This vignette from the R package JMDplots version 1.2.19-9 shows chemical metrics for proteins that are differentially expressed in liver cancer compared to normal tissue. The analysis is described in more detail in a paper (Dick, 2021). Abbreviations:
Differences are calculated as (median value for up-regulated proteins) - (median value for down-regulated proteins). Dashed lines enclose the 50% confidence region for highest probability density.
In the table, values of ΔZC and ΔnH2O are multiplied by 1000, values of ΔMW are multiplied by 100, and negative values are shown in bold. Abbreviations:
| set | reference (description) | ndown | nup | ΔZC | ΔnH2O | ΔnAA | ΔMW |
|---|---|---|---|---|---|---|---|
| a |
LHT+04 (T / N)
|
79 | 44 | 29 | -28 | 16 | 83 |
| b |
BLP+05 (T / N)
|
61 | 22 | 33 | 10 | 53 | 6 |
| c |
LTZ+05 (T / N)
|
35 | 39 | -3 | -18 | -16 | 50 |
| d |
DTS+07 (T / N)
|
20 | 23 | 23 | 15 | 90 | 4 |
| e |
SXS+07 (T / N)
|
46 | 21 | 17 | 55 | 168 | 112 |
| f |
CHN+08 (T / N)
|
93 | 58 | 51 | -24 | 92 | 48 |
| g |
RLA+10 (T / N rat transitional endoplasmic reticulum)
|
130 | 135 | 26 | 38 | -24 | -23 |
| h |
LMG+11 (T / N nuclear)
|
411 | 264 | 28 | 20 | 80 | -18 |
| i |
LMG+11 (T / N cytoskeletal)
|
280 | 273 | 39 | 18 | 84 | 26 |
| j |
LRL+12 (T / N)
|
102 | 161 | 32 | 4 | -7 | 114 |
| k |
KOK+13 (T / N)
|
53 | 38 | 19 | 49 | 54 | 108 |
| l |
MBK+13 (T / N)
|
192 | 280 | 17 | 11 | 1 | 102 |
| m |
XWS+14 (T / N)
|
135 | 504 | -27 | 15 | -82 | 144 |
| n |
BSG15 (T / N EGF transgenic mice)
|
34 | 71 | 30 | -39 | 104 | 163 |
| o |
RPM+15 (T / N)
|
131 | 479 | 40 | 9 | 72 | 118 |
| p |
NBM+16 (T / N G1)
|
42 | 80 | 18 | 8 | -56 | 69 |
| q |
NBM+16 (T / N G2)
|
81 | 82 | 32 | 0 | 154 | 29 |
| r |
NBM+16 (T / N G3)
|
237 | 556 | 29 | 9 | 33 | 124 |
| s |
NMB+16 (T / N)
|
121 | 426 | 16 | -6 | 166 | 126 |
| t |
QXC+16 (T / N T1)
|
198 | 132 | 20 | 25 | -106 | 63 |
| u |
QXC+16 (T / N T2)
|
193 | 172 | 7 | 36 | -82 | 112 |
| v |
QXC+16 (T / N T3)
|
202 | 185 | 27 | 4 | -54 | 51 |
| w |
GJZ+17 (T / N)
|
26 | 25 | 9 | 27 | 71 | -65 |
| x |
GWS+17 (T / N)
|
145 | 191 | 23 | 7 | -29 | 143 |
| y |
QPP+17 (T / N)
|
121 | 72 | 0 | 17 | -56 | 42 |
| z |
WLL+17 (T / N small)
|
48 | 31 | -28 | 37 | -140 | 60 |
| A |
WLL+17 (T / N medium)
|
21 | 39 | -1 | -1 | -8 | -116 |
| B |
WLL+17 (T / N large)
|
39 | 63 | 12 | 41 | -23 | -153 |
| C |
WLL+17 (T / N huge)
|
60 | 129 | -16 | 20 | 116 | 80 |
| D |
BOK+18 (T / N)
|
108 | 59 | -12 | 12 | 72 | 199 |
| E |
YXZ+18 (T / N)
|
145 | 111 | 28 | 34 | -140 | 64 |
| F |
BEM+20 (T / N mouse)
|
93 | 138 | 29 | 28 | 24 | -22 |
| G |
GZD+19 (T / N protein)
|
403 | 52 | 26 | 60 | 85 | 66 |
| H |
GZD+19 (T / N phosphoprotein)
|
213 | 68 | 36 | 22 | 140 | 216 |
| I |
JSZ+19 (T / N)
|
359 | 1649 | 22 | 30 | 80 | 85 |
| J |
ZZL+19 (T / N)
|
162 | 173 | 18 | 15 | -108 | 125 |
| K |
GZL+20 (T / N)
|
234 | 769 | 20 | 11 | -37 | 54 |
| L |
SCL+20 (T / N mitochondrial differential)
|
38 | 43 | 5 | 7 | -44 | -65 |
| M |
SCL+20 (T / N mitochondrial unique)
|
419 | 966 | 22 | -1 | 60 | 18 |
Gene names or other identifiers were converted to UniProt accession numbers using the UniProt mapping tool, except for IPI accession numbers, which were converted using the DAVID 6.7 conversion tool.
