Integrative multi-omics analysis reveals an 11-gene malignant–myeloid interaction signature and identifies TPST1 as a potential regulator of immunosuppressive microenvironment in glioma

Glioblastoma (GBM) is a highly aggressive brain tumor with a profoundly immunosuppressive tumor microenvironment (TME). Myeloid cells, especially tumor-associated macrophages, play a key role in immune evasion, yet regulators within tumor cells that shape myeloid-mediated immunosuppression remain poorly characterized. We integrated five single-cell RNA-seq datasets from glioma tissues, seven bulk RNA-seq cohorts with survival data, and seven immunotherapy transcriptomic datasets. Myeloid functional gene sets (microglia, border-associated macrophages, dendritic cells) were established via reference mapping. Weighted gene co-expression network analysis (WGCNA) identified a myeloid-associated module, whose overlap with malignant cell markers defined an 11-gene Malignant–Myeloid Interaction Signature (MMIS). Prognostic and immunotherapy response values were evaluated through meta-analysis. Immune infiltration, immunosuppression markers, and pathway activities were assessed using deconvolution algorithms and correlation analyses. The 11-gene MMIS (CLU, MAP1B, IGFBP7, NNMT, EMP1, EFEMP1, PAM, TPST1, MT2A, CHI3L1, ACTN1) was strongly correlated with myeloid function and poor prognosis. A risk score based on MMIS was constructed, which outperformed standard clinicopathological factors (including Age, IDH status, 1p/19q codeletion, and MGMT promoter methylation). The signature correlated positively with macrophage infiltration, immunosuppressive markers (e.g., CD163, TGFB1, IL10), and T cell exhaustion signatures (e.g., PDCD1, CTLA4, BATF). Among these, TPST1 was associated with immunotherapy resistance and was upregulated in high-grade glioma. TPST1-high tumor cells exhibited proliferative enrichment and potential interaction with myeloid cells via PTN-NCL and EREG/AREG-EGFR signaling pathways. We identified and validated an 11-gene signature that reflects malignant–myeloid crosstalk and predicts prognosis and immunotherapy response in GBM. These findings reveal novel mechanisms through which glioma cells modulate the immunosuppressive microenvironment and highlight TPST1 as a potential therapeutic target associated with T-cell exclusion and poor immunotherapy response mechanisms, though further validation in glioma-specific cohorts is required. Key findings What is known and what is new? What is the implication, and what should change now?

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Journal
Discover Oncology
Published
2026-03-16
DOI
https://doi.org/10.1007/s12672-026-04764-0
Primary Topic
Glioma Diagnosis and Treatment
Type
article
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article

Integrative multi-omics analysis reveals an 11-gene malignant–myeloid interaction signature and identifies TPST1 as a potential regulator of immunosuppressive microenvironment in glioma

Zhuwei Zhang, W. Xing, Jie Lu, Junwei Ren
Discover Oncology
Glioma Diagnosis and Treatment
article

Integrative multi-omics analysis reveals an 11-gene malignant–myeloid interaction signature and identifies TPST1 as a potential regulator of immunosuppressive microenvironment in glioma

Zhuwei Zhang, W. Xing, Jie Lu, Junwei Ren
article en

Abstract

Glioblastoma (GBM) is a highly aggressive brain tumor with a profoundly immunosuppressive tumor microenvironment (TME). Myeloid cells, especially tumor-associated macrophages, play a key role in immune evasion, yet regulators within tumor cells that shape myeloid-mediated immunosuppression remain poorly characterized. We integrated five single-cell RNA-seq datasets from glioma tissues, seven bulk RNA-seq cohorts with survival data, and seven immunotherapy transcriptomic datasets. Myeloid functional gene sets (microglia, border-associated macrophages, dendritic cells) were established via reference mapping. Weighted gene co-expression network analysis (WGCNA) identified a myeloid-associated module, whose overlap with malignant cell markers defined an 11-gene Malignant–Myeloid Interaction Signature (MMIS). Prognostic and immunotherapy response values were evaluated through meta-analysis. Immune infiltration, immunosuppression markers, and pathway activities were assessed using deconvolution algorithms and correlation analyses. The 11-gene MMIS (CLU, MAP1B, IGFBP7, NNMT, EMP1, EFEMP1, PAM, TPST1, MT2A, CHI3L1, ACTN1) was strongly correlated with myeloid function and poor prognosis. A risk score based on MMIS was constructed, which outperformed standard clinicopathological factors (including Age, IDH status, 1p/19q codeletion, and MGMT promoter methylation). The signature correlated positively with macrophage infiltration, immunosuppressive markers (e.g., CD163, TGFB1, IL10), and T cell exhaustion signatures (e.g., PDCD1, CTLA4, BATF). Among these, TPST1 was associated with immunotherapy resistance and was upregulated in high-grade glioma. TPST1-high tumor cells exhibited proliferative enrichment and potential interaction with myeloid cells via PTN-NCL and EREG/AREG-EGFR signaling pathways. We identified and validated an 11-gene signature that reflects malignant–myeloid crosstalk and predicts prognosis and immunotherapy response in GBM. These findings reveal novel mechanisms through which glioma cells modulate the immunosuppressive microenvironment and highlight TPST1 as a potential therapeutic target associated with T-cell exclusion and poor immunotherapy response mechanisms, though further validation in glioma-specific cohorts is required. Key findings What is known and what is new? What is the implication, and what should change now?

Discover Oncology
Linyi People's Hospital (CN), The Fifth People’s Hospital of Suzhou (CN)
No poverty
Openalex Percentile: Top 22%
Glioma Diagnosis and Treatment
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