Introduction

Clear cell renal cell carcinoma (ccRCC), the most common renal cell carcinoma (RCC) subtype, exhibits global health issues due to its growing incidence, extreme heterogeneity among patients and high mortality. It is estimated that 90% of the ccRCC patients died of tumor-specific recurrence and metastasis1. Regarding this, considerable research efforts have focused on develo** a model to predict the prognosis of ccRCC patients; however, these prognosis tools still require improvements to attain a high degree of accuracy2,3,4. Therefore, novel and robust prognostic models are urgently needed in clinical practice.

Metabolism is fundamental in maintaining all the biological processes necessary for life5. Tumors are typified by metabolic abnormalities as proliferating cells rewire their mechanism to sustain growth6. Notably, the rapidly proliferating cells in malignancies take up abundant glucose and glutamine to generate the proteins, lipids, and nucleic acids to support cell growth7. RCC is regarded as a metabolic disease with diabetes, obesity, and atherosclerosis considered as the risk factors8,9. CcRCC is a unique RCC subtypes based on the abnormally accumulated lipid droplets in the cytoplasm with research evidence implicating the lipid accumulation in disease progression10,11. Nevertheless, the underlying mechanism and the prognostic role of these metabolic genes remain largely unknown.

In the present study, we screened the differentially expressed metabolism-related genes and evaluated their clinical value based on the cancer genome atlas (TCGA) kidney renal clear cell carcinoma (KIRC) cohort. Subsequently, we selected a cluster of metabolism-related genes to construct a prognosis prediction signature using the least absolute shrinkage and selection operator (LASSO) regression algorithm. This signature was further validated in the clinical proteomic tumor analysis consortium (CPTAC) database. Moreover, our prognosis prediction could serve as an independent prognostic factor. Importantly, we investigated the underlying mechanism using the gene set enrichment analysis (GSEA) database and the Metascape database12.

Results

Construction of a risk signature associated with metabolism

A total of 105 differentially expressed metabolism-related genes were found between normal samples and tumor samples in TCGA_KIRC cohort (adj. P < 0.05 and |logFC|> 2). Specifically, 61 genes were downregulated, while 44 genes were upregulated (supplementary Table 1); the corresponding heatmap plot and volcano plot are shown in Fig. 1A,B. After that, we identified 12 genes (Fig. 1C) that were significantly associated with the patient’s overall survival (P < 0.001) by univariate Cox regression analysis; Lasso Cox regression algorithm was then utilized to screen out 10 genes as active covariates in evaluating the risk score for the patients (Fig. 1D–F). The ten genes included in the model were Fructose-Bisphosphatase 1 (FBP1), Hydroxyacid Oxidase 2 (HAO2), Phosphoserine Aminotransferase 1 (PSAT1), Aldehyde Dehydrogenase 6 Family Member A1 (ALDH6A1), Thymidine Phosphorylase (TYMP), Interleukin 4 Induced 1 (IL4I1), Prolyl 4-Hydroxylase Subunit Alpha 3 (P4HA3), Hexokinase 3 (HK3), and Carnitine Palmitoyltransferase 1B (CPT1B), Cytochrome P450 Family 26 Subfamily A Member 1 (CYP26A1). The Kaplan–Meier survivor analysis of the ten genes was plotted based on an online database called “ONCOLNC” (https://www.oncolnc.org) (supplementary figure 1). The risk score for each patient was calculated as follows: − 0.03677*Expression of ALDH6A1-0.00467* Expression of FBP1-0.001642* Expression of HAO2 + 0.001219* Expression of TYMP + 0.01105* Expression of PSAT1 + 0.018708* Expression of IL4I1 + 0.020459* Expression of P4HA3 + 0.023879*Expression of HK3 + 0.092914* Expression of CPT1B + 0.240626* Expression of CYP26A1. We separated patients into high- and low-risk groups based on the median risk score. Heatmap plot was depicted to reveal the clinicopathological characteristics between the low-risk and high-risk patients (Fig. 1G). The overall survival (OS) of the high-risk group was significantly poorer compared with the low-risk group in TCGA_KIRC Cohort (P < 0.0001; Fig. 2A). Moreover, this model was further validated in the CPTAC_ccRCC Cohort; patients in the low-risk group had a better prognosis than in the high-risk group (P < 0.05; Fig. 2B). The clinical characteristics of CPTAC_ccRCC Cohort patients are in supplementary table 2. The area under the curve (AUC) for 1-, 3-, and 5-year OS of risk score compared with age, grade, TNM stage, T, M and N were 0.708, 0.731, and 0.780 respectively (Fig. 2C–E, supplementary figure 2); the AUC for 1-, 3-, and 5-year OS of risk score compared with ALDH6A1, FBP1, HAO2, TYMP, PSAT1, IL4I1, P4HA3, HK3, CPT1B, and CYP26A1 were 0.739, 0.720, and 0.747 respectively (Fig. 2F–H). Altogether, these results indicated that the ten genes form a potential robust prognostic model. Finally, we constructed a predictive nomogram by combing the TNM stage, age, grade, T, M, N and the prognostic model (supplementary figure 3).

