat map showed independent expression patterns of the three key DMA-RMSGs in highand low-risk patients ( Figure 9H). Finally, the heat map verified that the prognostic panel was significantly correlated with distant metastasis, gender, pathological stage, histological grade, T stage, and N stage (p < 0.05, Figure 9I).

Validation of the Prognostic Panel Based on the Key DMA-RMSGs
To validate the stability of our risk model, we evaluated the expression patterns of key DMA-RMSGs using other databases. In the three GEO datasets, the expression of CFB and PPP1R18 was significantly higher in tumor samples, whereas that of TOM1L1 was significantly lower (Supplementary Figure S7A-C). In the ICGC dataset, CFB and PPP1R18 were significantly upregulated in tumor samples, whereas TOM1L1 was significantly downregulated in tumor samples (Supplementary Figure S7D). Moreover, in GSE53757 and GEPIA datasets, the expression of CFB and PPP1R18 increased with elevated pathological stage, whereas the expression of TOM1L1 decreased with increasing pathological stage (Supplementary Figure S7E-H). In addition, survival analysis using the Kaplan-Meier plotter database validated that CFB and PPP1R18 were significantly associated with poor OS in patients with ccRCC, whereas TOM1L1 was significantly correlated with favorable OS (Supplementary Figure S7I-K). Furthermore, we conducted a pan-cancer analysis based on the Timer 2.0 database (Supplementary Table S6). Among the 19 cancer types, PPP1R18/KIAA1949 (p = 2.98 × 10 −32 , Figure 9J) and TOM1L1 (p = 3.87 × 10 −30 , Figure 9L) exhibited the highest specificity for KIRC/ccRCC, and CFB also presented excellent specificity for KIRC/ccRCC (p = 3.76 × 10 −13 , Figure 9K).

Validation of the Prognostic Panel Based on the Key DMA-RMSGs
To validate the stability of our risk model, we evaluated the expression patterns of key DMA-RMSGs using other databases. In the three GEO datasets, the expression of CFB and PPP1R18 was significantly higher in tumor samples, whereas that of TOM1L1 was significantly lower (Supplementary Figure S7A-C). In the ICGC dataset, CFB and PPP1R18 were significantly upregulated in tumor samples, whereas TOM1L1 was significantly downregulated in tumor samples (Supplementary Figure S7D). Moreover, in GSE53757 and GEPIA datasets, the expression of CFB and PPP1R18 increased with elevated pathological stage, whereas the expression of TOM1L1 decreased with increasing pathological stage (Supplementary Figure S7E-H). In addition, survival analysis using the Kaplan-Meier plotter database validated that CFB and PPP1R18 were significantly

GSEA of the Key DMA-RMSG-Based Risk Score Model
To identify the potential molecular mechanism of the prognostic model, GSEA was performed between the different risk groups. The results showed that the pathways in low-risk patients with ccRCC were mostly related to mast cell immunity and carcinoma, including the vascular endothelial growth factor (VEGF) signaling pathway, TGF beta signaling pathway, proteasome, cytokine receptor interaction, and RCC ( Figure 9M). Interestingly, most of the gene sets enriched in the high-risk group were correlated with autophagy, such as regulation of autophagy, the MTOR signaling pathway, PPAR signaling pathway, snare interactions in vesicular transport, and cell cycle ( Figure 9N).

Relationship between the Prognostic Panel and RMC Infiltration
The characteristics of key RMCs varied between high-and low-risk patients with ccRCC. The raincloud plot showed that the infiltration scores of the RMCs in the low-risk group were significantly higher than those in the high-risk group (p < 0.0001, Figure 10A). Furthermore, the infiltration scores of RMCs significantly decreased with elevated risk scores (R = −0.4, p < 2.2 × 10 −16 ), further demonstrating the crucial prognostic value of RMCs ( Figure 10B). Patients were separated into four clusters according to their risk score and RMC infiltration. We observed that the group with high RMC infiltration and low risk scores had the best survival rate (p < 0.001, Figure 10C).