a. Table III of Li et al. (2004). b. Tables 2 and 3 of Blanc et al. (2005). c. Table 2 of Li et al. (2005). d. Table 1 of Dos Santos et al. (2007), including proteins identified in either tumor homogenates or laser microdissected samples. e. Supplemental Table S1 of Sun et al. (2007). f. Tables 1–3 of Chaerkady et al. (2008). g. Supplemental Tables S3A and S3C of Roy et al. (2010), filtered to include proteins with p-value < 0.05. h. i. IPI numbers from Supplemental Tables S3 (nuclear proteins) and S4 (cytoskeletal proteins) of Lee et al. (2011), filtered to include proteins with median fold-change > 2 or < 0.5. j. Supplemental Table 5 of Li et al. (2012). k. Supplementary Table 3 of Kimura et al. (2013). l. Supplemental Data S5 (sheet “LF_proteins”) of Megger et al. (2013). m. Supporting Information SI-S2 of Xu et al. (2014). n. Table 1 of Borlak, Singh & Gazzana (2015). o. Supplementary Table S1 of Reis et al. (2015), filtered to include proteins with > 1 peptide used for quantification and fold change > 2 in either direction. p. q. r. Supplementary Data Table S2 of Naboulsi et al. (2016a) (sheets “G1 vs C”, “G2 vs C”, and “G3 vs C”), filtered to include proteins with p-value < 0.05, quantified in at least half of both tumor and control samples, and median log2 fold change > 1 or < -1. s. Supporting Information Table S-4 (sheet “Filtered protein list”) of Naboulsi et al. (2016b). t. u. v. Supplementary Tables S3–S5 of Qi et al. (2016). w. Table 4 of Guo et al. (2017). x. Supplementary Table S5 of Gao et al. (2017). y. Supplementary Table 5 of Qiao et al. (2017), filtered to include proteins with median fold change > 2 or < 0.5 (UniProt IDs from Supplementary Table 1). z. A. B. C. Supplementary Table S3 of Wang et al. (2017), filtered to include proteins with p-value < 0.05 and fold change > 2 or < 0.5. D. Supplemental Table S2 of Buczak et al. (2018) (data for tumor vs. peritumor), filtered to include proteins with q-value < 0.05, quantified in at least 3 tumors, same direction of change in all tumors, and median log2 fold change > 1 or < -1. E. Supplementary Table S2 of Yang et al. (2018). F. Dataset of Berndt et al. (2020), filtered to include proteins quantified in more than half of each of control and cancer samples and median fold change > 2 or < 0.5. G. H. Supplemental Table S3 of Gao et al. (2019) (sheets “1. 1,274 DF proteins” and “2. 859 DF phosphoproteins”), filtered to include proteins with log2 fold change > 1 or < -1. I. Supplementary Table 6 of Jiang et al. (2019). J. Supporting Information Table S5 of Zhu et al. (2019) (sheet “c_proteins”), filtered to include proteins quantified in at least half of tumor and normal samples and with fold change > 2 or < 0.5. K. Supporting Information Table S4 of Gao et al. (2020) (sheet “B_Molecule”). L. M. Supplementary Tables S1–S2 (proteins with differential expression between non-tumor and tumor regions) and S3–S4 (all proteins identified in each region) of Shin et al. (2020). Common accession numbers in Tables S3 and S4 were eliminated to yield proteins uniquely identified in either tumor or non-tumor regions.
