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Background <p>Glioma is the most malignant intracranial tumor. Transient receptor potential (TRP) channel family has been found to be involved in malignant progression of many tumors. However, the relationship between TRP channel-related genes (TCRGs) and glioma remains unclear.</p> Methods <p>Gene expression profiles and clinical data of 1,475 glioma patients were obtained from TCGA, CGGA, and GEO databases. Prognostic TCRGs were screened and used to classify the patients. Lasso Cox regression analysis was used to construct a risk model, which was validated in external cohorts, and the patients were stratified into high- and low-risk groups. Immune infiltration and functional enrichment analyses were performed to explore the tumor microenvironment in two groups, while drug sensitivity predictions were conducted. Single-cell RNA sequencing data were analyzed to examine the cell type-specific expression of key model genes. Finally, RT-qPCR was performed on paired glioma and adjacent normal tissues to validate the expression of all model genes.</p> Results <p>Thirty-seven differentially expressed TCRGs were identified in glioma, of which 30 were associated with patient survival. Consensus clustering revealed three molecular subtypes with distinct prognoses, immune infiltration, and pathway enrichment. A 10-gene (TRPM6, PRKCB, CAMK2G, ADCY5, HTR2A, P2RY2, MAPK13, BDKRB1, PLA2G4D, and TRPV3) prognostic model stratified patients into high- and low-risk groups with significantly different overall survival, validated in external cohorts. High-risk patients exhibited higher immune cell infiltration and were predicted to be more sensitive to drugs including 5-Fluorouracil, Dasatinib, Gemcitabine, and Rapamycin, whereas low-risk patients were more sensitive to Vorinostat, Lapatinib, Gefitinib, and Osimertinib. Single-cell RNA sequencing showed that TRPV3 was expressed in exhausted CD8<sup>+</sup> T cells, supporting the model’s relevance to tumor immunity and patient prognosis. RT-qPCR verification indicated that all 10 genes in the model were expressed at lower levels in glioma tissues.</p> Conclusion <p>Based on the expression of TCRGs, we conducted the new subtype classification and a prognostic model for glioma, and is expected to provide theoretical basis for the development of new targets.</p>

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A novel TRP channel-related prognostic model of glioma based on transcriptomics and single cell sequencing analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Xiaochen Niu,
  • Aijie Guo,
  • Xuanchen Liu,
  • Hao Li,
  • Hongming Ji,
  • Chunhong Wang

摘要

Background

Glioma is the most malignant intracranial tumor. Transient receptor potential (TRP) channel family has been found to be involved in malignant progression of many tumors. However, the relationship between TRP channel-related genes (TCRGs) and glioma remains unclear.

Methods

Gene expression profiles and clinical data of 1,475 glioma patients were obtained from TCGA, CGGA, and GEO databases. Prognostic TCRGs were screened and used to classify the patients. Lasso Cox regression analysis was used to construct a risk model, which was validated in external cohorts, and the patients were stratified into high- and low-risk groups. Immune infiltration and functional enrichment analyses were performed to explore the tumor microenvironment in two groups, while drug sensitivity predictions were conducted. Single-cell RNA sequencing data were analyzed to examine the cell type-specific expression of key model genes. Finally, RT-qPCR was performed on paired glioma and adjacent normal tissues to validate the expression of all model genes.

Results

Thirty-seven differentially expressed TCRGs were identified in glioma, of which 30 were associated with patient survival. Consensus clustering revealed three molecular subtypes with distinct prognoses, immune infiltration, and pathway enrichment. A 10-gene (TRPM6, PRKCB, CAMK2G, ADCY5, HTR2A, P2RY2, MAPK13, BDKRB1, PLA2G4D, and TRPV3) prognostic model stratified patients into high- and low-risk groups with significantly different overall survival, validated in external cohorts. High-risk patients exhibited higher immune cell infiltration and were predicted to be more sensitive to drugs including 5-Fluorouracil, Dasatinib, Gemcitabine, and Rapamycin, whereas low-risk patients were more sensitive to Vorinostat, Lapatinib, Gefitinib, and Osimertinib. Single-cell RNA sequencing showed that TRPV3 was expressed in exhausted CD8+ T cells, supporting the model’s relevance to tumor immunity and patient prognosis. RT-qPCR verification indicated that all 10 genes in the model were expressed at lower levels in glioma tissues.

Conclusion

Based on the expression of TCRGs, we conducted the new subtype classification and a prognostic model for glioma, and is expected to provide theoretical basis for the development of new targets.