Hidalgo, M. et al. Addressing the challenges of pancreatic cancer: future directions for improving outcomes. Pancreatology 15, 8–18 (2015). DOI: 10.1016/j.pan.2014.10.001
Valle, S., Martin-Hijano, L., Alcalá, S., Alonso-Nocelo, M. & Sainz, B. Jr. The ever-evolving concept of the cancer stem cell in pancreatic cancer. Cancers 10, 33 (2018). DOI: 10.3390/cancers10020033
Garrido-Laguna, I. & Hidalgo, M. Pancreatic cancer: from state-of-the-art treatments to promising novel therapies. Nat. Rev. Clin. Oncol. 12, 319–334 (2015). DOI: 10.1038/nrclinonc.2015.53
Nevala-Plagemann, C., Hidalgo, M. & Garrido-Laguna, I. From state-of-the-art treatments to novel therapies for advanced-stage pancreatic cancer. Nat. Rev. Clin. Oncol. 17, 108–123 (2020). DOI: 10.1038/s41571-019-0281-6
Olive, K. P. et al. Inhibition of hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science 324, 1457–1461 (2009). DOI: 10.1126/science.1171362
Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56 (2011). DOI: 10.1038/nrg2918
Erten, S., Bebek, G., Ewing, R. M. & Koyutürk, M. DADA: degree-aware algorithms for network-based disease gene prioritization. BioData Min. 4, 19–19 (2011). DOI: 10.1186/1756-0381-4-19
Kaiser, M. H. & Ellenberg, S. S. Pancreatic cancer: adjuvant combined radiation and chemotherapy following curative resection. Arch. Surg. 120, 899–903 (1985). DOI: 10.1001/archsurg.1985.01390320023003
Riediger, H. et al. The lymph node ratio is the strongest prognostic factor after resection of pancreatic cancer. J. Gastrointest. Surg. 13, 1337 (2009). DOI: 10.1007/s11605-009-0919-2
Osipov, A. et al. Impact of margin status and lymphadenectomy on clinical outcomes in resected pancreatic adenocarcinoma: implications for adjuvant radiotherapy. J. Gastrointest. Oncol. 7, 239 (2016).
Schlitter, A. M. et al. Molecular, morphological and survival analysis of 177 resected pancreatic ductal adenocarcinomas (PDACs): Identification of prognostic subtypes. Sci. Rep. 7, 41064 (2017). DOI: 10.1038/srep41064
Liu, B. et al. Identification of prognostic biomarkers by combined mRNA and miRNA expression microarray analysis in pancreatic cancer. Transl. Oncol. 11, 700–714 (2018). DOI: 10.1016/j.tranon.2018.03.003
Xiong, G. et al. MiR-10a-5p targets TFAP2C to promote gemcitabine resistance in pancreatic ductal adenocarcinoma. J. Exp. Clin. Cancer Res. 37, 76 (2018). DOI: 10.1186/s13046-018-0739-x
Collisson, E. A. & Maitra, A. Pancreatic cancer genomics 2.0: profiling metastases. Cancer Cell 31, 309–310 (2017). DOI: 10.1016/j.ccell.2017.02.014
Roychowdhury, S. & Chinnaiyan, A. M. Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J. Clin. 66, 75–88 (2016). DOI: 10.3322/caac.21329
Chaika, N. V. et al. Differential expression of metabolic genes in tumor and stromal components of primary and metastatic loci in pancreatic adenocarcinoma. PLoS ONE 7, e32996 (2012). DOI: 10.1371/journal.pone.0032996
Mao, Y. et al. RNA sequencing analyses reveal novel differentially expressed genes and pathways in pancreatic cancer. Oncotarget 8, 42537 (2017). DOI: 10.18632/oncotarget.16451
Newhook, T. E. et al. A thirteen-gene expression signature predicts survival of patients with pancreatic cancer and identifies new genes of interest. PLoS ONE 9, e105631 (2014). DOI: 10.1371/journal.pone.0105631
Rao, M. et al. PO-277 single-cell RNA-seq analysis of human pancreatic ductal adenocarcinoma. ESMO Open 3, A336 (2018). DOI: 10.1136/esmoopen-2018-EACR25.791
Duconseil, P. et al. Transcriptomic analysis predicts survival and sensitivity to anticancer drugs of patients with a pancreatic adenocarcinoma. Am. J. Pathol. 185, 1022–1032 (2015). DOI: 10.1016/j.ajpath.2014.11.029
Stark, A. P. et al. Long-term survival in patients with pancreatic ductal adenocarcinoma. Surgery 159, 1520–1527 (2016). DOI: 10.1016/j.surg.2015.12.024
