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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Identification of a Four Cancer Stem Cell-Related Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Adenocarcinoma

Author(s): Shuanghua Li*, Rui Chen*, Wang Luo, Jinyu Lin, Yunlong Chen, Zhuangxiong Wang, Wenjun Lin, Baihong Li, Junfeng Wang and Jian Yang

Volume 25, Issue 12, 2022

Published on: 13 January, 2022

Page: [2070 - 2081] Pages: 12

DOI: 10.2174/1386207325666220113142212

Price: $65

Abstract

Background: Cancer stem cells (CSCs) are now being considered as the initial component in the development of pancreatic adenocarcinoma (PAAD). Our aim was to develop a CSCrelated signature to assess the prognosis of PAAD patients for the optimization of treatment.

Methods: Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue in the Cancer Genome Atlas (TCGA) were screened out, and the weighted gene correlation network analysis (WGCNA) was employed to identify the CSC-related gene sets. Then, univariate, Lasso Cox regression analyses and multivariate Cox regression were applied to construct a prognostic signature using the CSC-related genes. Its prognostic performance was validated in TCGA and ICGC cohorts. Furthermore, Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors in PAAD, and a prognostic nomogram was established.

Results: The Kaplan-Meier analysis, ROC curve and C-index indicated the good performance of the CSC-related signature at predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression revealed that the CSC-related signature was an independent prognostic factor in PAAD. The nomogram was superior to the risk model and AJCC stage in predicting OS. In terms of mutation and tumor immunity, patients in the high-risk group had higher tumor mutation burden (TMB) scores than patients in the low-risk group, and the immune score and the ESTIMATE score were significantly lower in the high-risk group. Moreover, according to the results of principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA), the low-risk and high-risk groups displayed different stemness statuses based on the risk model.

Conclusion: Our study identified four CSC-related gene signatures and established a prognostic nomogram that reliably predicts OS in PAAD. The findings may support new ideas for screening therapeutic targets to inhibit stem characteristics and the development of PAAD.

Keywords: Pancreatic adenocarcinoma (PAAD), cancer stem cell (CSC), weighted gene correlation network analysis (WGCNA), the cancer genome atlas (TCGA), international cancer genome consortium (ICGC), prognosis.

