Abstract：
BACKGROUND: Vasculogenic mimicry, a novel neovascularization pattern of aggressive tumors, is associated with poor clinical outcomes.
OBJECTIVE: The aim of this research was to establish a new model, termed VC score, to predict the prognosis, Tumor Microenvironment (TME) components, and immunotherapeutic response in Hepatocellular Carcinoma (HCC).
METHODS: The expression data of the public databases were used to develop the prognostic model. Consensus clustering was performed to confirm the molecular subtypes with ideal clustering efficacy. The high- and low-risk groups were stratified utilizing the VC score. Various methodologies, including survival analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), Tumor Immune Dysfunction and Exclusion scores (TIDE), Immunophenoscore (IPS), and nomogram, were utilized for verification of the model performance and to characterize the immune status of HCC tissues. GSEA was performed to mine functional pathway information.
RESULTS: The survival and immune characteristics varied between the three molecular subtypes. A five-gene signature (TPX2, CDC20, CFHR4, SPP1, and NQO1) was verified to function as an independent predictive factor for the prognosis of patients with HCC. The high-risk group exhibited lower Overall Survival (OS) rates and higher mortality rates in comparison to the low-risk group. Patients in the low-risk group were predicted to benefit from immune checkpoint inhibitor therapy and exhibit increased sensitivity to immunotherapy. Enrichment analysis revealed that signaling pathways linked to the cell cycle and DNA replication processes exhibited enrichment in the high-risk group.
CONCLUSIONS: The VC score holds the potential to establish individualized treatment plans and clinical management strategies for patients with HCC.
OBJECTIVE: The aim of this research was to establish a new model, termed VC score, to predict the prognosis, Tumor Microenvironment (TME) components, and immunotherapeutic response in Hepatocellular Carcinoma (HCC).
METHODS: The expression data of the public databases were used to develop the prognostic model. Consensus clustering was performed to confirm the molecular subtypes with ideal clustering efficacy. The high- and low-risk groups were stratified utilizing the VC score. Various methodologies, including survival analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), Tumor Immune Dysfunction and Exclusion scores (TIDE), Immunophenoscore (IPS), and nomogram, were utilized for verification of the model performance and to characterize the immune status of HCC tissues. GSEA was performed to mine functional pathway information.
RESULTS: The survival and immune characteristics varied between the three molecular subtypes. A five-gene signature (TPX2, CDC20, CFHR4, SPP1, and NQO1) was verified to function as an independent predictive factor for the prognosis of patients with HCC. The high-risk group exhibited lower Overall Survival (OS) rates and higher mortality rates in comparison to the low-risk group. Patients in the low-risk group were predicted to benefit from immune checkpoint inhibitor therapy and exhibit increased sensitivity to immunotherapy. Enrichment analysis revealed that signaling pathways linked to the cell cycle and DNA replication processes exhibited enrichment in the high-risk group.
CONCLUSIONS: The VC score holds the potential to establish individualized treatment plans and clinical management strategies for patients with HCC.
摘要：
背景：血管生成拟态，一种新的侵袭性肿瘤的新生血管形成模式，与不良临床结局相关。
目的：本研究的目的是建立一个新的模型，称为VC分数，预测预后，肿瘤微环境（TME）组件，和肝细胞癌（HCC）的免疫治疗反应。
方法：使用公共数据库的表达数据来建立预后模型。进行一致聚类以确认具有理想聚类功效的分子亚型。利用VC评分对高危组和低危组进行分层。各种方法，包括生存分析，单样本基因集富集分析(ssGSEA)，肿瘤免疫功能障碍和排斥评分（TIDE），免疫表型（IPS），和列线图，用于验证模型性能并表征HCC组织的免疫状态。进行GSEA以挖掘功能通路信息。
结果：三种分子亚型之间的生存和免疫特性不同。五个基因标签（TPX2，CDC20，CFHR4，SPP1和NQO1）被证实是HCC患者预后的独立预测因素。与低危组相比，高危组的总生存率（OS）较低，死亡率较高。低风险组的患者预计将从免疫检查点抑制剂治疗中受益，并表现出对免疫疗法的敏感性增加。富集分析显示，与细胞周期和DNA复制过程相关的信号通路在高风险组中表现出富集。
结论：VC评分具有为HCC患者建立个体化治疗计划和临床管理策略的潜力。
目的：本研究的目的是建立一个新的模型，称为VC分数，预测预后，肿瘤微环境（TME）组件，和肝细胞癌（HCC）的免疫治疗反应。
方法：使用公共数据库的表达数据来建立预后模型。进行一致聚类以确认具有理想聚类功效的分子亚型。利用VC评分对高危组和低危组进行分层。各种方法，包括生存分析，单样本基因集富集分析(ssGSEA)，肿瘤免疫功能障碍和排斥评分（TIDE），免疫表型（IPS），和列线图，用于验证模型性能并表征HCC组织的免疫状态。进行GSEA以挖掘功能通路信息。
结果：三种分子亚型之间的生存和免疫特性不同。五个基因标签（TPX2，CDC20，CFHR4，SPP1和NQO1）被证实是HCC患者预后的独立预测因素。与低危组相比，高危组的总生存率（OS）较低，死亡率较高。低风险组的患者预计将从免疫检查点抑制剂治疗中受益，并表现出对免疫疗法的敏感性增加。富集分析显示，与细胞周期和DNA复制过程相关的信号通路在高风险组中表现出富集。
结论：VC评分具有为HCC患者建立个体化治疗计划和临床管理策略的潜力。
