Bioinformatics analysis to identify key invasion related genes and construct a prognostic model for glioblastoma.

in Scientific reports by Jintao Tian, Jinxi Zhao, Zhixing Xu, Bohu Liu, Jun Pu, Hongwen Li, Qingchun Lei, Yu Zhao, Weilin Zhou, Xuhui Li, Xiaobin Huang

TLDR

  • The study identified five genes associated with glioblastoma prognosis and established a comprehensive prognostic model using genetic profiles, survival curves, immune infiltration, and radiotherapy face susceptibility.
  • The model demonstrated good predictive ability and can serve as an independent prognostic factor for patients with glioblastoma.

Abstract

Glioblastoma (GBM) is the most common and lethal brain tumor with limited therapeutic strategies and incomplete studies on its progression and mechanisms. This study aims to reveal potential prognostic marker genes associated with GBM cell invasion, and establish an effective prognostic model for GBM patients. Differentially expressed genes (DEGs) were screened from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), differentially invasive-related genes was obtained, qRT-PCR was used to verify gene expression. The risk scores of individual patients, univariate and multivariate Cox regression were analyzed to investigate the correlation between risk values and glioblastoma, Finally, the risk scores with the prognostic clinical characteristics of the patients, such as PFS, OS were used to build a comprehensive GBM prognostic model. Five DEGs (GZMB, COL22A1, MSTN, CRYGN and OSMR) were significantly associated with GBM prognosis. Pseudotemporal analysis, risk scores (PFS, OS) based on tumor cells revealed that prognostic genes were associated with tumor proliferation and progression. The final prognostic model was developed and validated with good performance with higher accuracy(C-index: 0.675), and it was found that the risk value can serve as an independent prognostic factor for patients with glioblastoma (p < 0.05). We constructed a comprehensive prognostic model related to invasion in GBM patients using genetic profiles, survival curves, immune infiltration, and radiotherapy face susceptibility. The model has good predictive ability.

Overview

  • The study aimed to identify potential prognostic marker genes associated with glioblastoma (GBM) cell invasion and establish an effective prognostic model for GBM patients.
  • Differentially expressed genes (DEGs) were screened from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), and qRT-PCR was used to verify gene expression.
  • The primary objective of the study was to develop a comprehensive GBM prognostic model using genetic profiles, survival curves, immune infiltration, and radiotherapy face susceptibility.

Comparative Analysis & Findings

  • Five DEGs (GZMB, COL22A1, MSTN, CRYGN, and OSMR) were significantly associated with GBM prognosis.
  • Pseudotemporal analysis and risk scores (PFS, OS) based on tumor cells revealed that prognostic genes were associated with tumor proliferation and progression.
  • The final prognostic model was developed and validated with good performance (C-index: 0.675), demonstrating its predictive ability.

Implications and Future Directions

  • The risk value can serve as an independent prognostic factor for patients with glioblastoma (p < 0.05), providing new insights for personalized treatment strategies.
  • Future studies could explore the mechanisms underlying the prognostic genes identified in this study and investigate their potential as therapeutic targets.
  • This comprehensive prognostic model can be used as a diagnostic tool to predict patient outcomes and optimize treatment strategies, potentially improving patient prognosis.
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