Difference in the TMB between the High-and Low-Risk Groups
TMB, a useful biomarker across many types of cancer, has been used to identify patients benefiting from immunotherapy. Therefore, genetic alterations in patients with ccRCC were investigated. The waterfall plot indicated that VHL had the highest alteration

Difference in the TMB between the High-and Low-Risk Groups
TMB, a useful biomarker across many types of cancer, has been used to identify patients benefiting from immunotherapy. Therefore, genetic alterations in patients with ccRCC were investigated. The waterfall plot indicated that VHL had the highest alteration frequency in the TCGA ccRCC dataset ( Figure 10D). Moreover, the TMB in the high-risk group was significantly higher ( Figure 10E). The TMB of patients with ccRCC significantly increased with elevated risk scores (R = 0.14, p = 0.0066, Figure 10F). Furthermore, the survival rate in the low-TMB group was significantly better (p < 0.001, Figure 10G). After classifying patients with ccRCC into four subgroups based on their risk scores and TMB, we found that the combination of TMB and risk scores had greater prognostic value than TMB alone, with the best prognosis associated with low TMB and low risk scores (p < 0.001, Figure 10H).

Correlation between the Risk Score Model and Immune Checkpoints
To explore the underlying role of the prognostic panel in immunotherapy responsiveness, we analyzed the expression patterns of key immune checkpoint molecules, such as PD-1 and its ligands (PD-L1 and PD-L2), CTLA-4, lymphocyte activation gene 3 (LAG3), T-cell immunoreceptor with Ig and ITIM domains (TIGIT), inducible costimulator (ICOS), B-and T-lymphocyte-associated (BTLA), B7-H3, and CD28. The results indicated that the risk scores were positively related to the expression of PD-1, PD-L2, CTLA-4, LAG3, TIGIT, ICOS, BTLA, B7-H, and CD28 and negatively correlated with PD-L1 expression (p < 0.05, Figure 10I). Moreover, we found a negative correlation between RMCs and other risk factors (risk score, TMB, and most immune checkpoints), further indicating a protective effect of RMCs produced in ccRCC ( Figure 10J).

Construction and Validation of the Nomogram
To identify independent prognostic factors in ccRCC, we conducted univariate and multivariate Cox regression analyses on multiple clinicopathological variables. The results indicated that age, risk score, pathological stage, histological grade, N stage, and distant metastasis were significantly associated with poor prognosis in patients with ccRCC (p < 0.001, Figure 11A). More importantly, age, stage, risk score, and distant metastasis were screened as prognostic indicators independent of other clinical phenotypes (p < 0.05, Figure 11B). Taking the four independent prognostic factors together, a nomogram was established in the TCGA ccRCC cohort ( Figure 11C). The calibration diagram exhibited good concordance between the nomogram-predicted OS and the observed OS after 1, 3, and 5 years ( Figure 11D). Furthermore, ROC analysis showed that the AUC of the nomogram for 1-, 3-, and 5-year OS were 0.872, 0.805, and 0.771, respectively, demonstrating the excellent predictive performance of the nomogram ( Figure 11E).

Implication of the Risk Score in Immunotherapy
To evaluate the relationship between the prognostic panel and immunotherapy response, ICI therapy scores of patients with ccRCC were calculated. In the TCIA cohort, compared with the low-risk group, the immunotherapy scores in high-risk patients were significantly increased in the PD-1-(p = 0.002, Figure 11G) and CTLA-4-positive treatment groups (p = 0.036, Figure 11H), indicating that high-risk patients were more sensitive to anti-PD-1 and anti-CTLA-4 immunotherapy. Furthermore, high-risk patients exhibited excellent sensitivity to the combination of anti-PD-1 and anti-CTLA-4 immunotherapy (p

