This vignette from the R package JMDplots version 1.2.19-9 shows chemical metrics for proteins that are differentially expressed in pancreatic 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 |
LHE+04 (T / adjacent N)
|
41 | 69 | 7 | 54 | -1 | 6 |
| b |
CYD+05 (T / N)
|
60 | 88 | 26 | 9 | 128 | 88 |
| c |
CGB+05 (T / N)
|
48 | 54 | 7 | 13 | 87 | -16 |
| d |
CTZ+09 (T / adjacent N)
|
28 | 29 | 18 | 47 | 132 | -134 |
| e |
MLC+11 (T / adjacent N)
|
38 | 45 | 10 | 28 | 18 | 61 |
| f |
PCS+11 (FFPE T / N)
|
207 | 152 | 35 | -19 | 130 | -30 |
| g |
TMW+11 (accessible T / N)
|
108 | 86 | -45 | -21 | 139 | 42 |
| h |
KBK+12 (FFPE T / N)
|
38 | 47 | 41 | 32 | -87 | 21 |
| i |
KHO+13 (T / N)
|
78 | 57 | -17 | 29 | -380 | 45 |
| j |
KPC+13 (T / adjacent N)
|
257 | 456 | 3 | 31 | -69 | 82 |
| k |
WLL+13a (T / adjacent N with DM)
|
208 | 219 | 32 | 14 | 86 | 24 |
| l |
WLL+13a (T / adjacent N without DM)
|
56 | 167 | 26 | 46 | 131 | -16 |
| m |
YKK+13 (T / adjacent N)
|
84 | 83 | 27 | -2 | 60 | 57 |
| n |
ZNWL13 (LCM T / adjacent N)
|
227 | 148 | 33 | 2 | 32 | -12 |
| o |
ISI+14 (T / adjacent N)
|
65 | 34 | 6 | -19 | -53 | 174 |
| p |
BZQ+14 (T / matched N)
|
55 | 98 | 26 | 1 | 102 | 6 |
| q |
MZH+14 (mouse tumor / healthy)
|
38 | 28 | 5 | -8 | -141 | -41 |
| r |
BHB+15 (mouse organoids T / N)
|
486 | 526 | -9 | 23 | 34 | -30 |
| s |
KKC+16 (mouse 10 w T / N)
|
37 | 108 | 18 | 34 | -46 | 17 |
| t |
CHO+18 (T / adjacent N)
|
109 | 129 | 21 | 2 | 120 | 109 |
| u |
SWW+18 (T / adjacent N)
|
284 | 324 | -11 | 0 | 4 | 38 |
| v |
ZAH+19 (T / N)
|
89 | 76 | 27 | -34 | 222 | 46 |
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. Tables 2 and 3 of Lu et al. (2004). b. Tables 1 and 2 of Chen et al. (2005). c. Table 2 of Crnogorac-Jurcevic et al. (2005). d. Table 1 of Cui et al. (2009). e. IPI numbers from Supplementary Table S2 of McKinney et al. (2011). f. Supplementary Table 3 of Pan et al. (2011). g. Extracted from the SI Table of Turtoi et al. (2011). h. Supplementary Tables 2 and 3 of Kojima et al. (2012). i. SI Table S3 of Kawahara et al. (2013), filtered to include proteins with an expression ratio >2 [or <0.5] in at least 5 of the 7 experiments and ratio >1 [or <1] in all experiments. j. Supplementary Table 2 of Kosanam et al. (2013). k. l. Supplementary Tables S3 and S4 of Wang et al. (2013), including proteins with >3/2 or <2/3 fold change in at least 3 of 4 iTRAQ experiments for different pooled samples. m. Supplementary Tables 2 and 3 of Yu et al. (2013) (data file provided by Youngsoo Kim). n. SI Table S5 of Zhu et al. (2013). o. SI Table S5 of Iuga et al. (2014), filtered to exclude proteins marked as “not passed”, i.e. having inconsistent regulation. p. Table S6, Sheet 2 of Britton et al. (2014). q. Table 1 of Mirus et al. (2014). r. Table S6 of Boj et al. (2015). s. Supplementary Table of Kuo et al. (2016). t. Supplementary Table S3 of Coleman et al. (2018). u. Table S1 of Song et al. (2018), filtered to exclude proteins with opposite expression changes in different patients. v. Gene names extracted from Figure 1b of Zhou et al. (2019).
