1,531
Views
2
CrossRef citations to date
0
Altmetric
Articles

DNA Methylation and Gene Expression with Clinical Covariates Explain Variation in Aggressiveness and Survival of Pancreatic Cancer Patients

ORCID Icon, , &
Pages 502-506 | Received 23 Mar 2019, Accepted 16 Aug 2020, Published online: 16 Sep 2020

References

  • Imaoka H, Shimizu Y, Senda Y, Natsume S, Mizuno N, Hara K, et al. Post-adjuvant chemotherapy CA19-9 levels predict prognosis in patients with pancreatic ductal adenocarcinoma: a retrospective cohort study. Pancreatology. 2016;16(4):658–664. doi:10.1016/j.pan.2016.04.007.
  • Lowenfels AB, Maisonneuve P. Epidemiology and prevention of pancreatic cancer. Jpn J Clin Oncol. 2004;34(5):238–244. doi:10.1093/jjco/hyh045.
  • Gonzalez-Reymundez A, Vazquez AI. Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin. bioRxiv. 2020;806323.
  • Bardeesy N, DePinho RA. Pancreatic cancer biology and genetics. Nat Rev Cancer. 2002;2(12):897–909. doi:10.1038/nrc949.
  • Hidalgo M. New insights into pancreatic cancer biology. Ann Oncol. 2012;23(Suppl_10):x135–x138. doi:10.1093/annonc/mds313.
  • Nones K, Waddell N, Song S, Patch A-M, Miller D, Johns A, Wu J, et al. Genome-wide DNA methylation patterns in pancreatic ductal adenocarcinoma reveal epigenetic deregulation of SLIT-ROBO, ITGA2 and MET signaling. Int J Cancer. 2014;135(5):1110–1118. doi:10.1002/ijc.28765.
  • Heyn H, Moran S, Hernando-Herraez I, Sayols S, Gomez A, Sandoval J, et al. DNA methylation contributes to natural human variation. Genome Res. 2013;23(9):1363–1372. doi:10.1101/gr.154187.112.
  • Storey JD, Madeoy J, Strout JL, Wurfel M, Ronald J, Akey JM. Gene-expression variation within and among human populations. Am J Hum Genet. 2007;80(3):502–509. doi:10.1086/512017.
  • Behring M, Shrestha S, Manne U, Cui X, Gonzalez-Reymundez A, Grueneberg A, et al. Integrated landscape of copy number variation and RNA expression associated with nodal metastasis in invasive ductal breast carcinoma. Oncotarget. 2018;9(96):36836–36848. doi:10.18632/oncotarget.26386.
  • Shafi A, Nguyen T, Peyvandipour A, Nguyen H, Draghici S. A multi-cohort and multi-omics meta-analysis framework to identify network-based gene signatures. Front Genet. 2019;10:159. doi:10.3389/fgene.2019.00159.
  • Yadav D, Lowenfels AB. The epidemiology of pancreatitis and pancreatic cancer. Gastroenterology. 2013;144(6):1252–1261. doi:10.1053/j.gastro.2013.01.068.
  • Tom JA, Reeder J, Forrest WF, Graham RR, Hunkapiller J, Behrens TW, et al. Identifying and mitigating batch effects in whole genome sequencing data. BMC Bioinformatics. 2017;18(1):351 doi:10.1186/s12859-017-1756-z.
  • Whaley FS. Optimizing the Wald-Wolfowitz runs statistic using a linkage tolerance: guidelines based on computer simulation. Commun Stat Theory Methods. 1987;16(7):2125–2138. doi:10.1080/03610928708829494.
  • Pérez P, de los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics. 2014;198(2):483–495. doi:10.1534/genetics.114.164442.
  • VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91(11):4414–4423. doi:10.3168/jds.2007-0980.
  • Vazquez A, Wiener H, Shrestha S, Tiwari H, de los Campos G. Integration of multi-layer omic data for prediction of disease risk in humans. Paper presented at: 10th World Congress of Genetics Applied to Livestock Production; 2014; Vancouver, BC, Canada.
  • de los Campos G, Gianola D, Rosa G, Weigel KA, Crossa J. Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet Res (Camb). 2010;92(4):295–308. doi:10.1017/S0016672310000285.
  • Vazquez AI, Veturi Y, Behring M, Shrestha S, Kirst M, Resende MFR, et al. Increased proportion of variance explained and prediction accuracy of survival of breast cancer patients with use of whole-genome multiomic profiles. Genetics. 2016;203(3):1425–1438. doi:10.1534/genetics.115.185181.
  • Gonzalez-Reymundez A, de los Campos G, Gutierrez L, Lunt SY, Vazquez AI. Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions. Eur J Hum Genet. 2017;25(5):538–544. doi:10.1038/ejhg.2017.12.
  • Wahba G. Spline models for observational data. Philadelphia (PA): Society for Industrial and Applied Mathematics; 1990.
  • Plummer M, Best N, Cowles K, Vines K. CODA: convergence diagnosis and output analysis for MCMC. Open Univ. 2006;6(1):7–11.
  • Yang J, Long Q, Li H, Lv Q, Tan Q, Yang X. The value of positive lymph nodes ratio combined with negative lymph node count in prediction of breast cancer survival. J Thorac Dis. 2017;9(6):1531–1537. doi:10.21037/jtd.2017.05.30.
  • Yu K-D, Jiang Y-Z, Chen S, Cao Z-G, Wu J, Shen Z-Z, et al. Effect of large tumor size on cancer-specific mortality in node-negative breast cancer. Mayo Clin Proc. 2012;87(12):1171–1180. doi:10.1016/j.mayocp.2012.07.023.
  • Bernal Rubio YL, González-Reymúndez A, Wu K-HH, Griguer CE, Steibel JP, de los Campos G, et al. Whole-genome multi-omic study of survival in patients with glioblastoma multiforme. G3 (Bethesda). 2018;8(11):3627–3636. doi:10.1534/g3.118.200391.
  • Raman P, Maddipati R, Lim KH, Tozeren A. Pancreatic cancer survival analysis defines a signature that predicts outcome. PLoS One. 2018;13(8):e0201751. doi:10.1371/journal.pone.0201751.
  • Funkhouser SA, Vazquez AI, Steibel JP, Ernst CW, de los Campos G. Deciphering sex-specific genetic architectures using local Bayesian regressions. bioRxiv. 2019;653386.