Implication of the Risk Score in Immunotherapy
To evaluate the relationship between the prognostic panel and immunotherapy response, ICI therapy scores of patients with ccRCC were calculated. In the TCIA cohort, compared with the low-risk group, the immunotherapy scores in high-risk patients were significantly increased in the PD-1-(p = 0.002, Figure 11G) and CTLA-4-positive treatment groups (p = 0.036, Figure 11H), indicating that high-risk patients were more sensitive to anti-PD-1 and anti-CTLA-4 immunotherapy. Furthermore, high-risk patients exhibited excellent sensitivity to the combination of anti-PD-1 and anti-CTLA-4 immunotherapy (p < 0.001, Figure 11I), revealing the potential immunotherapy regimen for patients with advanced ccRCC. However, no significant difference was observed between the high-and low-risk patients in PD-1-and CTLA-4-negative treatment groups (p = 0.953, Figure 11F). In the IMvigor210 cohort, low-risk patients were more sensitive to anti-PD-L1 therapy than high-risk patients ( Figure 11J). Furthermore, compared with patients with complete or partial response, risk scores were significantly elevated in patients with progressive or stable disease (p = 0.018, Figure 11K). These results are in agreement with our previous finding ( Figure 10I) that PD-1 and CTLA-4 were highly expressed in the high-risk group, whereas PD-L1 was highly expressed in the low-risk group.

Experimental Validation of the Three-Gene Signature Associated with Distant Metastasis
After rigorous screening, 33 paired ccRCC/normal tissue specimens were ultimately included in the current study. The detailed clinicopathologic information of the patients is shown in Supplementary Table S8. IHC was used to verify the expression levels of the three key DMA-RMSGs. In accordance with our previous findings of the database analysis, CFB and PPP1R18 had higher staining intensity in ccRCC tissues compared to normal kidney tissues ( Figure 12A,B), whereas TOM1L1 had lower staining intensity in ccRCC tissues ( Figure 12C). We also assessed the quantity of the positive strain in the IHC. The results showed that the positive cells of CFB and PPP1R18 were significantly higher in ccRCC tissues (p < 0.01, Figure 12G,I), whereas those of TOM1L1 were significantly lower (p < 0.01, Figure 12K). Compared with the nondistant metastasis samples, distant metastasis samples showed higher protein levels of CFB and PPP1R18 ( Figure 12D,E) but a lower level of TOM1L1 ( Figure 12F). Furthermore, compared to nondistant metastasis samples, CFB and PPP1R18 had a greater proportion of positive cells in distant metastasis samples (p < 0.01, Figure 12H,J), whereas TOM1L1 had fewer (p < 0.01, Figure 12L).  Figure 11I), revealing the potential immunotherapy regimen for patients with advanced ccRCC. However, no significant difference was observed between the high-and low-risk patients in PD-1-and CTLA-4-negative treatment groups (p = 0.953, Figure 11F). In the IMvigor210 cohort, low-risk patients were more sensitive to anti-PD-L1 therapy than high-risk patients ( Figure 11J). Furthermore, compared with patients with complete or partial response, risk scores were significantly elevated in patients with progressive or stable disease (p = 0.018, Figure 11K). These results are in agreement with our previous finding ( Figure 10I) that PD-1 and CTLA-4 were highly expressed in the high-risk group, whereas PD-L1 was highly expressed in the low-risk group.

Experimental Validation of the Three-gene Signature Associated with Distant Metastasis
After rigorous screening, 33 paired ccRCC/normal tissue specimens were ultimately included in the current study. The detailed clinicopathologic information of the patients is shown in Supplementary Table S8. IHC was used to verify the expression levels of the three key DMA-RMSGs. In accordance with our previous findings of the database analysis, CFB and PPP1R18 had higher staining intensity in ccRCC tissues compared to normal kidney tissues ( Figure 12A,B), whereas TOM1L1 had lower staining intensity in ccRCC tissues ( Figure 12C). We also assessed the quantity of the positive strain in the IHC. The results showed that the positive cells of CFB and PPP1R18 were significantly higher in ccRCC tissues (p < 0.01, Figure 12G,I), whereas those of TOM1L1 were significantly lower (p < 0.01, Figure 12K). Compared with the nondistant metastasis samples, distant metastasis samples showed higher protein levels of CFB and PPP1R18 ( Figure 12D,E) but a lower level of TOM1L1 ( Figure 12F). Furthermore, compared to nondistant metastasis samples, CFB and PPP1R18 had a greater proportion of positive cells in distant metastasis samples (p < 0.01, Figure 12H,J), whereas TOM1L1 had fewer (p < 0.01, Figure 12L).

