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Network-driven prioritization and functional phenotyping nominate TTC23 as a biomarker-informed target in chlorpromazine repurposing for glioblastoma.

Overview

Authors: Jianqiang Hao1, Hongbin Liu1, Yongqiang Ye1, Jianwei Lv1, Fang Xue1, Yanli Cai2
  1. Department of Neurosurgery, Ziyang Central Hospital, West China Hospital of Sichuan University-Ziyang Hospital, Ziyang, China
  2. Outpatient Department, Ziyang Central Hospital, West China Hospital of Sichuan University-Ziyang Hospital, Ziyang, China
Journal: Frontiers in pharmacology, volume 17, article 1797067
Dates: received 27 January 2026; accepted 25 March 2026; published online 4 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fphar.2026.1797067 · PMID 42158948 · PMCID PMC13181239 · OpenAlex W7160058662
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: other condition (population), clinical / translational (subfield)
Methods: Statistics, Machine learning, Preprocessing, Connectivity
Keywords: chlorpromazine, drug repurposing, glioblastoma, network pharmacology, prognostic modeling, TTC23, tumor immunity
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 65 references in the paper
Research resources: U251MG RRID:CVCL_0021, U87MG RRID:CVCL_0022, HEK293T RRID:CVCL_0063

Abstract

Background: Glioblastoma (GBM) remains a lethal brain tumor with limited therapeutic options and near-universal recurrence. Drug repurposing offers a practical strategy, but pleiotropic compounds require systematic target triage to yield actionable and testable vulnerabilities.

Methods: We integrated GBM transcriptomic dysregulation with curated chlorpromazine (CPZ)-associated targets to define drug–disease intersecting genes, constructed a protein–protein interaction network, and developed an outcome-linked Lasso–Cox prognostic model to prioritize core candidates. Structure-informed docking and coarse-grained conformational sampling were used to evaluate the plausibility of a TTC23–CPZ interaction. TTC23-associated pathway activity, oncogenic state features, and immune contexture were characterized using expression stratification, enrichment and state scoring, cancer–immunity cycle analysis, and immune infiltration estimation. Functional validation was performed in GBM cell models to assess migration, apoptosis, cell viability, and clonogenic potential under TTC23 perturbation with or without CPZ exposure.

Results: Integrated CPZ–GBM intersection analysis and network-based prognostic modeling consistently prioritized TTC23 as a clinically relevant candidate. Structure-based analyses supported a consistent TTC23–CPZ interaction hypothesis across conformational sampling. Elevated TTC23 expression was associated with coordinated pathway activation, malignant functional states, and distinct immune-associated features. Functionally, TTC23 depletion suppressed migratory capacity, increased apoptotic susceptibility, reduced short-term viability, and impaired long-term clonogenic survival, while sensitizing GBM cells to CPZ-associated anti-tumor phenotypes.

Conclusion: Our multi-layer framework nominates TTC23 as a functionally relevant determinant associated with CPZ response in GBM and supports the CPZ–TTC23 axis as a candidate for biomarker-informed drug repurposing.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Datasets cited

Data availability statement

The datasets analyzed in this study are publicly available. Bulk transcriptomic data were obtained from the Gene Expression Omnibus (GEO) under accession number GSE4290 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4290) and from The Cancer Genome Atlas (TCGA-GBM) project. Single-cell RNA-seq data used for cell-cycle–resolved analysis were obtained from GEO under accession number GSE146773 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146773). All other data generated during this study are included in the article and its supplementary material or are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 7 keywords, 65 references, 3 RRIDs.

Cite

This paper

Hao, J., Liu, H., Ye, Y., Lv, J., Xue, F., & Cai, Y. (2026). Network-driven prioritization and functional phenotyping nominate TTC23 as a biomarker-informed target in chlorpromazine repurposing for glioblastoma. Frontiers in pharmacology, 17, 1797067. https://doi.org/10.3389/fphar.2026.1797067

BibTeX

@article{hao2026network,
author = {Hao, Jianqiang and Liu, Hongbin and Ye, Yongqiang and Lv, Jianwei and Xue, Fang and Cai, Yanli},
title = {{Network-driven prioritization and functional phenotyping nominate TTC23 as a biomarker-informed target in chlorpromazine repurposing for glioblastoma}},
journal = {Frontiers in pharmacology},
year = {2026},
month = may,
volume = {17},
pages = {1797067},
publisher = {Frontiers Media SA},
issn = {1663-9812},
doi = {10.3389/fphar.2026.1797067},
url = {https://doi.org/10.3389/fphar.2026.1797067},
pmid = {42158948},
pmcid = {PMC13181239}
}

RIS

TY - JOUR
AU - Hao, Jianqiang
AU - Liu, Hongbin
AU - Ye, Yongqiang
AU - Lv, Jianwei
AU - Xue, Fang
AU - Cai, Yanli
TI - Network-driven prioritization and functional phenotyping nominate TTC23 as a biomarker-informed target in chlorpromazine repurposing for glioblastoma
T2 - Frontiers in pharmacology
J2 - Front Pharmacol
PY - 2026
DA - 2026/05/04
VL - 17
SP - 1797067
SN - 1663-9812
PB - Frontiers Media SA
DO - 10.3389/fphar.2026.1797067
UR - https://doi.org/10.3389/fphar.2026.1797067
LA - en
ER -

CSL-JSON

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