Bioinformatics Profiling Integrating A Ten Mesenchymal-related IncRNAs Signature As A Prognostic Model For Glioma Patients

Objective Glioma is one of the most aggressive primary malignant central nervous system tumors in humans,and glioblastoma(grade IV)is the most malignant.At present,the treatment of glioma is mainly based on surgery,supplemented by radiotherapy and chemotherapy.However,due to the recurrence of glioma and the resistance to radiotherapy and chemotherapy,its five-year survival rate is still very low,early detection and treatment are essential to improve the survival of glioma patients.With the continuous development of tumor molecular biology,the research of long non-coding RNA(lnc RNA)has been intensified.It is worth noting that the abnormal expression of mesenchymal(MES)subtype related lnc RNAs not only induces the recurrence of gliomas,but also significantly related to the poor prognosis of gliomas.In addition to identifying potential biomarkers,new advances in bioinformatics and genome sequencing technologies also help predict the prognosis of cancer patients.The prognostic models of autophagy-and immune-related lnc RNAs in gliomas have been widely studied,but there are few reports about the prognostic model of MES-related lnc RNAs.In this study,we screened ten MES-related lnc RNAs through the TCGA and Ivy GAP databases to construct a prognostic model,and verified it in the CGGA database.Further functional experiments reveled the influence of lnc RNAs in this model on the migration and invasion of glioma cell lines,which will provide new targets for predicting the prognosis and treatment of glioma patients.Methods(1)Using TCGA and Ivy GAP databases,303 MES-related genes were obtained.(2)Obtain 42 MES-related lnc RNAs through co-expression and differential expression analysis.(3)Ten prognostic-related lnc RNAs were screened by univariate and multivariate regression analysis,and the coefficients regression obtained by multivariate regression analysis and were used to construct risk scores.(4)The median risk score distinguishes high-and low-risk groups and is verified by the survival curve.(5)univariate factor and multivariate analysis confirmed that the risk score model has independent prognostic ability.(6)Principal component analysis(PCA)and nomogram confirm the predictability of the survival model.(7)Gene enrichment analysis(GSEA) shows that high-risk groups are enriched in the malignant biological process.(8)Gene expression profiling analysis TCGA and GETX database of glioma(GBM;LGG)and normal brain tissue expression levels of ten lnc RNAs.(9)The effect of the three lnc RNAs(DGCR10,HAR1 B,SNHG18)in this model on the migration and invasion ability was verified in the glioma cell lines.Result In this study,MES-related lnc RNAs was obtained by co-expression analysis of MES-related genes,these genes are obtained through the union of The Cancer Genome Atlas(TCGA)and Ivy Glioblastoma Atlas Project(Ivy GAP)dataset.Next,cox regression analysis was used to establish a prognostic model,which integrates ten MES-related lnc RNAs.Glioma patients in TCGA are divided into high-and low-risk group based on the median risk score calculated by multivariate regression analysis.Compared with the low-risk group,the survival time of glioma patients in the high-risk group is shorter.In addition,we used the ROC curve to measure the specificity and sensitivity of this model,uivariate and multivariate cox analysis showed that the prognostic model is an independent prognostic factor for glioma.The Chinese Human Glioma Genome Atlas(CGGA)dataset was used to validate the predictive power of these ten lnc RNAs and obtained similar results.In addition,Gene set enrichment analysis(GSEA)was used to detect functional annotations,and the results showed that the high-risk group were enriched in the malignant process of tumors.Finally,the protective factors DGCR10,HAR1 B and the risk factor SNHG18 were selected for functional verification.Knockdown of DGCR10,HAR1 B promotes the migration and invasion of glioma cell lines,while knockdown of SNHG18 inhibits the migration and invasion of glioma cell lines.Conclusion In summary,we screened ten MES-related lnc RNAs and distinguished high-and low-risk groups based on the median risk score,which can be used to identify glioma patients with poor prognosis.Further GSEA and functional experiments confirmed DGCR10,HAR1 B And SNHG18 are expected to become personalized biomarkers for predicting treatment outcomes.