A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation

Abstract

Background DNA Methylation is one of the most important epigenetic processes that are crucial to regulating the functioning of the human genome without altering the DNA sequence. DNA Methylation data for cancer patients are becoming more accessible than ever, which is attributed to newer DNA sequencing technologies, notably, the relatively low-cost DNA microarray technology by Illumina Infinium. This technology makes it possible to study DNA methylation at hundreds of thousands of different loci. Currently, most of the research found in the literature focuses on the discovery of DNA methylation markers for specific cancer types. A relatively small number of studies have attempted to find unified DNA methylation biomarkers that can diagnose different types of cancer (pan-cancer classification). Results In this study, the aim is to conduct a pan-classification of cancer disease. We retrieved individual data for different types of cancer patients from The Cancer Genome Atlas (TCGA) portal. We selected data for many cancer types: Breast Cancer (BRCA), Ovary Cancer (OV), Stomach Cancer (STOMACH), Colon Cancer (COAD), Kidney Cancer (KIRC), Liver Cancer (LIHC), Lung Cancer (LUSC), Prostate Cancer (PRAD) and Thyroid cancer (THCA). The data was pre-processed and later used to build the required dataset. The system that we developed consists of two main stages. The purpose of the first stage is to perform feature selection and, therefore, decrease the dimensionality of the DNA methylation loci (features). This is accomplished using an unsupervised metaheuristic technique. As for the second stage, we used supervised machine learning and developed deep neural network (DNN) models to help classify the samples’ malignancy status and cancer type. Experimental results showed that compared to recently published methods, our proposed system achieved better classification results in terms of recall, and similar and higher results in terms of precision and accuracy. The proposed system also achieved an excellent receiver operating characteristic area under the curve (ROC AUC) values varying from 0.85 to 0.89. Conclusions This research presented an effective new approach to classify different cancer types based on DNA methylation data retrieved from TCGA. The performance of the proposed system was compared to recently published works, using different performance metrics. It provided better results, confirming the effectiveness of the proposed method for classifying different cancer types based on DNA methylation data.

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“…In recent years, highly tissue-speci c DNA methylation patterns and even a small number of selected DNA methylation biomarkers have proven to be valuable tools for the diagnosis and differentiation of tumors and the prediction of their origin (1,3,12,(14)(15)(16)(17)(18)(19)(20)(21)(22)(23). Advances in technology have enabled accurate determination of the methylation status of selected regions (24).…”

The Binomial “Inflammation-Epigenetics” in Breast Cancer Progression and Bone Metastasis: IL-1β Actions Are Influenced by TET Inhibitor in MCF-7 Cell Line

“…In fact, DNA methylation modulation is implicated in various biological processes, including cell activation, proliferation, differentiation, and apoptosis. Its aberrant regulation is associated with tumor progression and metastasis in several tumor types [ 9 ]. In particular, Interleukin-1β (IL-1β) stimulation led to a strong remodulation of chromatin accessibility, in part determined by the remodulation of genome methylation that in tumor cells is often associated with tumor progression [ 10 , 11 , 12 , 13 ].…”