RNF43 p.G659fs 通过 PI3K/AKT/mTOR 信号通路和 HLA-E 上调导致 MSI-high 结直肠癌中 NK 细胞功能障碍
RNF43 p.G659fs leads to natural killer cell dysfunction in MSI-high colorectal cancer through PI3K/AKT/mTOR signaling and HLA-E up-regulation.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Impact of a new liver immune status index among patients with hepatocellular carcinoma after initial hepatectomy.
Impact of a new liver immune status index among patients with hepatocellular carcinoma after initial hepatectomy.
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我们的模型通过提供 LISI 来预测 NK 细胞的抗肿瘤效果,从而促进高危患者 RR 的预测。
自然杀伤(NK)细胞的抗肿瘤效应存在个体差异。肝脏NK细胞上表达的肿瘤坏死因子相关凋亡诱导配体(TRAIL)是免疫细胞治疗中抗肝细胞癌(HCC)细胞毒作用的标志物。本研究旨在开发一种肝脏免疫状态指数(LISI),用于预测低TRAIL表达,并验证其预测原发性HCC初次肝切除术后复发的能力。
对40例肝移植供体的肝NK细胞与白细胞介素-2共培养3天后进行了功能分析。通过多元logistic回归分析计算了LISI,该指数预测肝NK细胞中TRAIL低表达(25%四分位数:<33%)。随后,基于LISI对586例初次肝切除术病例进行了分析。
我们的模型基于Fibrosis-4指数+0.1(比值比[OR],1.33)、体重指数(OR,0.61)和白蛋白水平+0.1(OR,0.54)。LISI对低TRAIL表达的受试者工作特征曲线下面积(AUC)为0.89。复发率(RR)的分层显示,LISI是RR的独立预测因素(中风险:风险比,1.44;高风险:风险比,3.02)。LISI、白蛋白-吲哚菁绿评估分级、白蛋白-胆红素评分和老年营养风险指数在预测RR方面的AUC相似。在血管侵犯病例中,LISI比其他指标更有用。
A functional analysis of liver NK cells co-cultured with interleukin-2 for 3 days was performed of 40 liver transplant donors. The LISI, which predicted low TRAIL expression (25% quartile: <33%) in liver NK cells, was calculated using multiple logistic regression analysis. Next, 586 initial hepatectomy cases were analyzed based on the LISI.
Our model was based on the Fibrosis-4 index +0.1 (odds ratio [OR], 1.33), body mass index (OR, 0.61), and albumin levels +0.1 (OR, 0.54). The area under the receiver operating characteristic curve (AUC) of the LISI for low TRAIL expression was 0.89. Stratification of the recurrence rates (RR) revealed that LISI was an independent predictive factor of RR (moderate risk: hazard ratio, 1.44; high risk: hazard ratio, 3.02). The AUC was similar for the LISI, albumin-indocyanine green evaluation grade, albumin-bilirubin score, and geriatric nutritional risk index for predicting RR. Among the vascular invasion cases, the LISI was more useful than the other indexes.
Our model facilitates the prediction of RR in high-risk patients by providing LISI to predict the anti-tumor effects of NK cells.
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