Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets.

Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.

Gene body hypomethylation of pyroptosis-related genes NLRP7, NLRP2, and NLRP3 facilitate non-invasive surveillance of hepatocellular carcinoma.

Programmed cell death (PCD) resistance is a key driver of cancer occurrence and development. The prognostic relevance of PCD-related genes in hepatocellular carcinoma (HCC) has attracted considerable attention in recent years. However, there is still a lack of efforts to compare the methylation status of different types of PCD genes in HCC and their roles in its surveillance. The methylation status of genes related to pyroptosis, apoptosis, autophagy, necroptosis, ferroptosis, and cuproptosis was analyzed in tumor and non-tumor tissues from TCGA. Whole-genome bisulfite sequencing (WGBS) data of paired tumor tissue and buffy coat samples were used to filter the potential interference of blood leukocytes in cell-free DNA (cfDNA). The WGBS data of healthy individuals' and early-stage HCC patients' cfDNA were analyzed to evaluate the distinguishing ability. The average gene body methylation (gbDNAme) of pyroptosis-related genes (PRGs) was significantly altered in HCC tissues relative to normal tissues, and their distinguishing ability was higher compared to the other types of PCD-related genes. The gbDNAme of NLRP7, NLRP2, and NLRP3 was reflective of the hypomethylation in HCC tissues, and methylation levels of NLRP3 correlated positively with its expression level (r=0.51). The candidate hypomethylated PRGs could discriminate between early HCC patients and healthy controls in cfDNA analysis with high accuracy (area under the receiver operation curve, AUC=0.94). Furthermore, the hypomethylation of PRGs was associated with poor prognosis of HCC. Gene body hypomethylation of PRGs is a promising biomarker for early HCC detection, monitoring of tumor recurrence, and prognosis prediction.

Absent in melanoma 2 (AIM2) in rheumatoid arthritis: novel molecular insights and implications.

Absent in melanoma 2 (AIM2), a member of the Pyrin and HIN domain protein family, is a cytoplasmic receptor that recognizes double-stranded DNA. AIM2 exhibits limited expression under physiological conditions but is widely expressed in many human diseases, including autoimmune diseases, and plays an essential role in the immune response. Rheumatoid arthritis (RA) is an autoimmune disease that poses a severe threat to physical and mental health, and is caused by several genetic and metabolic factors. Multiple immune cells interact to form a complex inflammatory network that mediates inflammatory responses and bone destruction. Abnormal AIM2 expression in multiple immune cell populations (T cells, B cells, fibroblast-like synoviocytes, monocytes, and macrophages) may regulate multiple functional responses in RA through mechanisms such as pyroptosis, PANoptosis, and regulation of other molecules. In this review, we describe and summarize the functional regulation and impact of AIM2 expression in immune cells to improve our understanding of the complex pathological mechanisms. These insights may provide potential directions for the development of new clinical diagnostic strategies for RA.

A gene-based recessive diplotype exome scan discovers FGF6, a novel hepcidin-regulating iron-metabolism gene.

Standard analyses applied to genome-wide association data are well designed to detect additive effects of moderate strength. However, the power for standard genome-wide association study (GWAS) analyses to identify effects from recessive diplotypes is not typically high. We proposed and conducted a gene-based compound heterozygosity test to reveal additional genes underlying complex diseases. With this approach applied to iron overload, a strong association signal was identified between the fibroblast growth factor-encoding gene, FGF6, and hemochromatosis in the central Wisconsin population. Functional validation showed that fibroblast growth factor 6 protein (FGF-6) regulates iron homeostasis and induces transcriptional regulation of hepcidin. Moreover, specific identified FGF6 variants differentially impact iron metabolism. In addition, FGF6 downregulation correlated with iron-metabolism dysfunction in systemic sclerosis and cancer cells. Using the recessive diplotype approach revealed a novel susceptibility hemochromatosis gene and has extended our understanding of the mechanisms involved in iron metabolism.

Mechanisms of DNA Methylation in Virus-Host Interaction in Hepatitis B Infection: Pathogenesis and Oncogenetic Properties.

