Hybrid Metabolic Activity-Related Prognostic Model and Its Effect on Tumor in Renal Cell Carcinoma

Background Tumor cells with a hybrid metabolic state, in which glycolysis and oxidative phosphorylation (OXPHOS) can be used, usually have a strong ability to adapt to different stress environments due to their metabolic plasticity. However, few studies on tumor cells with this phenotype have been conducted in the field of renal cell carcinoma (RCC). Methods The metabolic pathway (glycolysis, OXPHOS) related gene sets were obtained from the Molecular Signatures Database (V7.5.1). The gene expression matrix, clinical information, and mutation data were obtained by Perl programming language (5.32.0) mining, the Cancer Genome Atlas and International Cancer Genome Consortium database. Gene Set Enrichment Analysis (GSEA) software (4.0.3) was utilised to analyse glycolysis-related gene sets. Analysis of survival, immune infiltration, mutation, etc. was performed using the R programming language (4.1.0). Results Eight genes that are highly associated with glycolysis and OXHPOS were used to construct the cox proportional hazards model, and risk scores were calculated based on this to predict the prognosis of clear cell RCC patients and to classify patients into risk groups. Gene Ontology, the Kyoto Encyclopaedia of Genes and Genomes, and GSEA were analysed according to the differential genes to investigate the signal pathways related to the hybrid metabolic state. Immunoinfiltration analysis revealed that CD8+T cells, M2 macrophages, etc., had significant differences in infiltration. In addition, the analysis of mutation data showed significant differences in the number of mutations of PBRM1, SETD2, and BAP1 between groups. Cell experiments demonstrated that the DLD gene expression was abnormally high in various tumor cells and is associated with the strong migration ability of RCC. Conclusions We successfully constructed a risk score system based on glycolysis and OXPHOS-related genes to predict the prognosis of RCC patients. Bioinformatics analysis and cell experiments also revealed the effect of the hybrid metabolic activity on the migration ability and immune activity of RCC and the possible therapeutic targets for patients.


Introduction
Kidney cancer is the second most common malignancy of the urinary system, ranking 6th in men and 9th in women [1]. Te most common pathological types of kidney cancer are clear cell renal cell carcinoma (ccRCC), chromophobe cell RCC, and papillary cell RCC, among which ccRCC accounts for approximately 70% of kidney cancers [2]. Owing to early screening and relatively diversifed treatment options, the mortality rate of renal cancer is not high, but some patients are still not sensitive to traditional targeted therapy and immunotherapy [3]. Terefore, the development of potential combination drugs has been a focus in the feld of RCC.
Cell metabolism has always been a popular feld in malignant tumors, in which glycolysis and oxidative phosphorylation are important metabolic pathways of cells, and their mutual transformation is an important research object in the study of the metabolic plasticity of tumor cells [4]. Studies related to breast cancer have shown that tumor cells with metabolic plasticity have strong metabolic adaptability, tumor proliferation, migration, and invasion ability [5]. Meanwhile, a single-cell sequencing analysis study about RCC suggested that a subcluster exists in tumor cells that have simultaneous glycolysis and oxidative phosphorylation (OXPHOS) activities [6]. In addition, previous studies related to RCC have shown that the transformation of the tumor metabolic status has signifcant impacts on the tumor immune microenvironment and the efcacy of targeted therapy and immunotherapy [7,8]. Terefore, the infuence of the hybrid metabolic status of tumor cells in RCC on the overall tumor and prognosis of patients should be further investigated.
Here, we use Pearson correlation coefcient analysis to screen the genes that are highly related to the OXPHOS signal pathway in the glycolysis-related gene set and screen the genes highly related to the glycolysis-signal pathway in the OXPHOS-related gene set in the same way and fnally combine the overlapping genes to construct the prognosis model in ccRCC. According to the prognostic model score, patients were divided into high and low-risk groups to further explore the infuence of the hybrid metabolic status on RCC. In addition, we further explored the efect of the gene in the prognostic model on the tumor cell function through fundamental experiments ( Figure 1). Tis study may provide new therapeutic targets for ccRCC with hybrid metabolic characteristics.

