Age-dependent prognostic significance of neutrophil-to-HDL ratio in hepatocellular carcinoma patients

中性粒细胞与高密度脂蛋白比值在肝细胞癌患者中的年龄依赖性预后意义

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Abstract

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy, with variability in molecular features, clinical presentations, and treatment responses. Postoperative recurrence and disease-free survival (DFS) are important prognostic indicators for patient outcomes. The neutrophil-to-HDL ratio (NHR) is recognized as an inflammatory-lipid marker, however its age-dependent predictive value in HCC remains unclear and is not established in clinical practice. Therefore, we aimed to evaluate the prognostic significance of NHR in HCC patients undergoing surgical resection, with a focus on its age-dependent effects. METHODS: We retrospectively analyzed 121 HCC patients undergoing surgical resection and randomly divided them into training (n = 95) and validation (n = 26) cohorts. Multivariate logistic regression, Receiver operating characteristic (ROC) analysis, and nomogram construction were used to evaluate the prognostic significance of NHR, with age-stratified analyses conducted to explore its differential effects. RESULTS: In the training cohort, both univariate and multivariate analysis identified NHR and age as statistically significant prognostic factors for HCC recurrence (P < 0.05). Age-stratified analysis further demonstrated that the prognostic value of NHR was significant in older patients (OR = 0.087, 95% CI: 0.009 - 0.835, P = 0.034), but not in younger patients. ROC analysis indicated good predictive performance for both NHR (AUC = 0.609) and age (AUC = 0.655). Similar trends were observed using the validation dataset. CONCLUSIONS: In this cohort of 121 HCC patients, NHR showed a potential association with prognosis in older patients; However, these findings are preliminary due to limited sample size and lack of stratified analyses by disease stage and prior treatment. Future studies should validate these findings in larger, well-characterized cohorts and investigate underlying mechanisms.

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