Hepatitis B virus (HBV), the well-studied oncovirus that contributes to the majority of hepatocellular carcinomas (HCC) worldwide, can cause a severe inflammatory microenvironment leading to genetic and epigenetic changes in hepatocyte clones. HBV replication contributes to the regulation of DNA methyltransferase gene expression, particularly by X protein (HBx), and subsequent methylation changes may lead to abnormal transcription activation of adjacent genes and genomic instability. Undoubtedly, the altered expression of these genes has been known to cause diverse aspects of infected hepatocytes, including apoptosis, proliferation, reactive oxygen species (ROS) accumulation, and immune responses. Additionally, pollutant-induced DNA methylation changes and aberrant methylation of imprinted genes in hepatocytes also complicate the process of tumorigenesis. Meanwhile, hepatocytes also contribute to epigenetic modification of the viral genome to affect HBV replication or viral protein production. Meanwhile, methylation levels of HBV integrants and surrounding host regions also play crucial roles in their ability to produce viral proteins in affected hepatocytes. Both host and viral changes can provide novel insights into tumorigenesis, individualized responses to therapeutic intervention, disease progress, and early diagnosis. As such, DNA methylation-mediated epigenetic silencing of cancer-related genes and viral replication is a compelling therapeutic goal to reduce morbidity and mortality from liver cancer caused by chronic HBV infection. In this review, we summarize the most recent research on aberrant DNA methylation associated with HBV infection, which is involved in HCC development, and provide an outlook on the future direction of the research.

Hypomethylation in HBV integration regions aids non-invasive surveillance to hepatocellular carcinoma by low-pass genome-wide bisulfite sequencing.

BACKGROUND: Circulating cell-free DNA (cfDNA) methylation has been demonstrated to be a promising approach for non-invasive cancer diagnosis. However, the high cost of whole genome bisulfite sequencing (WGBS) hinders the clinical implementation of a methylation-based cfDNA early detection biomarker. We proposed a novel strategy in low-pass WGBS (~ 5 million reads) to detect methylation changes in circulating cell-free DNA (cfDNA) from patients with liver diseases and hepatocellular carcinoma (HCC). METHODS: The effective small sequencing depth were determined by 5 pilot cfDNA samples with relative high-depth WGBS. CfDNA of 51 patients with hepatitis, cirrhosis, and HCC were conducted using low-pass WGBS. The strategy was validated in an independent WGBS cohort of 32 healthy individuals and 26 early-stage HCC patients. Fifteen paired tumor tissue and buffy coat samples were used to characterize the methylation of hepatitis B virus (HBV) integration regions and genome distribution of cfDNA. RESULTS: A significant enrichment of cfDNA in intergenic and repeat regions, especially in previously reported HBV integration sites were observed, as a feature of cfDNA and the bias of cfDNA release. Methylation profiles nearby HBV integration sites were a better indicator for hypomethylation of tumor genome comparing to Alu and LINE (long interspersed nuclear element) repeats, and were able to facilitate the cfDNA-based HCC prediction. Hypomethylation nearby HBV integration sites (5 kb flanking) was detected in HCC patients, but not in patients with hepatitis and cirrhosis (MethylHBV5k, median:0.61 vs 0.72, P = 0.0003). Methylation levels of integration sites certain candidate regions exhibited an area under the receiver operation curve (AUC) value > 0.85 to discriminate HCC from non-HCC samples. The validation cohort achieved the prediction performance with an AUC of 0.954. CONCLUSIONS: Hypomethylation around viral integration sites aids low-pass cfDNA WGBS to serve as a non-invasive approach for early HCC detection, and inspire future efforts on tumor surveillance for oncovirus with integration activity.

Reduced Anti-Histone Antibodies and Increased Risk of Rheumatoid Arthritis Associated with a Single Nucleotide Polymorphism in PADI4 in North Americans.

Autoantibodies against citrullinated proteins are a hallmark of rheumatoid arthritis, a destructive inflammatory arthritis. Peptidylarginine deiminase 4 (PAD4) has been hypothesized to contribute to rheumatoid arthritis by citrullinating histones to induce neutrophil extracellular traps (NETs), which display citrullinated proteins that are targeted by autoantibodies to drive inflammation and arthritis. Consistent with this theory, PAD4-deficient mice have reduced NETs, autoantibodies, and arthritis. However, PAD4's role in human rheumatoid arthritis is less clear. Here, we determine if single nucleotide polymorphism rs2240335 in PADI4, whose G allele is associated with reduced PAD4 in neutrophils, correlates with NETs, anti-histone antibodies, and rheumatoid arthritis susceptibility in North Americans. Control and rheumatoid arthritis subjects, divided into anti-cyclic citrullinated peptide (CCP) antibody positive and negative groups, were genotyped at rs2240335. In homozygotes, in vitro NETosis was quantified in immunofluorescent images and circulating NET and anti-histone antibody levels by enzyme linked immunosorbent assay (ELISA). Results were compared by t-test and correlation of rheumatoid arthritis diagnosis with rs2240335 by Armitage trend test. NET levels did not significantly correlate with genotype. G allele homozygotes in the CCP- rheumatoid arthritis group had reduced anti-native and anti-citrullinated histone antibodies. However, the G allele conferred increased risk for rheumatoid arthritis diagnosis, suggesting a complex role for PAD4 in human rheumatoid arthritis.

