Evidence map›Paper›PMID 38948951›Full record

ArticleCNS neuroscience & therapeutics2024

Multicenter integration analysis of TRP channels revealed potential mechanisms of immunosuppressive microenvironment activation and identified a machine learning-derived signature for improving outcomes in gliomas.

Yuntao Li, Yonggang Zhang, Shi Feng, Hua Zhu, Ying Xing, Xiaoxing Xiong, Qianxue Chen

Abstract readMulticenter Study
In one paragraph

Article in CNS neuroscience & therapeutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yuntao LiDepartment of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China.
Yonggang ZhangDepartment of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China.
Shi FengDepartment of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China.
Hua ZhuDepartment of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China.
Ying XingDepartment of Gastroenterology, 72nd Group Army Hospital, Huzhou University, Huzhou, Zhejiang, China.
Xiaoxing XiongDepartment of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China.ORCID 0000-0001-6983-8547
Qianxue ChenDepartment of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China.ORCID 0000-0002-9413-1030

Funding

Fundamental Research Funds for the Central Universities 2042022kf1216National Natural Science Foundation of China 82072764Zhejiang Provincial Natural Science Foundation of China LQ19H030001
6 · The paper itself

Abstract

aimThis study aimed to explore the mechanisms of transient receptor potential (TRP) channels on the immune microenvironment and develop a TRP-related signature for predicting prognosis, immunotherapy response, and drug sensitivity in gliomas.

methodsBased on the unsupervised clustering algorithm, we identified novel TRP channel clusters and investigated their biological function, immune microenvironment, and genomic heterogeneity. In vitro and in vivo experiments revealed the association between TRPV2 and macrophages. Subsequently, based on 96 machine learning algorithms and six independent glioma cohorts, we constructed a machine learning-based TRP channel signature (MLTS). The performance of the MLTS in predicting prognosis, immunotherapy response, and drug sensitivity was evaluated.

resultsPatients with high expression levels of TRP channel genes had worse prognoses, higher tumor mutation burden, and more activated immunosuppressive microenvironment. Meanwhile, TRPV2 was identified as the most essential regulator in TRP channels. TRPV2 activation could promote macrophages migration toward malignant cells and alleviate glioma prognosis. Furthermore, MLTS could work independently of common clinical features and present stable and superior prediction performance.

conclusionThis study investigated the comprehensive effect of TRP channel genes in gliomas and provided a promising tool for designing effective, precise treatment strategies.

Indexed as

Brain NeoplasmsGliomaMachine LearningTransient Receptor Potential ChannelsTumor MicroenvironmentAnimalsFemaleHumansMaleMiceTRPV Cation ChannelsTransient Receptor Potential ChannelsTRPV2 protein, humanTRPV Cation Channelsgliomasmachine learningTRP channeltumor immune microenvironment

Identifiers

PMID38948951
PMCPMC11215471

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.