Lung cancer is one of the deadliest diseases in the world. Non-small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.

A major objective of ‘omics’ technologies is to understand genetic causality of complex traits of human diseases. High-throughput omics technologies and their application to medicine open up remarkable opportunities for realising optimised medical treatment for individuals. Because many major breakthrough and discoveries in this field have been driven by the development of new omics technologies, in this review, the authors aim to provide an in-depth description of their underlying principles as a foundation of developing another new omics technology, and to introduce their emerging applications for personalised medicine. The systems biology approach is then introduced as a future direction towards actionable personalised medicine.

DNA methylation is an epigenetic phenomenon in which methyl groups get bonded to the cytosines of the DNA molecule altering the expression of the associated genes. Cancer is linked with hypo or hyper-methylation of specific genes as well as global changes in DNA methylation. In this study, the authors study the probability density function distribution of DNA methylation in various significant genes and across the genome in healthy and tumour samples. They propose a unique ‘average healthy methylation distribution’ based on the methylation values of several healthy samples. They then obtain the Kullback–Leibler and Jensen–Shannon distances between methylation distributions of the healthy and tumour samples and the average healthy methylation distribution. The distance measures of the healthy and tumour samples from the average healthy methylation distribution are compared and the differences in the distances are analysed as possible parameters for cancer. A classifier trained on these values was found to provide high values of sensitivity and specificity. They consider this to be a computationally efficient approach to predict tumour samples based on DNA methylation data. This technique can also be improvised to consider other differentially methylated genes significant in cancer or other epigenetic diseases.