A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning

Abstract

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

The Role of Artificial Intelligence in Early Cancer Diagnosis

“…Using central nervous system (CNS) tumours as an example, multi-omic data, including single-nucleotide polymorphism (SNP) mutations (e.g., TARDBP), gene methylation (e.g., 64-MMP) and transcriptome abnormalities (e.g., miRNA-21), are known to predict the progression of meningiomas [76]. A systematic review of multi-omic glioblastoma studies by Takahashi et al found that most utilised ML techniques for analysis, likely due to the size and complexity of the data [77]. In one study of 156 patients with oligodendrogliomas, mRNA expression arrays, microRNA sequencing and DNA methylation arrays were analysed using a multi-omics approach to better classify 1p/19q co-deleted tumours [78].…”

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

“…In addition, recently, traces of the entry of ML into the field of multi-omics data analysis from neuro-oncology research have been seen. So that the application of this advanced technology in various fields of cancer is getting more colorful and effective (Takahashi et al, 2021).…”

Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges

“…Along with radiomics, genomics, transcriptomics, and high-throughput proteomics are all examples of “omics” techniques that provide data for the examination of molecular constituents. In a general sense, the multi-omics analysis consists of three components: input data, technique, and output data ( 162 ). Multiple omics analysis is crucial in neuro-oncology research with limited sample sizes.…”