A proteogenomic analysis of clear cell renal cell carcinoma in a Chinese population

Abstract

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive subtype of renal cancer. Here we conduct a comprehensive proteogenomic analysis of 232 tumor and adjacent non-tumor tissue pairs from Chinese ccRCC patients. By comparing with tumor adjacent tissues, we find that ccRCC shows extensive metabolic dysregulation and an enhanced immune response. Molecular subtyping classifies ccRCC tumors into three subtypes (GP1-3), among which the most aggressive GP1 exhibits the strongest immune phenotype, increased metastasis, and metabolic imbalance, linking the multi-omics-derived phenotypes to clinical outcomes of ccRCC. Nicotinamide N-methyltransferase (NNMT), a one-carbon metabolic enzyme, is identified as a potential marker of ccRCC and a drug target for GP1. We demonstrate that NNMT induces DNA-dependent protein kinase catalytic subunit (DNA-PKcs) homocysteinylation, increases DNA repair, and promotes ccRCC tumor growth. This study provides insights into the biological underpinnings and prognosis assessment of ccRCC, revealing targetable metabolic vulnerabilities.

Fig. 1. Proteogenomic Landscape of Chinese ccRCC.

a Schematic representation of the multiomics analyses of ccRCC, including sample preparation, protein identification, WES, and function verification. b Genomic profile and associated clinical features of 224 ccRCC patients. c Comparison of frequently mutated genes among Chinese, Japanese, European, and TCGA cohorts. P values derived from two-sided Fisher’s exact test. d Overview of proteomic profiles of pairwise ccRCC samples. The dashed curves fitted by lasso regression show the distribution of protein identifications. The shading that underlies the lasso curves denotes the 95% confidence intervals. e The upper Venn diagram shows the overlap of proteins identified in tumors and adjacent normal tissues. The lower Venn diagram shows that proteins identified in this study cover most of the proteins identified in the CPTAC ccRCC cohort.

Fig. 2. Profiles of CNAs and Effects of CNA on Somatic Mutations, Proteome, and Overall survival.

a Frequency of SCNAs. Copy number gains and losses are indicated in red and blue, respectively. The dotted line indicates the frequency of arm-level CNA events. b, c Cox regression analysis of significant arm-level CNA events and CN. d Kaplan–Meier curves of OS for patients with different 3p loss burden in the Chinese and TCGA cohorts (two-sided log-rank test). e Correlations of CNA (x axes) with protein abundance (y axes). Significant (q < 0.01) positive (red) and negative (blue) correlations are shown. f GSEA of patients with LB 3p loss (n = 93) compared to patients with HB 3p loss (n = 93). NES, normalized enrichment score. g Prioritizing genes in chromosome 3p. Chromosome 3p gene encoded proteins, with prognostic values (HR > 1, p < 0.05), were annotated by red. h Kaplan–Meier curves of PFS for patients with different SLC4A7 abundances in the Chinese and TCGA cohorts (two-sided log-rank test). i The correlation between SLC4A7 protein expression and lactate abundance (two-sided Spearman’s correlation test) (n = 370). j Comparison of SMAD3 and SMAD4 activities between LB 3p loss group (n = 93) and HB 3p loss group (n = 93). P values are derived from two-sided t test. Boxplots show the median (central line), the 25–75% interquartile range (IQR) (box limits), the ±1.5×IQR (whiskers). k Pathways and proteins, involved in EMT, significantly associated with SMAD3 or SMAD4 activities. The left panel shows Spearman’s correlation between SMAD3 activities and pathway scores/protein abundances. l Proposed model of the pH imbalance induced EMT impairment in 3p loss ccRCC.

Fig. 3. Proteomic Alterations in ccRCC Compared to Adjacent Tissues.

