lunes, 13 de noviembre de 2017

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

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



 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.

Kim YH1,2Jeong DC3Pak K4,2Goh TS1,5,2Lee CS6Han ME1Kim JY1Liangwen L1Kim CD7Jang JY2,8Cha W2,8Oh SO1.

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

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

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