Transcriptional Data and Clinical Information of Patients with ccRCC.
Te mRNA expression matrix and the clinical information of patients were extracted from the Cancer Genome Atlas (TCGA) database using the Perl programming language (5.32.0) and then standardized and combined by R programming language (4.1.0) [9,10]. Clinical information was collected including age, sex, grade, stage, TMN stage, duration of survival, and status. Te data in this article are available in open databases. We also used the Perl programming language (5.32.0) to collect information on renal cancer from the International Cancer Genome Consortium (ICGC) database with clinical feature data and gene expression matrix. We referred to the methods in the previous literature to standardize and remove the batch efect of these datasets. Te results suggest that the gene expression of these data sets has good consistency after merging ( Figure S1).

Gene Acquisition of Glycolysis and OXPHOS and Establishment of the Prognostic Model.
Gene sets related to glycolysis and OXPHOS are available in the Molecular Signatures Database (MSigDB) (V7.5.1) [11]. We also used the R software package Limma (version 3.40.6) for diferential analysis (|logFC| > 1, P < 0.05) to obtain the diferential genes between tumor tissues and normal tissues. Pearson correlation coefcient analysis was performed on the gene sets of these two metabolic pathways [12]. P < 0.05 and |r| ≥ 0.3 are the criteria for signifcant correlation. Finally, stepwise Cox regression using the R software package survival was applied to further screen the genes and construct the prognostic model. Te prediction of risk degree is based on the following formula: risk score � n i�0 Coef(i) × x(i), where Coef (i) and x (i) represent the estimated regression coefcient and the expression of genes, respectively [13].

Evaluation and Validation of the Prognostic Model.
Univariate and multivariate cox regression analyses were used to analyse the relationship between the screened genes and prognosis. Te Kaplan-Meier (K-M) survival curve was used to visualise the diferences in survival between high-risk and low-risk groups. Te receiver operating characteristic (ROC) curve was used to assess the accuracy of the prognostic model. Heatmap and scatter plot were used to exhibit the relationship between the risk score and the gene expression and between the risk score and the clinical characteristics, respectively. Te nomogram based on multifactor regression analyses integrating risk scores and clinical characteristics was used to determine the extent to which risk scores and clinical features contributed to the outcome variables [14].

Deconvolution of Immune Cell Infltration in Tumor
Microenvironment (TME). Here, on the basis of our gene expression matrix, the CIBERSORT method was selected to calculate the immunoinfltrating cell score of each sample using R software package IOBR, a computing tool for immunotumor biology research [15,16].

Calculation of Mutation Landscape and Microsatellite
Instability (MSI) of Tumor. We extracted the mutation data of ccRCC from the TCGA database using the Perl language. A total of 336 samples were tested for mutations, of which 283 (84.2%) were mapped. In addition, we extracted the mutation score, the number of mutations per million bases, for each sample using the Perl programming language. We calculated the MSI score of the ccRCC in the TCGA database using the PreMSIm package in R software [17].

Gene Set Enrichment Analysis.
We obtained the Gene Set Enrichment Analysis (GSEA) software (version 3.0) from the GSEA website. We also divided the samples into two groups according to the risk score and downloaded the c2.cp.biocarta.v7.4.symbols.gmt subset from the MsigDB to evaluate the relevant pathways and molecular mechanisms [11,18]. Meanwhile, we used the R software package Limma to obtain the diferential genes (|logFC| > 1, P < 0.05) in the high-and low-risk groups and then conducted the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis to fnd the related signal pathways. P < 0.05 and FDR < 0.25 were considered statistically signifcant.

Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR).
Using Trizol (Invitrogen, USA) for RNA extraction from the RCC cell lines according to the manufacturer's instructions. miRNA was reverse-transcribed into cDNA using MiR-XTM miRNA frst-strand synthesis (Takara, JPN). Te total RNA was reversed transcribed into cDNA using PrimeScript RT Master Mix (Takara, JPN). A standard SYBR Green PCR kit (Takara, JPN) was used to perform qRT-PCR. Te following forward and reverse primer sequences are shown in Table S1.

Western
Blotting. Te total proteins of the RCC cells were lysed in RIPA bufer (KeyGene biotech) supplemented with protease inhibitors. Protein was separated by 10% SDS/ PAGE after boiling the samples for 15 min. Ten, the lysates were transfected onto PVDF membranes in a transfer bufer. Te PVDF membranes were blocked in 5% nonfat milk with Tris-bufered saline with Tween (TBST) for 3 h, then the PVDF membranes were treated overnight at 4°C with the following primary antibodies: DLD and GAPDH (Abcam, UK). After cleaning with TBST, the PVDF membranes were treated with the secondary antibody (Abcam, UK) of the corresponding species and fnally exposed to the ECL luminometer and collected the image.  Assay. Te cells were placed into the 96-well plate (1.5 * 10 3 per well), and 10 μl CCK-8 solution (Beyotime Biotechnology, Shanghai, China) was added into each well at 24, 48, 72, and 96 h. Te cells were then incubated at 37°C for 1.5 h and then the optical density was measured at 450 nm by an absorbance reader (Termo Scientifc, USA).

Clone Formation Assay.
Te 786-O and Caki-1 cells were inoculated into 6-well plates (1.0 * 10 3 per well) and incubated at 37°C for 10 days. Afterwards, the cells were washed with 2 ml PBS (Beyotime Biotechnology, Shanghai, China) and fxed with 4% paraformaldehyde (Beyotime Biotechnology, Shanghai, China) and stained by hematoxylin. Each well was observed and photographed by a highdefnition camera.

Cell Migration Assay.
Te Transwell upper chambers were added with 200 μl serum-free medium and 1 * 10 5 cells, and then they were placed in a 24-well plate containing 600 ml medium containing 20% fetal bovine serum in each well and cultured at 37°C for 24 h (786-O) and 36 h (Caki-1). Te Transwell upper chambers were then rinsed with PBS to remove unmigrated cells and fxed with 4% paraformaldehyde before staining with hematoxylin. Finally, they were observed and photographed under the optical microscope.  2.14. Statistical Analysis. We normalized the mRNA expression matrix from the TCGA database through log2 transformation for further analysis. Te criterion for the statistically signifcant diference of all t-tests was P < 0.05. Statistical analysis and fgure drawing were carried out through R software and GraphPad Prism 8.

Screening Genes Related to Hybrid Metabolic Activity in the Gene Sets of Glycolysis and OXPHOS in ccRCC.
A total of 525 patients with renal cell carcinoma from the TCGA database were enrolled in the TCGA in the subsequent study, and their corresponding clinical characteristics were shown in Table 1. Based on the gene expression matrix of all the patients with renal cell carcinoma from the TCGA database, we obtained the list of diferential genes between cancer and adjacent tissues. We also downloaded gene sets related to glycolysis and OXPHOS from MsigDB and intersected them with the diferential gene set (Figure 2(a)). Six duplicate genes were enrolled in the subsequent model construction considering that they were involved in both metabolic pathways. Meanwhile, we analysed the correlation between the diferential genes associated with the two metabolic pathways in renal cancer. Te correlation between the target genes and the metabolic activity was assessed using the number of genes in the metabolic pathway-related diferential gene set that were highly correlated with the target gene (P < 0.01 and |r| ≥ 0.3). Te top 10 genes in the glycolysis-related differential gene set with the highest correlation with OXPHOS and the top 10 genes in the OXPHOS-related diferential gene set with the highest correlation with glycolysis were selected. A follow-up analysis was performed based on the above 26 genes.