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[1]
Kleeff, J.; Korc, M.; Apte, M.; La Vecchia, C.; Johnson, C.D.; Biankin, A.V.; Neale, R.E.; Tempero, M.; Tuveson, D.A.; Hruban, R.H.; Neoptolemos, J.P. Pancreatic cancer. Nat. Rev. Dis. Primers, 2016, 2, 16022.
[http://dx.doi.org/10.1038/nrdp.2016.22] [PMID: 27158978]
[2]
Rahib, L.; Smith, B.D.; Aizenberg, R.; Rosenzweig, A.B.; Fleshman, J.M.; Matrisian, L.M. Projecting cancer incidence and deaths to 2030: The unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res., 2014, 74(11), 2913-2921.
[http://dx.doi.org/10.1158/0008-5472.CAN-14-0155] [PMID: 24840647]
[3]
Wolfgang, C.L.; Herman, J.M.; Laheru, D.A.; Klein, A.P.; Erdek, M.A.; Fishman, E.K.; Hruban, R.H. Recent progress in pancreatic cancer. CA Cancer J. Clin., 2013, 63(5), 318-348.
[http://dx.doi.org/10.3322/caac.21190] [PMID: 23856911]
[4]
Maeda, S.; Shinchi, H.; Kurahara, H.; Mataki, Y.; Noma, H.; Maemura, K.; Aridome, K.; Yokomine, T.; Natsugoe, S.; Aikou, T.; Takao, S. Clinical significance of midkine expression in pancreatic head carcinoma. Br. J. Cancer, 2007, 97(3), 405-411.
[http://dx.doi.org/10.1038/sj.bjc.6603879] [PMID: 17622248]
[5]
Rombouts, S.J.; Vogel, J.A.; van Santvoort, H.C.; van Lienden, K.P.; van Hillegersberg, R.; Busch, O.R.; Besselink, M.G.; Molenaar, I.Q. Systematic review of innovative ablative therapies for the treatment of locally advanced pancreatic cancer. Br. J. Surg., 2015, 102(3), 182-193.
[http://dx.doi.org/10.1002/bjs.9716] [PMID: 25524417]
[6]
De Luca, R.; Blasi, L.; Alù, M.; Gristina, V.; Cicero, G. Clinical efficacy of nab-paclitaxel in patients with metastatic pancreatic cancer. Drug Des. Devel. Ther., 2018, 12, 1769-1775.
[http://dx.doi.org/10.2147/DDDT.S165851] [PMID: 29950811]
[7]
Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics. CA Cancer J. Clin., 2019, 69(1), 7-34.
[8]
Kamisawa, T.; Wood, L.D.; Itoi, T.; Takaori, K. Pancreatic cancer. Lancet, 2016, 388(10039), 73-85.
[http://dx.doi.org/10.1016/S0140-6736(16)00141-0] [PMID: 26830752]
[9]
Heestand, G.M.; Murphy, J.D.; Lowy, A.M. Approach to patients with pancreatic cancer without detectable metastases. J. Clin. Oncol., 2015, 33(16), 1770-1778.
[http://dx.doi.org/10.1200/JCO.2014.59.7930] [PMID: 25918279]
[10]
Cheng, Y.; Wang, K.; Geng, L.; Sun, J.; Xu, W.; Liu, D.; Gong, S.; Zhu, Y. Identification of candidate diagnostic and prognostic bi-omarkers for pancreatic carcinoma. EBioMedicine, 2019, 40, 382-393.
[http://dx.doi.org/10.1016/j.ebiom.2019.01.003] [PMID: 30639415]
[11]
Karaayvaz, M.; Cristea, S.; Gillespie, S.M.; Patel, A.P.; Mylvaganam, R.; Luo, C.C.; Specht, M.C.; Bernstein, B.E.; Michor, F.; Ellisen, L.W. Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq. Nat. Commun., 2018, 9(1), 3588.
[http://dx.doi.org/10.1038/s41467-018-06052-0] [PMID: 30181541]
[12]
Kim, C.; Gao, R.; Sei, E.; Brandt, R.; Hartman, J.; Hatschek, T.; Crosetto, N.; Foukakis, T.; Navin, N.E. Chemoresistance evolution in tri-ple-negative breast cancer delineated by single-cell sequencing. Cell, 2018, 173(4), 879-893.e13.
[http://dx.doi.org/10.1016/j.cell.2018.03.041] [PMID: 29681456]
[13]
Clarke, M.F.; Dick, J.E.; Dirks, P.B.; Eaves, C.J.; Jamieson, C.H.; Jones, D.L.; Visvader, J.; Weissman, I.L.; Wahl, G.M. Cancer stem cells--perspectives on current status and future directions: AACR Workshop on cancer stem cells. Cancer Res., 2006, 66(19), 9339-9344.
[http://dx.doi.org/10.1158/0008-5472.CAN-06-3126] [PMID: 16990346]
[14]
Qiu, H.; Fang, X.; Luo, Q.; Ouyang, G. Cancer stem cells: A potential target for cancer therapy. Cell. Mol. Life Sci., 2015, 72(18), 3411-3424.
[http://dx.doi.org/10.1007/s00018-015-1920-4] [PMID: 25967289]
[15]