Cell line donor genotype and its influence on experimental phenotype: Toll-like receptor SNPs and potential variability in innate immunity.

Cell lines are used to model a disease and provide valuable information regarding phenotype, mechanism, and response to novel therapies. Derived from individuals of diverse genetic backgrounds, the cell's genetic complement predicts the phenotype, and although some lines have been sequenced, little emphasis has been placed on genotyping. Toll-like receptors (TLRs) are essential in initiating the inflammatory cascade in response to infection. TLR single nucleotide polymorphism (SNP) alleles may predict an altered innate immune response: a SNP can affect TLR-dependent pathways and may alter experimental results. Thus, genotype variation may have far-reaching implications when using cell lines to model phenotypes. We recommend that cell lines be genotyped and cataloged in a fashion similar to that used for bacteria, with cumulative information being archived in an accessible central database to facilitate the understanding of SNP cell phenotypes reported in the literature.

SeqHBase: a big data toolset for family based sequencing data analysis.

BACKGROUND: Whole-genome sequencing (WGS) and whole-exome sequencing (WES) technologies are increasingly used to identify disease-contributing mutations in human genomic studies. It can be a significant challenge to process such data, especially when a large family or cohort is sequenced. Our objective was to develop a big data toolset to efficiently manipulate genome-wide variants, functional annotations and coverage, together with conducting family based sequencing data analysis. METHODS: Hadoop is a framework for reliable, scalable, distributed processing of large data sets using MapReduce programming models. Based on Hadoop and HBase, we developed SeqHBase, a big data-based toolset for analysing family based sequencing data to detect de novo, inherited homozygous, or compound heterozygous mutations that may contribute to disease manifestations. SeqHBase takes as input BAM files (for coverage at every site), variant call format (VCF) files (for variant calls) and functional annotations (for variant prioritisation). RESULTS: We applied SeqHBase to a 5-member nuclear family and a 10-member 3-generation family with WGS data, as well as a 4-member nuclear family with WES data. Analysis times were almost linearly scalable with number of data nodes. With 20 data nodes, SeqHBase took about 5 secs to analyse WES familial data and approximately 1 min to analyse WGS familial data. CONCLUSIONS: These results demonstrate SeqHBase's high efficiency and scalability, which is necessary as WGS and WES are rapidly becoming standard methods to study the genetics of familial disorders.

Use of an electronic medical record to create the marshfield clinic twin/multiple birth cohort.

Population-based genetic analyses, such as the Genome-Wide Association Study (GWAS), have proven powerful for describing the genetic complexities of common disease in epidemiologic research. However, the significant challenges faced by population-based study designs have resulted in revitalization of family-based approaches, including twin studies. Twin studies are unique in their ability to ascertain both heritable and environmental contributions to human disease. Several regional and national twin registries have been constructed using a variety of methods to identify potential twins. A significant challenge in constructing these large twin registries includes the substantial resources required to recruit participants, collect phenotypic data, and update the registries as time progresses. Here we describe the use of the Marshfield Clinic electronic medical record (EMR) to identify a cohort of 19,226 patients enriched for twins or multiples. This cohort defines the Marshfield Clinic Twin/Multiple Birth Cohort (MCTC). An EMR system provides both a mechanism to identify potential twins and a source of detailed phenotypic data in near real time without the need for patient contact outside standard medical care. To demonstrate that the MCTC can be used for genetic-based epidemiologic research, concordance rates for muscular dystrophy (MD) and fragile-X syndrome-two highly heritable diseases-were assessed. Observations indicate that both MD and fragile-X syndrome are highly correlated among affected twins in the MCTC (P ≅ 3.7 × 10(-6) and 1.1 × 10(-4) , respectively). These findings suggest that EMR systems may not only be an effective resource for predicting families of twins, but can also be rapidly applied to epidemiologic research.

Prevalence estimation for monogenic autosomal recessive diseases using population-based genetic data.

