Pancreatic Cancer

This vignette from the R package canprot reproduces calculations of compositional oxidation state and hydration state that are described in a paper published in PeerJ (Dick, 2017).


T (tumor), N (normal), CP (chronic pancreatitis), AIP (autoimmune pancreatitis), PC (pancreatic cancer), DM (diabetes mellitus), PDAC (pancreatic ductal adenocarcinoma), ANT (adjacent normal tissue), FFPE (Formalin-fixed paraffin-embedded), LCM (laser-capture microdissection), NP (normal pancreas).

Summary Table

This table compares the chemical compositions of groups of human proteins that are relatively down- and up-expressed (n1 and n2, respectively) in pancreatic cancer compared to non-tumor tissue.

datasets <- pdat_pancreatic()
comptab <- lapply_canprot(datasets, function(dataset) {
  pdat <- get_pdat(dataset, "pdat_pancreatic")
  get_comptab(pdat,, mfun="mean")
}, varlist="pdat_pancreatic")
set reference (description) n1 n2 MD ES p-value MD ES p-value
a LHE+04 (T / N) 41 69 0.002 53 6e-01 0.062 66 5e-03
b CYD+05 (T / N) 60 88 0.025 61 2e-02 -0.009 48 7e-01
c CGB+05 (T / N) 48 54 0.015 59 1e-01 -0.014 48 8e-01
d CBP+07 (CP / N) 19 95 0.009 54 6e-01 -0.053 30 6e-03
e CTZ+09 (T / N) 28 29 0.023 64 8e-02 0.027 58 3e-01
f MLC+11 (T / N) 38 45 0.021 56 4e-01 0.015 58 2e-01
g PCS+11 (FFPE T / N) 207 152 0.042 69 2e-09 -0.018 45 1e-01
h TMW+11 (accessible T / N) 108 86 -0.031 35 5e-04 -0.026 41 4e-02
i KBK+12 (FFPE T / N) 38 47 0.039 69 3e-03 0.024 59 1e-01
j KHO+13 (T / N) 78 57 -0.011 43 2e-01 0.041 60 5e-02
k KPC+13 (T / N) 257 456 0.006 52 5e-01 0.036 60 2e-05
l PKB+13 (FFPE PC / AIP) 29 73 -0.015 42 2e-01 0.023 56 3e-01
m PKB+13 (FFPE PC / CP) 53 73 -0.016 45 3e-01 0.054 66 2e-03
n WLL+13 (low-grade T / N) 83 32 0.008 58 2e-01 -0.036 40 1e-01
o WLL+13 (high-grade T / N) 224 176 0.022 62 3e-05 -0.003 49 7e-01
p WLL+13a (T / N (no DM)) 208 219 0.034 68 1e-10 0.000 50 9e-01
q WLL+13a (T / N (DM)) 56 167 0.034 66 4e-04 0.040 63 4e-03
r ZNWL13 (LCM PDAC / ANT) 227 148 0.035 67 1e-08 -0.007 49 6e-01
s ISI+14 (T / N) 65 34 0.002 53 6e-01 -0.050 36 3e-02
t KKC+16 (mouse 2.5 w T / N) 35 51 -0.015 44 4e-01 -0.004 46 6e-01
u KKC+16 (mouse 3.5 w T / N) 40 73 -0.019 42 1e-01 0.045 65 1e-02
v KKC+16 (mouse 5 w T / N) 49 84 0.015 57 2e-01 -0.005 49 9e-01
w KKC+16 (mouse 10 w T / N) 37 108 0.014 58 2e-01 0.024 58 2e-01