Chen, R. et al. Stromal galectin-1 expression is associated with long-term survival in resectable pancreatic ductal adenocarcinoma. Cancer Biol. Therapy 13, 899–907 (2012). DOI: 10.4161/cbt.20842
Zhao, L., Zhao, H. & Yan, H. Gene expression profiling of 1200 pancreatic ductal adenocarcinoma reveals novel subtypes. BMC Cancer 18, 603 (2018). DOI: 10.1186/s12885-018-4546-8
Menche, J. et al. Integrating personalized gene expression profiles into predictive disease-associated gene pools. NPJ Syst. Biol. Appl. 3, 10 (2017). DOI: 10.1038/s41540-017-0009-0
Puleo, F. et al. Stratification of pancreatic ductal adenocarcinomas based on tumor and microenvironment features. Gastroenterology 155, 1999–2013 (2018). DOI: 10.1053/j.gastro.2018.08.033
Bailey, P. et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47 (2016). DOI: 10.1038/nature16965
Peran, I., Madhavan, S., Byers, S. W. & McCoy, M. D. Curation of the pancreatic ductal adenocarcinoma subset of the cancer genome Atlas is essential for accurate conclusions about survival related molecular mechanisms. Clin. Cancer Res. 24, 3813–3819 (2018). DOI: 10.1158/1078-0432.CCR-18-0290
Presson, A. P. et al. Protein expression based multimarker analysis of breast cancer samples. BMC Cancer 11, 230 (2011). DOI: 10.1186/1471-2407-11-230
Wirapati, P. et al. Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res. 10, R65 (2008). DOI: 10.1186/bcr2124
Wang, L. et al. Gene networks and microRNAs implicated in aggressive prostate cancer. Cancer Res. 69, 9490–9497 (2009). DOI: 10.1158/0008-5472.CAN-09-2183
Giulietti, M., Occhipinti, G., Principato, G. & Piva, F. Weighted gene co-expression network analysis reveals key genes involved in pancreatic ductal adenocarcinoma development. Cell. Oncol. 39, 379–388 (2016). DOI: 10.1007/s13402-016-0283-7
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008). DOI: 10.1186/1471-2105-9-559
Sharma, A. et al. Integration of molecular interactome and targeted interaction analysis to identify a COPD disease network module. Sci. Rep. 8, 14439 (2018). DOI: 10.1038/s41598-018-32173-z
Piñero, J. et al. The DisGeNET knowledge platform for disease genomics. Nucl. Acids Res. 48, D845–D855 (2019).
Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385 (2018). DOI: 10.1016/j.cell.2018.02.060
Kim, J. H., Byun, S. J., Park, S. G., Oh, Y. K. & Baek, S. K. Interval between surgery and radiation therapy is an important prognostic factor in treatment of rectal cancer. Cancer Res. Treat. 44, 187–194 (2012). DOI: 10.4143/crt.2012.44.3.187
Haider, S. et al. A multi-gene signature predicts outcome in patients with pancreatic ductal adenocarcinoma. Genome Med. 6, 105 (2014). DOI: 10.1186/s13073-014-0105-3
Biankin, A. V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399 (2012). DOI: 10.1038/nature11547
Zhu, T. et al. Genome-scale analysis identifies GJB2 and ERO1LB as prognosis markers in patients with pancreatic cancer. Oncotarget 8, 21281 (2017). DOI: 10.18632/oncotarget.15068
Raman, P., Maddipati, R., Lim, K. H. & Tozeren, A. Pancreatic cancer survival analysis defines a signature that predicts outcome. PLoS ONE 13, e0201751 (2018). DOI: 10.1371/journal.pone.0201751
Osaki, M., Oshimura, M. & Ito, H. PI3K-Akt pathway: its functions and alterations in human cancer. Apoptosis 9, 667–676 (2004). DOI: 10.1023/B:APPT.0000045801.15585.dd
Glazer, E. S., Welsh, E., Pimiento, J. M., Teer, J. K. & Malafa, M. P. TGFβ1 overexpression is associated with improved survival and low tumor cell proliferation in patients with early-stage pancreatic ductal adenocarcinoma. Oncotarget 8, 999 (2017). DOI: 10.18632/oncotarget.13533
Paniccia, A., Merkow, J., Edil, B. H. & Zhu, Y. Immunotherapy for pancreatic ductal adenocarcinoma: an overview of clinical trials. Chin. J. Cancer Res. 27, 376–391 (2015).
