Glioblastoma multiforme (GBM) is the most common and malignant brain tumor of the adult central nervous system and is associated with poor prognosis. The present study aimed to identify the hub genes in GBM in order to improve the current understanding of the underlying mechanism of GBM. The rna-seq data were downloaded from The cancer Genome atlas database. The edger package in r software was used to identify differentially expressed genes (deGs) between two groups: Glioblastoma samples and normal brain samples. Gene ontology (Go) functional enrichment analysis and the Kyoto encyclopedia of Genes and Genomes pathway enrichment analysis were performed using database for annotation, Visualization and integrated discovery software. additionally, cytoscape and Search Tool for the retrieval of interacting Genes/Proteins tools were used for the protein-protein interaction network, while the highly connected modules were extracted from this network using the Minimal common oncology data elements plugin. Next, the prognostic significance of the candidate hub genes was analyzed using ualcan. in addition, the identified hub genes were verified by reverse transcription-quantitative (RT-q) PCR. In total, 1,483 DEGs were identified between GBM and control samples, including 954 upregulated genes and 529 downregulated genes (P<0.01; fold-change >16) and these genes were involved in different Go terms and signaling pathways. Furthermore, CDK1, BUB1, BUB1B, CENPA and GNG3 were identified as key genes in the GBM samples. The UALCAN tool verified that higher expression level of CENPA was relevant to poorer overall survival rates. in conclusion, CDK1, BUB1, BUB1B, CENPA and GNG3 were found to be potential biomarkers for GBM. additionally, 'cell cycle' and 'γ-aminobutyric acid signaling' pathways may serve a significant role in the pathogenesis of GBM.

Background Primary headache is a disorder with a high incidence and low diagnostic accuracy; the incidence of migraine and tension-type headache ranks first among primary headaches. Artificial intelligence (AI) decision support systems have shown great potential in the medical field. Therefore, we attempt to use machine learning to build a clinical decision-making system for primary headaches. Methods The demographic data and headache characteristics of 173 patients were collected by questionnaires. Decision tree, random forest, gradient boosting algorithm and support vector machine (SVM) models were used to construct a discriminant model and a confusion matrix was used to calculate the evaluation indicators of the models. Furthermore, we have carried out feature selection through univariate statistical analysis and machine learning. Results In the models, the accuracy, F1 score were calculated through the confusion matrix. The logistic regression model has the best discrimination effect, with the accuracy reaching 0.84 and the area under the ROC curve also being the largest at 0.90. Furthermore, we identified the most important factors for distinguishing the two disorders through statistical analysis and machine learning: nausea/vomiting and photophobia/phonophobia. These two factors represent potential independent factors for the identification of migraines and tension-type headaches, with the accuracy reaching 0.74 and the area under the ROC curve being at 0.74. Conclusions Applying machine learning to the decision-making system for primary headaches can achieve a high diagnostic accuracy. Among them, the discrimination effect obtained by the integrated algorithm is significantly better than that of a single learner. Second, nausea/vomiting, photophobia/phonophobia may be the most important factors for distinguishing migraine from tension-type headaches.

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