Figure 1
figure 1

Identification of a metabolism-related signature by Cox proportional hazards model in TCGA cohort. (A,B) Heatmap and Volcano plot of 105 differentially expressed metabolism-related genes. (C) Forrest plot of results of the univariate Cox regression analysis (P < 0.001). (D,E) Lasso Cox regression algorithm. (F) Coefficient value for each of the 10 active covariates genes (red represents upregulated in tumor, while green indicates the downregulated), X-axis represents gene name, and Y-axis represents the coefficient value of the model. (G) Heatmap of the 10 genes of the signature based on the risk score value.

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Figure 2
figure 2

Kaplan–Meier analysis and time-dependent ROC analysis for the 10-gene signature in ccRCC. (A) Kaplan–Meier curve with Log-rank test in TCGA_KIRC Cohort. (B) Kaplan–Meier curve with Log-rank test in CPTAC_ccRCC Cohort. (C–E) ROC curve of model and clinical characteristics predicting 1-, 3- and 5-year survival based on TCGA training set. (F–H) ROC curve of model and single gene predicting 1-, 3- and 5-year survival based on TCGA training set.

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Independence role of the prognostic model

First, univariate Cox regression analysis indicated that age, grade, stage, T, M, N and risk score were correlated with OS (Fig. 3A). Subsequently, multivariate Cox regression analysis adjusted for age, grade, stage, T, M, N and risk score revealed that risk score could serve as an independent prognostic factor (P < 0.001, Hazard Ratio:1.849–4.14) (Fig. 3B).

Figure 3
figure 3

Forrest plot of the univariate and multivariate Cox regression analysis in TCGA_KIRC Cohort. (A) The univariate Cox proportional regression analysis. (B) The age, grade, stage, T stage, N stage, M stage and risk score were adjusted in our multivariate Cox regression model.

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Patients with a higher risk score had a poorer clinical outcome

The Kaplan–Meier survival analysis was applied to determine OS in different subgroups of patients according to the risk score level. The results demonstrated that risk score may be a potential prognostic biomarker for patients with the following characteristics: aged ≤ 60 or > 60 years old (Fig. 4A,B), Female or Male (Fig. 4C,D), G1 + G2 or G3 + G4 (Fig. 4E,F), Stage I + II or Stage III + IV (Fig. 4G,H), T1 + T2 or T3 + T4 (Fig. 4I,J), M0 (Fig. 4K) and N0 (Fig. 4L).

Figure 4
figure 4

Kaplan–Meier analysis of subgroup patients based on risk scores. (A–L). The Kaplan–Meier curve revealed the survival of patients was significantly poorer in subgroup patients with high risk scores.

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The risk score system was associated with various clinicopathological parameters

We explored the correlation between the risk score and different clinicopathological factors. Chi-square test revealed that the risk scores were significantly associated with patients’ gender, histological grade, TNM stage, T stage, N stage, M stage and Vital status (Table 1). Furthermore, subgroup analysis confirmed that higher risk scores were significantly correlated with higher TNM stage, higher tumor T stage, distant metastasis, lymph node metastasis, and higher histologic grade (Fig. 5A–F).

