Table 8_Multi-omics and single-cell approaches reveal molecular subtypes and key cell interactions in hepatocellular carcinoma.xlsx

IntroductionHepatocellular carcinoma is a highly aggressive and heterogeneous malignancy with limited understanding of its heterogeneity.
MethodsIn this study, we applied ten multi-omics classification algorithms to identify three distinct molecular subtypes of HCC (C1–C3). To further explore the immune microenvironment of these molecular subtypes, we leveraged single-cell transcriptomic data and employed CIBERSORTx to deconvolute their immune landscape.
ResultsAmong them, C3 exhibited the worst prognosis, whereas C1 and C2 were associated with relatively better clinical outcomes. Patients in the C3 group exhibited a high burden of copy number variations, mutation load, and methylation silencing. Our results revealed that compared to C1 and C2, C3 had a lower proportion of hepatocytes but a higher proportion of cholangiocytes and macrophages. Through analyses of hepatocyte, cholangiocyte, and macrophage subpopulations, we characterized their functional states, spatial distribution preferences, evolutionary relationships, and transcriptional regulatory networks, ultimately identifying cell subpopulations significantly associated with patient survival. Furthermore, we identified key ligand-receptor interactions, such as APOA1-TREM2 and APOA2-TREM2 in hepatocyte-macrophage crosstalk, and VTN-PLAUR in cholangiocyte-macrophage communication.
DiscussionFinally, we employed machine learning methods to construct a prognostic model for HCC patients and identified novel potential compounds for high risk patients. In summary, our novel multi-omics classification of HCC provides valuable insights into tumor heterogeneity and prognosis, offering potential clinical applications for precision oncology.

引言：肝细胞癌（Hepatocellular carcinoma, HCC）是一种高度侵袭性且异质性显著的恶性肿瘤，目前对其异质性的认知仍较为有限。
方法：本研究应用十种多组学分类算法，鉴定出肝细胞癌的三种独特分子亚型（C1—C3）。为进一步探究这些分子亚型的免疫微环境，本研究借助单细胞转录组数据，并采用CIBERSORTx算法解析其免疫景观。
结果：其中，C3亚型的预后最差，而C1与C2亚型则对应相对更优的临床结局。C3组患者表现出较高的拷贝数变异负荷、突变负荷以及甲基化沉默水平。研究结果显示，相较于C1和C2亚型，C3亚型的肝细胞占比更低，但胆管细胞与巨噬细胞占比更高。通过对肝细胞、胆管细胞及巨噬细胞亚群的分析，本研究刻画了这些亚群的功能状态、空间分布偏好、演化关系以及转录调控网络，最终鉴定出与患者生存显著相关的细胞亚群。此外，本研究还识别出关键的配体-受体互作对，例如肝细胞-巨噬细胞互作中的APOA1-TREM2与APOA2-TREM2，以及胆管细胞-巨噬细胞通讯中的VTN-PLAUR。
讨论：最后，本研究采用机器学习方法构建了肝细胞癌患者的预后模型，并为高危患者筛选出潜在的新型治疗化合物。综上，本研究提出的新型肝细胞癌多组学分类体系，为肿瘤异质性与预后研究提供了宝贵见解，同时为肿瘤精准医学的临床应用提供了潜在可能。