The Prognostic Value of aspartate aminotransferase to lymphocyte ratio and systemic immune-inflammation index for Overall Survival of Hepatocellular Carcinoma Patients Treated with palliative Treatments

作者：Liyun Zhao, Dongdong Yang, Xiaokun Ma, Mengmeng Liu, Dong‐Hao Wu, Xiaoping Zhang, Dan‐Yun Ruan, Jin-Xiang Lin, Jing‐Yun Wen, Jie Chen, Qu Lin, Min Dong, Jingjing Qi, Peishan Hu, Zhao-Lei Zeng, Zhan‐Hong Chen, Xiangyuan Wu · 发表于：Journal of Cancer · 年份：2019 · DOI：10.7150/jca.30663 · 被引用次数：64 · 研究领域：Inflammatory Biomarkers in Disease Prognosis、Liver Disease Diagnosis and Treatment、MicroRNA in disease regulation

Background: Lymphocytes were reported to play a significant part in host anticancer immune responses and influence tumour prognosis. Few studies have focused on the prognostic values of aspartate aminotransferase (AST) to lymphocyte ratio (ALRI), aspartate aminotransferase to platelet count ratio index (APRI) and systemic immune-inflammation index (SII) in hepatocellular carcinoma (HCC) treated with palliative treatments. Methods: Five hundred and ninety-eight HCC patients treated with palliative therapies were retrospectively analysed. We randomly assigned patients into the training cohort (429 patients) and the validation cohort I (169 patients). Receiver operating characteristic (ROC) curves were used to identify the best cut-off values for the ALRI, APRI and SII in the training cohort and the values were further validated in the validation cohort I. Correlations between ALRI and other clinicopathological factors were also analysed. A prognostic nomogram including ALRI was established. We validated the prognostic value of the ALRI, SII and APRI with two independent cohorts, the validation cohort II of 82 HCC patients treated with TACE and the validation cohort III of 150 HCC patients treated with curative resection. In the training cohort and all the validation cohorts, univariate analyses by the method of Kaplan-Meier and multivariate analysis by Cox proportional hazards regression model were carried out to identify the independent prognostic factors.