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This study integrates gene expression and clinical data from 1,475 patients across TCGA, CGGA, and GEO to screen prognostic TCRGs, build and validate a Lasso Cox risk model, and stratify patients into high- and low-risk groups. Immune infiltration, pathway enrichment, drug sensitivity, single-cell RNA-seq cell-type expression, and RT-qPCR validation support the model.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-novel-trp-channel-related-prognostic-model-of-glioma-based-on-transcriptomics-and-single-cell-sequencing-analysis/455612/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-novel-trp-channel-related-prognostic-model-of-glioma-based-on-transcriptomics-and-single-cell-sequencing-analysis/455612.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-08","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How were prognostic TRP channel-related genes (TCRGs) identified in the study?","Question",{"text":112,"@type":113},"Gene expression profiles and clinical data from 1,475 glioma patients were collected from TCGA, CGGA, and GEO. Prognostic TCRGs were screened, then used to classify patients and build the risk model.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is included in the final prognostic model and what does it do?",{"text":117,"@type":113},"The final model is a 10-gene signature (TRPM6, PRKCB, CAMK2G, ADCY5, HTR2A, P2RY2, MAPK13, BDKRB1, PLA2G4D, TRPV3). It stratifies patients into high- and low-risk groups with significantly different overall survival and is validated in external cohorts.",{"name":119,"@type":110,"acceptedAnswer":120},"How did the researchers connect the model to the tumor microenvironment and drug response?",{"text":121,"@type":113},"They assessed immune infiltration and functional pathway enrichment between high- and low-risk groups. They also predicted drug sensitivity differences, showing higher-risk patients were more sensitive to drugs such as 5-Fluorouracil, Dasatinib, Gemcitabine, and Rapamycin.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},455612,1790795804,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":31},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Niu etal. Discover Oncology (2026) 17:29 [https://doi.org/10.1007/s12672-025-04220-5](https://doi.org/10.1007/s12672-025-04220-5)  \nDiscover Oncology  \nRESEARCH Open Access  \nA novel TRP channel-related prognostic model of glioma based on transcriptomics and single cell sequencing analysis  \nXiaochen Niu 1,2,3,4†, Aijie Guo 1,2,3,4†, Xuanchen Liu 1,2,3,4, Hao Li5,6, Hongming Ji 1,2,3,4 and Chunhong Wang 1,2,3,4*  \n†Xiaochen Niu and Aijie Guo have contributed equally to this work.  \n*Correspondence:  \nChunhong Wang [wang15934150741@163.com](wang15934150741@163.com)[ ](wang15934150741@163.com)1Department of Neurosurgery, Shanxi Provincial People’s Hospital, Taiyuan 030012, China  \n2The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, China  \n3Shanxi Provincial Key Laboratory of intelligent Brain Tumor,  \nTaiyuan 030012, China  \n4Shanxi Provincial Key Laboratory of intelligent, big data and digital neurosurgery, Taiyuan  \n030012, China  \n5Department of Epidemiology and Biostatistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu 610041, China 6Department of Nutrition and Food Hygiene, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu 610041, China  \nAbstract  \nBackground 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.  \nMethods 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.  \nResults Thirty-seven differential ly 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 tobe 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.  \nConclusion Based on the expression ofTCRGs, 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.  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third par","cbCaikU74IqvzIFc","https://ap.wps.com/l/cbCaikU74IqvzIFc","pdf",3548364,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Keywords\n# 1 Introduction","[{\"question\":\"How were prognostic TRP channel-related genes (TCRGs) identified in the study?\",\"answer\":\"Gene expression profiles and clinical data from 1,475 glioma patients were collected from TCGA, CGGA, and GEO. Prognostic TCRGs were screened, then used to classify patients and build the risk model.\"},{\"question\":\"What is included in the final prognostic model and what does it do?\",\"answer\":\"The final model is a 10-gene signature (TRPM6, PRKCB, CAMK2G, ADCY5, HTR2A, P2RY2, MAPK13, BDKRB1, PLA2G4D, TRPV3). It stratifies patients into high- and low-risk groups with significantly different overall survival and is validated in external cohorts.\"},{\"question\":\"How did the researchers connect the model to the tumor microenvironment and drug response?\",\"answer\":\"They assessed immune infiltration and functional pathway enrichment between high- and low-risk groups. They also predicted drug sensitivity differences, showing higher-risk patients were more sensitive to drugs such as 5-Fluorouracil, Dasatinib, Gemcitabine, and Rapamycin.\"}]","A novel TRP channel-related prognostic model of glioma based on transcriptomics and single cell sequencing analysis | PDF",1790743632]