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Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients
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....
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652797/ https://www.ncbi.nlm.nih.gov/pubmed/29100405 http://dx.doi.org/10.18632/oncotarget.20548 |
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author | Kim, Yun Hak Jeong, Dae Cheon Pak, Kyoungjune Goh, Tae Sik Lee, Chi-Seung Han, Myoung-Eun Kim, Ji-Young Liangwen, Liu Kim, Chi Dae Jang, Jeon Yeob Cha, Wonjae Oh, Sae-Ock |
author_facet | Kim, Yun Hak Jeong, Dae Cheon Pak, Kyoungjune Goh, Tae Sik Lee, Chi-Seung Han, Myoung-Eun Kim, Ji-Young Liangwen, Liu Kim, Chi Dae Jang, Jeon Yeob Cha, Wonjae Oh, Sae-Ock |
author_sort | Kim, Yun Hak |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5652797 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-56527972017-11-02 Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients Kim, Yun Hak Jeong, Dae Cheon Pak, Kyoungjune Goh, Tae Sik Lee, Chi-Seung Han, Myoung-Eun Kim, Ji-Young Liangwen, Liu Kim, Chi Dae Jang, Jeon Yeob Cha, Wonjae Oh, Sae-Ock Oncotarget Research Paper 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. Impact Journals LLC 2017-08-24 /pmc/articles/PMC5652797/ /pubmed/29100405 http://dx.doi.org/10.18632/oncotarget.20548 Text en Copyright: © 2017 Kim et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (http://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Kim, Yun Hak Jeong, Dae Cheon Pak, Kyoungjune Goh, Tae Sik Lee, Chi-Seung Han, Myoung-Eun Kim, Ji-Young Liangwen, Liu Kim, Chi Dae Jang, Jeon Yeob Cha, Wonjae Oh, Sae-Ock Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients |
title | Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients |
title_full | Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients |
title_fullStr | Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients |
title_full_unstemmed | Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients |
title_short | Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients |
title_sort | gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652797/ https://www.ncbi.nlm.nih.gov/pubmed/29100405 http://dx.doi.org/10.18632/oncotarget.20548 |
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