Huang Yuan, Li Shi-Rong, Gao Ying-Jie, Zhu Yan-Hua, Zhang Xiao-Feng
Department of Biochemistry and Molecular Biology, School of Bioscience and Technology, Chengdu Medical College, Chengdu, Sichuan, China.
Laboratory of Animal Tumor Models, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
J Oncol. 2023 Feb 21;2023:6114976. doi: 10.1155/2023/6114976. eCollection 2023.
Current research studies have suggested that glucose deprivation (GD)-based tumor microenvironment (TME) can promote epithelial-mesenchymal transition (EMT) of tumor cells, leading to tumor invasion and metastasis. However, no one has yet studied detailedly the synthetic studies that include GD features in TME with EMT status. In our research, we comprehensively developed and validated a robust signature regarding GD and EMT status to provide prognostic value for patients with liver cancer.
GD and EMT status were estimated with transcriptomic profiles based on WGCNA and t-SNE algorithms. Two cohorts of training (TCGA_LIHC) and validation (GSE76427) datasets were analyzed with the Cox regression and logistic regression analyses. We identified a 2-mRNA signature to establish a GD-EMT-based gene risk model for the prediction of HCC relapse.
Patients with significant GD-EMT status were divided into two subgroups: GD/EMT and GD/EMT, with the latter having significantly worse recurrence-free survival ( < 0.01). We employed the least absolute shrinkage and selection operator (LASSO) technique as a method for HNF4A and SLC2A4 filtering and constructing a risk score for risk stratification. In the multivariate analysis, this risk score predicted recurrence-free survival (RFS) in both the discovery and validation cohorts and remained valid in patients stratified by TNM stage and age at diagnosis. The nomogram that combines risk score and TNM stage as well as age produces improved performance and net benefits in the analysis of calibration and decision curves in training and validation groups.
The GD-EMT-based signature predictive model may provide a prognosis classifier for HCC patients with a high risk of postoperative recurrence to decrease the relapse rate.
目前的研究表明，基于葡萄糖剥夺（GD）的肿瘤微环境（TME）可促进肿瘤细胞的上皮-间质转化（EMT），从而导致肿瘤侵袭和转移。然而，尚未有人详细研究过包含TME中GD特征与EMT状态的综合研究。在我们的研究中，我们全面开发并验证了一个关于GD和EMT状态的强大特征，以为肝癌患者提供预后价值。
基于WGCNA和t-SNE算法，利用转录组图谱评估GD和EMT状态。通过Cox回归和逻辑回归分析对两个训练（TCGA_LIHC）和验证（GSE76427）数据集队列进行分析。我们鉴定出一个双信使核糖核酸（mRNA）特征，以建立基于GD-EMT的基因风险模型来预测肝癌复发。
具有显著GD-EMT状态的患者被分为两个亚组：GD/EMT和GD/EMT，后者的无复发生存期明显更差（<0.01）。我们采用最小绝对收缩和选择算子（LASSO）技术作为筛选肝细胞核因子4A（HNF4A）和溶质载体家族2成员4（SLC2A4）并构建风险分层风险评分的方法。在多变量分析中，该风险评分在发现和验证队列中均能预测无复发生存期（RFS），并且在按TNM分期和诊断时年龄分层的患者中仍然有效。结合风险评分、TNM分期以及年龄的列线图在训练组和验证组的校准分析和决策曲线分析中表现出更好的性能和净效益。
基于GD-EMT的特征预测模型可能为术后复发风险高的肝癌患者提供预后分类器，以降低复发率。
