Data Mining of Microarray Datasets in Translational Neuroscience

Abstract

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Keywords: biological samples; biomarker discovery; circular RNA (circRNA); long non-coding RNA (lncRNA); messenger RNA (mRNA); microRNA (miRNA); microarray analysis; multi-omics integration; therapeutic development; translational neuroscience.

Figure 1

Data mining of different types of RNA in various biological samples for translational neuroscience applications. (A) Various types of biological samples, including post-mortem brain tissues, CSF, peripheral blood, and human stem cells. (B) Different types of coding (mRNA) and non-coding RNA (miRNA, circRNA, and lncRNA) obtained from biological samples. (C) Data mining of microarray datasets associated with neurodegenerative diseases. Different genes detected by the microarray analysis are illustrated by different colors. (D) Translational neuroscience applications including drug discovery, the elucidation of disease mechanisms, and biomarker identification. The figure was created using BioRender.

Figure 2

Pipeline for the data mining of microarray gene expression. (A) Searching for suitable microarray datasets to be analyzed using web-based tools or command line scripts. Different genes identified by microarray analysis are represented by different colors. (B) Pre-processing of datasets via normalization, quality control, and feature selection. (C) Statistical tests to obtain DEGs with corresponding P-values (significance) and LogFC (fold-change). Downregulated genes are illustrated in red and upregulated genes are illustrated in purple. (D) Enrichment analysis and visualization using pathway analysis, gene set enrichment analysis (GSEA), and network analysis to provide a biological interpretation of the DEGs. Different pathways or functional annotations of the DEGs are illustrated by different colors. The figure was created using BioRender.