R.L. Siegel, et al., Cancer statistics, 2023. CA Cancer J. Clin. 73(1), 17–48 (2023)
E.A. Collisson, et al., Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat. Med. 17(4), 500–503 (2011)
CAS PubMed PubMed Central Google Scholar
J.D. Mizrahi, et al., Pancreatic cancer. Lancet 395(10242), 2008–2020 (2020)
J. Encarnacion-Rosado, A.C. Kimmelman, Harnessing metabolic dependencies in pancreatic cancers. Nat. Rev. Gastroenterol. Hepatol. 18(7), 482–492 (2021)
PubMed PubMed Central Google Scholar
J. Kim, R.J. DeBerardinis, Mechanisms and implications of metabolic heterogeneity in cancer. Cell Metab. 30(3), 434–446 (2019)
CAS PubMed PubMed Central Google Scholar
S. Barthel, et al., Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy. Nat. Cancer 4(4), 454–467 (2023)
PubMed PubMed Central Google Scholar
Y. Li, et al., Metabolic classification suggests the GLUT1/ALDOB/G6PD axis as a therapeutic target in chemotherapy-resistant pancreatic cancer. Cell Rep. Med. 4(9), 101162 (2023)
CAS PubMed PubMed Central Google Scholar
Z. Jin, Y.D. Chai, S. Hu, Fatty acid metabolism and cancer. Adv. Exp. Med. Biol. 1280, 231–241 (2021)
S.M. Rossi, G. Konstantinidou, Targeting long chain acyl-CoA synthetases for cancer therapy. Int. J. Mol. Sci. 20(15), 3624 (2019)
M. Lopes-Marques, et al., Diversity and history of the long-chain acyl-CoA synthetase (Acsl) gene family in vertebrates. BMC Evol. Biol. 13, 271 (2013)
PubMed PubMed Central Google Scholar
S. Hanzelmann, R. Castelo, J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 14, 7 (2013)
A. Subramanian, et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102(43), 15545–15550 (2005)
CAS PubMed PubMed Central Google Scholar
D.A. Barbie, et al., Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462(7269), 108–112 (2009)
CAS PubMed PubMed Central Google Scholar
J. Gao, P.W. Kwan, D. Shi, Sparse kernel learning with LASSO and Bayesian inference algorithm. Neural Netw. 23(2), 257–264 (2010)
D.W. Huang, B.T. Sherman, R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4(1), 44–57 (2009)
P. Langfelder, S. Horvath, WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 9, 559 (2008)
R.A. Moffitt, et al., Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47(10), 1168–1178 (2015)
J.P. Brunet, et al., Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. U. S. A. 101(12), 4164–4169 (2004)
CAS PubMed PubMed Central Google Scholar
K. Yoshihara, et al., Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013)
B. Ru, et al., TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics 35(20), 4200–4202 (2019)
A. Mayakonda, et al., Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 28(11), 1747–1756 (2018)
CAS PubMed PubMed Central Google Scholar
D. Maeser, R.F. Gruener, R.S. Huang, oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 22(6), bbab260 (2021)
PubMed PubMed Central Google Scholar
Y. Shu, C.W. Chua, An organoid assay for long-term maintenance and propagation of mouse prostate luminal epithelial progenitors and cancer cells. Methods Mol. Biol. 1940, 231–254 (2019)
M.A. Fleming, P. Storz, Mimicking and manipulating pancreatic acinar-to-ductal metaplasia in 3-dimensional cell culture. J. Vis. Exp. (144) (2019)
J.A. Menendez, R. Lupu, Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat. Rev. Cancer 7(10), 763–777 (2007)
N. Zaidi, et al., Lipogenesis and lipolysis: the pathways exploited by the cancer cells to acquire fatty acids. Prog. Lipid Res. 52(4), 585–589 (2013)
CAS PubMed PubMed Central Google Scholar
W.C. Huang, et al., A novel miR-365-3p/EHF/keratin 16 axis promotes oral squamous cell carcinoma metastasis, cancer stemness and drug resistance via enhancing beta5-integrin/c-met signaling pathway. J. Exp. Clin. Cancer Res. 38(1), 89 (2019)
PubMed PubMed Central Google Scholar
F. Matsuzawa, et al., Mesothelin blockage by Amatuximab suppresses cell invasiveness, enhances gemcitabine sensitivity and regulates cancer cell stemness in mesothelin-positive pancreatic cancer cells. BMC Cancer 21(1), 200 (2021)
CAS PubMed PubMed Central Google Scholar
T. Arumugam, et al., S100P promotes pancreatic cancer growth, survival, and invasion. Clin. Cancer Res. 11(15), 5356–5364 (2005)
R. Fischer-Colbrie, A. Laslop, R. Kirchmair, Secretogranin II: molecular properties, regulation of biosynthesis and processing to the neuropeptide secretoneurin. Prog. Neurobiol. 46(1), 49–70 (1995)
T. Takeuchi, M. Hosaka, Sorting mechanism of peptide hormones and biogenesis mechanism of secretory granules by secretogranin III, a cholesterol-binding protein, in endocrine cells. Curr. Diabetes Rev. 4(1), 31–38 (2008)
I. Comerford, et al., A myriad of functions and complex regulation of the CCR7/CCL19/CCL21 chemokine axis in the adaptive immune system. Cytokine Growth Factor Rev. 24(3), 269–283 (2013)
J.D. Klement, et al., An osteopontin/CD44 immune checkpoint controls CD8+ T cell activation and tumor immune evasion. J. Clin. Invest. 128(12), 5549–5560 (2018)
PubMed PubMed Central Google Scholar
C. Wang, et al., CD276 expression enables squamous cell carcinoma stem cells to evade immune surveillance. Cell Stem Cell 28(9), 1597–1613.e7 (2021)
CAS PubMed PubMed Central Google Scholar
X. Jiang, et al., Role of the tumor microenvironment in PD-L1/PD-1-mediated tumor immune escape. Mol. Cancer 18(1), 10 (2019)
PubMed PubMed Central Google Scholar
R. Wang, et al., Interferon gamma-induced interferon regulatory factor 1 activates transcription of HHLA2 and induces immune escape of hepatocellular carcinoma cells. Inflammation 45(1), 308–330 (2022)
M.R. Stratton, P.J. Campbell, P.A. Futreal, The cancer genome. Nature 458(7239), 719–724 (2009)
CAS PubMed PubMed Central Google Scholar
M.R. Stratton, Exploring the genomes of cancer cells: progress and promise. Science 331(6024), 1553–1558 (2011)
N.A. Rizvi, et al., Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348(6230), 124–128 (2015)
CAS PubMed PubMed Central Google Scholar
A. Snyder, et al., Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371(23), 2189–2199 (2014)
PubMed PubMed Central Google Scholar
L. Liu, et al., Combination of TMB and CNA stratifies prognostic and predictive responses to immunotherapy across metastatic cancer. Clin. Cancer Res. 25(24), 7413–7423 (2019)
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