Establishment and Evaluation of the Prognostic Model
Related to Hybrid Metabolic Activity in ccRCC. We frst performed univariate regression analysis to observe the relationship between these genes and the survival prognosis of patients with renal cancer and identifed nine protective genes and two risk genes (Figure 2(b)). Ten, stepwise COX regression analysis was used to further screen the genes and construct the prognostic model related to the hybrid metabolic activity (Figure 2(c)). Finally, a total of eight genes were included in the prediction model, and the coefcients associated with each variable are presented in Table S2. According to the risk score calculated by the prognostic model, the patients were divided into the high-and the lowrisk groups and the K-M curves of the two groups illustrate that the survival prognosis of the low-risk group was signifcantly better than that of the high-risk group (Figure 2(d)). Te scatter plot visualises the risk scores of all the patients and exhibits the survival status of the corresponding high-and low-risk groups. Te gene expression profles used to construct the prognostic model were also mapped ( Figure 2(e)). In addition, we compared the relationship between the key regulatory genes in the glycolysis pathway ( Figure 2(f )) and the OXPHOS pathway ( Figure 2(g)) and the risk score and found that the activity of the two metabolic pathways in the high-risk group is higher than that in the low-risk group, suggesting that the tumor cells in these patients were more likely to have a confounding metabolic activity.
To further evaluate the efcacy of the model, ROC curves were plotted to evaluate the reliability of the risk score in predicting patient survival outcomes in 1-, 3-and 5-year, with all AUCs approaching or exceeding 0.70 (Figure 3(a)). We also grouped the patients according to the pathological grade, clinical stage, and other clinical characteristics of their tumor to compare the risk scores among the groups and observed signifcant positive associations between them ( Figure 3(b)). Finally, we combined the risk score calculated by the hybrid metabolic activity-related prognostic model and the clinical characteristics, including age, gender, clinical stage, T stage, and M stage to construct the nomogram to accurately estimate the 1-, 3-, and 5-year survival and prognosis of these RCC patients. We found that the risk score made an excellent contribution to the prediction of the survival status (Figure 3(c)), and the calibration curve also elucidated that the predicted and real 3-year OS are highly consistent (Figure 3(d)). We collected the survival information and gene expression matrix of 157 patients from the ICGC database and calculated the risk score of each patient with the above model (Table S3). Te ROC curve shows that the prediction model still has a certain prediction efciency in the validation set, and univariate regression analysis suggests that the risk score is a signifcant risk factor (Figures 3(f ) and 3(g)). Tese results indicate that the prognostic model related to the hybrid metabolic activity has good predictive efcacy.  KEGG analysis shows that lipid metabolism-related signaling pathways such as peroxisome proliferator-activated receptor (PPAR), fatty acid degradation, and amino acid metabolism signaling pathways such as leucine, tryptophan, and valine were signifcantly enriched (Figure 4(c)). In the GO analysis, the cell component analysis suggests that the hybrid metabolic activity afected the biological processes that mainly occurred in extracellular regions. Molecular function analysis enriched the cell's active transmembrane transport activity and signal receptor binding the related signaling pathways. Biological process analysis further confrmed that the transmembrane transport of tumor cells was signifcantly altered, and the response and regulation of cells to external stimuli such as drugs and chemicals were also signifcantly afected (Figure 4(d)).
In addition, to capture the efect of subtle coordination changes between genes on biological signaling pathways, we performed GSEA analysis based on patient subgroups developed by the hybrid metabolic activity-related prognostic model and the gene expression matrix and identifed signifcant enrichment signaling pathways in high-risk patients (Figure 4(e)). Among them, the peroxisome signaling pathway involved in intracellular material transport and catabolism and the adhesion junction signaling pathway related to cell movement and intercellular connection were signifcantly enriched. Meanwhile, the mTOR and ERBB receptor tyrosine kinase signaling pathways related to RCC therapies were also signifcantly correlated with the confessor metabolic activity. Te lipid metabolism signaling pathways mentioned in the previous analysis, such as fatty acid and sphingomyelin metabolism, were enriched again.