Thanks to Youngsoo Kim for providing a data file.
Boj SF, Hwang C-I, Baker LA, Chio IIC, Engle DD, Corbo V, Jager M, Ponz-Sarvise M, Tiriac H, Spector MS, Gracanin A, Oni T, Yu KH, Boxtel R van, Huch M, Rivera KD, Wilson JP, Feigin ME, Öhlund D, Handly-Santana A, Ardito-Abraham CM, Ludwig M, Elyada E, Alagesan B, Biffi G, Yordanov GN, Delcuze B, Creighton B, Wright K, Park Y, Morsink FH, Molenaar IQ, Rinkes IHB, Cuppen E, Hao Y, Jin Y, Nijman IJ, Iacobuzio-Donahue C, Leach SD, Pappin DJ, Hammell M, Klimstra DS, Basturk O, Hruban RH, Offerhaus GJ, Vries RG, Clevers H, Tuveson DA. 2015. Organoid models of human and mouse ductal pancreatic cancer. Cell 160:324–338. DOI: 10.1016/j.cell.2014.12.021.
Britton D, Zen Y, Quaglia A, Selzer S, Mitra V, Lößner C, Jung S, Böhm G, Schmid P, Prefot P, Hoehle C, Koncarevic S, Gee J, Nicholson R, Ward M, Castellano L, Stebbing J, Zucht HD, Sarker D, Heaton N, Pike I. 2014. Quantification of pancreatic cancer proteome and phosphorylome: Indicates molecular events likely contributing to cancer and activity of drug targets. PLOS One 9:e90948. DOI: 10.1371/journal.pone.0090948.
Chen R, Yi EC, Donohoe S, Pan S, Eng J, Cooke K, Crispin DA, Lane Z, Goodlett DR, Bronner MP, Aebersold R, Brentnall TA. 2005. Pancreatic cancer proteome: The proteins that underlie invasion, metastasis, and immunologic escape. Gastroenterology 129:1187–1197. DOI: 10.1053/j.gastro.2005.08.001.
Coleman O, Henry M, O’Neill F, Roche S, Swan N, Boyle L, Murphy J, Meiller J, Conlon NT, Geoghegan J, Conlon KC, Lynch V, Straubinger NL, Straubinger RM, McVey G, Moriarty M, Meleady P, Clynes M. 2018. A comparative quantitative LC-MS/MS profiling analysis of human pancreatic adenocarcinoma, adjacent-normal tissue, and patient-derived tumour xenografts. Proteomes 6:45. DOI: 10.3390/proteomes6040045.
Crnogorac-Jurcevic T, Gangeswaran R, Bhakta V, Capurso G, Lattimore S, Akada M, Sunamura M, Prime W, Campbell F, Brentnall TA, Costello E, Neoptolemos J, Lemoine NR. 2005. Proteomic analysis of chronic pancreatitis and pancreatic adenocarcinoma. Gastroenterology 129:1454–1463. DOI: 10.1053/j.gastro.2005.08.012.
Cui Y, Tian M, Zong M, Teng M, Chen Y, Lu J, Jiang J, Liu X, Han J. 2009. Proteomic analysis of pancreatic ductal adenocarcinoma compared with normal adjacent pancreatic tissue and pancreatic benign cystadenoma. Pancreatology 9:89–98. DOI: 10.1159/000178879.
Iuga C, Seicean A, Iancu C, Buiga R, Sappa PK, Völker U, Hammer E. 2014. Proteomic identification of potential prognostic biomarkers in resectable pancreatic ductal adenocarcinoma. Proteomics 14:945–955. DOI: 10.1002/pmic.201300402.
Kawahara T, Hotta N, Ozawa Y, Kato S, Kano K, Yokoyama Y, Nagino M, Takahashi T, Yanagisawa K. 2013. Quantitative proteomic profiling identifies DPYSL3 as pancreatic ductal adenocarcinoma-associated molecule that regulates cell adhesion and migration by stabilization of focal adhesion complex. PLOS One 8:e79654. DOI: 10.1371/journal.pone.0079654.
Kojima K, Bowersock GJ, Kojima C, Klug CA, Grizzle WE, Mobley JA. 2012. Validation of a robust proteomic analysis carried out on formalin-fixed paraffin-embedded tissues of the pancreas obtained from mouse and human. Proteomics 12:3393–3402. DOI: 10.1002/pmic.201100663.
