Screening and Identification of Key Biomarkers in Pancreatic Cancer: Evidence from Bioinformatic Analysis.

Pancreatic cancer (PC) whose mortality is comparable to morbidity is a highly fatal disease. Early approaches of diagnosis and treatment for PC are quite limited, so it is of great urgency to figure out the exact tumorigenesis and development mechanism of PC. To identify the related molecular markers of pancreatic oncogenesis, we downloaded three microarray datasets (GSE63111, GSE101448, and GSE107610) from Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) among them were identified, and the corresponding function enrichment analyses were accomplished. The protein-protein interaction network was conducted by Search Tool for the Retrieval of Interacting Genes (STRING), and the corresponding module analysis was accomplished by Cytoscape. There were 55 DEGs found in total. The molecular function and biological processes (BP) of these DEGs mainly include cytokinesis, mitotic nuclear division, cell division, cell proliferation, microtubule-based movement, and mineral absorption. Among the 55 DEGs, 14 hub genes were further confirmed and it was concluded that they mainly function in mitotic cytokinesis, microtubule-based movement, mitotic chromosome condensation, and mitotic spindle assembly from the BP analysis. The survival analysis showed that all the 14 hub genes, especially nucleolar and spindle associated protein 1 and abnormal spindle microtubule assembly, may involve in the tumorigenesis and development of PC. And they might be used as new biomarkers for auxiliary diagnosis and potential targets for immunotherapy of PC.

TPM2 as a potential predictive biomarker for atherosclerosis.

Cardiac-cerebral vascular disease (CCVD), is primarily induced by atherosclerosis, and is a leading cause of mortality. Numerous studies have investigated and attempted to clarify the molecular mechanisms of atherosclerosis; however, its pathogenesis has yet to be completely elucidated. Two expression profiling datasets, GSE43292 and GSE57691, were obtained from the Gene Expression Omnibus (GEO) database. The present study then identified the differentially expressed genes (DEGs), and functional annotation of the DEGs was performed. Finally, an atherosclerosis animal model and neural network prediction model was constructed to verify the relationship between hub gene and atherosclerosis. The results identified a total of 234 DEGs between the normal and atherosclerosis samples. The DEGs were mainly enriched in actin filament, actin binding, smooth muscle cells, and cytokine-cytokine receptor interactions. A total of 13 genes were identified as hub genes. Following verification of animal model, the common DEG, Tropomyosin 2 (TPM2), was found, which were displayed at lower levels in the atherosclerosis models and samples. In summary, DEGs identified in the present study may assist clinicians in understanding the pathogenesis governing the occurrence and development of atherosclerosis, and TPM2 exhibits potential as a promising diagnostic and therapeutic biomarker for atherosclerosis.

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.