Identification of Potential Hub Genes of Atherosclerosis Through Bioinformatic Analysis.

Cardiovascular and cerebrovascular diseases, which mainly consist of atherosclerosis (AS), are major causes of death. A great deal of research has been carried out to clarify the molecular mechanisms of AS. However, the etiology of AS remains poorly understood. To screen the potential genes of AS occurrence and development, GSE43292 and GSE57691 were obtained from the Gene Expression Omnibus (GEO) database in this study for bioinformatic analysis. First, GEO2R was used to identify differentially expressed genes (DEGs) and the functional annotation of DEGs was performed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The Search Tool for the Retrieval of Interacting Genes (STRING) tool was used to construct the protein-protein interaction network and the most important modules and core genes were mined. The results show that a total of 211 DEGs are identified. The functional changes of DEGs are mainly associated with the cellular process, catalytic activity, and protein binding. Eighteen genes were identified as core genes. Bioinformatic analysis showed that the core genes are mainly enriched in numerous processes related to actin. In conclusion, the DEGs and hub genes identified in this study may help us understand the potential etiology of the occurrence and development of AS.

Progress in the correlation between PTPN12 gene expression and human tumors.

BACKGROUND: The global morbidity of cancer is rising rapidly. Despite advances in molecular biology, immunology, and cytotoxic and immune-anticancer therapies, cancer remains a major cause of death worldwide. Protein tyrosine phosphatase non-receptor type 12 (PTPN12) is a new member of the cytoplasmic protein tyrosine phosphatase family, isolated from a cDNA library of adult colon tissue. Thus far, no studies have reviewed the correlation between PTPN12 gene expression and human tumors. METHODS: This article summarizes the latest domestic and international research developments on how the expression of PTPN12 relates to human tumors. The extensive search in Web of Science and PubMed with the keywords including PTPN12, tumor, renal cell carcinoma, proto-oncogenes, tumor suppressor genes was undertaken. RESULTS: More and more studies have shown that a tumor is essentially a genetic disease, arising from a broken antagonistic function between proto-oncogenes and tumor suppressor genes. When their antagonistic effect is out of balance, it may cause uncontrolled growth of cells and lead to the occurrence of tumors. PTPN12 is a tumor suppressor gene, so inhibiting its activity will lead directly or indirectly to the occurrence of tumors. CONCLUSION: The etiology, prevention, and treatment of tumors have become the focus of research around the world. PTPN12 is a tumor suppressor gene. In the future, PTPN12 might serve as a novel molecular marker to benefit patients, and even the development of tumor suppressor gene activation agents can form a practical research direction.

Identification of biomarkers for the diagnosis and treatment of primary colorectal cancer based on microarray technology.

BACKGROUND: Primary colorectal cancer (PCRC) is one of the most common malignant tumors in clinic, and is characterized by high heterogeneity occurring between tumors and intracellularly. Therefore, this study aimed to explore potential gene targets for the diagnosis and treatment of PCRC via bioinformatic technology. METHODS: Gene Expression Omnibus (GEO) was used to download the data used in this study. Differently expressed genes (DEGs) were identified with GEO2R, and the gene set enrichment analysis (GSEA) was implemented for enrichment analysis. Then, the researchers constructed a protein-protein interaction (PPI) network, a significant module, and a hub genes network. RESULTS: The GSE81558 dataset was downloaded, and a total of 97 DEGs were found. There were 23 up-regulated DEGs and 74 down-regulated DEGs in the PCRC samples, compared with the control group. The PPI network included a total of 42 nodes and 63 edges. One module network consisted of 11 nodes and 25 edges. Another module network consisted of 4 nodes and 6 edges. The hub genes network was created by cytoHubba using GCG, GUCA2B, CLCA4, ZG16, TMIGD1, GUCA2A, CHGA, PYY, SST, and MS4A12. CONCLUSIONS: Ten hub genes were found from the genomic samples of patients with PCRC and normal controls by bioinformatics analysis. The hub genes might provide novel ideas and evidence for the diagnosis and targeted therapy of PCRC.

Exploration of the Differentially Expressed Long Noncoding RNAs and Genes of Morphine Tolerance via Bioinformatic Analysis.

Morphine tolerance is one of the most common complications in patients with chronic pain. Many patients with morphine tolerance have poor efficacy in the treatment of primary pain, and are accompanied by the side effects. Previous studies have found that many mechanisms are involved in morphine tolerance, but few researches could fully explain morphine tolerance, and no effective treatment for morphine tolerance has been found. One expression profiling data set was downloaded from the Gene Expression Omnibus (GEO) database. The probes would be transformed into the homologous gene symbol by means of the platform's annotation information. GEO2R was used to search for differentially expressed long noncoding RNAs (lncRNAs) and differentially expressed genes (DEGs) that were differentially expressed between spinal cord samples. Receiver operator characteristic curve analysis was performed to determine the ability of the hub lncRNAs to predict morphine tolerance. Through the principal component analysis, the intragroup data repeatability is fine in the GSE110115. A total of 10 genes were identified as hub genes from the protein-protein interaction network with degrees ≥10. Compared with the normal saline group, the expression levels of LncRNA XR_006440, XR_009493, AF196267, MRAK150340, and MRAK037188 were more downregulated, while the expression levels of MRAK046606, XR_005988, DQ266361, uc.167-, and uc.468+ were more upregulated in the morphine tolerance group. LncRNAs and DEGs were differentially expressed between the morphine tolerance group and nonmorphine tolerance group, which may be involved in the development of morphine tolerance, especially LncRNA DQ266361, uc.167-, and Mmp9, CCL7 genes.