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Single-cell data for FGFR3-driven gene regulatory network analysis reveals a pro-tumoral role for p63 in luminal bladder tumors

GSE315628 Homo sapiens; Mus musculus Expression profiling by high throughput sequencing 6 samples Submitted 2026/01/06 Platform GPL24676Platform GPL25526
Summary
FGFR3 (Fibroblast Growth Factor Receptor 3) is one of the most frequently altered genes in bladder cancer, primarily through activating mutations that drive oncogenesis and are enriched in luminal tumors. However, the underlying gene regulatory network (GRN) remains poorly characterized. Here, we inferred an FGFR3-mutated GRN using a bottom-up bioinformatics approach, integrating transcriptomic data from bladder cancer cell lines, FGFR3-mutated tumors, and FGFR3-perturbation experiments in human and mouse models. Using CRISPR-Cas9 screen public data, we identified transcription factors (TFs) from this GRN that regulate the viability of FGFR3-mutated cells, with a focus on p63 (TP63). We showed that FGFR3 activation upregulates p63 in patient-derived xenografts and cell lines, while single-cell RNA sequencing revealed heterogeneous p63 activation associated with basal differentiation. Functional studies, including TP63 knock-down in FGFR3-dependent in vitro and in vivo models and RNA-seq along with p63 ChIP-seq, demonstrated that p63 directly promotes cell proliferation and migration, and uncovered a positive feedback loop between FGFR3 and p63. Together, these findings identified p63 as a pro-tumorigenic regulator in FGFR3 mutated tumors despite their luminal differentiation and provide a detailed FGFR3-driven GRN, offering insights into FGFR3-induced oncogenic dependency and potential strategies to circumvent resistance to FGFR inhibitors.
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Direct links to NCBI, no account and no request form: the whole study as GSE315628_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 6 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1398356 and SRA study SRP659813. Searching any of these in the dataset finder brings you back here.

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