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

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.

RETRACTED: Cheng et al. Using Comorbidity Pattern Analysis to Detect Reliable Methylated Genes in Colorectal Cancer Verified by Stool DNA Test. Genes 2021, 12, 1539.

The Genes journal retracts the article "Using Comorbidity Pattern Analysis to Detect Reliable Methylated Genes in Colorectal Cancer Verified by Stool DNA Test" [...].

Using Comorbidity Pattern Analysis to Detect Reliable Methylated Genes in Colorectal Cancer Verified by Stool DNA Test.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide in 2020. Colonoscopy and the fecal immunochemical test (FIT) are commonly used as CRC screening tests, but both types of tests possess different limitations. Recently, liquid biopsy-based DNA methylation test has become a powerful tool for cancer screening, and the detection of abnormal DNA methylation in stool specimens is considered as an effective approach for CRC screening. The aim of this study was to develop a novel approach in biomarker selection based on integrating primary biomarkers from genome-wide methylation profiles and secondary biomarkers from CRC comorbidity analytics. A total of 125 differential methylated probes (DMPs) were identified as primary biomarkers from 352 genome-wide methylation profiles. Among them, 51 biomarkers, including 48 hypermethylated DMPs and 3 hypomethylated DMPs, were considered as suitable DMP candidates for CRC screening tests. After comparing with commercial kits, three genes (ADHFE1, SDC2, and PPP2R5C) were selected as candidate epigenetic biomarkers for CRC screening tests. Methylation levels of these three biomarkers were significantly higher for patients with CRC than normal subjects. The sensitivity and specificity of integrating methylated ADHFE1, SDC2, and PPP2R5C for CRC detection achieved 84.6% and 92.3%, respectively. Through an integrated approach using genome-wide DNA methylation profiles and electronic medical records, we could design a biomarker panel that allows for early and accurate noninvasive detection of CRC using stool samples.

Cross-species identification of in silico microsatellite biomarkers for genetic disease.

Microsatellites appear widely in genomes of diverse species. Variants of repeat number of microsatellites often correlate with risks of genetic disorder or severity of diseases. Using cross-species comparison, the proposed system comprehensively verifies microsatellites of specific genes related to 16 genetic disorders. Genomic information retrieved from 14 frequently used model organisms in biomedical study was thoroughly analyzed, emphasizing conserved and diverse traits. Features of microsatellite sequences among different organisms, including appearing frequency, position, pattern and distribution, could be determined automatically for stating genetically functional conservation and evolutionary correlation. This research found that among mammals and fishes, the microsatellite sequences are conserved in the genes of epidermal growth factor receptor, ataxia telangiectasia mutated and androgen receptor corresponding to cancers, ataxia telangiectasia and hepatocellular carcinoma, respectively. Still, except fruit fly conserved CAG repeats in Huntington and Spinocerebellar ataxia type 2 genes, no microsatellites were conserved in those genes linked to neurological/neurodegenerative disorders among mammal and fish species. In comparison of mammalian species, microsatellite biomarkers identified from 17 genetic disorder-related genes revealed high repeat conservation, especially in human, gorilla and macaque. Obviously, this comparative analysis illustrates microsatellite repeats affecting genetic disorders, highly correlated to evolutionary distance of species. Chief contribution of this in silico research lies in assisting biologists to identify disease-related microsatellite biomarkers and employ appropriate model organisms for further biomedical studies relying on microsatellite conservation information. Database http://ssrtc.cs.ntou.edu.tw is for academic use.

Identification of simple sequence repeat biomarkers through cross-species comparison in a tag cloud representation.

Simple sequence repeats (SSRs) are not only applied as genetic markers in evolutionary studies but they also play an important role in gene regulatory activities. Efficient identification of conserved and exclusive SSRs through cross-species comparison is helpful for understanding the evolutionary mechanisms and associations between specific gene groups and SSR motifs. In this paper, we developed an online cross-species comparative system and integrated it with a tag cloud visualization technique for identifying potential SSR biomarkers within fourteen frequently used model species. Ultraconserved or exclusive SSRs among cross-species orthologous genes could be effectively retrieved and displayed through a friendly interface design. Four different types of testing cases were applied to demonstrate and verify the retrieved SSR biomarker candidates. Through statistical analysis and enhanced tag cloud representation on defined functional related genes and cross-species clusters, the proposed system can correctly represent the patterns, loci, colors, and sizes of identified SSRs in accordance with gene functions, pattern qualities, and conserved characteristics among species.

Identifying significant associations of orthologous simple sequence repeats with gene ontologies.

Simple Sequence Repeats (SSRs), also known as microsatellites, regulate gene functions. SSR mutations in a disease gene may cause various genetic disorders. To identify putative functional SSRs, a web-based system, Gene Ontology SSR Hierarchy (GOSH), was developed to facilitate discovery of significant associations between SSRs and Gene Ontology (GO) terms. Using the GO hierarchy term structure, GOSH assists users with selecting functional or biological gene subsets. Significant SSR patterns are retrieved and identified via comprehensive overrepresentation analysis within a target gene subset and by comparing results with orthologous genes. Pattern relationships between different biological subsets or supersets can be observed by using the GO hierarchy structure directly. GOSH also supports GO searching through identified significant SSR patterns and all GO terms possessing such patterns are listed for consultation. GOSH is the first comprehensive and efficient online mining tool for discovering significant orthologous SSR patterns in GO terms and is available at http://gosh.cs.ntou.edu.tw/.