Department of Medical Oncology 2, The People's Hospital of Guangxi Zhuang Autonomous & Institute of Oncology, Guangxi Academy of Medical Sciences, Nanning, China.
Department of Nephrology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
Front Immunol. 2023 Jul 28;14:1216585. doi: 10.3389/fimmu.2023.1216585. eCollection 2023.
The heterogeneity of tumor immune microenvironments is a major factor in poor prognosis among hepatocellular carcinoma (HCC) patients. Neutrophils have been identified as playing a critical role in the immune microenvironment of HCC based on recent single-cell studies. However, there is still a need to stratify HCC patients based on neutrophil heterogeneity. Therefore, developing an approach that efficiently describes "neutrophil characteristics" in HCC patients is crucial to guide clinical decision-making.
We stratified two cohorts of HCC patients into molecular subtypes associated with neutrophils using bulk-sequencing and single-cell sequencing data. Additionally, we constructed a new risk model by integrating machine learning analysis from 101 prediction models. We compared the biological and molecular features among patient subgroups to assess the model's effectiveness. Furthermore, an essential gene identified in this study was validated through molecular biology experiments.
We stratified patients with HCC into subtypes that exhibited significant differences in prognosis, clinical pathological characteristics, inflammation-related pathways, levels of immune infiltration, and expression levels of immune genes. Furthermore, A risk model called the "neutrophil-derived signature" (NDS) was constructed using machine learning, consisting of 10 essential genes. The NDS's RiskScore demonstrated superior accuracy to clinical variables and correlated with higher malignancy degrees. RiskScore was an independent prognostic factor for overall survival and showed predictive value for HCC patient prognosis. Additionally, we observed associations between RiskScore and the efficacy of immune therapy and chemotherapy drugs.
Our study highlights the critical role of neutrophils in the tumor microenvironment of HCC. The developed NDS is a powerful tool for assessing the risk and clinical treatment of HCC. Furthermore, we identified and analyzed the feasibility of the critical gene in NDS as a molecular marker for HCC.
肿瘤免疫微环境的异质性是导致肝细胞癌（HCC）患者预后不良的一个主要因素。基于最近的单细胞研究，已经确定中性粒细胞在 HCC 的免疫微环境中发挥关键作用。然而，仍需要根据中性粒细胞的异质性对 HCC 患者进行分层。因此，开发一种能够有效地描述 HCC 患者“中性粒细胞特征”的方法对于指导临床决策至关重要。
我们使用批量测序和单细胞测序数据将两个 HCC 患者队列分层为与中性粒细胞相关的分子亚型。此外，我们通过整合来自 101 个预测模型的机器学习分析构建了一个新的风险模型。我们比较了患者亚组之间的生物学和分子特征，以评估该模型的有效性。此外，在这项研究中鉴定的一个关键基因通过分子生物学实验进行了验证。
我们将 HCC 患者分为具有显著预后、临床病理特征、炎症相关途径、免疫浸润水平和免疫基因表达水平差异的亚型。此外，我们使用机器学习构建了一个名为“中性粒细胞衍生特征”（NDS）的风险模型，该模型由 10 个关键基因组成。NDS 的 RiskScore 表现出优于临床变量的准确性，并与更高的恶性程度相关。RiskScore 是总生存期的独立预后因素，对 HCC 患者的预后具有预测价值。此外，我们观察到 RiskScore 与免疫治疗和化疗药物疗效之间存在关联。
我们的研究强调了中性粒细胞在 HCC 肿瘤微环境中的关键作用。开发的 NDS 是评估 HCC 风险和临床治疗的有力工具。此外，我们鉴定并分析了 NDS 中关键基因作为 HCC 分子标志物的可行性。