Genetic methods can complement epidemiological surveys and clinical registries in determining prevalence of monogenic autosomal recessive diseases. Several large population-based genetic databases, such as the NHLBI GO Exome Sequencing Project, are now publically available. By assuming Hardy-Weinberg equilibrium, the frequency of individuals homozygous in the general population for a particular pathogenic allele can be directly calculated from a sample of chromosomes where some harbor the pathogenic allele. Further assuming that the penetrance of the pathogenic allele(s) is known, the prevalence of recessive phenotypes can be determined. Such work can inform public health efforts for rare recessive diseases. A Bayesian estimation procedure has yet to be applied to the problem of estimating disease prevalence from large population-based genetic data. A Bayesian framework is developed to derive the posterior probability density of monogenic, autosomal recessive phenotypes. Explicit equations are presented for the credible intervals of these disease prevalence estimates. A primary impediment to performing accurate disease prevalence calculations is the determination of truly pathogenic alleles. This issue is discussed, but in many instances remains a significant barrier to investigations solely reliant on statistical interrogation--functional studies can provide important information for solidifying evidence of variant pathogenicity. We also discuss several challenges to these efforts, including the population structure in the sample of chromosomes, the treatment of allelic heterogeneity, and reduced penetrance of pathogenic variants. To illustrate the application of these methods, we utilized recently published genetic data collected on a large sample from the Schmiedeleut Hutterites. We estimate prevalence and calculate 95% credible intervals for 13 autosomal recessive diseases using these data. In addition, the Bayesian estimation procedure is applied to data from a central European study of hereditary fructose intolerance. The methods described herein show a viable path to robustly estimating both the expected prevalence of autosomal recessive phenotypes and corresponding credible intervals using population-based genetic databases that have recently become available. As these genetic databases increase in number and size with the advent of cost-effective next-generation sequencing, we anticipate that these methods and approaches may be helpful in recessive disease prevalence calculations, potentially impacting public health management, health economic analyses, and treatment of rare diseases.

Differential Lipid Response to Statins Is Associated With Variants in the BUD13-APOA5 Gene Region.

Genetic variants within the BUD13-APOA5 gene region are known to be associated with high-density lipoprotein cholesterol (HDL-C) and triglyceride (TG) levels. Recent studies suggest that single nucleotide polymorphisms (SNPs) within this region affect HDL-C response to statin-fibrate combination therapy and low-density lipoprotein cholesterol (LDL-C) response to statin therapy. We hypothesized that SNPs within the BUD13-APOA5 region are associated with TG, HDL-C, and LDL-C response to statin therapy. We examined 1520 observations for 1086 patients from the Personalized Medicine Research Project, a large biorepository at the Marshfield Clinic Research Foundation, who had received statin therapy and been previously genotyped for polymorphisms in the 11q23 chromosomal region. A significant differential response to statin therapy was observed for 3 SNPs. The minor allele at rs11605293 significantly attenuated TG-lowering response to pravastatin (P = 0.000159), whereas the minor allele at rs12806755 was associated with a similar response to lovastatin (P = 0.000192). Genotypes at rs947990 significantly attenuated LDL-C reduction to atorvastatin therapy (P = 0.000668) with some patients with the minor allele having LDL-C increase after therapy. No SNPs within the BUD13-APOA5 region were associated with a significant effect on HDL-C reduction in response to statin therapy. In conclusion, this study suggests that common SNPs within the BUD13-APOA5 can affect TG and LDL-C response to statin therapy in a North American population.

gwid: an R package and Shiny application for Genome-Wide analysis of IBD data.

Summary: Genome-wide identity by descent (gwid) is an R package developed for the analysis of identity-by-descent (IBD) data pertaining to dichotomous traits. This package offers a set of tools to assess differential IBD levels for the two states of a binary trait, yielding informative and meaningful results. Furthermore, it provides convenient functions to visualize the outcomes of these analyses, enhancing the interpretability and accessibility of the results. To assess the performance of the package, we conducted an evaluation using real genotype data derived from the SNPs to investigate rheumatoid arthritis susceptibility from the Marshfield Clinic Personalized Medicine Research Project. Availability and implementation: gwid is available as an open-source R package. Release versions can be accessed on CRAN (https://cran.r-project.org/package=gwid) for all major operating systems. The development version is maintained on GitHub (https://github.com/soroushmdg/gwid) and full documentation with examples and workflow templates is provided via the package website (http://tinyurl.com/gwid-tutorial). An interactive R Shiny dashboard is also developed (https://tinyurl.com/gwid-shiny).

Structural variants in linkage disequilibrium with GWAS-significant SNPs.

