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Although several studies have shown that osteogenic differentiation of different mesenchymal stem cell (MSC) lines can be guided by the 3D scaffold with growth factors or biochemical agent, the key mechanism regulating osteogenic differentiation is not known yet. Here, this study was designed to investigate key genes that regulate the induction of osteogenesis by different MSC lines in different ways. Expression profiling by array (GSE58919 and GSE18043) was downloaded and analyzed using weighted gene co-expression network analysis (WGCNA) to narrow genes associated with osteogenic differentiation. A protein-protein interactive (PPI) network was built to find the key genes and the role of these key genes was confirmed by statistical analysis. To understand the function of genes associated with osteogenesis, gene ontology (GO) and the Kyoto encyclopedia of genes and genomes (KEGG) were analyzed, which showed that key genes in MSC osteogenic differentiation induced by a biochemical agent involve regulation of cell apoptosis and proliferation while key genes in MSC osteogenic differentiation induced by the 3D scaffold with growth factors involve regulation of cajal body and centromeres. Furthermore, 58 key genes are involved in Wnt signaling pathway, ion response and focal adhesion. Proteasome also played a key role in osteogenic differentiation. Seven potential key genes were found essential in the osteogenic differentiation of MSCs in the PPI network, especially the five key genes, CCT2, NOP58, FBL, EXOSC8 and SNRPD1. This study will provide important targets of MSC osteogenic differentiation that will help us understand the mechanism of osteogenic differentiation in MSCs. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer‐related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug‐in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug‐in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC. [ABSTRACT FROM AUTHOR]

Schizophrenia is a chronic, debilitating neuropsychiatric disorder. Multiple transcriptomic gene expression profiling analysis has been used to identify schizophrenia-associated genes, unravel disease-associated biomarkers, and predict clinical outcomes. We aimed to identify gene expression regulation, underlying pathways, and their roles in schizophrenia pathogenesis. We searched the Gene Expression Omnibus (GEO) database for microarray studies of fibroblasts, lymphoblasts, and post-mortem brains of schizophrenia patients. Our analysis demonstrated high FOS expression in non-neural peripheral samples and low FOS expression in brain tissues of schizophrenia patients compared with healthy controls. FOS exhibited predictive value for schizophrenia patients in these datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that "amphetamine addiction" was among the top 10 significantly enriched KEGG pathways. FOS and FOSB, which are implicated in the amphetamine addiction pathway, were up-regulated in schizophrenia fibroblast samples. Protein–protein interaction (PPI) network analysis revealed that proteins closely interacting with FOS-encoded protein were also involved in the amphetamine addiction pathway. Pearson correlation test indicated that FOS showed positive correlation with genes in the amphetamine pathway. The results revealed that FOS was acceptable as a biomarker for schizophrenia and may be involved in schizophrenia pathogenesis. [ABSTRACT FROM AUTHOR]

Background: Colon cancer is one of most malignant cancers around worldwide. Nearly 20% patients were diagnosed at colon cancer with metastasis. However, the lack of understanding regarding its pathogenesis brings difficulties to study it. Methods: In this study, we acquired high‐sequence data from GEO dataset, and performed integrated bioinformatic analysis including differently expressed genes, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways analysis, protein–protein analysis, survival analysis to analyze the development of colon cancer. Results: By comparing the colon cancer tissues with normal colon tissues, 109 genes were dysregulated; among them, 83 genes were downregulated and 26 genes were upregulated. Two clusters were founded based on the STRING database and MCODE plugin of cytoscape software. Then, six genes with prognostic value were filtered out in UALCAN website. Conclusion: We found that SPP1, VIP, COL11A1, CA2, ADAM12, INHBA could provide great significant prognostic value for colon cancer. [ABSTRACT FROM AUTHOR]