Prognosis and Clinical features Analysis of EMT-related Signature and Tumor Immune Microenvironment in Glioma

EMT-related Subtypes of glioblastoma

  • Zheng Xiao Department of Neurosurgery, Zhujiang Hospital, Southern Medical University
  • Xiaoyan Liu
  • Yixiang Mo
  • Weibo Chen
  • Shizhong Zhang
  • Yingwei Yu
  • Huiwen Weng
DOI: https://doi.org/10.5937/jomb0-39234

Sažetak


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 thecomplex tumor microenvironment.

Methods: Six groups of samples from four datasets were included in this study. We used consensusClusterPlus 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.

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Objavljeno
2022/08/31
Broj časopisa
Vol. 42 Br. 1 (2023): Journal of Medical Biochemistry
Rubrika
Original paper

Sva prava zadržana (c) 2022 Zheng Xiao, Xiaoyan Liu, Yixiang Mo, Weibo Chen, Shizhong Zhang, Yingwei Yu, Huiwen Weng

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