Efect of Hybrid Metabolic Activity on Tumor Immune
Microenvironment in RCC. Te metabolic reprogramming of tumor cells often afects the diferentiation, proliferation, and function of immune cells in the TME. Terefore, we delineated the immune cell infltration in the RCC of patients in the high-( Figure 5(a)) and low-risk ( Figure 5(b)) groups and investigated the diferences in immune cell infltration between the two groups using the paired T test. Combined with the bar diagram and the corresponding violin diagram ( Figure 5(c)), we found that most T cell types, including CD8+T cells and regulatory T cells, greatly the tumor tissues of patients in the high-risk group except for memory CD4+T cells in the resting state. Patients in the high-risk group also have more active NK cells in their tumor tissue. In low-risk patients the overall macrophages infltration is higher, especially in the M2 subtype. Considering the diference in the infltration of CD8+T cells between high and low risk groups, we compared the expression of CD8+T cell function-related genes (GZMB, IFN-c, CD40LG, CD69) and human MHC class I genes, including classic Ia genes (HLA-A, B, C) and nonclassic Ib genes (HLA-D, E, F), in high and low risk groups ( Figure 5(d)). Te results show that the expressions of HLA-B, C, and E are higher in the high-risk group, while the expressions of IFN-c and CD40LG are higher in the low-risk group. To understand the association between various types of immune cells that infltrated ccRCC, we also conducted a correlation analysis (Figures 5(e) and 5(f)). Interestingly, a signifcant positive association between CD8+T and Treg cells was found in both the high risk and low risk groups, and the association was stronger in the high-risk group. Memory CD4+Tcells were also   Journal of Healthcare Engineering strongly associated with CD8+T cells in the high-risk group. Te diferences in the infltration of these immune cells suggest that hybrid metabolic activity signifcantly alters the immune microenvironment of ccRCC.

Diferences in Tumor Mutation Landscape in Patients with Diferent Hybrid Metabolic Activities and Possible Terapeutic
Targets. We describe the mutation profles of the top 15 genes with the most mutations in ccRCC in the high and low risk groups (Figure 6(a)). After comparison by the chisquare test, we found that the mutation incidence of STED2 and BAP1 was higher in the high-risk group, while the mutation incidence of PBRM1 was higher in the low-risk group. We then compared the overall tumor mutation burden ( Figure 6(b)) and the MSI (Figure 6(c)) between the two groups and found no statistical diferences. Considering that targeted therapy and immunotherapy are the classical systemic therapies for ccRCC, they may also be potential therapeutic options for patients with high hybrid metabolic activity. We plotted the linear relationship between these target genes and the risk score, and the results show that the expressions of HIF1A and mTOR are signifcantly positively correlated with the risk score, while VEFFA, EGFR, HIF2A, and CD274 were negatively correlated with the risk score ( Figure 6(d), Table S4). Tis result suggests that drugs targeting HIF1A and mTOR may be more suitable for these patients with a high hybrid metabolic activity.

Weakening of Metabolic Plasticity May Afect the Metastatic Ability of Renal Cell Carcinoma.
Previous analyses have shown that DLD, ALDH6A1, and SLC25A4 genes have a prominent impact on the survival and prognosis of patients with ccRCC in univariate analysis ( Figure 3€). We used qRT-PCR to examine the transcriptional diferences of these three genes in the normal renal cell line HK-2 and in RCC cell lines Caki-1, Caki-2, and 786-O (Figure 7(a)). Te results show that the expression of the DLD gene in the three RCC cell lines was signifcantly higher than that in the normal renal tissue cell line and we further validated this fnding at the protein level (Figure 7(b)). Te expression trend of the DLD gene in most of the paired tissues of renal cell carcinoma patients is also consistent with the above results (Figure 7(c)).

