Abstract

Hepatocellular carcinoma is the third leading cause of deaths from cancer worldwide. Infection with the hepatitis B virus is one of the leading risk factors for developing hepatocellular carcinoma, particularly in East Asia1. Although surgical treatment may be effective in the early stages, the five-year overall rate of survival after developing this cancer is only 50–70%2. Here, using proteomic and phospho-proteomic profiling, we characterize 110 paired tumour and non-tumour tissues of clinical early-stage hepatocellular carcinoma related to hepatitis B virus infection. Our quantitative proteomic data highlight heterogeneity in early-stage hepatocellular carcinoma: we used this to stratify the cohort into the subtypes S-I, S-II and S-III, each of which has a different clinical outcome. S-III, which is characterized by disrupted cholesterol homeostasis, is associated with the lowest overall rate of survival and the greatest risk of a poor prognosis after first-line surgery. The knockdown of sterol O-acyltransferase 1 (SOAT1)—high expression of which is a signature specific to the S-III subtype—alters the distribution of cellular cholesterol, and effectively suppresses the proliferation and migration of hepatocellular carcinoma. Finally, on the basis of a patient-derived tumour xenograft mouse model of hepatocellular carcinoma, we found that treatment with avasimibe, an inhibitor of SOAT1, markedly reduced the size of tumours that had high levels of SOAT1 expression. The proteomic stratification of early-stage hepatocellular carcinoma presented in this study provides insight into the tumour biology of this cancer, and suggests opportunities for personalized therapies that target it.

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Data availability

The data that support the findings of this study—including clinical information, and proteome, phospho-proteome and gene expression data—are available within the paper and its Supplementary Information, or from CNHPP liver data portal (http://liver.cnhpp.ncpsb.org/). The thermo.raw files of proteome and phospho-proteome datasets can be obtained from PRIDE database (www.ebi.ac.uk/pride/archive, accession numbers PXD006512 and PXD008373)63 or iProX database (www.iprox.org, accession number IPX0000937000)64. Gene expression profiles by RNA-seq can be obtained from Gene Expression Omnibus (accession number GSE124535).

The uploading and sharing of individual genetic data from this project is not permissible, according to a review by the Human Genetic Resources Administration of China on the basis of regulations documented in the Interim Measures for the Administration of Human Genetic Resources. We have summaries of the data that are as detailed as possible, and which are available to other researchers. This includes the exonic and splicing mutations, given in Supplementary Table 3. Researchers who wish to gain access to allele frequency information and other summary statistics data are required to fill in a simple application form (http://liver.cnhpp.ncpsb.org/) and send an email to F.H. for identity verification purposes, to adhere to the Chinese regulations.

The TCGA RNA-seq data are publicly available at the Genomic Data Commons Data Portal (CDC, http://www.ncbi.nlm.nih.gov/geo). Additional publically available microarray data for HCC (Fudan cohort) was analysed from NCBI GEO repository (accession number GSE14520).

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

This work was supported by Chinese Human Proteome Project, and partially supported by Chinese State Key Projects for Basic Research (973 Program) (no. 2014CBA02001), National Key R&D Program of China (no. 2016YFC0902400, 2017YFC0906603, 2018YFA0507502, 2016YFF0101405, 2013ZX10002009 and 2009ZX09503-002), Program of International S&T Cooperation (no. 2014DFB30020,2014DFB30030, 2014DFB30010, 2009DFB33070 and 2010DFA31260), National Natural Science Foundation of China (no. 81770581, 81570526, 81772551, 81802364, 8153077, 81672839, 81572823, 81772578, 8153077 and 81123001), Innovation project (16CXZ027), Beijing Science and Technology Project (Z161100002616036) and Shanghai 111 Project (B14019). We acknowledge the assistance of Z. Jin and P. Zhang for the immune-related data analyses, and C. Chang for the method development and evaluation of the missing-value imputing analyses (National Center for Protein Sciences (Beijing)). We thank L. Shen, W. Xue and Z. Li (Cancer Hospital & Institute, Peking University) for their examination of tumour cellularity and immune-cell infiltration; and B. Zhen, L. Tang and Y. Wang (State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics) for scientific management.