Table 1 Associations between risk model and clinicopathological parameters of patients.
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Figure 5
figure 5

Risk score was closely associated with various clinicopathological characteristics. (A) Stage. (B) T stage. (C) Grade. (D) M stage. (E) N stage. (F) Status.

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RT-qPCR and external validation of expression level

We used online database to validate the expression level of the 10 genes constructing our prognostic model. Consistent with our results, ALDH6A1, FBP1, HAO2 and PSAT1 were found to be significantly downregulated in tumor samples compared with normal samples in both Oncomine and TIMER database (supplementary figure 3A,B); TYMP, HK3, and P4HA3 were found to be significantly overexpressed in tumor samples in both Oncomine and TIMER database, while IL4I1 and CYP26A1 were found over-expressed only in TIMER database (supplementary figure 4A,B). Meanwhile, RT-qPCR assays revealed that ALDH6A1, FBP1, HAO2 and PSAT1 were markedly under-expressed in tumor tissues in exceeding 60% cases, and that TYMP, HK3, P4HA3, IL4I1, CYP26A1 and CPT1B were over-expressed in tumor tissues in exceeding 70% cases (Fig. 6A–J). Furthermore, we retrieved the HPA database and found that the protein expression of ALDH6A1, FBP1 and PSAT1 were downregulated in KIRC; the protein expression level of TYMP, P4HA3 and IL4I1 were upregulated in KIRC (Fig. 7).

Figure 6
figure 6

RT-qPCR verification of the expression level of 10 genes. (A) ALDH6A1, 14 cases with downregulated expression compared with corresponding normal tissue. (B) FBP1, 12 cases with downregulated expression compared with corresponding normal tissue. (C) HAO2, 10 cases with downregulated expression compared with corresponding normal tissue. (D) PSAT1, 11 cases with downregulated expression compared with corresponding normal tissue. (E) TYMP, 13 cases with upregulated expression compared with corresponding normal tissue. (F) IL4I1, 15 cases with upregulated expression compared with corresponding normal tissue. (G) P4HA3, 15 cases with upregulated expression compared with corresponding normal tissue. (H) HK3, 12 cases with upregulated expression compared with corresponding normal tissue. (I) CPT1B, 11 cases with upregulated expression compared with corresponding normal tissue. (J) CYP26A1, 13 cases with upregulated expression compared with corresponding normal tissue. Log2T/N: − △△CT.

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Figure 7
figure 7

The protein expression of six predictive genes. The representative protein expression of the six gene in ccRCC and normal kidney tissue, four genes were not found. Data was retrieved from the Human Protein Atlas (https://www.proteinatlas.org). ALDH6A1, FBP1, and PSAT1 were significantly underexpressed in ccRCC. While TYMP, P4HA3 and IL4I1 were found significantly overexpressed in ccRCC.

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Functional annotation of 10-gene signature

To investigate the potentially underlying mechanism between the two risk groups, GSEA was performed in TCGA_KIRC cohort and we found 34 significantly enriched pathway. Top 10 enriched KEGG pathway were shown in Fig. 8A. Fatty-acid metabolism, TGF-βsignaling pathway, Renal cell carcinoma, Propanoate metabolism and Inositol-phosphate metabolism were significantly enriched in low risk group patients. Moreover, differentially expressed genes analysis between low risk patients and high risk patients revealed that 96 were downregulated and 400 were upregulated in high risk patients (adj. P-value < 0.05 and |logFC|> 2); “Metascape” analysis indicated that top 20 enriched clusters of downregulated genes and upregulated genes (Fig. 8B,C; supplementary figure 5).

Figure 8
figure 8

The functional annotation of the 10-gene signature. (A) Ten representative KEGG pathway in high-risk and low-risk patients. (B) Functional annotation of the upregulated genes in high-risk patients with Metascape database. (C) Functional annotation of the downregulated genes in high-risk patients with Metascape database. Balls together with the same color indicates one biological process.