Discussion
Patients with advanced ccRCC have an adverse prognosis and limited therapeutic choices [5]. Accumulating evidence suggests that immunotherapy plays a non-negligible role in the treatment of patients with ccRCC with distant metastases [4,29]. Characterization of the composition and cellular states of TIICs helps to elucidate how the immune system might contribute to the immunotherapy response in advanced ccRCC [30]. Furthermore, screening of biomarkers is key to select better treatments, reduce costs, and enhance the outcomes of advanced ccRCC [31]. Although some studies have constructed immunerelated predictive models that could influence ccRCC progression or prognosis [18,19,32], a risk signature based on distant metastasis-associated TIICs in ccRCC is still lacking.
As a critical component of the TME, TIICs affect the progression, prognosis, and treatment of various cancers. For example, tumor-infiltrating macrophages have been reported to blunt the antitumor response in glioblastoma by secreting TGF-β and IL-10 and recruiting regulatory T (Treg) cells [33]. In vulvar squamous cell carcinoma, subtypes of cytotoxic lymphocytes and NK cells infiltrating cancer nests are associated with patient prognosis [34]. Byrne et al. [35] suggested that patients with advanced breast cancer with high levels of tissue-resident memory T cells have elevated response rates to anti-PD-1 therapy. However, the roles of TIICs in advanced ccRCC were not fully understood until recently. Here, we assessed the distinct expression patterns of TIICs in distant and nondistant metastatic tissues. After evaluating the prognostic power of TIICs, we identified RMCs as the key TIICs that significantly inhibit distant metastasis and benefit OS and PFS in ccRCC.
Tumor-infiltrating mast cells (TIMCs) derived from myeloid stem cells activate immune cells and induce inflammation in the TME [36]. TIMCs exert protumorigenic or antitumorigenic effects according to the type of carcinoma, degree of tumor progression, and location of TIMCs in tumor tissues [37]. RMCs, an important type of TIMCs, are distinguished by their high content of electron-dense material evenly distributed throughout the major part of each granule in the cytoplasm [38]. RMCs are particularly important to the progression and treatment of various cancers [39]. For instance, a high RMC density is associated with a favorable prognosis in hepatocellular carcinoma [40]. However, Xie et al. [41] suggested that the distribution of RMCs in meningioma tissues is significantly higher than that in normal controls. Currently, studies on the role of RMCs in ccRCC, especially metastatic ccRCC are limited. Pan et al. [42] demonstrated that RMCs can act as protective factors for patients with ccRCC; however, a model underlying RMCs has not been constructed. Although Cui et al. [43] suggested that the infiltration of RMCs was negatively linked with the histological grade of ccRCC, the significance of RMCs in ccRCC metastasis has not been investigated. Here, based on the identified key RMCs, we screened RMSGs using WGCNA. Functional analysis revealed that RMSGs were significantly enriched in immune-and inflammation-related pathways, revealing that the immune system and inflammation may be involved in RMC-related distant metastasis in ccRCC. Compared to other studies that merely utilized bulk RNA-seq to filter key genes in metastatic ccRCC [44,45], ignoring the complex interaction of tumor cells and the associated nontumor constituents in TME, we identified ccRCC distant metastatic marker genes after systematic analysis of scRNA-seq data. Then, 12 prognosis-related DMA-RMSGs were screened by univariate Cox regression analysis and validated by KM prognosis analysis.
With the development of immunotherapy, it is crucial to screen the most sensitive group to improve immunotherapy response and prognosis in ccRCC. To achieve individual treatment, we categorized ccRCC patients into two immune clusters according to 12 prognosis-related DMA-RMSGs. According to the survival analysis, the ccRCC patients in subtype one had a worse OS and PFS. Furthermore, malignant phenotypes, such as distant metastasis, higher grade, higher stage, and higher TMB, were enriched in patients in cluster one. Subsequently, by conducting a series of rigorous analyses, three independent prognostic factors (TOM1L1, CFB, and PPP1R18) related to distant metastasis in ccRCC were identified.
