ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1610683
This article is part of the Research TopicInnovative Therapeutic Approaches for Complex Cancers: Exploring New Strategies in Glioblastoma, Urogenital, and Bladder CancersView all 11 articles
Meta-Analysis of Multi-Center Transcriptomic Profiles and Machine Learning Reveal Phospholipase Cβ4 as a Wnt/Ca²⁺ Signaling Mediator in Glioblastoma Immunotherapy
Provisionally accepted- 1Department of Neurosurgery, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
- 2Suzhou Medical College of Soochow University, Suzhou, China
- 3Suzhou Institute of Nano-tech and Nano-bionics, Chinese Academy of Sciences (CAS), Suzhou, Jiangsu Province, China
- 4Soochow University, Suzhou, China
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Glioblastoma (GBM) is a highly aggressive brain tumor characterized by pronounced invasiveness, rapid progression, frequent recurrence, and poor clinical prognosis. Current treatment strategies remain inadequate due to the lack of effective molecular targets, underscoring the urgent need to identify novel therapeutic avenues. In this study, we employed weighted gene co-expression network analysis and meta-analysis, incorporating clinical immunotherapy datasets, to identify ten candidate genes associated with GBM initiation, progression, prognosis, and response to immunotherapy. Multi-omics analyses across glioma and pan-cancer datasets revealed that these genes play pivotal roles in cancer biology. Among them, Phospholipase Cβ4 (PLCB4) showed a negative correlation with tumor grade in clinical samples, suggesting its potential role as a tumor suppressor. Evidence indicated that PLCB4 expression is modulated by Wnt signaling, and its overexpression may activate the calcium ion signaling pathway. Notably, PLCB4 is strongly associated with aberrant tumor proliferation, making it a compelling therapeutic target. Through structure-based virtual screening, five small molecules with high predicted affinity for PLCB4 were identified as potential drug candidates. This study's integrative approach-combining target identification, pathway inference, and in silico drug screening-offers a promising framework for rational drug development in GBM. The findings may reduce unnecessary experimental screening and medical costs, and represent a significant step toward improving therapeutic outcomes and prognosis for GBM patients.
Keywords: Glioblastoma, PLCB4, machine learning, Tumor Microenvironment, multi-omics, Immunotherapy
Received: 12 Apr 2025; Accepted: 02 Jul 2025.
Copyright: © 2025 Song, Wang, Yang, Guo, Li, Huang, Ling, Cheng, Chen, Zhu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhouqing Chen, Soochow University, Suzhou, China
Zhanchi Zhu, Suzhou Institute of Nano-tech and Nano-bionics, Chinese Academy of Sciences (CAS), Suzhou, 215123, Jiangsu Province, China
Zhong Wang, Department of Neurosurgery, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
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