Screening of differentially expressed lncRNAs for predicting Low Grade Glioma between different genders by Computational Biology

Abstract

Abstract In this study, we aim to find out the high-risk gene prediction model of Low Grade Glioma (LGG) male patient. After analysis of over 500 LGG patient RNA data and clinical data from the Cancer Genome Atlas (TCGA) database, the regulatory network of the differentially expressed lncRNA (DElncRNA) and differentially expressed miRNA (DEmiRNA) was established. 8 DElncRNAs, IGF2-AS, TTTY14, LINC00305, XIST, LINC00276, ZFY-AS1, TTTY15 and C8orf49, and related survival data of the patients were analyzed by the multivariable COX regression model and Akaike Information Criterion (AIC). Furthermore, XIST, ZFY-AS1, IGF2-AS and C8orf49 were kept for ROC and AUC analysis. Based on the 4 DElncRNAs, closely related assessing risk-score and risk level of the LGG patients, we drawn a survival kmplot curve (p=5.153e-06) and a ROC curve (AUC=0.599), and the true positive rate (TPR) and false positive rate (FPR) were shown in the ROC curve. We believe that the XIST, ZFY-AS1, IGF2-AS and C8orf49 model can give us an effective way for the diagnosis of LGG male patients.