Fan, Z. et al. Critical role of KRAS mutation in pancreatic ductal adenocarcinoma. Transl. Cancer Res. 7, 1728–1736 (2018). DOI: 10.21037/tcr.2018.10.19
Altieri, F. et al. Epigenetic alterations of gastrokine 1 gene expression in gastric cancer. Oncotarget 8, 16899 (2017). DOI: 10.18632/oncotarget.14817
Yao, H. et al. Glypican-3 and KRT19 are markers associating with metastasis and poor prognosis of pancreatic ductal adenocarcinoma. Cancer Biomark. 17, 397–404 (2016). DOI: 10.3233/CBM-160655
Takehara, A. et al. Novel tumor marker REG4 detected in serum of patients with resectable pancreatic cancer and feasibility for antibody therapy targeting REG4. Cancer Sci. 97, 1191–1197 (2006). DOI: 10.1111/j.1349-7006.2006.00297.x
Lin, X. et al. TSPAN8 serves as a prognostic marker involving Akt/MAPK pathway in nasopharyngeal carcinoma. Ann. Transl. Med. 7, 18 (2019). DOI: 10.21037/atm.2018.12.02
Halbrook, C. J. & Lyssiotis, C. A. Employing metabolism to improve the diagnosis and treatment of pancreatic cancer. Cancer Cell 31, 5–19 (2017). DOI: 10.1016/j.ccell.2016.12.006
Shurin, M. R. Immunological targets for cancer therapy: new recognition. ImmunoTargets Therapy 7, 83 (2018). DOI: 10.2147/ITT.S191821
Oldham, M. C., Horvath, S. & Geschwind, D. H. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc. Natl. Acad. Sci. 103, 17973–17978 (2006). DOI: 10.1073/pnas.0605938103
Wang, J. et al. Single-cell co-expression analysis reveals distinct functional modules, co-regulation mechanisms and clinical outcomes. PLoS Comput. Biol. 12, e1004892 (2016). DOI: 10.1371/journal.pcbi.1004892
Ballouz, S., Verleyen, W. & Gillis, J. Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics 31, 2123–2130 (2015). DOI: 10.1093/bioinformatics/btv118
Hur, C. et al. Early pancreatic ductal adenocarcinoma survival is dependent on size: positive implications for future targeted screening. Pancreas 45, 1062 (2016). DOI: 10.1097/MPA.0000000000000587
Marchegiani, G. et al. Does the surgical waiting list affect pathological and survival outcome in resectable pancreatic ductal adenocarcinoma?. HPB 20, 411–417 (2018). DOI: 10.1016/j.hpb.2017.10.017
Zhou, L. et al. Suppression of stromal-derived Dickkopf-3 (DKK3) inhibits tumor progression and prolongs survival in pancreatic ductal adenocarcinoma. Sci. Transl. Med. 10, eaat3487 (2018). DOI: 10.1126/scitranslmed.aat3487
Stone, B. et al. Can gene expression profiling identify pancreatic ductal adenocarcinoma patients with short or long-term prognosis?. J. Pancreas 19, 118–125 (2018).
Dal Molin, M. & Wood, L. D. Very long-term survival in pancreatic cancer. Aging (Albany NY) 7, 360 (2015). DOI: 10.18632/aging.100771
Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor-and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47, 1168 (2015). DOI: 10.1038/ng.3398
Waddell, N. et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature 518, 495 (2015). DOI: 10.1038/nature14169
Marino, F. Z. et al. Molecular heterogeneity in lung cancer: from mechanisms of origin to clinical implications. Int. J. Med. Sci. 16, 981 (2019). DOI: 10.7150/ijms.34739
Zhou, H. et al. Multi-region exome sequencing reveals the intratumoral heterogeneity of surgically resected small cell lung cancer. Nat. Commun. 12, 1–11 (2021). DOI: 10.1038/s41467-020-20314-w
Decaup, E., Rochotte, J., Pyronnet, S., Bousquet, C. & Jean, C. Focal adhesion kinase: a promising therapeutic target in pancreatic adenocarcinoma. Clin. Res. Hepatol. Gastroenterol. 41, 246–248 (2017). DOI: 10.1016/j.clinre.2016.10.010
Weniger, M., Honselmann, K. & Liss, A. The extracellular matrix and pancreatic cancer: a complex relationship. Cancers 10, 316 (2018). DOI: 10.3390/cancers10090316
McCleary-Wheeler, A. L., McWilliams, R. & Fernandez-Zapico, M. E. Aberrant signaling pathways in pancreatic cancer: a two compartment view. Mol. Carcinog. 51, 25–39 (2012). DOI: 10.1002/mc.20827
Le Large, T. Y. S. et al. Key biological processes driving metastatic spread of pancreatic cancer as identified by multi-omics studies. Semin. Cancer Biol. 44, 153–169 (2017). DOI: 10.1016/j.semcancer.2017.03.008