With the recent expansion of structural variant identification in the human genome, understanding the role of these impactful variants in disease architecture is critically important. Currently, a large proportion of genome-wide-significant genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) are functionally unresolved, raising the possibility that some of these SNPs are associated with disease through linkage disequilibrium with causal structural variants. Hence, understanding the linkage disequilibrium between newly discovered structural variants and statistically significant SNPs may provide a resource for further investigation into disease-associated regions in the genome. Here we present a resource cataloging structural variant-significant SNP pairs in high linkage disequilibrium. The database is composed of (i) SNPs that have exhibited genome-wide significant association with traits, primarily disease phenotypes, (ii) newly released structural variants (SVs), and (iii) linkage disequilibrium values calculated from unphased data. All data files including those detailing SV and GWAS SNP associations and results of GWAS-SNP-SV pairs are available at the SV-SNP LD Database and can be accessed at 'https://github.com/hliang-SchrodiLab/SV_SNPs. Our analysis results represent a useful fine mapping tool for interrogating SVs in linkage disequilibrium with disease-associated SNPs. We anticipate that this resource may play an important role in subsequent studies which investigate incorporating disease causing SVs into disease risk prediction models.

Understanding the function of the GABAergic system and its potential role in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a highly disabling chronic autoimmune disease. Multiple factors contribute to the complex pathological process of RA, in which an abnormal autoimmune response, high survival of inflammatory cells, and excessive release of inflammatory factors lead to a severe chronic inflammatory response. Clinical management of RA remains limited; therefore, exploring and discovering new mechanisms of action could enhance clinical benefits for patients with RA. Important bidirectional communication occurs between the brain and immune system in inflammatory diseases such as RA, and circulating immune complexes can cause neuroinflammatory responses in the brain. The gamma-aminobutyric acid (GABA)ergic system is a part of the nervous system that primarily comprises GABA, GABA-related receptors, and GABA transporter (GAT) systems. GABA is an inhibitory neurotransmitter that binds to GABA receptors in the presence of GATs to exert a variety of pathophysiological regulatory effects, with its predominant role being neural signaling. Nonetheless, the GABAergic system may also have immunomodulatory effects. GABA/GABA-A receptors may inhibit the progression of inflammation in RA and GATs may promote inflammation. GABA-B receptors may also act as susceptibility genes for RA, regulating the inflammatory response of RA via immune cells. Furthermore, the GABAergic system may modulate the abnormal pain response in RA patients. We also summarized the latest clinical applications of the GABAergic system and provided an outlook on its clinical application in RA. However, direct studies on the GABAergic system and RA are still lacking; therefore, we hope to provide potential therapeutic options and a theoretical basis for RA treatment by summarizing any potential associations.

Role of the granzyme family in rheumatoid arthritis: Current Insights and future perspectives.

Rheumatoid arthritis (RA) is a complex autoimmune disease characterized by chronic inflammation that affects synovial tissues of multiple joints. Granzymes (Gzms) are serine proteases that are released into the immune synapse between cytotoxic lymphocytes and target cells. They enter target cells with the help of perforin to induce programmed cell death in inflammatory and tumor cells. Gzms may have a connection with RA. First, increased levels of Gzms have been found in the serum (GzmB), plasma (GzmA, GzmB), synovial fluid (GzmB, GzmM), and synovial tissue (GzmK) of patients with RA. Moreover, Gzms may contribute to inflammation by degrading the extracellular matrix and promoting cytokine release. They are thought to be involved in RA pathogenesis and have the potential to be used as biomarkers for RA diagnosis, although their exact role is yet to be fully elucidated. The purpose of this review was to summarize the current knowledge regarding the possible role of the granzyme family in RA, with the aim of providing a reference for future research on the mechanisms of RA and the development of new therapies.

Pan-precancer and cancer DNA methylation profiles revealed significant tissue specificity of interrupted biological processes in tumorigenesis.

