Liu Zhiqiang, Qi Yong, Wang Haibo, Zhang Qikun, Wu Zhengsheng, Wu Wenyong
Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Front Genet. 2022 Sep 15;13:1000652. doi: 10.3389/fgene.2022.1000652. eCollection 2022.
Owing to the heterogeneity displayed by hepatocellular carcinoma (HCC) and the complexity of tumor microenvironment (TME), it is noted that the long-term effectiveness of the cancer therapy poses a severe clinical challenge. Hence, it is essential to categorize and alter the treatment intervention decisions for these tumors. "ConsensusClusterPlus" tool was used for developing a secure molecular classification system that was based on the cuproptosis-linked gene expression. Furthermore, all clinical properties, pathway characteristics, genomic changes, and immune characteristics of different cell types involved in the immune pathways were also assessed. Univariate Cox regression and the least absolute shrinkage and selection operator (Lasso) analyses were used for designing the prognostic risk model associated with cuproptosis. Three cuproptosis-linked subtypes (clust1, clust2, and clust3) were detected. Out of these, Clust3 showed the worst prognosis, followed by clust2, while Clust1 showed the best prognosis. Three subtypes had significantly different enrichment in pathways related to Tricarboxylic Acid (TCA) cycle, cell cycle, and cell senescence ( < 0.01). The clust3 subtype with poor prognosis had a low "ImmuneScore" and low immune cell infiltration, and the three subtypes had significant differences in the antigen processing and presentation pathway of the macrophages. Clust1 had a low TIDE score and was sensitive to immunotherapy. Then, according to the prognosis-related genes of cuproptosis, a prognosis risk model related to cuproptosis was constructed, containing seven genes (KIF2C, PTTG1, CENPM, CDC20, CYP2C9, SFN, and CFHR3). "High" group had a higher TIDE score compared to the TIDE score value shown by the "Low" group, which benefited less from immunotherapy, whereas the "High" group patients were more sensitive to the conventional drugs. Finally, the prognosis risk model related to cuproptosis was combined with clinical pathological characteristics to further improve the prognostic model and survival prediction. Three new molecular subgroups based on cuproptosis-linked genes were revealed, and a cuproptosis-related prognostic risk model comprising seven genes was established in this study, which could assist in predicting the prognosis and identifying the patients benefit from immunotherapy.
由于肝细胞癌（HCC）表现出的异质性以及肿瘤微环境（TME）的复杂性，癌症治疗的长期有效性面临严峻的临床挑战。因此，对这些肿瘤的治疗干预决策进行分类和调整至关重要。使用“ConsensusClusterPlus”工具开发了一种基于铜死亡相关基因表达的可靠分子分类系统。此外，还评估了免疫途径中不同细胞类型的所有临床特性、通路特征、基因组变化和免疫特征。采用单因素Cox回归和最小绝对收缩和选择算子（Lasso）分析来设计与铜死亡相关的预后风险模型。检测到三种铜死亡相关亚型（clust1、clust2和clust3）。其中，Clust3预后最差，其次是clust2，而Clust1预后最好。三种亚型在与三羧酸（TCA）循环、细胞周期和细胞衰老相关的通路中富集程度有显著差异（<0.01）。预后较差的clust3亚型“免疫评分”较低，免疫细胞浸润较少，且三种亚型在巨噬细胞的抗原加工和呈递途径上有显著差异。Clust1的TIDE评分较低，对免疫治疗敏感。然后，根据铜死亡的预后相关基因，构建了一个与铜死亡相关的预后风险模型，包含七个基因（KIF2C、PTTG1、CENPM、CDC20、CYP2C9、SFN和CFHR3）。“高”组的TIDE评分高于“低”组，从免疫治疗中获益较少，而“高”组患者对传统药物更敏感。最后，将与铜死亡相关的预后风险模型与临床病理特征相结合，进一步完善预后模型和生存预测。本研究揭示了基于铜死亡相关基因的三个新分子亚组，并建立了一个包含七个基因的铜死亡相关预后风险模型，可有助于预测预后并识别从免疫治疗中获益的患者。
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