Objective. This study is aimed at understanding the molecular mechanisms and exploring potential therapeutic targets for atrial fibrillation (AF) by multiomics analysis. Methods. Transcriptomics and methylation data of AF patients were retrieved from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and differentially methylated sites between AF and normal samples were screened. Then, highly expressed and hypomethylated and lowly expressed and hypermethylated genes were identified for AF. Weighted gene coexpression network analysis (WGCNA) was presented to construct AF-related coexpression networks. 52 AF blood samples were used for whole exome sequence. The mutation was visualized by the maftools package in R. Key genes were validated in AF using independent datasets. Results. DEGs were identified between AF and controls, which were enriched in neutrophil activation and regulation of actin cytoskeleton. RHOA, CCR2, CASP8, and SYNPO2L exhibited abnormal expression and methylation, which have been confirmed to be related to AF. PCDHA family genes had high methylation and low expression in AF. We constructed two AF-related coexpression modules. Single-nucleotide polymorphism (SNP) was the most common mutation type in AF, especially T > C . MUC4 was the most frequent mutation gene, followed by PHLDA1, AHNAK2, and MAML3. There was no statistical difference in expression of AHNAK2 and MAML3, for AF. PHLDA1 and MUC4 were confirmed to be abnormally expressed in AF. Conclusion. Our findings identified DEGs related to DNA methylation and mutation for AF, which may offer possible therapeutic targets and a new insight into the pathogenesis of AF from a multiomics perspective.

Background: Data regarding the parameters of the coracoid process and glenoid width are insufficient, and information on gender, age, and ethnic differences in the parameters appear lacking in the Chinese population. This study aimed to investigate the morphometric parameters in the coracoid process and glenoid width. Methods: Using our institution's electronic database, we selected 84 patients (55 males and 29 females) who underwent a shoulder computed tomography (CT) scan from January 2017 to May 2018 in this study. Mimics19.0 software was used for three-dimensional (3D) reconstruction of CT and to measure the morphometric parameters of the coracoid process and glenoid width. Subgroup analyses stratified by gender and age were conducted and the parameters were compared with previously published reports. All data were statistically analysed by SPSS23.0 Statistical Package. Results: A positive and significant relationship between the coracoid process and the glenoid width (R > 0.758, P < 0.01) was found. The midpoint width represents 52% (41-62%) of the glenoid width; the midpoint height, 40% (31-53%) of the glenoid width. Significant differences in all parameters between males and females were noted (P < 0.05). No significant differences among the age groups were observed (P > 0.05), whereas significant differences in almost all parameters between the ethnic groups were observed (P < 0.05). Conclusion: Our results could supplement the information in the shoulder joint database with morphometric parameters and provide a reference for theoretical research on coracoid osteotomy, which may in turn help surgeons in the evaluation of coracoid process transfer.

Objective. Multiple genes have been identified to cause dilated cardiomyopathy (DCM). Nevertheless, there is still a lack of comprehensive elucidation of the molecular characteristics for DCM. Herein, we aimed to uncover putative molecular features for DCM by multiomics analysis. Methods. Differentially expressed genes (DEGs) were obtained from different RNA sequencing (RNA-seq) datasets of left ventricle samples from healthy donors and DCM patients. Furthermore, protein-protein interaction (PPI) analysis was then presented. Differentially methylated genes (DMGs) were identified between DCM and control samples. Following integration of DEGs and DMGs, differentially expressed and methylated genes were acquired and their biological functions were analyzed by the clusterProfiler package. Whole exome sequencing of blood samples from 69 DCM patients was constructed in our cohort, which was analyzed the maftools package. The expression of key mutated genes was verified by three independent datasets. Results. 1407 common DEGs were identified for DCM after integration of the two RNA-seq datasets. A PPI network was constructed, composed of 171 up- and 136 downregulated genes. Four hub genes were identified for DCM, including C3 ( degree = 24 ), GNB3 ( degree = 23 ), QSOX1 ( degree = 21 ), and APOB ( degree = 17 ). Moreover, 285 hyper- and 321 hypomethylated genes were screened for DCM. After integration, 20 differentially expressed and methylated genes were identified, which were associated with cell differentiation and protein digestion and absorption. Among single-nucleotide variant (SNV), C>T was the most frequent mutation classification for DCM. MUC4 was the most frequent mutation gene which occupied 71% across 69 samples, followed by PHLDA1, AHNAK2, and MAML3. These mutated genes were confirmed to be differentially expressed between DCM and control samples. Conclusion. Our findings comprehensively analyzed molecular characteristics from the transcriptome, epigenome, and genome perspectives for DCM, which could provide practical implications for DCM.

Sarcopenia is an aging syndrome characterized by decreased muscle function and skeletal muscle strength or quality. It is often accompanied by atherosclerosis (AS), which poses a great threat to the health and quality of life of the elderly. However, the relationship between sarcopenia and AS is poorly understood, so the clinical establishment of preventive measures for sarcopenia combined with AS requires further investigation. This paper aims to explore the genetic and environmental risk factors of sarcopenia complicated with AS. The clinical data of 252 non-blood-related patients with sarcopenia admitted to our hospital from May 2016 to May 2020 were retrospectively analyzed and allocated to AS and non-AS groups basing on whether they had concurrent AS. Univariate analysis found statistically significant (P <0.05) differences between the two groups’ vitamin D receptor (VDR) FokI loci genotypes and alleles, XbaI XX loci genotypes and alleles, ages, family history of AS, merge inflammation, growth hormone (GH) levels, insulin-like growth factor 1 (IGF-1) levels, testosterone levels, estrogen levels, insulin resistance (IR), vitamin D deficiency, and bone density. A multivariable logistic regression analysis showed VDR FokI loci genotypes and alleles, XbaI XX loci genotypes and alleles, merge inflammation, GH levels, IGF-1 levels, low testosterone levels, low estrogen levels, and vitamin D deficiency were independent risk factors of sarcopenia combined with AS. Therefore, corresponding preventive measures should be taken for the above risk factors in clinical practice to reduce the incidence of sarcopenia combined with AS.