Discussion
An important characteristic that diferentiates cancer cells from normal cells is the reprogramming of the glucose metabolism, which allows them to dynamically switch between glycolysis and OXPHOS under hypoxic or aerobic conditions, allowing cancer cells to survive in diferent TMEs [19]. Under aerobic conditions, tumor cells usually preferentially metabolise glucose through the glycolysis pathway to obtain ATP efciently, namely, the Warburg efect, and this phenomenon mainly occurs in highly proliferative tumor cells [20]. Recently, the up-regulation of peroxisome proliferator-related genes has been found to promote mitochondrial biosynthesis and OXPHOS and is associated with a more aggressive phenotype of tumors [21], which is also consistent with the results of our enrichment analysis and cell experiments. In addition, a single-cell sequencing analysis study also reported that primary tumors showed higher glycolysis metabolism, while micrometastases exhibited relatively higher OXHPOS metabolism [22]. Terefore, under the infuence of reactive oxygen species (ROS), oncogene activation, and TME stimulation, cancer cells with a dual hybrid metabolic activity could continuously switch the preferred glucose metabolic pathway for the development of tumors from the initial growth to late metastasis [5,23,24]. However, studies on the hybrid metabolic activity are basically blank in RCC, and its related potential therapeutic targets and prognostic markers have not been revealed. Although drugs targeting the key molecules or genes of the metabolic pathway, such as glutaminase and HIF-2α, have been developed and applied in clinical trials for RCC, their objective response rate and safety are unsatisfactory [25,26], highlighting the necessity of our study. Among the genes included in the model and signifcantly related to survival, the expression of the DLD gene in the three types of RCC cell lines was higher than that in the normal cell line. Te dihydrolipoamide dehydrogenase encoded by the DLD gene is a homologous favin-dependent enzyme that catalyses NAD + -dependent dihydrolipoamide oxidation. It is involved in regulating apoptosis by producing ROS, and its enzyme activity is associated with tumor and apoptotic cell death [27]. We hypothesised that higher expression of the DLD gene in the high-risk group might indicate more ROS production. Te oxidative stress in the TME may promote the formation of the hybrid metabolic activity in the high-risk group, suggesting that their tumors have a stronger ability to adapt to the TME. Cell experiments in this study suggest that the DLD gene is abnormally, highly expressed in RCC, and participates in regulating the migration ability of tumor cells. In conclusion, the DLD gene may promote the production of ROS to form a state of oxidative stress in the tumor and then stimulate tumor cells to obtain metabolic plasticity to enhance their invasion ability.
In the subsequent pathway enrichment analysis, we found some meaningful signaling pathways. Previous studies related to RCC have shown that the PPARα, c involved in the PPAR signaling pathway are involved in regulating the aggressiveness of tumor cells and may be used    as prognostic markers of RCC [28,29]. Moreover, a breast cancer study revealed that PPAR-gamma coactivator 1 alpha (PGC-1α) promotes cancer cell metastasis by mediating mitochondrial biosynthesis and OXPHOS (21). Te increased OXPHOS activity of cancer cells induced by SETD2 deletion in RCC may also be associated with the PGC1α mediated metabolic network [30], which is consistent with the high SETD2 mutation rate found in the high-risk group. Te mTOR signaling pathway, as a classic targeted molecule for the systemic therapy of RCC, has also been shown to promote and regulate the proliferation and invasion of cancer cells and is regulated by ECHS1, a key enzyme in fatty acid metabolism [31]. In addition, studies on breast cancer have shown that during the collective migration of tumor cells, the following cells guided by the lead cells will show a high level of intercellular adhesion, which also depends on the high expression of the adhesion junction signaling pathway [32]. Metabolic reprogramming can occur not only in tumor cells but also in immune cells, thus afecting the infltration and function of immune cells [33]. Our study found more infltration of antitumor immune cells and infltration of immunosuppressive T cells, known as Tregs in the high-risk group. Tis seems to indicate that the RCC in the high-risk group is more immunogenic, presenting more tumor neoantigens to promote the activation of antitumor immune cells and chemotaxis to the TME. Studies on immune cells have shown that consistency with nonproliferative cells, OXPHOS is the dominant metabolic pathway in resting naive T cells. However, when T cells encounter tumor neoantigens, they change into a stronger glycolytic activity to enhance the proliferation and immune function [34], and during T cell chemotaxis, they must also constantly switch preferred metabolic pathways to face the constantly changing microenvironment. In addition, studies have shown that NK cells with a hybrid metabolic activity also have stronger antitumor activity [35]. Terefore, we consider that the TME of the RCC with a hybrid metabolic activity will infltrate more antitumor immune cells. However, the presence of the Warburg efect causes tumor cells to consume large amounts of nutrients in the TME, thus placing metabolic limitations on T cells [36]. Glucose deprivation can prevent T cells in the TME from secreting cytokines to prevent them from further exerting the function of specifc tumor killing [37]. Meanwhile, studies have shown that selective deletion of AMP-activated protein kinase, a key enzyme in the OXPHOS metabolic pathway, can inhibit the antitumor efect of CD8+Tcells by inhibiting the secretion of     IFN-c and granzyme B [38]. In addition, CD4+Treg cells mainly rely on OXPHOS and lipid oxidation rather than glucose consumption to produce ATP, indicating that the TME of RCC with hybrid metabolic activity is more suitable for its function of limiting antitumor immunity [39]. Tese studies, together with the comparison of the expression of genes related to MHC-I molecules, granzyme B, etc., suggested that although the RCC in the high-risk group may infltrate more immune cells, the antitumor immune function of these cells may be signifcantly limited. However, the patients in the high-risk group may be better candidates for immunotherapy that promotes the activation of CD8+T cells and improves their cytotoxicity, as well as inhibits the expansion of Treg [40,41].
In the mutation landscape diference analysis, in addition to SETD2 mentioned above, BAP1 also had a signifcantly higher mutation rate in the high-risk group. RCC with BAP1 mutation has signifcant morphological overlap with Xp11 translocation RCC and is also associated with a higher tumor stage. Renal vein invasion is common in this type of RCC and the rate of metastasis is up to 50% [42]. PBMR1, which exhibits a signifcantly higher mutation rate in the low-risk group, was found to be associated with poorer survival in patients receiving immunotherapy [6]. Te deletion of PBMR1 induces the formation of a nonimmunogenic tumor microenvironment in RCC and reduces the presentation of tumor neoantigens, thus inducing resistance to immune checkpoint blocking therapy [43], which is consistent with our results in immune cell infltration analysis. In addition, in the analysis that searched for possible therapeutic targets, we found that HIF-1α and mTOR had a signifcant positive correlation with the risk score. HIF-1α, as an important subunit of HIF-1, is closely involved in the adaptive response of tumor cells to a hypoxic environment [44]. Te high expression of mTOR can also upregulate the expression of HIF-1α and stimulates the function of its transactivation domain [45]. Tese two genes may be important targets to block the formation of metabolic plasticity in tumors of the high-risk group.
In this study, we constructed a prognostic model with great predictive efcacy. Furthermore, we explored the efect of the hybrid metabolic activity on the tumor cells in RCC from the aspects of the signaling pathway, immunity, tumor mutation, and cell function. We found that the hybrid metabolic activity may be involved in tumor migration in several aspects and explored possible therapeutic options and prognostic indicators for patients with this type of RCC. Although the underlying mechanism is not fully revealed, this study may be helpful for further studies on the metabolic aspects of RCC.