Display options
Cite
Kim YH, Jeong DC, Pak K, et al. Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients. Oncotarget. 2017;8(44):77515-77526doi: 10.18632/oncotarget.20548.
Kim, Y. H., Jeong, D. C., Pak, K., Goh, T. S., Lee, C. S., Han, M. E., Kim, J. Y., Liangwen, L., Kim, C. D., Jang, J. Y., Cha, W., & Oh, S. O. (2017). Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients. Oncotarget, 8(44), 77515-77526. https://doi.org/10.18632/oncotarget.20548
Kim, Yun Hak, et al. "Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients." Oncotarget vol. 8,44 (2017): 77515-77526. doi: https://doi.org/10.18632/oncotarget.20548
Kim YH, Jeong DC, Pak K, Goh TS, Lee CS, Han ME, Kim JY, Liangwen L, Kim CD, Jang JY, Cha W, Oh SO. Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients. Oncotarget. 2017 Aug 24;8(44):77515-77526. doi: 10.18632/oncotarget.20548. eCollection 2017 Sep 29. PMID: 29100405; PMCID: PMC5652797.
Copy Download .nbib Format: NLM
Share it on

Oncotarget. 2017 Aug 24;8(44):77515-77526. doi: 10.18632/oncotarget.20548. eCollection 2017 Sep 29.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients.

Oncotarget

Yun Hak Kim, Dae Cheon Jeong, Kyoungjune Pak, Tae Sik Goh, Chi-Seung Lee, Myoung-Eun Han, Ji-Young Kim, Liu Liangwen, Chi Dae Kim, Jeon Yeob Jang, Wonjae Cha, Sae-Ock Oh

Affiliations

  1. Department of Anatomy, School of medicine, Pusan National University, Yangsan, 50612, Republic of Korea.
  2. BEER, Busan society of Evidence-based mEdicine and Research, Busan 49241, Republic of Korea.
  3. Department of Statistics, Korea University, Seoul 02841, Republic of Korea.
  4. Department of Nuclear Medicine, Pusan National University Hospital, Busan 49241, Republic of Korea.
  5. Department of Orthopaedic Surgery, Pusan National University Hospital, Busan 49241, Republic of Korea.
  6. Biomedical Research Institute, Pusan National University Hospital and School of Medicine, Pusan National University, Busan 49241, Republic of Korea.
  7. Department of Pharmacology, School of medicine, Pusan National University, Yangsan, 50612, Republic of Korea.
  8. Department of Otorhinolaryngology-Head and Neck Surgery, Pusan National Hospital, Busan 49241, Republic of Korea.

PMID: 29100405 PMCID: PMC5652797 DOI: 10.18632/oncotarget.20548

Abstract

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

Keywords: breast cancer; gene network; gene signature; network-regularized high-dimensional Cox-regression (Net); prognosis

Conflict of interest statement

CONFLICTS OF INTEREST The authors have declared no conflicts of interest.

References

  1. Cancer Res. 2011 Sep 1;71(17):5635-45 - PubMed
  2. Breast Cancer Res Treat. 1994;32(3):281-90 - PubMed
  3. Clin Cancer Res. 2007 Jun 1;13(11):3207-14 - PubMed
  4. Nat Rev Clin Oncol. 2013 Sep;10(9):494-506 - PubMed
  5. Nature. 2002 Jan 31;415(6871):530-6 - PubMed
  6. Mol Oncol. 2014 Oct;8(7):1278-89 - PubMed
  7. Stat Med. 2011 May 10;30(10):1105-17 - PubMed
  8. Carcinogenesis. 2013 Oct;34(10):2300-8 - PubMed
  9. Bioinformatics. 2015 Jul 15;31(14):2318-23 - PubMed
  10. Cancer Inform. 2015 May 05;14(Suppl 2):129-38 - PubMed
  11. N Engl J Med. 2004 Dec 30;351(27):2817-26 - PubMed
  12. Lancet. 2005 Feb 19-25;365(9460):671-9 - PubMed
  13. Nature. 2012 Apr 18;486(7403):346-52 - PubMed
  14. Stat Med. 1997 Feb 28;16(4):385-95 - PubMed
  15. Cancer Discov. 2012 May;2(5):401-4 - PubMed
  16. Lancet Oncol. 2013 Dec;14(13):1295-306 - PubMed
  17. Nat Commun. 2016 May 10;7:11479 - PubMed
  18. Nat Rev Cancer. 2014 May;14(5):299-313 - PubMed
  19. Stat Sin. 2012;22:27-294 - PubMed
  20. Breast Cancer Res. 2010;12(5):R66 - PubMed
  21. Gastroenterology. 2013 Jul;145(1):176-87 - PubMed
  22. N Engl J Med. 2007 Jan 4;356(1):11-20 - PubMed
  23. Stat Sin. 2014 Jul;24(3):1433-1459 - PubMed

Publication Types