Hub genes for early diagnosis and therapy of adamantinomatous craniopharyngioma.

BACKGROUND: Adamantinomatous craniopharyngioma (ACP) is a subtype of craniopharyngioma, a neoplastic disease with a benign pathological phenotype but a poor prognosis in the sellar region. The disease has been considered the most common congenital tumor in the skull. Therefore, this article aims to identify hub genes that might serve as genetic markers of diagnosis, treatment, and prognosis of ACP. METHODS: The procedure of this research includes the acquisition of public data, identification and functional annotation of differentially expressed genes (DEGs), construction and analysis of protein-protein interaction network, and the mining and analysis of hub genes by Spearman-rho test, multivariable linear regression, and receiver operator characteristic curve analysis. Quantitative real-time polymerase chain reaction was used to detect the level of mRNA of relative genes. RESULTS: Among 2 datasets, a total of 703 DEGs were identified, mainly enriched in chemical synaptic transmission, cell adhesion, odontogenesis of the dentin-containing tooth, cell junction, extracellular region, extracellular space, structural molecule activity, and structural constituent of cytoskeleton. The protein-protein interaction network was composed of 4379 edges and 589 nodes. Its significant module had 10 hub genes, and SYN1, SYP, and GRIA2 were significantly down-regulated with ACP. CONCLUSION: In a word, we find out the DEGs between ACP patients and standard samples, which are likely to play an essential role in the development of ACP. At the same time, these DEGs are of great value in tumors' diagnosis and targeted therapy and could even be mined as biological molecular targets for diagnosing and treating ACP patients.

Prognosis of Oligodendroglioma Patients Stratified by Age: A SEER Population-Based Analysis.

PURPOSE: Glioma may affect patients of any age. So far, only a limited number of big data studies have been conducted concerning oligodendroglioma (OG) in diverse age groups. This study evaluated the risk factors for OG in different age groups using the Surveillance, Epidemiology, and End Results (SEER) database built by the National Cancer Institute, which is part of the National Institutes of Health. PATIENTS AND METHODS: A total of 5437 cases within the SEER database were included. These patients were divided into seven age groups. The Kaplan-Meier method was employed for survival analysis. The independent risk factors for the survival of OG patients were identified using the Cox regression model. A nomogram was drawn with R software based on the independent risk factors. The X-tile software was adopted to find the optimal age group at diagnosis. RESULTS: The all-cause mortality and the tumor-specific mortality increased with age. The univariate analysis showed that the patients' age, gender, primary lesion location, side affected by the primary lesion (left or right), surgery for the primary lesion, and tumor size were correlated with survival (P<0.05). Multivariate Cox regression analysis showed that age was an independent risk factor for the survival of OG patients (P<0.05). The optimal cutoff value of age in terms of overall survival (OS) and cause-specific survival (CSS) were identified as 48 and 61 years and 48 and 59 years, respectively. CONCLUSION: The older the age, the worse the survival would be. That's, the mortality increased with age. In the clinic, healthcare professionals should be fully aware of the variability in the prognosis of OG patients in different age groups. Therefore, individualized treatments are recommended to OG patients in different age groups to optimize the prognosis.

Hub biomarkers for the diagnosis and treatment of glioblastoma based on microarray technology.

BACKGROUND: Glioblastoma (GBM) is the most common clinical intracranial malignancy worldwide, and the most common supratentorial tumor in adults. GBM mainly causes damage to the brain tissue, which can be fatal. This research explored potential gene targets for the diagnosis and treatment of GBM using bioinformatic technology. METHODS: Public data from patients with GBM and controls were downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were identified by Gene Expression Profiling Interactive Analysis (GEPIA) and Gene Expression Omnibus 2R (GEO2R). Construction of the protein-protein interaction network and the identification of a significant module were performed. Subsequently, hub genes were identified, and their expression was examined and compared by real-time quantitative (RT-q)PCR between patients with GBM and controls. RESULTS: GSE122498 (GPL570 platform), GSE104291 (GPL570 platform), GSE78703_DMSO (GPL15207 platform), and GSE78703_LXR (GPL15207 platform) datasets were obtained from the GEO. A total of 130 DEGs and 10 hub genes were identified by GEPIA and GEO2R between patients with GBM and controls. Of these, strong connections were identified in correlation analysis between CCNB1, CDC6, KIF23, and KIF20A. RT-qPCR showed that all 4 of these genes were expressed at significantly higher levels in patients with GBM compared with controls. CONCLUSIONS: The hub genes CCNB1, CDC6, KIF23, and KIF20A are potential biomarkers for the diagnosis and treatment of GBM.

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.

Identification and Functional Enrichment Analysis of Potential Diagnostic and Therapeutic Targets in Adamantinomatous Craniopharyngioma.

