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

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.

Prognostic Significance of EIF4G1 in Patients with Pancreatic Ductal Adenocarcinoma<strong> </strong>

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“…Surgical resection is performed only in 10 to 20% of cases [3]. Because most of the cases are belong to advanced stage at the time of diagnosis [4,5].…”

SAC3D1: a novel prognostic marker in hepatocellular carcinoma

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Development of a risk scoring system for patients with papillary thyroid cancer

“…A novel variable selection method, so-called Network-Regularized high-dimensional Cox-regression, has been developed that takes into account signalling pathways and gene networks with the addition of an optional gene-gene correlation matrix. 7,13 A new strategy that uses individual information is crucial to accurately stratify patients with PTC. Therefore, we aimed to develop a novel risk scoring system for PTC based on gene networks using The Cancer Genome Atlas (TCGA).…”