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<p>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 (<i>p</i> &lt; 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月28日

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

摘要

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.