DNA methylation (DNAme) alterations are known to initiate from the precancerous stage of tumorigenesis. Herein, we investigated the global and local patterns of DNAme perturbations in tumorigenesis by analysing the genome-wide DNAme profiles of the cervix, colorectum, stomach, prostate, and liver at precancerous and cancer stages. We observed global hypomethylation in tissues of both two stages, except for the cervix, whose global DNAme level in normal tissue was lower than that of the other four tumour types. For alterations shared by both stages, there were common hyper-methylation (sHyperMethyl) and hypo-methylation (sHypoMethyl) changes, of which the latter type was more frequently identified in all tissues. Biological pathways interrupted by sHyperMethyl and sHypoMethyl alterations demonstrated significant tissue specificity. DNAme bidirectional chaos indicated by the enrichment of both sHyperMethyl and sHypoMethyl changes in the same pathway was observed in most tissues and was a common phenomenon, particularly in liver lesions. Moreover, for the same enriched pathways, different tissues may be affected by distinct DNAme types. For the PI3K-Akt signalling pathway, sHyperMethyl enrichment was observed in the prostate dataset, but sHypoMethyl enrichment was observed in the colorectum and liver datasets. Nevertheless, they did not show an increased possibility in survival prediction of patients in comparison with other DNAme types. Additionally, our study demonstrated that gene-body DNAme changes of tumour suppressor genes and oncogenes may persist from precancerous lesions to the tumour. Overall, we demonstrate the tissue specificity and commonality of cross-stage alterations in DNA methylation profiles in multi-tissue tumorigenesis.

DNA methylome and transcriptome profiling reveal key electrophysiology and immune dysregulation in hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease. However, a detailed DNA methylation (DNAme) landscape has not yet been elucidated. Our study combined DNAme and transcriptome profiles for HCM myocardium and identify aberrant DNAme associated with altered myocardial function in HCM. The transcription of methylation-related genes did not significantly differ between HCM and normal myocardium. Nevertheless, the former had an altered DNAme profile compared with the latter. The hypermethylated and hypomethylated sites in HCM tissues had chromosomal distributions and functional enrichment of correlated genes differing from those of their normal tissue counterparts. The GO analysis of network underlying the genes correlated with DNAme alteration and differentially expressed genes (DEGs) shows functional clusters centred on immune cell function and muscle system processes. In KEGG analysis, only the calcium signalling pathway was enriched either by the genes correlated with changes in DNAme or DEGs. The protein-protein interactions (PPI) underlying the genes altered at both the DNAme and transcriptional highlighted two important functional clusters. One of these was related to the immune response and had the estrogen receptor-encoding ESR1 gene as its node. The other cluster comprised cardiac electrophysiology-related genes. Intelliectin-1 (ITLN1), a component of the innate immune system, was transcriptionally downregulated in HCM and had a hypermethylated site within 1500 bp upstream of the ITLN1 transcription start site. Estimates of immune infiltration demonstrated a relative decline in immune cell population diversity in HCM. A combination of DNAme and transcriptome profiles may help identify and develop new therapeutic targets for HCM.

Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis.

Objective: To investigate the potential association between Anoikis-related genes, which are responsible for preventing abnormal cellular proliferation, and rheumatoid arthritis (RA). Methods: Datasets GSE89408, GSE198520, and GSE97165 were obtained from the GEO with 282 RA patients and 28 healthy controls. We performed differential analysis of all genes and HLA genes. We performed a protein-protein interaction network analysis and identified hub genes based on STRING and cytoscape. Consistent clustering was performed with subgrouping of the disease. SsGSEA were used to calculate immune cell infiltration. Spearman's correlation analysis was employed to identify correlations. Enrichment scores of the GO and KEGG were calculated with the ssGSEA algorithm. The WGCNA and the DGIdb database were used to mine hub genes' interactions with drugs. Results: There were 26 differentially expressed Anoikis-related genes (FDR = 0.05, log2FC = 1) and HLA genes exhibited differential expression (P < 0.05) between the disease and control groups. Protein-protein interaction was observed among differentially expressed genes, and the correlation between PIM2 and RAC2 was found to be the highest; There were significant differences in the degree of immune cell infiltration between most of the immune cell types in the disease group and normal controls (P < 0.05). Anoikis-related genes were highly correlated with HLA genes. Based on the expression of Anoikis-related genes, RA patients were divided into two disease subtypes (cluster1 and cluster2). There were 59 differentially expressed Anoikis-related genes found, which exhibited significant differences in functional enrichment, immune cell infiltration degree, and HLA gene expression (P < 0.05). Cluster2 had significantly higher levels in all aspects than cluster1 did. The co-expression network analysis showed that cluster1 had 51 hub differentially expressed genes and cluster2 had 72 hub differentially expressed genes. Among them, three hub genes of cluster1 were interconnected with 187 drugs, and five hub genes of cluster2 were interconnected with 57 drugs. Conclusion: Our study identified a link between Anoikis-related genes and RA, and two distinct subtypes of RA were determined based on Anoikis-related gene expression. Notably, cluster2 may represent a more severe state of RA.