Kosanam H, Prassas I, Chrystoja CC, Soleas I, Chan A, Dimitromanolakis A, Blasutig IM, Rückert F, Gruetzmann R, Pilarsky C, Maekawa M, Brand R, Diamandis EP. 2013. Laminin, gamma 2 (LAMC2): A promising new putative pancreatic cancer biomarker identified by proteomic analysis of pancreatic adenocarcinoma tissues. Molecular & Cellular Proteomics 12:2820–2832. DOI: 10.1074/mcp.M112.023507.
Kuo K-K, Kuo C-J, Chiu C-Y, Liang S-S, Huang C-H, Chi S-W, Tsai K-B, Chen C-Y, Hsi E, Cheng K-H, Chiou S-H. 2016. Quantitative proteomic analysis of differentially expressed protein profiles involved in pancreatic ductal adenocarcinoma. Pancreas 45:71–83. DOI: 10.1097/MPA.0000000000000388.
Lu Z, Hu L, Evers S, Chen J, Shen Y. 2004. Differential expression profiling of human pancreatic adenocarcinoma and healthy pancreatic tissue. Proteomics 4:3975–3988. DOI: 10.1002/pmic.200300863.
McKinney KQ, Lee Y-Y, Choi H-S, Groseclose G, Iannitti DA, Martinie JB, Russo MW, Lundgren DH, Han DK, Bonkovsky HL, Hwang S-I. 2011. Discovery of putative pancreatic cancer biomarkers using subcellular proteomics. Journal of Proteomics 74:79–88. DOI: 10.1016/j.jprot.2010.08.006.
Mirus JE, Zhang Y, Hollingsworth MA, Solan JL, Lampe PD, Hingorani SR. 2014. Spatiotemporal proteomic analyses during pancreas cancer progression identifies serine/threonine stress kinase 4 (STK4) as a novel candidate biomarker for early stage disease. Molecular & Cellular Proteomics 13:3484–3496. DOI: 10.1074/mcp.M113.036517.
Pan S, Chen R, Stevens T, Bronner MP, May D, Tamura Y, McIntosh MW, Brentnall TA. 2011. Proteomics portrait of archival lesions of chronic pancreatitis. PLOS One 6:e27574. DOI: 10.1371/journal.pone.0027574.
Song Y, Wang Q, Wang D, Li J, Yang J, Li H, Wang X, Jin X, Jing R, Yang J-H, Su H. 2018. Label-free quantitative proteomics unravels carboxypeptidases as the novel biomarker in pancreatic ductal adenocarcinoma. Translational Oncology 11:691–699. DOI: 10.1016/j.tranon.2018.03.005.
Turtoi A, Musmeci D, Wang Y, Dumont B, Somja J, Bevilacqua G, De Pauw E, Delvenne P, Castronovo V. 2011. Identification of novel accessible proteins bearing diagnostic and therapeutic potential in human pancreatic ductal adenocarcinoma. Journal of Proteome Research 10:4302–4313. DOI: 10.1021/pr200527z.
Wang W-S, Liu X-H, Liu L-X, Jin D-Y, Yang P-Y, Wang X-L. 2013. Identification of proteins implicated in the development of pancreatic cancer-associated diabetes mellitus by iTRAQ-based quantitative proteomics. Journal of Proteomics 84:52–60. DOI: 10.1016/j.jprot.2013.03.031.
Yu J, Kim K, Kang M, Kim H, Kim SW, Jang J-Y, Kim Y. 2013. Development of candidate biomarkers for pancreatic ductal adenocarcinoma using multiple reaction monitoring. Biotechnology and Bioprocess Engineering 18:1038–1047. DOI: 10.1007/s12257-013-0421-2.
Zhou Q, Andersson R, Hu D, Bauden M, Kristl T, Sasor A, Pawłowski K, Pla I, Hilmersson KS, Zhou M, Lu F, Marko-Varga G, Ansari D. 2019. Quantitative proteomics identifies brain acid soluble protein 1 (BASP1) as a prognostic biomarker candidate in pancreatic cancer tissue. EBioMedicine 43:282–294. DOI: 10.1016/j.ebiom.2019.04.008.
Zhu J, Nie S, Wu J, Lubman DM. 2013. Target proteomic profiling of frozen pancreatic CD24+ adenocarcinoma tissues by immuno-laser capture microdissection and nano-LC–MS/MS. Journal of Proteome Research 12:2791–2804. DOI: 10.1021/pr400139c.