结合 bulk 和单细胞 RNA-seq 数据，基于集成机器学习框架开发用于肝细胞癌的 NK 细胞相关预后特征。

BACKGROUND: The application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients' prognosis and immunotherapeutic response. METHODS: NK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA-LIHC data set, GSE14520 data set, GSE76427 data set and ICGC-LIRI-JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set. RESULTS: With the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients' prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS-PD1 blocker, IPS-CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group. CONCLUSIONS: Overall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.

背景：分子靶向治疗和免疫治疗的应用显著延长了肝细胞癌（HCC）患者的生存时间。然而，HCC 的多药耐药性和高分子异质性仍然阻碍了临床获益的进一步提高。肿瘤浸润自然杀伤（NK）细胞功能障碍与 HCC 进展和 HCC 患者的生存获益密切相关。因此，构建了一个与 NK 细胞相关的预后标志物，以预测 HCC 患者的预后和免疫治疗反应。

方法：从 GSE162616 数据集的 scRNA-Seq 数据中选择 NK 细胞标志物。开发了一个共识机器学习框架，包括总共 77 种算法，以在 TCGA-LIHC 数据集、GSE14520 数据集、GSE76427 数据集和 ICGC-LIRI-JP 数据集中建立基因特征。此外，通过 GSE91061 数据集和 PRJEB23709 数据集对 ICI 反应的预测效果进行了外部验证。

结果：通过 LASSO 和 CoxBoost 算法的组合，在 77 种算法中具有最高的 C 指数，建立了一个由 11 个基因组成的标志物，将患者分为高风险和低风险组。该预后标志物在 TCGA、GEO 和 ICGC 队列中对总生存率具有良好的预测性能、中高度预测准确性，并且是 HCC 患者预后的独立危险因素。与高风险组相比，低风险组显示出更高的 IPS-PD1 阻滞剂、IPS-CTLA4 阻滞剂、常见免疫检查点表达，但更低的 TIDE 评分，这表明低风险组可能更容易受益于 ICI 治疗。此外，真实世界的 PRJEB23709 队列也显示低风险组的免疫治疗反应更好。

结论：总之，本研究基于 NK 细胞相关基因开发了一个基因标志物，为 HCC 患者的预后和免疫治疗反应评估提供了一个新的平台。