Scholarlink

<p>We present GBM-cRegMap, an online resource providing a comprehensive coregulatory influence network perspective on glioblastoma (GBM) heterogeneity and plasticity. Using representation learning algorithms, we derived two components of this resource: GBM-CoRegNet, a highly specific coregulatory network of tumor cells, and GBM-CoRegMap, a unified network influence map based on 1612 tumors from 16 studies. As a widely applicable closed-loop system connecting cellular models and tumors, GBM-cRegMap will provide the GBM research community with an easy-to-use web tool (<a href="https://gbm.cregmap.com">https://gbm.cregmap.com</a>) that maps any existing or newly generated transcriptomic “query” data to a reference coregulatory network and a large-scale manifold of disease heterogeneity. Using GBM-cRegMap, we demonstrated the synergy between the two components by refining the molecular classification of GBM, identifying potential key regulators, and aligning the transcriptional profiles of tumors and in vitro models. Through the amalgamation of a vast dataset, we validated the proneural (PN)-mesenchymal (MES) axis and identified three subclasses of classical (CL) tumors: astrocyte-like (CL-A), epithelial basal-like (CL-B), and cilium-rich (CL-C). We revealed the CL-C subclass, an intermediate state demonstrating the plasticity of GBM cells along the PN-MES axis under chemotherapy. We identified key regulators, such as PAX8, and NKX2.5, potentially involved in temozolomide (TMZ) resistance. Notably, NKX2.5, more expressed in higher-grade gliomas, negatively impacts patient survival, and regulates genes involved in glucose metabolism.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A coregulatory influence map of glioblastoma heterogeneity and plasticity

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月15日

查看全文
  • Chloé Bernhard,
  • Konstantinos Geles,
  • Geoffrey Pawlak,
  • Wajdi Dhifli,
  • Aurélien Dispot,
  • Jules Dusol,
  • Maria Kondratova,
  • Sophie Martin,
  • Mélissa Messé,
  • Damien Reita,
  • David Tulasne,
  • Isabelle Van Seuningen,
  • Natacha Entz-Werle,
  • Silvia Anna Ciafrè,
  • Monique Dontenwill,
  • Mohamed Elati

摘要

We present GBM-cRegMap, an online resource providing a comprehensive coregulatory influence network perspective on glioblastoma (GBM) heterogeneity and plasticity. Using representation learning algorithms, we derived two components of this resource: GBM-CoRegNet, a highly specific coregulatory network of tumor cells, and GBM-CoRegMap, a unified network influence map based on 1612 tumors from 16 studies. As a widely applicable closed-loop system connecting cellular models and tumors, GBM-cRegMap will provide the GBM research community with an easy-to-use web tool (https://gbm.cregmap.com) that maps any existing or newly generated transcriptomic “query” data to a reference coregulatory network and a large-scale manifold of disease heterogeneity. Using GBM-cRegMap, we demonstrated the synergy between the two components by refining the molecular classification of GBM, identifying potential key regulators, and aligning the transcriptional profiles of tumors and in vitro models. Through the amalgamation of a vast dataset, we validated the proneural (PN)-mesenchymal (MES) axis and identified three subclasses of classical (CL) tumors: astrocyte-like (CL-A), epithelial basal-like (CL-B), and cilium-rich (CL-C). We revealed the CL-C subclass, an intermediate state demonstrating the plasticity of GBM cells along the PN-MES axis under chemotherapy. We identified key regulators, such as PAX8, and NKX2.5, potentially involved in temozolomide (TMZ) resistance. Notably, NKX2.5, more expressed in higher-grade gliomas, negatively impacts patient survival, and regulates genes involved in glucose metabolism.