Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China.
Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang, China.
Int Immunopharmacol. 2024 Dec 5;142(Pt B):113231. doi: 10.1016/j.intimp.2024.113231. Epub 2024 Sep 26.
The highly heterogeneity of the tumor microenvironment (TME) in hepatocellular carcinoma (HCC) results in diverse clinical outcomes and therapeutic responses. This study aimed to investigate potential intercellular crosstalk and its impact on clinical outcomes and therapeutic responses.
Single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST) and bulk RNA sequencing (RNA-seq) datasets were integrated to comprehensively analyze the intercellular interactions within the TME. Multiplex immunohistochemistry was conducted to validate the intercellular interactions. A machine learning-based integrative procedure was used in bulk RNA-seq datasets to generate a risk model to predict prognosis and therapeutic responses.
Survival analyses based on the bulk RNA-seq datasets revealed the negative impact of the naïve Cluster of Differentiation 4 (CD4) T cells and Secreted Phosphoprotein 1 (SPP1) macrophages on prognosis. Furthermore, their intricate intercellular crosstalk and spatial colocalization were also observed by scRNA-seq and ST analyses. Based on this crosstalk, a machine learning model, termed the naïve CD4 T cell and SPP1 macrophage prognostic score (TMPS), was established in the bulk-RNA seq datasets for prognostic prediction. The TMPS achieved C-index values of 0.785, 0.715, 0.692 and 0.857, respectively, across 4 independent cohorts. A low TMPS was associated with a significantly increased survival rates, improved response to immunotherapy and reduced infiltration of immunosuppressive cells, such as. regulatory T cells. Finally, 8 potential sensitive drugs and 6 potential targets were predicted for patients based on their TMPS.
The crosstalk between naïve CD4 T cells and SPP1 macrophages play a crucial role in the TME. TMPS can reflect this crosstalk and serve as a valuable tool for prognostic stratification and guiding clinical decision-making.
肝癌（HCC）肿瘤微环境（TME）的高度异质性导致了不同的临床结局和治疗反应。本研究旨在探讨潜在的细胞间相互作用及其对临床结局和治疗反应的影响。
整合单细胞 RNA 测序（scRNA-seq）、空间转录组学（ST）和批量 RNA 测序（RNA-seq）数据集，全面分析 TME 中的细胞间相互作用。通过多重免疫组织化学验证细胞间相互作用。在批量 RNA-seq 数据集中使用基于机器学习的综合程序生成风险模型，以预测预后和治疗反应。
基于批量 RNA-seq 数据集的生存分析显示，幼稚 CD4 细胞和分泌磷蛋白 1（SPP1）巨噬细胞对预后有负面影响。此外，通过 scRNA-seq 和 ST 分析也观察到它们复杂的细胞间相互作用和空间共定位。基于这种相互作用，在批量 RNA-seq 数据集中建立了一种机器学习模型，称为幼稚 CD4 细胞和 SPP1 巨噬细胞预后评分（TMPS），用于预后预测。TMPS 在 4 个独立队列中的 C 指数值分别为 0.785、0.715、0.692 和 0.857。低 TMPS 与生存率显著提高、免疫治疗反应改善和抑制性细胞浸润减少相关，如调节性 T 细胞。最后，根据 TMPS 预测了 8 种潜在敏感药物和 6 种潜在靶点。
幼稚 CD4 细胞和 SPP1 巨噬细胞之间的相互作用在 TME 中起着关键作用。TMPS 可以反映这种相互作用，并作为预后分层和指导临床决策的有价值工具。