Birnbaum, D. J. et al. A 25-gene classifier predicts overall survival in resectable pancreatic cancer. BMC Med. 15, 170 (2017). DOI: 10.1186/s12916-017-0936-z
Isik, Z., Baldow, C., Cannistraci, C. V. & Schroeder, M. Drug target prioritization by perturbed gene expression and network information. Sci. Rep. 5, 17417 (2015). DOI: 10.1038/srep17417
Mullen, J., Cockell, S. J., Woollard, P. & Wipat, A. An integrated data driven approach to drug repositioning using gene-disease associations. PLoS ONE 11, e0155811 (2016). DOI: 10.1371/journal.pone.0155811
Xie, C.-M., Wei, W. & Sun, Y. Role of SKP1-CUL1-F-box-protein (SCF) E3 ubiquitin ligases in skin cancer. J. Genet. Genom. 40, 97–106 (2013). DOI: 10.1016/j.jgg.2013.02.001
Vallejo, A. et al. An integrative approach unveils FOSL1 as an oncogene vulnerability in KRAS-driven lung and pancreatic cancer. Nat. Commun. 8, 14294 (2017). DOI: 10.1038/ncomms14294
Awasthi, N. et al. Therapeutic efficacy of anti-MMP9 antibody in combination with nab-paclitaxel-based chemotherapy in pre-clinical models of pancreatic cancer. J. Cell. Mol. Med. 23, 3878–3887 (2019). DOI: 10.1111/jcmm.14242
Rajurkar, M. et al. The activity of Gli transcription factors is essential for Kras-induced pancreatic tumorigenesis. Proc. Natl. Acad. Sci. 109, E1038–E1047 (2012). DOI: 10.1073/pnas.1114168109
Danilova, L. et al. Programmed cell death ligand-1 (PD-L1) and CD8 expression profiling identify an immunologic subtype of pancreatic ductal adenocarcinomas with favorable survival. Cancer Immunol. Res. 7, 886–895 (2019). DOI: 10.1158/2326-6066.CIR-18-0822
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014). DOI: 10.1093/bioinformatics/btu170
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). DOI: 10.1093/bioinformatics/bts635
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015). DOI: 10.1093/bioinformatics/btu638
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). DOI: 10.1186/s13059-014-0550-8
Zhang, W., Liu, S., Zhan, H., Yan, Z. & Zhang, G. Transcriptome sequencing identifies key pathways and genes involved in gastric adenocarcinoma. Mol. Med. Rep. 18, 3673–3682 (2018).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics J. Integ. Biol. 16, 284–287 (2012). DOI: 10.1089/omi.2011.0118
Marchler-Bauer, A. et al. CDD/SPARCLE: functional classification of proteins via subfamily domain architectures. Nucl. Acids Res. 45, D200–D203 (2006). DOI: 10.1093/nar/gkw1129
Therneau T. A package for survival analysis in S. (R package version 2.38 2015)
Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics 24, 719–720 (2008). DOI: 10.1093/bioinformatics/btm563
Liu, R., Cheng, Y., Yu, J., Lv, Q.-L. & Zhou, H.-H. Identification and validation of gene module associated with lung cancer through coexpression network analysis. Gene 563, 56–62 (2015). DOI: 10.1016/j.gene.2015.03.008
Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009). DOI: 10.1093/bioinformatics/btp101
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003). DOI: 10.1101/gr.1239303
Anderson, M. J. Permutation tests for univariate or multivariate analysis of variance and regression. Can. J. Fish. Aquat. Sci. 58, 626–639 (2001). DOI: 10.1139/f01-004
Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucl. Acids Res. 37, W305–W311 (2009). DOI: 10.1093/nar/gkp427
Galili, T. dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics 31, 3718–3720 (2015). DOI: 10.1093/bioinformatics/btv428
De Troyer E, Otava M. RcmdrPlugin. BiclustGUI:’Rcmdr’Plug-in GUI for Biclustering (2016)
Kamburov, A., Stelzl, U., Lehrach, H. & Herwig, R. The ConsensusPathDB interaction database: 2013 update. Nucl. Acids Res. 41, D793-800 (2012). DOI: 10.1093/nar/gks1055
Dimitrakopoulos, C. et al. Network-based integration of multi-omics data for prioritizing cancer genes. Bioinformatics 34, 2441–2448 (2018). DOI: 10.1093/bioinformatics/bty148
Kassambara, A., Kosinski, M. & Biecek, P. Survminer: drawing survival curves using’ggplot2’. R package version 0.3 1 (2017).
Kolde R, Kolde MR. Package ‘pheatmap’. R package, 1 (2015)
