An integrated analysis of genes prognostic signature.. Integrated Analysis of a Six Genes Prognostic Signature with Its Competitive Endogenous RNA Network and Candidate Drugs in Meningioma

Background: Meningiomas was the most common primary intracranial tumor, but its pathogenesis is still unclear. However, there is lacking systematical assessment of molecular indicators related with the treatment and prognosis of malignant meningioma. Methods: We systematically analyzed the gene expression characteristics of 10 benign meningioma tumors and 10 malignant meningioma tumors. Then, we created a competitive endogenous RNA network with the dys-regulated genes. Moreover, one-class logistic regression machine learning algorithm was performed to determine the gene signatures of predicting the overall survival of meningioma patients. Results: The competitive endogenous RNA network including 3052 lncRNA–miRNA–mRNA interactions. MIR181A1HG, SLFN13, SPARC, EXTL2, DGKD, and BVES genes were screened by the one-class logistic regression machine learning algorithm. Adrenergic receptor agonist, MEK inhibin, and PKC inhibitor respectively were the top hits of potential compounds for targeting meningioma. Conclusions: The competitive endogenous RNA network may increase our understanding to the malignant progression of meningioma. Six prognostic signatures including may be closely related to tumorigenesis in meningioma.