Berndt N, Egners A, Mastrobuoni G, Vvedenskaya O, Fragoulis A, Dugourd A, Bulik S, Pietzke M, Bielow C, van Gassel R, Damink SWO, Erdem M, Saez-Rodriguez J, Holzhütter H-G, Kempa S, Cramer T. 2020. Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer. British Journal of Cancer 122:233–244. DOI: 10.1038/s41416-019-0659-3.
Blanc J-F, Lalanne C, Plomion C, Schmitter J-M, Bathany K, Gion J-M, Bioulac-Sage P, Balabaud C, Bonneu M, Rosenbaum J. 2005. Proteomic analysis of differentially expressed proteins in hepatocellular carcinoma developed in patients with chronic viral hepatitis C. Proteomics 5:3778–3789. DOI: 10.1002/pmic.200401194.
Borlak J, Singh P, Gazzana G. 2015. Proteome mapping of epidermal growth factor induced hepatocellular carcinomas identifies novel cell metabolism targets and mitogen activated protein kinase signalling events. BMC Genomics 16:124. DOI: 10.1186/s12864-015-1312-z.
Buczak K, Ori A, Kirkpatrick JM, Holzer K, Dauch D, Roessler S, Endris V, Lasitschka F, Parca L, Schmidt A, Zender L, Schirmacher P, Krijgsveld J, Singer S, Beck M. 2018. Spatial tissue proteomics quantifies inter- and intratumor heterogeneity in hepatocellular carcinoma (HCC). Molecular & Cellular Proteomics 17:810–825. DOI: 10.1074/mcp.RA117.000189.
Chaerkady R, Harsha HC, Nalli A, Gucek M, Vivekanandan P, Akhtar J, Cole RN, Simmers J, Schulick RD, Singh S, Torbenson M, Pandey A, Thuluvath PJ. 2008. A quantitative proteomic approach for identification of potential biomarkers in hepatocellular carcinoma. Journal of Proteome Research 7:4289–4298. DOI: 10.1021/pr800197z.
Dos Santos A, Thiers V, Sar S, Derian N, Bensalem N, Yilmaz F, Bralet M-P, Ducot B, Bréchot C, Demaugre F. 2007. Contribution of laser microdissection-based technology to proteomic analysis in hepatocellular carcinoma developing on cirrhosis. Proteomics - Clinical Applications 1:545–554. DOI: 10.1002/prca.200600474.
Gao Y, Wang X, Sang Z, Li Z, Liu F, Mao J, Yan D, Zhao Y, Wang H, Li P, Ying X, Zhang X, He K, Wang H. 2017. Quantitative proteomics by SWATH-MS reveals sophisticated metabolic reprogramming in hepatocellular carcinoma tissues. Scientific Reports 7:45913. DOI: 10.1038/srep45913.
Gao H, Zhang F, Liang S, Zhang Q, Lyu M, Qian L, Liu W, Ge W, Chen C, Yi X, Zhu J, Lu C, Sun P, Liu K, Zhu Y, Guo T. 2020. Accelerated lysis and proteolytic digestion of biopsy-level fresh-frozen and FFPE tissue samples using pressure cycling technology. Journal of Proteome Research 19:1982–1990. DOI: 10.1021/acs.jproteome.9b00790.
Gao Q, Zhu H, Dong L, Shi W, Chen R, Song Z, Huang C, Li J, Dong X, Zhou Y, Liu Q, Ma L, Wang X, Zhou J, Liu Y, Boja E, Robles AI, Ma W, Wang P, Li Y, Ding L, Wen B, Zhang B, Rodriguez H, Gao D, Zhou H, Fan J. 2019. Integrated proteogenomic characterization of hbv-related hepatocellular carcinoma. Cell 179:561–577. DOI: 10.1016/j.cell.2019.08.052.
Guo J, Jing R, Zhong J-H, Dong X, Li Y-X, Liu Y-K, Huang T-R, Zhang C-Y. 2017. Identification of CD14 as a potential biomarker of hepatocellular carcinoma using iTRAQ quantitative proteomics. Oncotarget 8:62011–62028. DOI: 10.18632/oncotarget.18782.