Adamantinomatous craniopharyngioma (ACP) is a congenital epithelial tumor in the sellar region with benign histological manifestation but invasive. Currently, surgery is the main treatment for it, but its recurrence rate is high. Therefore, it is of great importance to explore the mechanism of occurrence and development of ACP and to identify new molecules. One gene expression profile, GSE94349, was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by the limma package. Gene set enrichment analysis was used to make enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, we performed the construction and analysis of the protein-protein interaction (PPI) network and significant module. The analysis of the GSE94349 dataset identified 109 DEGs, consisting of 80 upregulated genes and 29 downregulated genes in ACP samples compared with normal brain tissues. Functional and pathway enrichment analysis of DEGs provided a comprehensive overview of some major pathophysiological mechanisms in ACP: RNA polymerase II promoter, glutamate receptor binding, and so on. A total of 10 hub genes of DEGs were obtained from the PPI network, which provided potential therapeutic targets for the ACP. In summary, there were DEGs between ACP tissues and normal brain tissues, which may be involved in the mechanisms of occurrence and development of ACP, especially via the regulation of RNA polymerase II promoter and glutamate receptor binding. Key genes in DEGs could serve as new research targets for the diagnosis and treatment of ACP.

Screening and authentication of molecular markers in malignant glioblastoma based on gene expression profiles.

Glioblastoma (GBM) is a malignant tumor of the central nervous system with high mortality rates. Gene expression profiling may determine the chemosensitivity of GBMs. However, the molecular mechanisms underlying GBM remain to be determined. To screen the novel key genes in its occurrence and development, two glioma databases, GSE122498 and GSE104291, were analyzed in the present study. Bioinformatics analyses were performed using the Database for Annotation, Visualization and Integrated Discovery, the Search Tool for the Retrieval of Interacting Genes, Cytoscape, cBioPortal, and Gene Expression Profiling Interactive Analysis softwares. Patients with recurrent GBM showed worse overall survival rate. Overall, 341 differentially expressed genes (DEGs) were authenticated based on two microarray datasets, which were primarily enriched in 'cell division', 'mitotic nuclear division', 'DNA replication', 'nucleoplasm', 'cytosol, nucleus', 'protein binding', 'ATP binding', 'protein C-terminus binding', 'the cell cycle', 'DNA replication', 'oocyte meiosis' and 'valine'. The protein-protein interaction network was composed of 1,799 edges and 237 nodes. Its significant module had 10 hub genes, and CDK1, BUB1B, NDC80, NCAPG, BUB1, CCNB1, TOP2A, DLGAP5, ASPM and MELK were significantly associated with carcinogenesis and the development of GBM. The present study indicated that the DEGs and hub genes, identified based on bioinformatics analyses, had significant diagnostic value for patients with GBM.

Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates.

The prevalence of overweight-obesity has increased sharply among undergraduates worldwide. In 2016, approximately 52% of adults were overweight-obese. This cross-sectional study aimed to investigate the prevalence of overweight-obesity and explore in depth the connection between eating habits and overweight-obesity among Chinese undergraduates.The study population included 536 undergraduates recruited in Shijiazhuang, China, in 2017. They were administered questionnaires for assessing demographic and daily lifestyle characteristics, including sex, region, eating speed, number of meals per day, and sweetmeat habit. Anthropometric status was assessed by calculating the body mass index (BMI). The determinants of overweight-obesity were investigated by the Pearson χ test, Spearman rho test, multivariable linear regression, univariate/multivariate logistic regression, and receiver operating characteristic curve analysis.The prevalence of undergraduate overweight-obesity was 13.6%. Sex [male vs female, odds ratio (OR): 1.903; 95% confidence interval (95% CI): 1.147-3.156], region (urban vs rural, OR: 1.953; 95% CI: 1.178-3.240), number of meals per day (3 vs 2, OR: 0.290; 95% CI: 0.137-0.612), and sweetmeat habit (every day vs never, OR: 4.167; 95% CI: 1.090-15.933) were significantly associated with overweight-obesity. Eating very fast was positively associated with overweight-obesity and showed the highest OR (vs very slow/slow, OR: 5.486; 95% CI: 1.622-18.553). However, the results of multivariate logistic regression analysis indicated that only higher eating speed is a significant independent risk factor for overweight/obesity (OR: 17.392; 95% CI, 1.614-187.363; P = .019).Scoremeng = 1.402 × scoresex + 1.269 × scoreregion + 19.004 × scoreeatin speed + 2.546 × scorenumber of meals per day + 1.626 × scoresweetmeat habit and BMI = 0.253 × Scoremeng + 18.592. These 2 formulas can help estimate the weight status of undergraduates and predict whether they will be overweight or obese.