a Cox regression analysis of significant arm-level CNA events for PFS. b Kaplan–Meier curves of PFS for patients with or without 12q gain (two-sided log-rank test). c Cis-effect of 12q gain on NAP1L1 in this study and CPTAC cohort. P values are derived from two-sided Spearman’s correlation test. d Kaplan–Meier curves of PFS for patients with different NAP1L1 abundances (two-sided log-rank test). e Negative correlations between NAP1L1 and CDKN1C abundances in this study and CPTAC cohort. P values are derived from two-sided Spearman’s correlation test. f Positive correlations between NAP1L1 abundances and MGPS scores in this study (two-sided Spearman’s correlation test). g Comparison of NAP1L1 abundances between tumors with different MKI67 IHC results (MKI67 positive ≥ 10%, n = 44; MKI67 positive < 10%, n = 139). P value is derived from two-sided t test. Boxplots show the median (central line), the 25–75% IQR (box limits), the ±1.5×IQR (whiskers). h Proposed model explaining the 12q gain-induced disease progression in ccRCC. i Up, 12q gains were enriched in SP group compared with LP group. P value is derived from two-sided Fisher’s exact test. Down, Kaplan–Meier curves of PFS for SP group and LP group (two-sided log-rank test). j GSEA of SP group patients compared with LP group patients. NES, normalized enrichment score. k Enrichment plots of MTORC1 signaling in the SP group and tight junction in the LP group. l Comparison of MTORC1 signaling and tight junction scores between the SP (n = 38) and LP (n = 149) groups and the associations of MTORC1 signaling and tight junction scores with PFS. P values are derived from two-sided t test. Boxplots show the median (central line), the 25–75% IQR (box limits), the ±1.5×IQR (whiskers). m Heatmap of plasma proteins significantly upregulated in SP group than LP group. Higher expressions of these proteins were associated with shorter PFS. n The area under the receiver operating characteristic (AUROC) of the 20 plasma proteins predictor.

Fig. 4. Proteomic Alterations in ccRCC Compared to Adjacent Tissues Reveal Tumorigenic Changes and Biomarker Candidates.

a PCA of 7,267 proteins in 232 paired tumor and adjacent tissue samples. Orange, tumor tissue; purple, tumor adjacent tissue. b Volcano plot showing DEPs (two-sided paired t test, Benjamini–Hochberg-adjusted p value < 0.01, FC > 2) in tumor and adjacent tissues. Proteins that were significantly overexpressed in tumor/adjacent tissues are presented with orange/purple filled scatters. c DEPs in tumors and adjacent tissues, and their associated biological pathways. d Dysregulation of metabolic bioprocesses in ccRCC. Alterations of representative proteins depicted as log2 FC (T/TA) (n = 232). Boxplots show the median (central line), the 25–75% IQR (box limits), the ±1.5×IQR (whiskers). e, Differentially expressed one-carbon metabolic enzymes between tumor and adjacent tissues (two-sided t test) and their associations with clinical outcomes (two-sided log-rank test). f Hcy concentrations in tumor and adjacent tissues (n = 24). P values are derived from two-sided paired t test. Data are shown as mean ± SD. g Transcription factors showed both increased/decreased protein expressions and activities in ccRCC tumors. h The increased activity of STAT1 and STAT2 in ccRCC and their association with prognosis (two-sided log-rank test). i Regulatory networks of the TFs and their downstream target proteins. j Abundance fold changes (FCs) for selected highly elevated proteins annotated with potential clinical utilities (n = 232). Drug (FDA-approved drug target), CD marker, and enzyme were annotated by HPA. Metabolism (metabolism-related protein) was annotated by Reactome. Boxplots show the median (central line), the 25–75% IQR (box limits), the min–max (whiskers). k “HPA staining proportions” indicate the proportion of ccRCC sections staining positive for the specific marker in the HPA database.

Fig. 5. Proteomic Subtypes of ccRCC and Associations with Genetic Features and Clinical Outcomes.

a Relative abundances of upregulated proteins in the three proteomic subtypes and associations of proteomic subtypes with multiple variables, including CPTAC subtype, TNM stage, ISUP grade, status of progression and genetic features (Fisher’s exact test). b Kaplan–Meier curves of OS for the three subtypes (two-sided log-rank test). c Kaplan–Meier curves of OS for subtypes GP1 and GP2&3 at different TNM stages (stage I&II vs. III&IV) (two-sided log-rank test). d Relative percentage of each mutation signatures in the three subtypes. e Ternary plot showing the distribution of significant arm-level events in the three subtypes. f Genes with differential mutation rates in each subtype (One-sided Fisher’s exact test). g Upregulated pathways enriched in the three subtypes. h Transcription factor activities significantly upregulated in GP1 compared with GP2&3 by GSEA analysis. i GSEA plot showing the upregulated STAT1 and STAT2 activities of GP1 tumors. j Comparison of ssGSEA inferred activities of STAT1 and STAT2 among three proteomic subtypes (GP1, n = 55; GP2, n = 99, GP3, n = 78). P values are derived from Kruskal–Wallis test. Boxplots show the median (central line), the 25–75% IQR (box limits), the ±1.5×IQR (whiskers). k Kaplan–Meier curves of OS for patients with different STAT1 and STAT2 activities (two-sided log-rank test). l STAT1 activities were significantly correlated with immune scores (Two-sided Spearman’s correlation test). Shaded region indicates 95% confidence interval for the correlation. m Correlations between STAT1 activities and cytokine abundances in the CPTAC cohort. n Left panel showing the IFN-γ-induced STAT signaling in GP1 ccRCC tumors. Right panel showing Cluster diagram representing pathways enriched by significantly upregulated STAT1/2 targets in GP1 using Metascape (https://metascape.org/). The top 5 clusters by p value are highlighted. o STAT1 protein levels are correlated with response to Ruxolitinib across ccRCC cell lines from the GDSC2 (two-sided Pearson’s correlation test). p Drug target candidates for ccRCC. Left, HPA annotations. Middle, protein abundance. Right, HR for OS of each protein, error bars indicates 95% confidence interval for HR (tumor samples, n = 232).

