An Effective DNA Methylation Biomarker Screening Mechanism for Amyotrophic Lateral Sclerosis (ALS) Based on Comorbidities and Gene Function Analysis

Abstract

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. We also applied word2vec to these two clinical diagnostic medical databases to measure similarities between ALS and other similar diseases and evaluated the statistical assessment of the odds ratio to discover significant comorbidities for ALS subjects. Important and representative DNA methylation biomarker candidates could be effectively selected by cross-comparing similar diseases to ALS, comorbidity-related genes, and differentially expressed methylation loci for ALS subjects. The screened epigenomic and comorbidity-related biomarkers were clustered based on their genetic functions. The candidate DNA methylation biomarkers associated with ALS were comprehensively discovered. Gene ontology annotations were then applied to analyze and cluster the candidate biomarkers into three different groups based on gene function annotations. The results showed that a potential testing kit for ALS detection can be composed of SOD3, CACNA1H, and ERBB4 for effective early screening of ALS using blood samples. By developing an effective DNA methylation biomarker screening mechanism, early detection and prophylactic treatment of high-risk ALS patients can be achieved.

Keywords: DNA methylation; amyotrophic lateral sclerosis (ALS); bioinformatics; biomarker screening platform; comorbidity.

Figure 1

Flow chart showing the pipeline of exploring functional representative biomarkers by integrating primary biomarkers from DNA methylation analysis and secondary biomarkers from related comorbidity patterns.

Figure 2

Pipeline operations to discover primary DMP biomarkers by using GEO methylation profiling data.

Figure 3

Functional annotation results of the nine candidate biomarkers. The horizontal axis represents the number of genes corresponding to each function. The purple, yellow and green legends were the corresponding annotation results of candidate biomarkers in three main ontologies: biological processes, cellular components, and molecular functions. The red legend represents the annotation results of KEGG biological pathways.