Li Yuyao, Li Er, Zheng Wenlan, Shi Jia, Yu Shihan, Zhang Xuemei, Zheng Liming, Du Wurong, Liu Hao, Feng Hai, Guo Jianfeng, Yu Zhuo
Department of Liver Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, People's Republic of China.
Institute of Infectious Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, People's Republic of China.
J Hepatocell Carcinoma. 2025 Sep 3;12:2017-2034. doi: 10.2147/JHC.S533398. eCollection 2025.
Anoikis is an anchorage-dependent programmed cell death implicated in multiple pathological processes of cancers; however, the prognostic value of anoikis-related genes (ANRGs) in hepatocellular carcinoma (HCC) remains unclear. Our study aims to develop an ANRGs-based prediction model to improve prognostic assessment in HCC patients.
The RNA-seq profile was performed to estimate the expression of ANRGs in HCC patients. The univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were applied in the model construction to predict the prognosis in terms of differentially expressed ANRGs in the Cancer Genome Atlas (TCGA) training cohort. The TCGA test cohort and the International Cancer Genome Consortium (ICGC)-originated cohort were set to verify the predictive capacity. Nomogram was built on the basis of risk score (RS), gender, age, grade, and T_stage, with the hope of extending the predictive ability of ANRGs to evaluate the HCC prognosis. The expression of differentially expressed ANRGs was assessed in HCC cell lines and orthotopic tumor-bearing mice.
Six ANRGs ( and ) demonstrated the critical prognostic significance in HCC patients. The prognostic model on the basis of these ANRGs was capable of properly predicting 1-, 3-, and 5-year survivals. The Kaplan-Meier results displayed the increased death and decreased survival in the high-risk group. The acted as the independent factor for the survival evaluation. Our nomogram model was able to directly reflect the survival probabilities of each patient, which was confirmed through various validations. The transcription and translation of six ANRGs were significantly enhanced in HCC cell lines and tumor tissues.
Despite the lack of mechanistic validation, our anoikis-linked model serves as a promising tool for predicting HCC prognosis in clinical practice, and provides valuable insights into the decision of individualized therapeutic approaches.
失巢凋亡是一种锚定依赖性程序性细胞死亡，与癌症的多种病理过程相关；然而，失巢凋亡相关基因（ANRGs）在肝细胞癌（HCC）中的预后价值仍不清楚。我们的研究旨在开发一种基于ANRGs的预测模型，以改善对HCC患者的预后评估。
进行RNA测序分析以评估HCC患者中ANRGs的表达。单变量Cox回归和最小绝对收缩和选择算子（LASSO）Cox回归分析应用于模型构建，以根据癌症基因组图谱（TCGA）训练队列中差异表达的ANRGs预测预后。设置TCGA测试队列和国际癌症基因组联盟（ICGC）来源的队列以验证预测能力。基于风险评分（RS）、性别、年龄、分级和T分期构建列线图，希望扩展ANRGs的预测能力以评估HCC预后。在HCC细胞系和原位荷瘤小鼠中评估差异表达的ANRGs的表达。
六个ANRGs（……此处原文缺失六个基因具体信息……）在HCC患者中显示出关键的预后意义。基于这些ANRGs的预后模型能够准确预测1年、3年和5年生存率。Kaplan-Meier结果显示高危组死亡增加、生存减少。……此处原文缺失具体因素信息……作为生存评估的独立因素。我们的列线图模型能够直接反映每位患者的生存概率，这通过各种验证得到证实。六个ANRGs的转录和翻译在HCC细胞系和肿瘤组织中显著增强。
尽管缺乏机制验证，但我们的失巢凋亡相关模型是临床实践中预测HCC预后的有前景的工具，并为个体化治疗方法的决策提供了有价值的见解。