Reviewer information

Nature thanks Josep M. Llovet and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. A list of participants and their affiliations appears in the online version of the paper.

  2. These authors contributed equally: Ying Jiang, Aihua Sun, Yang Zhao, Wantao Ying, Huichuan Sun, Xinrong Yang, Baocai Xing.

Affiliations

  1. State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China

    • Ying Jiang
    • , Aihua Sun
    • , Yang Zhao
    • , Wantao Ying
    • , Wei Sun
    • , Liangliang Ren
    • , Chaoying Li
    • , Ning Chen
    • , Manli Zhang
    • , Yin Huang
    • , Jinan Zhou
    • , Yan Zhao
    • , Mingwei Liu
    • , Yang Qiu
    • , Yanjun Sun
    • , Meng Yan
    • , Mingchao Wang
    • , Fang Tian
    • , Huali Xu
    • , Zhenyu Wu
    • , Weimin Zhu
    • , Jun Qin
    • , Xiaohong Qian
    • , Fuchu He
    • , Fuchu He
    • , Xiaohong Qian
    • , Jun Qin
    • , Ying Jiang
    • , Wantao Ying
    • , Wei Sun
    • , Yunping Zhu
    • , Weimin Zhu
    • , Yi Wang
    • , Dong Yang
    • , Wanlin Liu
    • , Qiongming Liu
    • , Xiaoming Yang
    • , Bei Zhen
    •  & Zhenyu Wu
  2. College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China

    • Yang Zhao
    •  & Xiaohong Qian
  3. Department of Liver Surgery & Transplantation, Liver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China

    • Huichuan Sun
    • , Xinrong Yang
    • , Bo Hu
    • , Xiaodong Zhu
    • , Cheng Huang
    • , Jian Zhou
    • , Jia Fan
    • , Jia Fan
    • , Huichuan Sun
    • , Juying Qian
    •  & Tao Hong
  4. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China

    • Baocai Xing
    • , Wei Liu
    • , Lin Shen
    •  & Baocai Xing
  5. Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China

    • Li Zhang
    •  & Tieliu Shi
  6. Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, China

    • Guangrong Qin
    • , Menghuan Zhang
    •  & Lu Xie
  7. Department of Chemistry and Institutes of Biomedical Science, Shanghai Medical School, Fudan University, Shanghai, China

    • Pengyuan Yang
    • , Huali Shen
    •  & Lijuan Zhang
  8. Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical, Beijing, China

    • Shujun Cheng
    • , Jianqiang Cai
    • , Xiaohang Zhao
    • , Yulin Sun
    • , Ting Xiao
    •  & Yousheng Mao
  9. General Hospital of Chinese People’s Liberation Army, Beijing, China

    • Xiangmei Chen
    • , Di Wu
    • , Lin Chen
    •  & Jun Dong
  10. MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China

    • Haiteng Deng
  11. Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

    • Minjia Tan
    •  & Zhixing Wu
  12. Xijing Hospital of Digestive Diseases, The Fourth Military Medical University, Xi’an, China

    • Qingchuan Zhao
  13. General Hospital of Chinese People’s Armed Police Force, Beijing, China

    • Zhongyang Shen
    •  & Xinguo Chen
  14. Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China

    • Youhe Gao
    •  & Wei Sun
  15. College of Life Science and Technology, Jinan University, Guangzhou, China

    • Tong Wang
  16. BGI-Shenzhen, Shenzhen, China

    • Siqi Liu
    • , Liang Lin
    •  & Jin Zi
  17. Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China

    • Xiaomin Lou
  18. Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    • Rong Zeng
    •  & Yi Wu
  19. Fudan University Shanghai Cancer Center, Shanghai, China

    • Sanjun Cai
  20. Nanfang Hospital, Southern Medical University, Guangzhou, China

    • Bo Jiang
  21. Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

    • Anqing Chen
  22. Peking University Third Hospital, Beijing, China

    • Zijian Li
  23. Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    • Fuquan Yang
    • , Xiulan Chen
    •  & Yuna Sun
  24. No. 307 Hospital of the Chinese People’s Liberation Army, Beijing, China