Data Sources

a. Pooled tissue samples of PC and matched normal tissue from 12 patients. Source: Tables 2 and 3 of Lu et al. (2004). b. Two PC and two normal pancreas samples. Source: Tables 1 and 2 of Chen et al. (2005). c. Large-scale immunoblotting (PowerBlot) of 8 tissue specimens of pancreatic intraepithelial neoplasia compared to normal pancreas and CP. Source: Table 2 of Crnogorac-Jurcevic et al. (2005). d. Tissue specimens from patients with CP (without any findings of pancreatic cancer) and 10 control specimens from patients with normal pancreas. Source: Table 1 of Chen et al. (2007). e. 12 carcinoma samples (PDAC), 12 benign pancreatic adenocarcinomas and 10 normal tissues adjacent to the PDAC primary mass. Source: Table 1 of Cui et al. (2009). f. Source: extracted from Suppl. Table S2 of McKinney et al. (2011). g. PDAC compared to normal pancreas. Source: Suppl. Table 3 of Pan et al. (2011). h. Potentially accessible proteins in fresh samples of PC tumors (three patients) vs normal tissue (two patients with normal pancreas and one with CP). Source: extracted from the SI Table of Turtoi et al. (2011). i. 11 tissue specimens containing >50% cancer and 8 unmatched, uninvolved tissues adjacent to pancreatitis. Source: Suppl. Tables 2 and 3 of Kojima et al. (2012). j. Fresh-frozen PDAC tissue specimens from 7 patients vs a pooled mixture of 3 normal main pancreatic duct tissue samples. Source: extracted from SI Table S3 of Kawahara et al. (2013), including 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. k. Frozen samples of PDAC tumors vs adjacent benign tissue from four patients. Source: Suppl. Table 2 of Kosanam et al. (2013). l. m. Tissue samples from 3 patients with PC vs 3 patients with AIP or 3 patients with CP. Source: extracted from Tables 2, 3, and 4 of Paulo et al. (2013). n. o. 12 samples each (pooled) of low-grade tumor or high-grade tumor vs non-tumor. Source: extracted from Suppl. Tables S4 and S5 of Wang et al. (2013a), including proteins with ratios ≥1.5 or ≤0.667 for at least 2 of the 4 groups, and with expression differences for all 4 groups in the same direction. p. q. Source: extracted from Suppl. Tables S3 and S4 of Wang et al. (2013b), including proteins with >3/2 or <2/3 fold change in at least 3 of 4 iTRAQ experiments for different pooled samples. r. LCM of CD24+ cells from PDAC vs CD24- cells from adjacent normal tissue (ANT). Source: SI Table S5 of Zhu et al. (2013). s. Matched PDAC and normal tissue from nine patients. Source: extracted from SI Table S5 of Iuga et al. (2014), excluding proteins marked as “not passed”, i.e. having inconsistent regulation. t. PDAC tumors in transgenic mice vs pancreas in normal mice, analyzed at time points of 2.5, 3.5, 5 and 10 weeks. Source: Suppl. Table of Kuo et al. (2016).

Mean Differences

The datasets comparing chronic pancreatitis or low-grade tumor to normal proteomes are highlighted in red.

col <- rep("black", length(datasets))
col[grepl("=low", datasets)] <- "red"
diffplot(comptab, col=col)


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.

Chen R., Brentnall TA., Pan S., Cooke K., Moyes KW., Lane Z., Crispin DA., Goodlett DR., Aebersold R., Bronner MP. 2007. Quantitative proteomics analysis reveals that proteins differentially expressed in chronic pancreatitis are also frequently involved in pancreatic cancer. Molecular & Cellular Proteomics 6:1331–1342. DOI: 10.1074/mcp.M700072-MCP200.

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.

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 6:1–12. DOI: 10.1371/journal.pone.0027574.

Paulo JA., Kadiyala V., Brizard S., Banks PA., Steen H., Conwell DL. 2013. A proteomic comparison of formalin-fixed paraffin-embedded pancreatic tissue from autoimmune pancreatitis, chronic pancreatitis, and pancreatic cancer. Journal of the Pancreas 14:405–414. DOI: 10.6092/1590-8577/1508.

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., Lou W-H., Jin D-Y., Yang P-Y., Wang X-L. 2013a. ITRAQ-based quantitative proteomics reveals myoferlin as a novel prognostic predictor in pancreatic adenocarcinoma. Journal of Proteomics 91:453–465. DOI: 10.1016/j.jprot.2013.06.032.

Wang W-S., Liu X-H., Liu L-X., Jin D-Y., Yang P-Y., Wang X-L. 2013b. 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.

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.