Department of Clinical Laboratory, Jinhua Municipal Central Hospital, Jinhua, Zhejiang, China.
Department of Ultrasound Medicine, Chongqing University Cancer Hospital, Chongqing, China.
Technol Health Care. 2023;31(4):1429-1449. doi: 10.3233/THC-220738.
Due to the complexity and heterogeneity of hepatocellular carcinoma, the existing clinical staging criterias are insufficient to accurately reflect the tumor microenvironment and predict the prognosis of HCC patients. Aggrephagy, as a type of selective autophagy, is associated with various phenotypes of malignant tumors.
This study aimed to identify and validate a prognostic model based on aggrephagy-related LncRNAs to assess the prognosis and immunotherapeutic response of HCC patients.
Based on the TCGA-LIHC cohort, aggrephagy-related LncRNAs were identified. Univariate Cox regression analysis and lasso and multivariate Cox regression were used to construct a risk-scoring system based on eight ARLs. CIBERSORT, ssGSEA, and other algorithms were used to evaluate and present the immune landscape of tumor microenvironment.
The high-risk group had a worse overall survival (OS) than the low-risk group. Patients in the high-risk group are more likely to benefit from immunotherapy because of their high infiltration level and high immune checkpoint expression.
The ARLs signature is a powerful predictor of prognosis for HCC patients, and the nomogram based on this model can help clinicians accurately determine the prognosis of HCC patients and screen for specific subgroups of patients who are more sensitive to immunotherapy and chemotherapy.
由于肝细胞癌的复杂性和异质性，现有的临床分期标准不足以准确反映肿瘤微环境，预测 HCC 患者的预后。聚集体自噬作为一种选择性自噬，与恶性肿瘤的各种表型有关。
本研究旨在识别和验证基于聚集体自噬相关 LncRNAs 的预后模型，以评估 HCC 患者的预后和免疫治疗反应。
基于 TCGA-LIHC 队列，鉴定了聚集体自噬相关 LncRNAs。采用单因素 Cox 回归分析、lasso 和多因素 Cox 回归分析，基于 8 个 ARLs 构建风险评分系统。采用 CIBERSORT、ssGSEA 等算法评估和呈现肿瘤微环境的免疫景观。
高危组的总生存期（OS）明显差于低危组。高危组患者更有可能从免疫治疗中获益，因为他们的免疫浸润水平更高，免疫检查点表达更高。
ARLs 特征是预测 HCC 患者预后的有力指标，基于该模型的列线图可帮助临床医生准确判断 HCC 患者的预后，并筛选出对免疫治疗和化疗更敏感的特定亚组患者。