    • Quanli Wang
  25. Xuanwu Hospital, Capital Medical University, Beijing, China

    • Yi Zhang
  26. Tianjin Baodi Hospital, Tianjin, China

    • Guangshun Wang
  27. Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China

    • Zhuchu Chen
  28. Nanjing General Hospital of Nanjing Military Area Command, Nanjing, China

    • Weisong Qin
  29. Shenzhen Second People’s Hospital, Shenzhen, China

    • Zesong Li

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Consortia

  1. Chinese Human Proteome Project (CNHPP) Consortium

Contributions

F.H., X.Q. J.F. and Y.J. directed and designed research; H.S., B.X., Y.J., W.S., N.C., A.S., X.Z., C.H., M.L. and W.L. coordinated acquisition, distribution and quality evaluation of HCC tumour, and nearby tissue, samples; X.Q., J.Q., W.Y., Yang Zhao, L.R., Manli Zhang, Yan Zhao, M.Y., M.W. and F.T. directed and performed analyses and quality control of mass spectrometry data; X.Y., B.H., Jian Zhou, and J.F. performed the tissue microarray, immunohistochemistry experiments and PDX mouse models experiments; C.L. and Jinan Zhou performed HCC cell-line experiments and the cholesterol staining and quantification experiments; Yang Zhao, G.Q., L.X., Menghuan Zhang, L.Z. and T.S. adapted algorithms and software for data analysis; Y.J., A.S., Yang Zhao and W.S. interpreted data in context of HCC biology; W.Z., Y.H., Y.Q., Y.S., H.X. and Z.W. designed and constructed the database of this study; Y.J., Yang Zhao, A.S., W.S., W.Y., X.Q. and F.H. wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Jia Fan or Xiaohong Qian or Fuchu He.

Extended data figures and tables

  1. Extended Data Fig. 1 Overview of multi-omics landscape of HBV-related early-stage HCC and quality control for the mass spectrometry platform.

    a, Schematic of the multi-omics analyses of HBV-related early-stage HCC. The number of proteins, phosphorylation sites, expressed genes and genomic mutations identified in the proteome, phospho-proteome, transcriptome and whole-genome sequencing (WGS) data are displayed on the right of the middle panel. b, Illustration of 110 paired HCC cases used in the individual omics experiments. The proteome, phospho-proteome, genome and transcriptome experiments are coloured red, orange, blue and green, respectively. For each omics dataset, the top panel represents the non-tumour tissues and the bottom panel represents the tumour tissues. c, Longitudinal quality control of mass spectrometry using tryptic digest of HEK293T cells. The bottom-left half of the panel represents the pairwise Spearman’s correlation coefficients of the samples, and the top-right half of the panel depicts the pairwise scatter plots from the same comparison. d, Scatter plots and Spearman’s correlation coefficients for replicate proteome profiling of six HCC samples (three paired tumour and non-tumour samples). The x and y axes represent the log2-transformed protein intensities in each pairwise comparison. Notably, repeat experiments with the same samples have good reproducibility, with a high level of correlation (average, 0.9; range, 0.88–0.92). Source data

  2. Extended Data Fig. 2 Quality assessment of proteomic data and genomic landscape of HBV-related early-stage HCC.

    a, Distribution of log2-transformed iBAQ abundance of identified proteins in 199 proteome samples that passed quality control. Green presents non-tumour samples (n = 98), red denotes tumour samples (n = 101). In the box plots, the middle bar represents the median, and the box represents the interquartile range; bars extend to 1.5× the interquartile range. b, Cumulative number of protein identifications. Blue presents non-tumour samples (n = 98), red denotes tumour samples (n = 101). c, Cumulative number of phosphorylation site identifications. Blue presents non-tumour samples (n = 103), red denotes tumour samples (n = 103). d, The genomic landscape and mutational signatures in HBV-related early-stage HCC. Top, individual tumour mutation rates. Middle, AFP and MVI status of the 93 patients with HCC. Bottom, recurrently mutated genes. Mutation types are indicated in the legend at the bottom. The bar plot on the left shows the number of alterations for each gene in the 93 patients. Source data

