FGF1-FGFR2 axis regulated by nuclear receptor RORγ represents an effective strategy in intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) is a highly aggressive malignancy with limited therapeutic options. Although targeted therapies like pemigatinib provide partial clinical benefits, acquired resistance remains a significant challenge. Through integrative bioinformatics analysis of public datasets and immunohistochemical validation, we identified the retinoid-related orphan receptor gamma (RORγ) as markedly upregulated in iCCA. Genetic silencing and pharmacological inhibition of RORγ (GSK805/XY101) suppressed proliferation, induced apoptosis in vitro, and significantly reduced xenograft tumor growth in vivo. Mechanistically, RORγ promoted fibroblast growth factor receptor 2 (FGFR2) signaling via two complementary mechanisms: direct transcriptional activation of FGFR2 and induction of fibroblast growth factor 1 (FGF1) expression and secretion, which in turn activated FGFR2. Inhibition of RORγ markedly decreased FGF1 levels in conditioned media, whereas exogenous FGF1 restored tumor growth. Notably, RORγ antagonists synergized with pemigatinib to overcome resistance in pemigatinib-refractory models. Collectively, these findings identify the RORγ-FGF1-FGFR2 axis as a critical oncogenic driver in iCCA and highlight RORγ inhibition as a promising therapeutic strategy to suppress tumor progression and enhance sensitivity to FGFR inhibitors. HUCCT-1 cells were treated with vehicle, XY101 (5 µM) for 48 hours. Total RNA was extracted, and RNA-seq libraries were constructed using Illumina TruSeq Kit. RNA sequencing (RNA-seq) was performed by Beijing Genomics Institute (BGI, Shenzhen, China). Briefly, poly(A)⁺ mRNA was enriched using oligo(dT) magnetic beads. cDNA was synthesized under high-temperature conditions and purified by the kit. Sequencing was carried out on the BGISEQ platform. The resulting reads were aligned to the reference human genome assembly (GRCh37/hg19) using BWA and Bowtie 2. Gene expression levels were quantified from the RNA-seq data using RSEM, and a heatmap was subsequently generated to visualize the expression profiles.