Wang Haoren, Yu Shizhe, Cai Qiang, Ma Duo, Yang Lingpeng, Zhao Jian, Jiang Long, Zhang Xinyi, Yu Zhiyong
Department of Hepatobiliary Surgery, The Affiliated Hospital of Yunnan University, Kunming, China.
Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Cell Dev Biol. 2021 Sep 29;9:737723. doi: 10.3389/fcell.2021.737723. eCollection 2021.
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and heterogeneity of HCC is the major barrier in improving patient outcome. To stratify HCC patients with different degrees of malignancy and provide precise treatment strategies, we reconstructed the tumor evolution trajectory with the help of scRNA-seq data and established a 30-gene prognostic model to identify the malignant state in HCC. Patients were divided into high-risk and low-risk groups. C-index and receiver operating characteristic (ROC) curve confirmed the excellent predictive value of this model. Downstream analysis revealed the underlying molecular and functional characteristics of this model, including significantly higher genomic instability and stronger proliferation/progression potential in the high-risk group. In summary, we established a novel prognostic model to overcome the barriers caused by HCC heterogeneity and provide the possibility of better clinical management for HCC patients to improve their survival outcomes.
肝细胞癌(HCC)是全球癌症相关死亡的主要原因之一,而HCC的异质性是改善患者预后的主要障碍。为了对不同恶性程度的HCC患者进行分层并提供精确的治疗策略,我们借助单细胞RNA测序(scRNA-seq)数据重建了肿瘤进化轨迹,并建立了一个30基因的预后模型来识别HCC中的恶性状态。患者被分为高风险组和低风险组。一致性指数(C-index)和受试者工作特征(ROC)曲线证实了该模型具有出色的预测价值。下游分析揭示了该模型潜在的分子和功能特征,包括高风险组中显著更高的基因组不稳定性和更强的增殖/进展潜力。总之,我们建立了一种新型的预后模型,以克服由HCC异质性造成的障碍,并为HCC患者提供更好的临床管理可能性,从而改善他们的生存结局。