ArticleFrontiers in pharmacology2025
Exploring HSP90α and hs-CRP using AI models to predict prognosis in advanced hepatocellular carcinoma treated with PD-1 inhibitors and targeted therapy.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study investigates the roles of heat shock protein 90α (HSP90α) and high-sensitivity C-reactive protein (hs-CRP) in the progression and prognosis of advanced hepatocellular carcinoma (HCC) patients undergoing immunotherapy. By integrating these biomarkers with artificial intelligence (AI), we aim to elucidate the complex interactions between tumor stress, immune responses, and tumor progression. Methods: This retrospective analysis includes 644 patients with advanced HCC who received PD-1 inhibitors and targeted therapy across 3 tertiary hospitals in China from 2016 to 2023. The patients were randomly divided into training (70%) and validation (30%) sets. Independent prognostic factors for overall survival (OS) were identified using LASSO and stepwise Cox regression. Five machine learning models were built, and their performance was evaluated using Receiver Operating Characteristic (ROC) curves, Decision Curve Analysis (DCA), and calibration curves. Results: Patients with high HSP90α expression had a median OS of 7.7 months compared to 20.6 months for those with low expression (p < 0.001). Similarly, high hs-CRP levels were associated with OS of 11.6 months versus 30.8 months for low CRP (p < 0.001). LASSO and stepwise Cox regression identified age, CRP, HSP90α, Child-Pugh classification, tumor number, metastatic (M) status, and portal vein tumor thrombosis (PVTT) as independent prognostic markers. The Random Survival Forests (RSF) model achieved the highest C-index of 0.679, and in the validation set, it demonstrated AUC-ROC values of 0.803 at 6 months, 0.801 at 12 months, and 0.761 at 18 months. The RSF model demonstrated good calibration across all time points, and DCA showed consistently higher net benefit compared with "Treat All" and "Treat None" strategies. Additionally, High levels of CRP and HSP90α were also associated with advanced tumor stage and higher Child-Pugh classification. Conclusion: HSP90α and hs-CRP, plays a critical role in the prognosis of advanced HCC. Integrating these biomarkers with machine learning models enhances OS prediction accuracy, offering a personalized approach to cancer treatment.
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