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Discussion

Globally, RCC accounts for more than 2% of neoplasms in humans worldwide with the incidence and mortality persistently increasing1. Recently, gene signatures based on specific characteristic-related to predict prognosis have become a hotspot in cancer research13,14,15. In the present study, to the best of our knowledge, we constructed the first novel metabolism-related risk signature with the potential application of predicting the prognosis and progression of ccRCC.

We identified a novel robust 10-gene metabolic prognostic signature based on the TCGA_KIRC dataset and further validated it in CPTAC_ccRCC dataset. Our risk score model could efficiently stratify patients’ survival with various clinicopathological parameters and be an independent prognostic factor; moreover, the risk scores increased with the patients’ increasing malignancy. Besides, the performance of our model in the prediction of the 5-year overall survival was superior to other parameters, indicating that it might facilitate the development of a long-term follow-up plan; meantime, the prediction ability of 1-, 3- and 5-years overall survival surpassed a single gene. Altogether, these results revealed our 10-gene metabolic prognostic signature has a great potential in ccRCC. However, in the future, validating our signature in more independent cohorts is still required.

We performed GSEA analysis to investigate the underlying molecular mechanism of the signature. Notably, we found out that fatty acid metabolism was significantly enriched in the low-risk patients, while the high-risk patients were more associated with the non-metabolic pathways. Research evidence reveals that abnormal accumulation of lipid droplets plays a crucial role in ccRCC progression16. Our study provided insights by suggesting that low-risk patients could benefit more from fatty acid metabolism targeted therapy in the future. Moreover, the gene annotation and analysis results revealed that overexpressed genes in the high-risk patients are involved in incretin synthesis, secretion, and inactivation, and that the down-regulated genes participate in complement and coagulation cascades, metabolism of xenobiotics by cytochrome P450, response to glucose. Hua et al.17 identified an immune-related risk signature for predicting prognosis of ccRCC, they found that tumors from high-risk patients had higher relative abundance of T follicular helper cells, regulatory T cells than low-risk patients. Moreover, the time-dependent ROC analysis revealed that the AUC was 0.753, 0.686, and 0.637 at 1, 3, and 5 years. However, in our study, we extracted a cluster of metabolism-related genes and constructed a multi-gene signature, the AUC at 3 and 5 years were 0.731 and 0.780 respectively, which exhibit a better predictive performance.

Most of the genes in our signature have previously been shown to be involved in cancers. However, several genes’ role in ccRCC remain unclear. The Fructose-Bisphosphatase 1 (FBP1) is a rate-limiting enzyme in gluconeogenesis, which is suppressed in kidney tumors and thus feeds ccRCC; FBP1 is associated with impaired cell proliferation, glycolysis and the pentose phosphate pathway in ccRCC in a catalytic-activity-independent manner via direct interaction with the HIF inhibitory domain18. PSAT1 is up-regulated in many cancers and acts as an oncogene that exerts a vital role in cancer progression and metastasis19,20,21. Interestingly, through mining public database and RT-qPCR, we found that PSAT1 in ccRCC patients is down-regulated compared with normal tissue. In the future, we will further investigate the underlying mechanism. Hydroxyacid oxidase 2 (HAO2) encodes peroxisomal proteins with 2-hydroxy acid oxidase activity, which participated in the production of reactive oxygen species and cellular breakdown22; HAO2 has already been shown downregulated in hepatocellular carcinoma (HCC) tissues and ccRCC, overexpression of which restrained HCC and RCC cells proliferation by eliminating lipid droplet accumulation23,Statistical analysis

Chi-square test was conducted to detect the difference between two groups of patients with regard to clinicopathological features; differences in risk score between various cllinicopathological parameters were tested by Student’s t test. All statistical analysis were performed using GraphPad Prism7.0, SPSS version 16.0 software (SPSS Inc., Chicago, IL, USA); all data for bioinformatic analysis in the study were processed by Software R (version 3.6.1, https://www.r-project.org/). Data are presented as the mean ± SEM. If not specified above, P < 0.05 was regarded as statistically significant.