|
1
|
Kamisawa T, Wood LD, Itoi T and Takaori K:
Pancreatic cancer. Lancet. 388:73–85. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Michaud D: Epidemiology of pancreatic
cancer. Minerva Chir. 59:99–111. 2004.PubMed/NCBI
|
|
3
|
Siegel R, Ma J, Zou Z and Jemal A: Cancer
statistics, 2014. CA Cancer J Clin. 64:9–29. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Risch HA: Etiology of pancreatic cancer,
with a hypothesis concerning the role of N-nitroso compounds and
excess gastric acidity. J Natl Cancer Inst. 95:948–960. 2003.
View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Kern SE, Shi C and Hruban RH: The
complexity of pancreatic ductal cancers and multidimensional
strategies for therapeutic targeting. J Pathol. 223:295–306. 2011.
View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Grasso C, Jansen G and Giovannetti E: Drug
resistance in pancreatic cancer: Impact of altered energy
metabolism. Crit Rev Oncol Hematol. 114:139–152. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Eskelinen M and Haglund U: Developments in
serologic detection of human pancreatic adenocarcinoma. Scand J
Gastroenterol. 34:833–844. 1999. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Bass AJ, Lawrence MS, Brace LE, Ramos AH,
Drier Y, Cibulskis K, Sougnez C, Voet D, Saksena G, Sivachenko A,
et al: Genomic sequencing of colorectal adenocarcinomas identifies
a recurrent VTI1A-TCF7L2 fusion. Nat Genet. 43:964–968. 2011.
View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Sjöblom T, Jones S, Wood LD, Parsons DW,
Lin J, Barber TD, Mandelker D, Leary RJ, Ptak J, Silliman N, et al:
The consensus coding sequences of human breast and colorectal
cancers. Science. 314:268–274. 2006. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Wood LD, Parsons DW, Jones S, Lin J,
Sjöblom T, Leary RJ, Shen D, Boca SM, Barber T, Ptak J, et al: The
genomic landscapes of human breast and colorectal cancers. Science.
318:1108–1113. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Janky R, Binda MM, Allemeersch J, Van den
Broeck A, Govaere O, Swinnen JV, Roskams T, Aerts S and Topal B:
Prognostic relevance of molecular subtypes and master regulators in
pancreatic ductal adenocarcinoma. BMC Cancer. 16:6322016.
View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Barrett T, Wilhite SE, Ledoux P,
Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH,
Sherman PM, Holko M, et al: NCBI GEO: Archive for functional
genomics data sets-update. Nucleic Acids Res 41 (Database Issue).
D991–D995. 2013.
|
|
13
|
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW,
Shi W and Smyth GK: Limma powers differential expression analyses
for RNA-sequencing and microarray studies. Nucleic Acids Res.
43:e472015. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Zhang G, Schetter A, He P, Funamizu N,
Gaedcke J, Ghadimi BM, Ried T, Hassan R, Yfantis HG, Lee DH, et al:
DPEP1 inhibits tumor cell invasiveness, enhances chemosensitivity
and predicts clinical outcome in pancreatic ductal adenocarcinoma.
PLoS One. 7:e315072012. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Zhang G, He P, Tan H, Budhu A, Gaedcke J,
Ghadimi BM, Ried T, Yfantis HG, Lee DH, Maitra A, et al:
Integration of metabolomics and transcriptomics revealed a fatty
acid network exerting growth inhibitory effects in human pancreatic
cancer. Clin Cancer Res. 19:4983–4993. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Xing Z, Chu C, Chen L and Kong X: The use
of Gene Ontology terms and KEGG pathways for analysis and
prediction of oncogenes. Biochim Biophys Acta. 1860:2725–2734.
2016. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Kanehisa M, Sato Y, Furumichi M, Morishima
K and Tanabe M: New approach for understanding genome variations in
KEGG. Nucleic Acids Res. 47:D590–D595. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Kanehisa M, Furumichi M, Tanabe M, Sato Y
and Morishima K: KEGG: New perspectives on genomes, pathways,
diseases and drugs. Nucleic Acids Res. 45:D353–D361. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Kanehisa M and Goto S: KEGG: Kyoto
encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30.