Jiang Y, Sun A, Zhao Y, Ying W, Sun H, Yang X, Xing B, Sun W, Ren L, Hu B, Li C, Zhang L, Qin G, Zhang M, Chen N, Zhang M, Huang Y, Zhou J, Zhao Y, Liu M, Zhu X, Qiu Y, Sun Y, Huang C, Yan M, Wang M, Liu W, Tian F, Xu H, Zhou J, Wu Z, Shi T, Zhu W, Qin J, Xie L, Fan J, Qian X, He F, He F, Qian X, Qin J, Jiang Y, Ying W, Sun W, Zhu Y, Zhu W, Wang Y, Yang D, Liu W, Liu Q, Yang X, Zhen B, Wu Z, Fan J, Sun H, Qian J, Hong T, Shen L, Xing B, Yang P, Shen H, Zhang L, Cheng S, Cai J, Zhao X, Sun Y, Xiao T, Mao Y, Chen X, Wu D, Chen L, Dong J, Deng H, Tan M, Wu Z, Zhao Q, Shen Z, Chen X, Gao Y, Sun W, Wang T, Liu S, Lin L, Zi J, Lou X, Zeng R, Wu Y, Cai S, Jiang B, Chen A, Li Z, Yang F, Chen X, Sun Y, Wang Q, Zhang Y, Wang G, Chen Z, Qin W, Li Z, Chinese Human Proteome Project Consortium (CNHPP). 2019. Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma. Nature 567:257–261. DOI: 10.1038/s41586-019-0987-8.
Kimura K, Ojima H, Kubota D, Sakumoto M, Nakamura Y, Tomonaga T, Kosuge T, Kondo T. 2013. Proteomic identification of the macrophage-capping protein as a protein contributing to the malignant features of hepatocellular carcinoma. Journal of Proteomics 78:362–373. DOI: 10.1016/j.jprot.2012.10.004.
Lee Y-Y, McKinney KQ, Ghosh S, Iannitti DA, Martinie JB, Caballes FR, Russo MW, Ahrens WA, Lundgren DH, Han DK, Bonkovsky HL, Hwang S-I. 2011. Subcellular tissue proteomics of hepatocellular carcinoma for molecular signature discovery. Journal of Proteome Research 10:5070–5083. DOI: 10.1021/pr2005204.
Li C, Hong Y, Tan Y-X, Zhou H, Ai J-H, Li S-J, Zhang L, Xia Q-C, Wu J-R, Wang H-Y, Zeng R. 2004. Accurate qualitative and quantitative proteomic analysis of clinical hepatocellular carcinoma using laser capture microdissection coupled with isotope-coded affinity tag and two-dimensional liquid chromatography mass spectrometry. Molecular & Cellular Proteomics 3:399–409. DOI: 10.1074/mcp.M300133-MCP200.
Li C, Ruan H-Q, Liu Y-S, Xu M-J, Dai J, Sheng Q-H, Tan Y-X, Yao Z-Z, Wang H-Y, Wu J-R, Zeng R. 2012. Quantitative proteomics reveal up-regulated protein expression of the SET complex associated with hepatocellular carcinoma. Journal of Proteome Research 11:871–885. DOI: 10.1021/pr2006999.
Li C, Tan Y-X, Zhou H, Ding S-J, Li S-J, Ma D-j, Man X-b, Hong Y, Zhang L, Li L, Xia Q-C, Wu J-R, Wang H-Y, Zeng R. 2005. Proteomic analysis of hepatitis B virus-associated hepatocellular carcinoma: Identification of potential tumor markers. Proteomics 5:1125–1139. DOI: 10.1002/pmic.200401141.
Megger DA, Bracht T, Kohl M, Ahrens M, Naboulsi W, Weber F, Hoffmann A-C, Stephan C, Kuhlmann K, Eisenacher M, Schlaak JF, Baba HA, Meyer HE, Sitek B. 2013. Proteomic differences between hepatocellular carcinoma and nontumorous liver tissue investigated by a combined gel-based and label-free quantitative proteomics study. Molecular & Cellular Proteomics 12:2006–2020. DOI: 10.1074/mcp.M113.028027.
Naboulsi W, Bracht T, Megger DA, Reis H, Ahrens M, Turewicz M, Eisenacher M, Tautges S, Canbay AE, Meyer HE, Weber F, Baba HA, Sitek B. 2016a. Quantitative proteome analysis reveals the correlation between endocytosis-associated proteins and hepatocellular carcinoma dedifferentiation. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1864:1579–1585. DOI: 10.1016/j.bbapap.2016.08.005.
