Objective:The differentially expressed genes were screened from microarray data in the patients with non-syndromic craniosynostosis, and a protein interaction network was established to screen and predict hub genes related to the disease.Methods:The data set of GSE50796 were downloaded from the GEO database, which included seven samples of the closed cranial suture tissues from the non-syndromic craniosynostosis patients, and seven samples of the unclosed cranial suture tissues from the non-syndromic craniosynostosis patients. Analyze the differentially expressed genes were collected and analyzed with GEO2R, a GEO database online tool. P<0.05 and |logFC|> 2 were set as filter criteria. The ggplot2 of R package was applied for GO enrichment analysis, and the KEGG pathway analysis was completed with Enrichr. Gene set enrichment analysis (GSEA) was performed via GSEA 3.0 to analyze the correlation between gene sets and phenotypes. Secondly, the STRING database was used to analyze the interaction relationships between differentially expressed proteins in different tissues, and then Cytoscape and related plug-ins were used to establish the differentially expressed protein interaction network and screen the hub genes. Meanwhile, the key modules, important biological processes, and multiple co-expression relationships were analyzed. Results:A total of 255 differentially expressed genes based on the above screening conditions were obtained. The regulation of neural development screened by GO enrichment analysis, the PI3K-Akt signaling pathway screened by KEGG enrichment analysis, the important biological pathways (DNA replication, cell cycle, cytokine and receptor interaction) screened by GSEA enrichment analysis, and the positive regulation of osteoblast differentiation screened by ClueGO analysis, might be closely related to the etiology of non-syndromic craniosynostosis. The up-regulated hub genes such as CLEC12A, MS4A3 and DNTT in the group with closed sutures were screened by protein-protein interaction network and literature analysis, which might play a vital role in the pathogenic processes of non-syndromic craniosynostosis.Conclusions:With the multi-dimensional enrichment analysis of the differentially expressed genes and the establishment of protein interaction networks, we have deepened our understanding of differentially expressed genes, important biological processes and signaling pathways involved in the pathogenesis of non-syndromic craniosynostosis. The selected hub genes may become early diagnostic markers and potential molecular therapeutic targets.

Objective:The differentially expressed genes were screened from microarray data in the patients with non-syndromic craniosynostosis, and a protein interaction network was established to screen and predict hub genes related to the disease.Methods:The data set of GSE50796 were downloaded from the GEO database, which included seven samples of the closed cranial suture tissues from the non-syndromic craniosynostosis patients, and seven samples of the unclosed cranial suture tissues from the non-syndromic craniosynostosis patients. Analyze the differentially expressed genes were collected and analyzed with GEO2R, a GEO database online tool. P<0.05 and |logFC|> 2 were set as filter criteria. The ggplot2 of R package was applied for GO enrichment analysis, and the KEGG pathway analysis was completed with Enrichr. Gene set enrichment analysis (GSEA) was performed via GSEA 3.0 to analyze the correlation between gene sets and phenotypes. Secondly, the STRING database was used to analyze the interaction relationships between differentially expressed proteins in different tissues, and then Cytoscape and related plug-ins were used to establish the differentially expressed protein interaction network and screen the hub genes. Meanwhile, the key modules, important biological processes, and multiple co-expression relationships were analyzed. Results:A total of 255 differentially expressed genes based on the above screening conditions were obtained. The regulation of neural development screened by GO enrichment analysis, the PI3K-Akt signaling pathway screened by KEGG enrichment analysis, the important biological pathways (DNA replication, cell cycle, cytokine and receptor interaction) screened by GSEA enrichment analysis, and the positive regulation of osteoblast differentiation screened by ClueGO analysis, might be closely related to the etiology of non-syndromic craniosynostosis. The up-regulated hub genes such as CLEC12A, MS4A3 and DNTT in the group with closed sutures were screened by protein-protein interaction network and literature analysis, which might play a vital role in the pathogenic processes of non-syndromic craniosynostosis.Conclusions:With the multi-dimensional enrichment analysis of the differentially expressed genes and the establishment of protein interaction networks, we have deepened our understanding of differentially expressed genes, important biological processes and signaling pathways involved in the pathogenesis of non-syndromic craniosynostosis. The selected hub genes may become early diagnostic markers and potential molecular therapeutic targets.

Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease accompanied by cholestasis, with the histological feature of non-purulent cholangitis. This article briefly describes the advantages and limitations of the traditional pathological staging systems such as Rubin stage, Scheuer stage, and Ludwig stage and the latest Nakanuma stage. Among them, Nakanuma stage refines the histological grading and staging standards to reduce the chance of missed diagnosis due to sampling errors, thus providing more adequate diagnostic and prognostic information for the clinic. A combination of new and traditional staging systems can provide guidance to the diagnosis, treatment, and research of PBC.