Background: As the most common primary malignant intracranial tumor, glioblastoma has a poor prognosis with limited treatment options. It has a high propensity for recurrence, invasion, and poor immune prognosis due to the complex tumor microenvironment.
Methods: Six groups of samples from four datasets were included in this study. We used consensus ClusterPlus to establish two subgroups by the EMT-related gene. The difference in clinicopathological features, genomic characteristics, immune infiltration, treatment response and prognoses were evaluated by multiple algorithms. By using LASSO regression, multi-factor Cox analysis, stepAIC method, a prognostic risk model was constructed based on the final screened genes.
Results: The consensusClusterPlus analyses revealed two subtypes of glioblastoma (C1 and C2), which were characterized by different EMT-related gene expression patterns. C2 subtype with the worse prognosis had the more malignant clinical and pathology manifestations, higher Immune infiltration and tumor-associated molecular pathways scores, and poorer response to treatment. Additionally, our EMT-related genes risk prediction model can provide valuable support for clinical evaluations of glioma.
Conclusions: The assessment system and prediction model displayed good performance in independent prognostic risk assessment and individual patient treatment response prediction. This can help with clinical treatment decisions and the development of effective treatments.
Uvod: Kao najčešći primarni maligni intrakranijalni tumor, glioblastom ima lošu prognozu sa ograničenim mogućnostima lečenja. Ima visoku sklonost ka recidivu, invaziji i lošu imunološku prognozu zbog kompleksnog mikrookruženja tumora.
Metode: U ovu studiju uključeno je šest grupa uzoraka iz četiri skupa podataka. Koristili smo konsenzus ClusterPlus da uspostavimo dve podgrupe pomoću gena povezanog sa EMT Razlika u kliničko-patološkim karakteristikama, genomskim karakteristikama, imunološkoj infiltraciji, odgovoru na lečenje i prognozama je procenjena pomoću više algoritama. Korišćenjem LASSO regresije, multifaktorske Cok analize, stepAIC metode, konstruisan je model prognostičkog rizika na osnovu finalnih skrining gena.
Keywords: epithelial mesenchymal transition; glioma; immunotherapy; risk score; tumor microenvironment.
2023 Zheng Xiao, Xiaoyan Liu, Yixiang Mo, Weibo Chen, Shizhong Zhang, Yingwei Yu, Huiwen Weng, published by CEON/CEES.