Construction of Immune-related lncRNA Prognosis Models and Development Related Networks and Drugs in LGG

Abstract Glioma is the most important tumor of the nervous system. LncRNA plays an important role in the occurrence, development, and metastasis of glioma. The immune status of glioma plays an important role in its treatment and prognosis. In lower-grade glioma (LGG), based on the results of the Weighted correlation network analysis (WCGNA) of lncRNA, we used lasso regression, random forest model, and stepwise regression to establish the lncRNA prognostic model: 0.58349*CYTOR + 0. 47804*LINC01831 + 0. 24933*HOTAIRM1 + 0. 73600*AC022034 + 0. 62351*AC104407 = lncRNA model prognosis score (LMPS). Concomitantly, we explored the correlation of LMPS with the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE), Tumor IMmune Estimation Resource (TIMER) infiltrating immune cells. We also established the reciprocal network of the model and verified the significant prognostic significance of the hub genes of the network and the significant impact on immunity. Finally, by connectivity Map (cMap) analysis, we obtained the drugs about the WCGNA module where the model is. Our study highlights the close role of the lncRNA model with patient prognosis, immunity, and clinical staging, and can guide immunotherapy. The network is constructed with the model as the core. Its core genes also have a guiding role in patient prognosis, immunity, and treatment. We identified loratadine and fidaxomicin as possible drugs.