Naboulsi W, Megger DA, Bracht T, Kohl M, Turewicz M, Eisenacher M, Voss DM, Schlaak JF, Hoffmann A-C, Weber F, Baba HA, Meyer HE, Sitek B. 2016b. Quantitative tissue proteomics analysis reveals versican as potential biomarker for early-stage hepatocellular carcinoma. Journal of Proteome Research 15:38–47. DOI: 10.1021/acs.jproteome.5b00420.
Qiao Z, Pan X, Parlayan C, Ojima H, Kondo T. 2017. Proteomic study of hepatocellular carcinoma using a novel modified aptamer-based array (SOMAscan™) platform. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1865:434–443. DOI: 10.1016/j.bbapap.2016.09.011.
Qi Y, Xu F, Chen L, Li Y, Xu Z, Zhang Y, Wei W, Su N, Zhang T, Fan F, Wang X, Qin X, Zhang L, Liu Y, Xu P. 2016. Quantitative proteomics reveals FLNC as a potential progression marker for the development of hepatocellular carcinoma. Oncotarget 7:68242–68252. DOI: 10.18632/oncotarget.11921.
Reis H, Pütter C, Megger DA, Bracht T, Weber F, Hoffmann A-C, Bertram S, Wohlschläger J, Hagemann S, Eisenacher M, Scherag A, Schlaak JF, Canbay A, Meyer HE, Sitek B, Baba HA. 2015. A structured proteomic approach identifies 14-3-3Sigma as a novel and reliable protein biomarker in panel based differential diagnostics of liver tumors. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1854:641–650. DOI: 10.1016/j.bbapap.2014.10.024.
Roy L, LaBoissière S, Abdou E, Thibault G, Hamel N, Taheri M, Boismenu D, Lanoix J, Kearney RE, Paiement J. 2010. Proteomic analysis of the transitional endoplasmic reticulum in hepatocellular carcinoma: An organelle perspective on cancer. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1804:1869–1881. DOI: 10.1016/j.bbapap.2010.05.008.
Shin H, Cha H-J, Lee MJ, Na K, Park D, Kim C-Y, Han DH, Kim H, Paik Y-K. 2020. Identification of ALDH6A1 as a potential molecular signature in hepatocellular carcinoma via quantitative profiling of the mitochondrial proteome. Journal of Proteome Research 19:1684–1695. DOI: 10.1021/acs.jproteome.9b00846.
Sun W, Xing B, Sun Y, Du X, Lu M, Hao C, Lu Z, Mi W, Wu S, Wei H, Gao X, Zhu Y, Jiang Y, Qian X, He F. 2007. Proteome analysis of hepatocellular carcinoma by two-dimensional difference gel electrophoresis: Novel protein markers in hepatocellular carcinoma tissues. Molecular & Cellular Proteomics 6:1798–1808. DOI: 10.1074/mcp.M600449-MCP200.
Wang Y, Liu H, Liang D, Huang Y, Zeng Y, Xing X, Xia J, Lin M, Han X, Liao N, Liu X, Liu J. 2017. Reveal the molecular signatures of hepatocellular carcinoma with different sizes by iTRAQ based quantitative proteomics. Journal of Proteomics 150:230–241. DOI: 10.1016/j.jprot.2016.09.008.
Xu B, Wang F, Song C, Sun Z, Cheng K, Tan Y, Wang H, Zou H. 2014. Large-scale proteome quantification of hepatocellular carcinoma tissues by a three-dimensional liquid chromatography strategy integrated with sample preparation. Journal of Proteome Research 13:3645–3654. DOI: 10.1021/pr500200s.
Yang J, Xie Q, Zhou H, Chang L, Wei W, Wang Y, Li H, Deng Z, Xiao Y, Wu J, Xu P, Hong X. 2018. Proteomic analysis and NIR-II imaging of MCM2 protein in hepatocellular carcinoma. Journal of Proteome Research 17:2428–2439. DOI: 10.1021/acs.jproteome.8b00181.
Zhu Y, Zhu J, Lu C, Zhang Q, Xie W, Sun P, Dong X, Yue L, Sun Y, Yi X, Zhu T, Ruan G, Aebersold R, Huang S, Guo T. 2019. Identification of protein abundance changes in hepatocellular carcinoma tissues using PCT–SWATH. Proteomics: Clinical Applications 13:1700179. DOI: 10.1002/prca.201700179.