  3. Extended Data Fig. 3 Protein identification and gene expression in HBV-related early-stage HCC.

    a, Box plots of the proteins identified in tumour (red, n = 98) and paired non-tumour (green, n = 98) samples. b, Box plots of the protein identifications in AFPlow (grey, n = 51), AFPhigh (red, n = 34), MVI (grey, n = 51) and MVI+ (red, n = 22) patients from the tumour (red, n = 98) or non-tumour (green, n = 98) samples. To eliminate interaction effects, the patients that are both AFPlow and MVI are simultaneously considered to be AFPlow or MVI patients in this analysis. c, Overview of the number of expressed genes in HBV-related early-stage HCC. Similar to the protein identification, the number of genes expressed in the tumour samples is higher than that in the paired non-tumour samples. The MVI+ or AFPhigh patients also have a higher level of gene expression than the MVI or AFPlow patients in the tumour samples, but there are no statistical differences between the MVI+ or AFPhigh patients and the MVI or AFPlow patients in the non-tumour samples. The samples are arranged in the same order as in Fig. 1a. Dashed lines denote paired samples from the same cases of HCC. Dashed curves are fitted by lasso regression. The shading that underlies the lasso curves represents the 95% confidence intervals of those lines. d, Box plots of the number of expressed genes in the tumour (red, n = 31) and paired non-tumour (red, n = 31) samples. e, Box plots of the expressed genes in AFPlow (grey, n = 12), AFPhigh (red, n = 14), MVI (grey, n = 12) and MVI+ (red, n = 9) patients from the tumour (red, n = 31) or non-tumour (green, n = 31) samples. To eliminate interaction effects, patients that are both AFPlow and MVI are simultaneously considered to be AFPlow or MVI patients in this analysis. In the box plots, the middle bar represents the median, and the box represents the interquartile range; bars extend to 1.5× the interquartile range. P values are calculated by the two-sided Wilcoxon rank-sum test and are shown on the top of the boxes. NS, not significant, *P < 0.05, **P < 0.01, ***P < 0.001. Source data

  4. Extended Data Fig. 4 Pathway alterations and the effects of genomic mutations on protein expression or phosphorylation in HBV-related early-stage HCC.

    a, Pathway alterations in HBV-related early-stage HCC. Left, alteration pathways in the tumours (n = 101) versus the non-tumour tissues (n = 98). Middle and right, alteration pathways in the tumour samples from the AFPhigh (n = 34) versus AFPlow (n = 67) and MVI+ (n = 22) versus MVI (n = 79) patients, respectively. P values are calculated by two-sided Fisher’s exact test based on the MSigDB database. Upregulated and downregulated pathways are indicated in red (right) and blue (left) bars. b, The activated pathways in the tumour samples (n = 92) are characterized by phospho-proteome data. The most significant pathways for the phospho-proteome are plotted on the x axis as the −log2 of the P value, compared with the proteome. P values are calculated by two-sided Fisher’s exact test based on the MSigDB database. c, Heat map of hyper-phosphorylated proteins across tumour (n = 92) and non-tumour (n = 92) tissues. The numbers of hyper- phosphorylation sites are shown for each protein and coloured in red, and proteins with no hyper-phosphorylation sites are coloured in white. Biological functions related to these proteins are denoted in the colour panel on the left. The samples are arranged in the same order as in Fig. 1. d, Heat map of dysregulated proteins by TSC1 and TSC2 mutation. The downstream substrate of mTOR, RPS6, was hyper-phosphorylated at the pS236, pS240 and pS244 sites. e, Heat map of dysregulated proteins by CTNNB1 mutation. The liver-related WNT targets GLUL and CYP1A2 were upregulated in tumour samples with CTNNB1 mutations. Source data

