Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPis) are used to treat BRCA -mutated (BRCAm) cancer patients; however, resistance has been observed. Therefore, biomarkers to indicate PARPi resistance and combination therapy to overcome that are urgently needed. We identified a high prevalence of activated FGF receptor 3 (FGFR3) in BRCAm triple-negative breast cancer (TNBC) cells with intrinsic and acquired PARPi resistance. FGFR3 phosphorylated PARP1 at tyrosine 158 (Y158) to recruit BRG1 and prolong chromatin-loaded MRE11, thus promoting homologous recombination (HR) to enhance PARPi resistance. FGFR inhibition prolonged PARP trapping and synergized with PARPi in vitro and in vivo. High-level PARP1 Y158 phosphorylation (p-Y158) positively correlated with PARPi resistance in TNBC patient–derived xenograft models, and in PARPi-resistant TNBC patient tumors. These findings reveal that PARP1 p-Y158 facilitates BRG1-mediated HR to resolve the PARP-DNA complex, and PARP1 p-Y158 may indicate PARPi resistance that can be relieved by combining FGFR inhibitors (FGFRis) with PARPis. In summary, we show that FGFRi restores PARP trapping and PARPi antitumor efficacy in PARPi-resistant breast cancer by decreasing HR through the PARP1 p-Y158/BRG1/MER11 axis, suggesting that PARP1 p-Y158 is a biomarker for PARPi resistance that can be overcome by combining FGFRis with PARPis.

Background: Glypican-3 (GPC3) is frequently overexpressed in hepatocellular carcinoma (HCC) and serves as a circulating biomarker. Limited evidence exists regarding whether plasma or serum constitutes the optimal matrix for GPC3 measurement. This study aimed to investigate this gap. Methods: Between December 2024 and September 2025, 100 participants were prospectively enrolled, including 33 healthy controls, 29 individuals with chronic liver disease, and 38 patients with HCC. Paired serum and plasma samples were analyzed under fresh conditions and after storage for seven days at 4 °C and −70 °C. GPC3 concentrations were compared across groups. Subsequently, correlation and area under the receiver operating characteristic curve (AUROC) analyses were conducted. Results: In fresh samples of the controls, median plasma GPC3 levels were significantly higher than those in serum (82.36 pg/mL, IQR: 67.56–92.42 vs. 30.89 pg/mL, IQR: 20.36–41.12; p < 0.001). After seven days of storage, plasma GPC3 concentrations declined markedly at both 4 °C (41.73 pg/mL, IQR: 32.49–55.37; p < 0.001) and −70 °C (45.53 pg/mL, IQR: 25.30–55.65; p < 0.001), with no significant difference between the two storage conditions (p = 0.610). In contrast, serum GPC3 levels remained relatively stable across fresh, 4 °C (31.10 pg/mL, IQR: 16.84–38.60), and −70 °C (25.31 pg/mL, IQR: 14.36–40.74) conditions (p = 0.645). Both matrices under −70 °C storage effectively discriminated HCC from non-HCC cases, although serum demonstrated a significantly better diagnostic performance (AUROC: 0.836, 95% CI: 0.749–0.902 vs. 0.772, 95% CI: 0.677–0.850; p = 0.013). Conclusions: Although plasma offers operational convenience and higher baseline GPC3 levels, serum provides both greater stability and superior diagnostic accuracy under frozen conditions, thus supporting its use as the preferred specimen matrix in clinical and research applications.

Background: Hepatocellular carcinoma (HCC) remains a major global health challenge with limited treatment options for advanced disease. Although immune checkpoint inhibitors (ICIs) have shown clinical benefits, response rates remain low, emphasizing the need for reliable biomarkers to guide patient selection. Given the critical role of metabolic reprogramming in immune modulation, this study aimed to identify a metabolic gene signature predictive of immunotherapy response in HCC. Methods: Three independent transcriptomic datasets (GSE279750, GSE215011, and GSE235863) comprising 35 ICI-treated HCC samples were integrated after quality control and ComBat batch correction. Differentially expressed genes were identified using DESeq2 and limma, followed by integration of the meta-analysis results. Machine learning models, including LASSO regression and random forest algorithms, were applied for feature selection, and a logistic regression model was developed for predictive scoring. Results: A five-gene metabolic signature (PLPPR1, CNTN3, HOXA10, HAGLR, and ENPP3) demonstrated good discriminative ability between responders and non-responders, with consistent performance observed across internal validation analyses. Functional enrichment analysis revealed significant involvement of metabolic pathways, with HOXA10 linked to immune evasion and CNTN3 associated with immune activation. Conclusions: This five-gene signature represents a biologically interpretable biomarker panel with potential utility for immunotherapy response stratification in HCC. The integrative analytical framework provides preliminary evidence supporting its value, warranting further validation in larger, independent clinical cohorts before clinical translation.