Abstract
Simple SummaryA deep learning model based on multi-omics data to classify Nottingham prognostic Index score levels. The model represents each omic dataset using 2-dimensional map before integrating all omics maps into the prediction model. The literature confirms the relationship between the extracted omics features with the progression and survival of breast cancer.The Nottingham Prognostics Index (NPI) is a prognostics measure that predicts operable primary breast cancer survival. The NPI value is calculated based on the size of the tumor, the number of lymph nodes, and the tumor grade. Next-generation sequencing advancements have led to measuring different biological indicators called multi-omics data. The availability of multi-omics data triggered the challenge of integrating and analyzing these various biological measures to understand the progression of the diseases. High-dimensional embedding techniques are incorporated to present the features in the lower dimension, i.e., in a 2-dimensional map. The dataset consists of three -omics: gene expression, copy number alteration (CNA), and mRNA from 1885 female patients. The model creates a gene similarity network (GSN) map for each omic using t-distributed stochastic neighbor embedding (t-SNE) before being merged into the residual neural network (ResNet) classification model. The aim of this work was to (i) extract multi-omics biomarkers that are associated with the prognosis and prediction of breast cancer survival; and (ii) build a prediction model for multi-class breast cancer NPI classes. We evaluated this model and compared it to different high-dimensional embedding techniques and neural network combinations. The proposed model outperformed the other methods with an accuracy of 98.48%, and the area under the curve (AUC) equals 0.9999. The findings in the literature confirm associations between some of the extracted omics and breast cancer prognosis and survival including CDCA5, IL17RB, MUC2, NOD2 and NXPH4 from the gene expression dataset; MED30, RAD21, EIF3H and EIF3E from the CNA dataset; and CENPA, MACF1, UGT2B7 and SEMA3B from the mRNA dataset.
Highlights
The results show that the artificial neural network (ANN) and support vector machine (SVM) outperformed some other standard classifiers with an accuracy higher than 95% for the three classes [15]
We proposed a residual neural network (ResNet) model based on the t-distributed stochastic neighbor embedding (t-SNE) embedding method to classify two classes of patients; with Nottingham Prognosis Index (NPI) < 3.4 versus NPI ≥ 3.4 where 3.4 is the cut-off between the high survival rate and low survival rate
Multi-omics features that are associated with NPI classes were extracted using a hybrid feature selection method
Summary
Breast cancer (BC) is one of the most common cancers and leading cause of cancer death in women worldwide [1], accounting for 30% of female cancers [2]. BC is a heterogeneous disease that consists of several subtypes which have been identified at the clinical, molecular, genomic, and histological levels. Detection and intervention are proven to be effective in increasing survival rates and improving prognosis [3]. The Nottingham Prognosis Index (NPI) is an index to determine prognosis following surgery for BC [4]. Various studies have validated the prognostic discrimination [5–7] and NPI is widely used in clinical practice
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