Li Yang, Ding Yinan, Wang Jinghao, Wu Xiaoxia, Zhang Dinghu, Zhou Han, Zhang Pengfei, Shao Guoliang
Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Clinical Research Center of Hepatobiliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, Zhejiang, China.
Front Immunol. 2024 Nov 26;15:1466069. doi: 10.3389/fimmu.2024.1466069. eCollection 2024.
Hepatocellular carcinoma (HCC) is heterogeneous and refractory with multidimensional features. This study aims to investigate its molecular classifications based on multidimensional molecular features scores (FSs) and support classification-guided precision medicine.
Data of bulk RNA sequencing, single nucleotide variation, and single-cell RNA sequencing were collected. Feature scores (FSs) from hallmark pathways, regulatory cell death pathways, metabolism pathways, stemness index, immune scores, estimate scores, etc. were evaluated and screened. Then, the unsupervised clustering on the core FSs was performed and the characteristics of the resulting clusters were identified. Subsequently, machine learning algorithms were used to predict the classifications and prognoses. Additionally, the sensitivity to immune therapy and biological roles of classification-related prognostic genes were also evaluated.
We identified four clusters with distinct characteristics. C1 is characterized by high TP53 mutations, immune suppression, and metabolic downregulation, with notable responsiveness to anti-PD1 therapy. C2 exhibited high tumor purity and metabolic activity, moderate TP53 mutations, and cold immunity. C3 represented an early phase with the most favorable prognosis, lower stemness and tumor mutations, upregulated stroma, and hypermetabolism. C4 represented a late phase with the poorest prognosis, highest stemness, higher TP53 mutations, cold immunity, and metabolic downregulation. We further developed practical software for prediction with good performance in the external validation. Additionally, FTCD was identified as a classification-specific prognostic gene with tumor-suppressing role and potential as a therapeutic target, particularly for C1 and C4 patients.
The four-layer classification scheme enhances the understanding of HCC heterogeneity, and we also provide robust predictive software for predicting classifications and prognoses. Notably, C1 is more sensitive to anti-PD1 therapies and FTCD is a promising therapeutic target, particularly for C1 and C4. These findings provide new insights into classification-guided precision medicine.
肝细胞癌（HCC）具有异质性且难治，具有多维度特征。本研究旨在基于多维度分子特征评分（FSs）探究其分子分类，并支持分类指导下的精准医学。
收集批量RNA测序、单核苷酸变异和单细胞RNA测序数据。评估并筛选来自标志性通路、调节性细胞死亡通路、代谢通路、干性指数、免疫评分、估计评分等的特征评分（FSs）。然后，对核心FSs进行无监督聚类，并确定所得聚类的特征。随后，使用机器学习算法预测分类和预后。此外，还评估了免疫治疗的敏感性以及分类相关预后基因的生物学作用。
我们识别出四个具有不同特征的聚类。C1的特征是高TP53突变、免疫抑制和代谢下调，对抗PD1治疗有显著反应。C2表现出高肿瘤纯度和代谢活性、中等TP53突变以及冷免疫。C3代表预后最有利的早期阶段，干性和肿瘤突变较低，基质上调，代谢亢进。C4代表预后最差的晚期阶段，干性最高，TP53突变较高，冷免疫，代谢下调。我们进一步开发了实用软件进行预测，在外部验证中表现良好。此外，FTCD被鉴定为具有肿瘤抑制作用和作为治疗靶点潜力的分类特异性预后基因，特别是对于C1和C4患者。
四层分类方案增强了对HCC异质性的理解，我们还提供了用于预测分类和预后的强大预测软件。值得注意的是，C1对抗PD1治疗更敏感，FTCD是一个有前景的治疗靶点，特别是对于C1和C4。这些发现为分类指导下的精准医学提供了新的见解。