The three key DMA-RMSGs play a non-negligible role in different cancers. Shimazaki et al. [46] suggested that high stromal CFB expression is associated with unfavorable clinical outcomes in patients with pancreatic ductal adenocarcinoma after surgery. Wu et al. [47] found that the expression of CFB in patients with N1 stage thyroid carcinoma was higher than that in those with N0 stage disease. However, there are very few reports on CFB in RCC. Cooley et al. [48] suggested that patients with RCC with increased CFB levels have faster disease progression and lower survival rates than those with decreased CFB levels. Furthermore, there are no relevant reports on the utilization of CFB expression in ccRCC. Studies on PPP1R18 in human cancers are also limited. Meng et al. [49] reported that PPP1R18 activates LCN2 expression, which is related to esophageal cancer invasion. PPP1R18 is highly expressed in ccRCC cell lines and tissues [50]. To the best of our knowledge, the roles of PPP1R18 in distant metastatic ccRCC are still unknown. Chevalier et al. [51] reported that TOM1L1 promotes ERBB2-induced breast cancer cell invasion. In cutaneous squamous cell carcinoma, TOM1L1 expression levels are decreased and related to precursor lesions [52]. However, there have been no related studies on TOM1L1 in kidney cancer. All of these studies further support the accuracy of our analyses of the three key DMA-RMSGs and the value of the current study in advanced ccRCC. Taken together, the three key DMA-RMSGs were utilized to construct a prognostic model in the TCGA dataset and validated in IGCG, GEPIA, and GEO cohorts, which yielded supportive results. Furthermore, IHC verified the independent protein expression levels of the three key DMA-RMSGs in ccRCC. After dividing patients with ccRCC into high-and low-risk groups, we found that patients in the low-risk group had better prognosis. Furthermore, we discovered that the risk model was closely associated with various clinical characteristics, indicating that it might be utilized as a useful index to predict clinical outcomes in patients with ccRCC. In addition, compared with the other clinical characteristics of patients with ccRCC, risk score had an independent influence on OS. In contrast to other studies that simply used common clinical variables to build nomograms in ccRCC [53,54], we integrated independent prognostic factors to construct the nomogram, improving the significance of the predictive model.
To investigate the possible mechanism by which RMCs regulate distant metastasis in ccRCC, we performed GSEA of the RMC-based signature. The analysis revealed that mast cell immune-and carcinoma-associated pathways were most abundant in the lowrisk group, including the VEGF signaling pathway [55], TGF beta signaling pathway [56], proteasome [57], cytokine-cytokine receptor interaction [58], and RCC, whereas autophagyassociated pathways were mainly enriched in the high-risk group. These results further demonstrate that RMCs may play a crucial role in distant metastasis and the prognosis of ccRCC, which could be achieved by regulating immune function and autophagy. Substantial evidence has suggested that TMB has become an emerging biomarker of immunotherapy response among multiple solid tumors [59]. To further evaluate the application of our risk signature in immunotherapy, we analyzed RMC infiltration and TMB. In our analysis, patients with high infiltration of RMCs and low risk scores exhibited an excellent survival rate, further validating the prognostic role of RMCs in ccRCC. Furthermore, patients with low TMB and low risk scores exhibited the best prognosis, revealing the predictive value of the combination of TMB and risk signature in ccRCC.
Tumor immune escape is a crucial step in avoiding antitumor immune responses during cancer metastasis [60]. During tumor immune escape, cancer cells express high levels of immune-inhibitory signaling proteins, represented by immune checkpoints. To explore the underlying effect of our model on the immune escape of ccRCC cells, we assessed the relationship between the RMC-based signature and ten immune checkpoints [61]. The results showed that PD-L1 was upregulated in low-risk patients, whereas all other nine immune checkpoints were upregulated in high-risk patients, which further explained the high distant metastasis rate and low survival rate in high-risk patients. Subsequently, we investigated the potential effect of the RMC-based signature in predicting the response to ICI immunotherapy. In accordance with the former results, high-risk patients were more sensitive to anti-PD-1/CTLA-4 immunotherapy, whereas low-risk patients had a better response rate to anti-PD-L1 immunotherapy. These results revealed that the signature based on RMCs could help to screen patients with ccRCC with overexpressed immune checkpoints who might therefore benefit from different ICI therapies. In addition, considering the possible role of autophagy in ccRCC distant metastasis, which is in agreement with the results of our previous study [62], we further discovered that RGS19 shared the highest correlation with the risk score, which was likely the key ATG regulated by the risk model. RGS19 codes a guanosine triphosphatase-activating protein related to autophagy dysregulation [63]. The upregulated expression of RGS19 was associated with poor prognosis of bladder cancer patients [64]. However, the role of RGS19 in the autophagy of ccRCC requires further investigation.

