Cargando…

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....

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2017
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
_version_ 1783273130949607424
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
work_keys_str_mv AT kimyunhak genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT jeongdaecheon genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT pakkyoungjune genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT gohtaesik genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT leechiseung genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT hanmyoungeun genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT kimjiyoung genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT liangwenliu genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT kimchidae genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT jangjeonyeob genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT chawonjae genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients
AT ohsaeock genenetworkinherentingenomicbigdataimprovestheaccuracyofprognosticpredictionforcancerpatients