Malta, T.M.; Sokolov, A.; Gentles, A.J.; Burzykowski, T.; Poisson, L.; Weinstein, J.N.; Kamińska, B.; Huelsken, J.; Omberg, L.; Gevaert, O.; Colaprico, A.; Czerwińska, P.; Mazurek, S.; Mishra, L.; Heyn, H.; Krasnitz, A.; Godwin, A.K.; Lazar, A.J.; Stuart, J.M.; Hoadley, K.A.; Laird, P.W.; Noushmehr, H.; Wiznerowicz, M. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell, 2018, 173(2), 338-354.e15.
[http://dx.doi.org/10.1016/j.cell.2018.03.034] [PMID: 29625051]
[16]
Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expres-sion data. Bioinformatics, 2010, 26(1), 139-140.
[http://dx.doi.org/10.1093/bioinformatics/btp616] [PMID: 19910308]
[17]
Langfelder, P.; Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9, 559.
[http://dx.doi.org/10.1186/1471-2105-9-559] [PMID: 19114008]
[18]
Emura, T.; Matsui, S.; Chen, H.Y. Univariate feature selection and compound covariate for predicting survival. Comput. Methods Programs Biomed., 2019, 168, 21-37.
[19]
Tibshirani, R. The lasso method for variable selection in the Cox model. Stat. Med., 1997, 16(4), 385-395.
[http://dx.doi.org/10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3] [PMID: 9044528]
[20]
Scosyrev, E.; Glimm, E. Power analysis for multivariable Cox regression models. Stat. Med., 2019, 38(1), 88-99.
[http://dx.doi.org/10.1002/sim.7964] [PMID: 30302784]
[21]
Yoshihara, K.; Shahmoradgoli, M.; Martínez, E.; Vegesna, R.; Kim, H.; Torres-Garcia, W.; Treviño, V.; Shen, H.; Laird, P.W.; Levine, D.A.; Carter, S.L.; Getz, G.; Stemke-Hale, K.; Mills, G.B.; Verhaak, R.G. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun., 2013, 4, 2612.
[http://dx.doi.org/10.1038/ncomms3612] [PMID: 24113773]
[22]
Kim, S.; Kang, D.; Huo, Z.; Park, Y.; Tseng, G.C. Meta-analytic principal component analysis in integrative omics application. Bioinformatics, 2018, 34(8), 1321-1328.
[http://dx.doi.org/10.1093/bioinformatics/btx765] [PMID: 29186328]
[23]
Li, Z.; Safo, S.E.; Long, Q. Incorporating biological information in sparse principal component analysis with application to genomic data. BMC Bioinformatics, 2017, 18(1), 332.
[http://dx.doi.org/10.1186/s12859-017-1740-7] [PMID: 28697740]
[24]
Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; Mesirov, J.P. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 2005, 102(43), 15545-15550.
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[25]
Song, J.; Xu, Q.; Zhang, H.; Yin, X.; Zhu, C.; Zhao, K.; Zhu, J. Five key lncRNAs considered as prognostic targets for predicting pancreat-ic ductal adenocarcinoma. J. Cell. Biochem., 2018, 119(6), 4559-4569.
[http://dx.doi.org/10.1002/jcb.26598] [PMID: 29239017]
[26]
Waddell, N.; Pajic, M.; Patch, A.M.; Chang, D.K.; Kassahn, K.S.; Bailey, P. Whole genomes redefine the mutational landscape of pancreat-ic cancer. Nature, 2015, 518(7540), 495-501.
[http://dx.doi.org/10.1038/nature14169]
[27]
Aghaalikhani, N.; Rashtchizadeh, N.; Shadpour, P.; Allameh, A.; Mahmoodi, M. Cancer stem cells as a therapeutic target in bladder cancer. J. Cell. Physiol., 2019, 234(4), 3197-3206.
[28]
Michieli, P.; Mazzone, M.; Basilico, C.; Cavassa, S.; Sottile, A.; Naldini, L. Targeting the tumor and its microenvironment by a dual-function decoy Met receptor. Cancer Cell, 2004, 6(1), 61-73.
[http://dx.doi.org/10.1016/j.ccr.2004.05.032]
[29]
Gherardi, E.; Birchmeier, W.; Birchmeier, C.; Vande, W.G. Targeting MET in cancer: Rationale and progress. Nat. Rev. Cancer, 2012, 12(2), 89-103.
[http://dx.doi.org/10.1038/nrc3205]
[30]
Takeuchi, K.; Ito, F. Suppression of adriamycin-induced apoptosis by sustained activation of the phosphatidylinositol-3′-OH kinase-Akt pathway. J. Biol. Chem., 2004, 279(2), 892-900.
[31]
Bowers, D.C.; Fan, S.; Walter, K.A.; Abounader, R.; Williams, J.A.; Rosen, E.M. Scatter factor/hepatocyte growth factor protects against cytotoxic death in human glioblastoma via phosphatidylinositol 3-kinase- and AKT-dependent pathways. Cancer Res., 2000, 60(15), 4277-4283.
[32]
Que, W.; Chen, J. Knockdown of c-Met inhibits cell proliferation and invasion and increases chemosensitivity to doxorubicin in human multiple myeloma U266 cells in vitro. Mol. Med. Rep., 2011, 4(2), 343-349.
