Novel Gene Signatures Promote Epithelial-Mesenchymal Transition (EMT) in Glucose Deprivation-Based Microenvironment to Predict Recurrence-Free Survival in Hepatocellular Carcinoma

Yuan Huang; Shi-Rong Li; Ying-Jie Gao; Yan-Hua Zhu; Xiao-Feng Zhang

doi:10.1155/2023/6114976

Novel Gene Signatures Promote Epithelial-Mesenchymal Transition (EMT) in Glucose Deprivation-Based Microenvironment to Predict Recurrence-Free Survival in Hepatocellular Carcinoma

J Oncol. 2023 Feb 21:2023:6114976. doi: 10.1155/2023/6114976. eCollection 2023.

Authors

Yuan Huang¹, Shi-Rong Li², Ying-Jie Gao¹, Yan-Hua Zhu¹, Xiao-Feng Zhang¹

Affiliations

¹ Department of Biochemistry and Molecular Biology, School of Bioscience and Technology, Chengdu Medical College, Chengdu, Sichuan, China.
² 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.

Abstract

Background: 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.

Methods: 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.

Results: Patients with significant GD-EMT status were divided into two subgroups: GD^low/EMT^low and GD^high/EMT^high, with the latter having significantly worse recurrence-free survival (P < 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.

Conclusions: 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.