Conclusions
In conclusion, we identified RMCs as the key TIIC, exerting a crucial role in the distant metastasis and prognosis of ccRCC. Subsequently, three key DMA-RMSGs predicting distant metastasis and prognosis in patients with ccRCC were identified to construct a prognostic panel and validated using different datasets and IHC. Furthermore, two immune clusters exhibiting distinct immune, clinical, and prognosis heterogeneity were distinguished. More importantly, the prognostic panel closely related to RMCs, TMB, and immune checkpoints also exhibited excellent performance in stratifying patients according to their responses to anti-PD-1/CTLA-4 and anti-PD-L1 immunotherapy. The limitations of our study mainly lie in the need to further investigate the specific molecular mechanisms by which RMCs and the DMA-RMSG-based signature affect ccRCC distant metastasis and prognosis. Taken together, our prognostic panel may help doctors predict clinical outcomes and design individual immunotherapies for patients with ccRCC.
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/cells12010180/s1, Figure S1: Kaplan-Meier curves of TIICs that abundantly infiltrated ccRCC tissues. Figure S2: Division of coexpression modules using WGCNA. (A) The cluster dendrogram of ccRCC tissues to detect outliers. Analysis of network topology to select the best soft thresholding power via scale independence (B) and mean connectivity (C). (D) The heat map depicts the topological overlap matrix among divided coexpression modules. Figure S3: The top four PCs and the associated genes in each PC according to PCA analysis. (A) Highly relevant genes in each PC according to correlation analysis. (B) Cluster analysis in each PC. Figure S4: Scatter plots of the differential expression patterns of the 17 DMA-RMSGs between the two clusters. Figure S5: Heat map depicts the correlation between DMA-RMSGs and marker genes of mast cells. Figure S6. OS analysis showing the prognostic value of the DMA-RMSGs. Figure Table S1: Identical DEGs between TCGA and ICGC screened by DESeq2. Table S2: Number of genes in the 12 coexpression modules. Table S3: Functional enrichment analysis of resting mast-cellsensitive genes. Table S4: Marker genes of mast cells. Table S5: The Akaike information criterion (AIC) of diverse gene combinations. Table S6: Pan-cancer analysis of the three key DMA-RMSGs. Table S7: Correlation analysis between the risk signature and ATGs. Table S8: Clinicopathologic information of ccRCC patients. Raw code: original code used in this study. Raw data: original data used in this study.