2000. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Yu G, Wang LG, Han Y and He QY:
clusterProfiler: An R package for comparing biological themes among
gene clusters. OMICS. 16:284–287. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Shannon P, Markiel A, Ozier O, Baliga NS,
Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A
software environment for integrated models of biomolecular
interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT and
Lin CY: cytoHubba: Identifying hub objects and sub-networks from
complex interactome. BMC Syst Biol. 8 (Suppl 4):S112014. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Chandrashekar DS, Bashel B, Balasubramanya
SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK and
Varambally S: UALCAN: A portal for facilitating tumor subgroup gene
expression and survival analyses. Neoplasia. 19:649–658. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Li B and Dewey CN: RSEM: Accurate
transcript quantification from RNA-Seq data with or without a
reference genome. BMC Bioinformatics. 12:3232011. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Wang X, Wang L, Mo Q, Dong Y, Wang G and
Ji A: Changes of Th17/Treg cell and related cytokines in pancreatic
cancer patients. Int J Clin Exp Pathol. 8:5702–5708.
2015.PubMed/NCBI
|
|
26
|
Shen S, Gui T and Ma C: Identification of
molecular biomarkers for pancreatic cancer with mRMR shortest path
method. Oncotarget. 8:41432–41439. 2017.PubMed/NCBI
|
|
27
|
Thomas PD: The gene ontology and the
meaning of biological function. Methods Mol Biol. 1446:15–24. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Duffy MJ, Duggan C, Mulcahy HE, McDermott
EW and O'Higgins NJ: Urokinase plasminogen activator: A prognostic
marker in breast cancer including patients with axillary
node-negative disease. Clin Chem. 44:1177–1183. 1998.PubMed/NCBI
|
|
29
|
Nielsen TO, Andrews HN, Cheang M, Kucab
JE, Hsu FD, Ragaz J, Gilks CB, Makretsov N, Bajdik CD, Brookes C,
et al: Expression of the insulin-like growth factor I receptor and
urokinase plasminogen activator in breast cancer is associated with
poor survival: Potential for intervention with 17-allylamino
geldanamycin. Cancer Res. 64:286–291. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Pavón MA, Arroyo-Solera I, Céspedes MV,
Casanova I, León X and Mangues R: uPA/uPAR and SERPINE1 in head and
neck cancer: Role in tumor resistance, metastasis, prognosis and
therapy. Oncotarget. 7:57351–57366. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Amos S, Redpath GT, Dipierro CG, Carpenter
JE and Hussaini IM: Epidermal growth factor receptor-mediated
regulation of urokinase plasminogen activator expression and
glioblastoma invasion via C-SRC/MAPK/AP-1 signaling pathways. J
Neuropathol Exp Neurol. 69:582–592. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Chaudhary A, Hilton MB, Seaman S, Haines
DC, Stevenson S, Lemotte PK, Tschantz WR, Zhang XM, Saha S, Fleming
T and St Croix B: TEM8/ANTXR1 blockade inhibits pathological
angiogenesis and potentiates tumoricidal responses against multiple
cancer types. Cancer Cell. 21:212–226. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Xu ZY, Chen JS and Shu YQ: Gene expression
profile towards the prediction of patient survival of gastric
cancer. Biomed Pharmacother. 64:133–139. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Liu P, Weng Y, Sui Z, Wu Y, Meng X, Wu M,
Jin H, Tan X, Zhang L and Zhang Y: Quantitative secretomic analysis
of pancreatic cancer cells in serum-containing conditioned medium.
Sci Rep. 6:376062016. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Borradori L and Sonnenberg A: Structure
and function of hemidesmosomes: More than simple adhesion
complexes. J Invest Dermatol. 112:411–418. 1999. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Järveläinen H, Sainio A, Koulu M, Wight TN
and Penttinen R: Extracellular matrix molecules: Potential targets
in pharmacotherapy. Pharmacol Rev. 61:198–223. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Yodsurang V, Tanikawa C, Miyamoto T, Lo
PHY, Hirata M and Matsuda K: Identification of a novel p53 target,
COL17A1, that inhibits breast cancer cell migration and invasion.
Oncotarget. 8:55790–55803. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Chowdhury N and Sapru S: Association of
protein translation and extracellular matrix gene sets with breast
cancer metastasis: Findings uncovered on analysis of multiple
publicly available datasets using individual patient data approach.
PLoS One. 10:e01296102015. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Rizwan A, Bulte C, Kalaichelvan A, Cheng
M, Krishnamachary B, Bhujwalla ZM, Jiang L and Glunde K: Metastatic
breast cancer cells in lymph nodes increase nodal collagen density.
Sci Rep. 5:100022015. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Zou X, Feng B, Dong T, Yan G, Tan B, Shen
H, Huang A, Zhang X, Zhang M, Yang P, et al: Up-regulation of type
I collagen during tumorigenesis of colorectal cancer revealed by
quantitative proteomic analysis. J Proteomics. 94:473–485. 2013.
View Article : Google Scholar : PubMed/NCBI
|