[33]
Lux, A.; Kahlert, C.; Grützmann, R.; Pilarsky, C. c-Met and PD-L1 on circulating exosomes as diagnostic and prognostic markers for pan-creatic cancer. Int. J. Mol. Sci., 2019, 20(13), 3305.
[34]
Rodrigo, J.P.; García-Carracedo, D.; García, L.A.; Menéndez, S.; Allonca, E.; González, M.V. Distinctive clinicopathological associations of amplification of the cortactin gene at 11q13 in head and neck squamous cell carcinomas. J. Pathol., 2009, 217(4), 516-523.
[35]
Hui, A.B.; Or, Y.Y.; Takano, H.; Tsang, R.K.; To, K.F.; Guan, X.Y. Array-based comparative genomic hybridization analysis identified cyclin D1 as a target oncogene at 11q13.3 in nasopharyngeal carcinoma. Cancer Res., 2005, 65(18), 8125-8133.
[36]
Brown, L.A.; Irving, J.; Parker, R.; Kim, H.; Press, J.Z.; Longacre, T.A. Amplification of EMSY, a novel oncogene on 11q13, in high grade ovarian surface epithelial carcinomas. Gynecol. Oncol., 2006, 100(2), 264-270.
[37]
Brown, L.A.; Kalloger, S.E.; Miller, M.A.; Shih, I.; McKinney, S.E.; Santos, J.L. Amplification of 11q13 in ovarian carcinoma. Genes Chromosomes Cancer, 2008, 47(6), 481-489.
[38]
Janssen, J.W.; Cuny, M.; Orsetti, B.; Rodriguez, C.; Vallés, H.; Bartram, C.R. MYEOV: a candidate gene for DNA amplification events occurring centromeric to CCND1 in breast cancer. Int. J. Cancer, 2002, 102(6), 608-614.
[39]
Janssen, J.W.; Imoto, I.; Inoue, J.; Shimada, Y.; Ueda, M.; Imamura, M. MYEOV, a gene at 11q13, is coamplified with CCND1, but epige-netically inactivated in a subset of esophageal squamous cell carcinomas. J. Hum. Genet., 2002, 47(9), 460-464.
[40]
Moreaux, J.; Hose, D.; Bonnefond, A.; Reme, T.; Robert, N.; Goldschmidt, H. MYEOV is a prognostic factor in multiple myeloma. Exp. Hematol., 2010, 38(12), 1189-1198.
[http://dx.doi.org/10.1016/j.exphem.2010.09.002]
[41]
Takita, J.; Chen, Y.; Okubo, J.; Sanada, M.; Adachi, M.; Ohki, K. Aberrations of NEGR1 on 1p31 and MYEOV on 11q13 in neuroblasto-ma. Cancer Sci., 2011, 102(9), 1645-1650.
[42]
Tang, R.; Ji, J.; Ding, J.; Huang, J.; Gong, B.; Zhang, X. Overexpression of MYEOV predicting poor prognosis in patients with pancreatic ductal adenocarcinoma. Cell Cycle, 2020, 19(13), 1602-1610.
[43]
Liang, E.; Lu, Y.; Shi, Y.; Zhou, Q.; Zhi, F. MYEOV increases HES1 expression and promotes pancreatic cancer progression by enhanc-ing SOX9 transactivity. Oncogene, 2020, 39(41), 6437-6450.
[http://dx.doi.org/10.1038/s41388-020-01443-4]
[44]
Luo, L.; McGarvey, P.; Madhavan, S.; Kumar, R.; Gusev, Y.; Upadhyay, G. Distinct lymphocyte antigens 6 (Ly6) family members Ly6D, Ly6E, Ly6K and Ly6H drive tumorigenesis and clinical outcome. Oncotarget, 2016, 7(10), 11165-11193.
[45]
Wang, J.; Fan, J.; Gao, W.; Wu, Y.; Zhao, Q.; Chen, B. LY6D as a chemoresistance marker gene and therapeutic target for laryngeal squamous cell carcinoma. Stem Cells Dev., 2020, 29(12), 774-785.
[46]
Kalloger, S.E.; Karasinska, J.M.; Keung, M.S.; Thompson, D.L.; Ho, J.; Chow, C. Stroma vs. epithelium-enhanced prognostics through histologic stratification in pancreatic ductal adenocarcinoma. Int. J. Cancer, 2020, 148(2), 481491.
[PMID: 32955725]
[47]
Pan, W.; Cheng, Y.; Zhang, H.; Liu, B.; Mo, X.; Li, T. CSBF/C10orf99, a novel potential cytokine, inhibits colon cancer cell growth through inducing G1 arrest. Sci. Rep., 2014, 4, 6812.
[48]
Zeng, D.; Li, M.; Zhou, R.; Zhang, J.; Sun, H.; Shi, M. Tumor microenvironment characterization in gastric cancer identifies prognostic and immunotherapeutically relevant gene signatures. Cancer Immunol. Res., 2019, 7(5), 737-750.
[http://dx.doi.org/10.1158/2326-6066.CIR-18-0436]
[49]
Steuer, C.E.; Ramalingam, S.S. Tumor mutation burden: Leading immunotherapy to the era of precision medicine? J. Clin. Oncol., 2018, 36(7), 631-632.

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