Fig. 6. NNMT Promotes Cancer Cell Proliferation through Hcy Accumulation.

a NNMT expression based on our proteomic data. P value is derived from two-sided paired t test (n = 232). b Association between NNMT protein expression and OS (two-sided log-rank test). c Upper panel, representative western blots of NNMT, MARS, K-Hcy, DNA-PKcs, DNA-PKcs (pS2056), p53, p53 (pS15) and γ-H₂AX in tumor and tumor tissues. Lower panel, quantified western blot results (n = 6). d Left panel, IHC results of NNMT expression in tumors and adjacent tissues (scale bars: 50 μm). Right panel, quantified IHC results of 12 sample pairs. TA = tumor adjacent, T = tumor. Results for other samples are shown in Supplementary Fig. 8. e, f Cell proliferation associated with various treatments (n = 5 repeats per group). g Representative plots of immunofluorescence staining of γ-H₂AX in cells under various treatments (scale bars: 20 μm). h Western blot analysis of γ-H₂AX and H₂AX in cells under various treatments. i Comet assay of DNA damage levels in cells subjected to various treatments. For each group, DNA damage levels in a total of 30 cells from five independent repeats were measured. j Relative metabolite levels in cells subjected to various treatments. k Comet assay of DNA damage levels in cells subjected to various treatments. l Cell proliferation associated with various treatments (n = 5 repeats per group). m Comet assay of DNA damage levels in cells subjected to various treatments. n Western blot analysis of γ-H₂AX and H₂AX in cells under various treatments. Data are shown as mean ± SD in panels c–f, i–l. P values are derived from two-sided t test. Not significant, ns.

Fig. 7. Lysine Homocysteinylation of DNA-PKcs Increases Cell DNA Repair through Facilitating DNA-PK Complex Formation.

a, b Comparison of K-Hcy levels in cells subjected to various treatments. c Structure of DNA-PK complex. K-Hcy sites in DNA-PKcs protein were highlighted in red. d Co-immunoprecipitation showing that endogenous DNA-PKcs interacts with endogenous MARS (n = 3 biological repeats). e Western blot analysis of K-Hcy levels of DNA-PKcs, DNA-PKcs (pS2056), and p53 (Ser15) in cells subjected to various treatments. f Comet assay of DNA damage levels in ccRCC tumor vs. adjacent normal tissues (n = 10). P value is derived from two-sided paired t test. g, h NNMT overexpression enhancess the interaction between endogenous DNA-PKcs and endogenous KU70 or KU80 in ACHN and 769-P cells. i In vitro DNA-PKcs activity assayed by monitoring its kinase activity in phosphorylating its substrate p53. j DNA-PKcs activity indicated by measuring ADP formation in an ADP-Glo-DNA-PK assay (n = 5 repeats per group). P values are derived from two-sided t test. Data are shown as the mean ± SD. k HTL increases the in vitro DNA-PKcs activity. l DNA-PK activity under different treatments (n = 5 repeats per group). P values are derived from two-sided t test. Data are shown as the mean ± SD. m, n DNA-PKcs KW and 3KW mutants exhibit enhanced binding affinity for KU70 and KU80 compared to wild-type DNA-PKcs. o DNA-PKcs 3KW mutant exhibits enhanced kinase activity. p, q Tumor size of cell xenografts under different treatments in normal and NAC-administered nude mice. r Model depicting NNMT-mediated DNA repair and cell proliferation in ccRCC.