Pan Junhan, Zhang Cong, Huang Huizhen, Zhu Yanyan, Zhang Yuhao, Wu Shuzhen, Zhao Yan-Ci, Chen Feng
Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Hepatobiliary and Pancreatic Interventional Treatment Center, Division of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
J Hepatocell Carcinoma. 2024 Nov 29;11:2373-2390. doi: 10.2147/JHC.S480358. eCollection 2024.
Macrotrabecular-massive (MTM) and vessels encapsulating tumor clusters (VETC)-hepatocellular carcinoma (HCC) are aggressive histopathological phenotypes with significant prognostic implications. However, the molecular markers associated with MTM-HCC and VETC-HCC and their implications for clinical outcomes and therapeutic strategies remain unclear.
Utilizing the TCGA-LIHC cohort, we employed machine learning techniques to develop a prognostic risk score based on MTM and VETC-related genes. The performance of the risk score was assessed by investigating various aspects including clinical outcomes, biological pathways, treatment responses, drug sensitivities, tumor microenvironment, and molecular subclasses. To validate the risk score, additional data from the ICGC-JP, GSE14520, GSE104580, GSE109211, and an in-house cohort were collected and analyzed.
The machine learning algorithm established a 4-gene-based risk score. High-risk patients had significantly worse prognosis compared to low-risk patients, with the risk score being associated with malignant progression of HCC. Functionally, the high-risk group exhibited enrichment in tumor proliferation pathways. Additionally, patients in the low-risk group exhibited improved response to TACE and sorafenib treatments compared to the high-risk group. In contrast, the high-risk group exhibited reduced sensitivity to immunotherapy and increased sensitivity to paclitaxel. In the in-house cohort, high-risk patients displayed higher rates of early recurrence, along with an increased frequency of elevated alpha-fetoprotein, microvascular invasion, and aggressive MRI features associated with HCC.
This study has successfully developed a risk score based on MTM and VETC-related genes, providing a promising tool for prognosis prediction and personalized treatment strategies in HCC patients.
大小梁-大块状（MTM）和包绕肿瘤细胞簇的血管（VETC）-肝细胞癌（HCC）是具有重要预后意义的侵袭性组织病理学表型。然而，与MTM-HCC和VETC-HCC相关的分子标志物及其对临床结局和治疗策略的影响仍不清楚。
利用TCGA-LIHC队列，我们采用机器学习技术，基于MTM和VETC相关基因开发了一个预后风险评分。通过研究包括临床结局、生物学途径、治疗反应、药物敏感性、肿瘤微环境和分子亚类等各个方面来评估风险评分的性能。为了验证风险评分，收集并分析了来自ICGC-JP、GSE14520、GSE104580、GSE109211的额外数据以及一个内部队列的数据。
机器学习算法建立了一个基于4个基因的风险评分。与低风险患者相比，高风险患者的预后明显更差，风险评分与HCC的恶性进展相关。在功能上，高风险组在肿瘤增殖途径中表现出富集。此外，与高风险组相比，低风险组患者对TACE和索拉非尼治疗的反应有所改善。相反，高风险组对免疫治疗的敏感性降低，对紫杉醇的敏感性增加。在内部队列中，高风险患者早期复发率更高，同时甲胎蛋白升高、微血管侵犯以及与HCC相关的侵袭性MRI特征的频率增加。
本研究成功开发了一种基于MTM和VETC相关基因的风险评分，为HCC患者的预后预测和个性化治疗策略提供了一个有前景的工具。
