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Background <p>Immunotherapy has demonstrated outstanding therapeutic success in solid cancers by regulating immunity through immunological components in the tumor microenvironment. However, the immunological phenotypes and immunosuppressive processes in glioblastoma (GBM) remain unknown.</p> Methods <p>The relative abundance of immune cells was determined, which was used to classify 167 GBM samples into high- and low-immune subtypes with ssGSEA analysis. Differentially expressed immune-related genes (DE-IRGs) were determined between these two immune subtypes, which were used for gene oncology, pathway network, survival, and nonnegative matrix factorization cluster analyses. Survival-related DE-IRGs was used to create DE-IRG signature with LASSO regression. DE-IRG signature-based risk score was calculated for determing high- and low-risk score groups. Differentially expressed genes (DEGs) were determined between high-and low-risk score groups, which were used for WGCNA coexpression gene modules analysis. A ggalluvial plot was used to examine the cross-talk between the LASSO groups and NMF clusters. Furthermore, DE-IRGs data were integrated with quantitative proteomics data of human GBMs to obtain key molecules, followed by functional analysis of key molecule in GBM cell models.</p> Results <p>A total of 115 DE-IRGs were identified in high- vs. low-immune subtypes in GBM. These DE-IRGs were mapped into <Emphasis Type="Underline">6</Emphasis> KEGG pathways, 88 important biological processes, and 25 important molecular functions. Seven DE-IRG prognostic signatures (GBX1, PF4V1, RETN, SOAT2, SUMO1P1, TNS4, and TTC22) based on DE-IRGs were generated via LASSO regression to identify GBM samples as high- or low-risk score groups. This signature was closely correlated with overall survival, clinical characteristics, immune cells, immunological scores, immune checkpoints, tumor mutation burden, gene mutations, and drug sensitivity in GBM patients. The LASSO groups had a substantial correlation with three NMF-based unique clusters in GBM, and a WGCNA of 452 DEGs was performed to distinguish high- and low-risk score groups. Furthermore, integrative analysis of 115 DE-IRGs and 608 differentially expressed proteins (DEPs) found a overlapped molecule C4BPA that promoted the phenotype of GBM tumor cells.</p> Conclusion <p>This study provided the complete IRG landscape and distribution of tumor microenvironment cells in GBM, which are promising indicators of prognosis and survival, and have the potential to monitor treatment schedules.</p> Graphical Abstract <p></p>

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Glioblastoma immune-related gene landscape and its prognostic significance identified with integrative multiomics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Jianbang Han,
  • Linting Luo,
  • Ke Yu,
  • Guangsheng Zhan,
  • Lijuan Feng,
  • Yajun Wang,
  • Yijing Chen,
  • Yule Han,
  • Yong U. Liu,
  • Xianquan Zhan,
  • Zongqin Xiang

摘要

Background

Immunotherapy has demonstrated outstanding therapeutic success in solid cancers by regulating immunity through immunological components in the tumor microenvironment. However, the immunological phenotypes and immunosuppressive processes in glioblastoma (GBM) remain unknown.

Methods

The relative abundance of immune cells was determined, which was used to classify 167 GBM samples into high- and low-immune subtypes with ssGSEA analysis. Differentially expressed immune-related genes (DE-IRGs) were determined between these two immune subtypes, which were used for gene oncology, pathway network, survival, and nonnegative matrix factorization cluster analyses. Survival-related DE-IRGs was used to create DE-IRG signature with LASSO regression. DE-IRG signature-based risk score was calculated for determing high- and low-risk score groups. Differentially expressed genes (DEGs) were determined between high-and low-risk score groups, which were used for WGCNA coexpression gene modules analysis. A ggalluvial plot was used to examine the cross-talk between the LASSO groups and NMF clusters. Furthermore, DE-IRGs data were integrated with quantitative proteomics data of human GBMs to obtain key molecules, followed by functional analysis of key molecule in GBM cell models.

Results

A total of 115 DE-IRGs were identified in high- vs. low-immune subtypes in GBM. These DE-IRGs were mapped into 6 KEGG pathways, 88 important biological processes, and 25 important molecular functions. Seven DE-IRG prognostic signatures (GBX1, PF4V1, RETN, SOAT2, SUMO1P1, TNS4, and TTC22) based on DE-IRGs were generated via LASSO regression to identify GBM samples as high- or low-risk score groups. This signature was closely correlated with overall survival, clinical characteristics, immune cells, immunological scores, immune checkpoints, tumor mutation burden, gene mutations, and drug sensitivity in GBM patients. The LASSO groups had a substantial correlation with three NMF-based unique clusters in GBM, and a WGCNA of 452 DEGs was performed to distinguish high- and low-risk score groups. Furthermore, integrative analysis of 115 DE-IRGs and 608 differentially expressed proteins (DEPs) found a overlapped molecule C4BPA that promoted the phenotype of GBM tumor cells.

Conclusion

This study provided the complete IRG landscape and distribution of tumor microenvironment cells in GBM, which are promising indicators of prognosis and survival, and have the potential to monitor treatment schedules.

Graphical Abstract