  5. Extended Data Fig. 5 NMF consensus-clustering identifies three proteomic subtypes of HBV-related early-stage HCC.

    a, Principal component analysis; first two principal components of protein intensities, with samples connected by centroids according to sample types (green, non-tumour; red, tumour). The tumour samples (red, n = 101) exhibit higher heterogeneity than the non-tumour (green, n = 98) samples. The ellipse presents the 0.9 confidence intervals for each type. b, c, Heat map of NMF consensus matrix, cophenetic correlation coefficient and average silhouette-width plots. The NMF input is the quantile-normalized iBAQ intensity matrix of the top 25% most-variant proteins, across 101 tumour samples. On the basis of visual inspection of the hierarchical clustering and the profiles of cophenetic correlation coefficient and average silhouette width for solutions with 2 to 7 clusters, we consider K = 3 to be the preferred solution (indicated by black triangles) and use this to arrange the samples shown in Fig. 2a (yielding the three clusters highlighted in blue, yellow and red). Average cophenetic correlations of each rank of clusters are displayed under the heat map matrix. The green and blue points or lines in the cophenetic correlation and average silhouette-width plots represent the non-smooth NMF (nsNMF) and the Brunet algorithm, respectively. Source data

  6. Extended Data Fig. 6 Association of proteomic subtypes with clinical features and prognosis signature proteins of HBV-related early stage HCC.

    a, b, Association of MVI and serum AFP levels with proteomic subtypes. c, Association of age with proteomic subtypes. S-I (n = 36) HCC developed in older patients (62.0 ± 8.9 years), whereas S-II (n = 32) and S-III (n = 33) HCC generally developed in relatively young patients (54.5 ± 12.0 and 53.4 ± 10.1 years, respectively). The black lines denote the average age of the patients (mean ± s.d.) in the subtypes. d, e, Kaplan–Meier plots of HCC recurrence of subtypes identified in the proteomic (d) and Fudan (e) cohorts. The proteomic subtypes of patients from the Fudan cohort are classified by the nearest template prediction algorithm, with signature genes of proteomic subtypes (Methods). f, Prognosis signature proteins in proteomic subtypes. Heat map shows the relative abundance of each protein. Spearman’s correlation coefficients (cor.) between mRNA and protein expression levels and the log2(hazard ratio) of each protein are displayed on the right. Some of the prognosis signatures identified by proteomic analysis are highlighted by asterisks. For each subtype, the signature proteins are ordered by increasing hazard ratio (HR) values. P values are calculated by one-sided Fisher's exact test (a, b), two tailed Student’s t-test (c) and two-sided log-rank test (d, e). Source data

  7. Extended Data Fig. 7 Pathway alterations in the proteomic subtypes.

    a, Heat map of alteration pathways in the proteomic subtypes. Left, alteration pathways identified from the proteome. Right, alteration pathways identified from the phospho-proteome. Colour of each cell represents the average ssGSEA enrichment scores of that subtype; red denotes activation and blue denotes inhibition. Blank cells represent non-enrichment. b, Details of alteration scores of integrin proteins, extracellular matrix proteins, metalloproteinase proteins and glycolysis proteins shown in Fig. 3a. c, Network diagram depicting the interaction of the Rho GTPase signalling pathway. The pink node represents proteins expressed at higher levels in the S-III tumours than in the S-I and S-II tumours; a red node border indicates the hyper-phosphorylation of that protein. d, Identification and quantification of hyper-phosphorylation sites for Rho GTPase activators (FGD3, DOCK2 and ARHGEF2) in S-I (n = 35), S-II (n = 30) and S-III (n = 29) tumours. In the box plots, the middle bar represents the median, and the box represents the interquartile range; bars extend to 1.5× the interquartile range. A grey circle under the box plot indicates that this phosphorylation site is not identified in these samples. P values are calculated by two-sided Wilcoxon rank-sum test. Source data

  8. Extended Data Fig. 8 Signature proteins in S-III tumours and the association of proteomic subtypes with genomic mutations and established transcriptomic signatures of HCC classifications.

    a, Global heat map of the relative abundance of the signature proteins in the S-III tumours. Biological functions related to these proteins are denoted by the colour panel on the right, and annotated according to the pathways based on enrichment of MSigDB ontologies. b, Association of the proteomic subtypes with p53 and CTNNB1 mutations, and mRNA-based HCC subclasses. Samples with positive results are coloured in red, and the negative samples are shown in grey. Samples with no statistical significance, or which are not available, are coloured in white. The immunosuppression subclass is labelled by the dashed box. Prediction of mRNA-based HCC subclasses and immune-cell types was performed using nearest template prediction analysis, and ssGSEA and model-based clustering analysis, respectively (Methods). c, Immune infiltration in proteomic subtypes. Samples with high immune infiltration are coloured in red and samples with low immune infiltration are coloured in grey. P values are calculated by one-sided Fisher’s exact test. Source data

  9. Extended Data Fig. 9 Cholesterol metabolic dysregulation in HBV-related early-stage HCC.

    a, Representative immunohistochemical staining of TMA with SOAT1 antibodies in an independent cohort of HCC (n = 254, biologically independent samples). Scale bars, 50 μm. b, Staining intensity of SOAT1 between non-tumour (N) and tumour (T) samples from TMA (n = 254). Data are mean ± s.d. c, Intensities of three cholesteryl esters in paired non-tumour and tumour HCC samples (n = 25) by electrospray ionization mass spectrometry. In the box plots, the middle bar represents the median, and the box represents the interquartile range; bars extend to 1.5× the interquartile range. d, e, Upregulation of the cholesterol-metabolism-related proteins (d) or their mRNAs (e) in paired non-tumour and tumour samples. The black lines in the middle of the plots indicate the mean of protein intensities (± s.e.m.) and mRNA abundance (± s.e.m.). The P values are calculated by two-sided Wilcoxon rank-sum test (be). Source data

  10. Extended Data Fig. 10 Downregulation of SOAT1 suppresses the proliferation and migration of HCC cells by reducing plasma-membrane cholesterol content, and then inhibiting the integrins and TGFβ signalling pathway.

    a, Western blot of SOAT1 and SOAT2 in PLC/PRF/5 and HepG2 cell lines, with or without treatment with shRNA (n = 3 biologically independent experiments). For full scans, see Supplementary Fig. 1. b, c, SOAT1 protein abundance in PLC/PRF/5 and HepG2 cells based on proteomic data (b, mean ± s.e.m., n = 3 biological repeats) and western blotting analysis (c, n = 3 biologically independent experiments). For full scans, see Supplementary Fig. 1. d, Alteration pathways and proteins in the PLC/PRF/5 cell line after the SOAT1 knockdown or treatment with avasimibe (n = 3 biological repeats). e, Schematic of mechanism that underlies the dysregulation of integrins and TGFβRs by SOAT1 inhibition. f, Body weight of PDX models treated by control (100 μl PBS) or avasimibe (15 mg kg−1 day−1) on the indicated days (n = 36 mice per group). Mean ( ± s.d.) of body weight is plotted. g, Growth curves of PDX models treated by control (100 μl PBS) or avasimibe (15 mg kg−1 day−1) on the indicated days (n = 6 mice per group). Circle and triangle denotes the mean volume of tumours ( ± s.e.m.). The immunohistochemical staining of SOAT1 in PDX models is labelled on the top panel. h, Relative expression of S-III-signature pathway-related proteins among three SOAT1high (SOAT1-H) PDX and three SOAT1low (SOAT1-L) PDX models. Red and blue denote upregulation and downregulation, respectively. Zigzags on the y axis indicate places at which the y axis is truncated. The P values are calculated by two-tailed Student’s t-test. NS, not significant, *P < 0.05, ***P < 0.001. Source data

Supplementary information

  1. Supplementary Information

    This file contains Supplementary Notes which shows the full Membership of the Chinese Human Proteome Project (CNHPP) Consortium.

  2. Reporting Summary

  3. Supplementary Figures

    This file contains Supplementary Figure 1.

  4. Supplementary Tables

    This file contains Supplementary Tables 1-18.

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https://doi.org/10.1038/s41586-019-0987-8

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