A novel transcription factor-based signature to predict prognosis and therapeutic response of hepatocellular carcinoma

作者：Yanbing Yang, Xuenian Ye, Haibin Zhang, Zhaowang Lin, Zhaowang Lin, Min Fang, Jian Wang, Yuyan Yu, Xuwen Hua, Hong-Xuan Huang, Weifeng Xu, Ling Liu, Zhan Lin, Zhan Lin · 发表于：Frontiers in Genetics · 年份：2023 · DOI：10.3389/fgene.2022.1068837 · 被引用次数：17 · 研究领域：Ferroptosis and cancer prognosis、Cancer, Lipids, and Metabolism、Cancer-related molecular mechanisms research

Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive malignancies with increasing incidence worldwide. The oncogenic roles of transcription factors (TFs) were increasingly recognized in various cancers. This study aimed to develop a predicting signature based on TFs for the prognosis and treatment of HCC. Methods: Differentially expressed TFs were screened from data in the TCGA-LIHC and ICGC-LIRI-JP cohorts. Univariate and multivariate Cox regression analyses were applied to establish a TF-based prognostic signature. The receiver operating characteristic (ROC) curve was used to assess the predictive efficacy of the signature. Subsequently, correlations of the risk model with clinical features and treatment response in HCC were also analyzed. The TF target genes underwent Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, followed by protein-protein-interaction (PPI) analysis. Results: A total of 25 differentially expressed TFs were screened, 16 of which were related to the prognosis of HCC in the TCGA-LIHC cohort. A 2-TF risk signature, comprising high mobility group AT-hook protein 1 (HMGA1) and MAF BZIP transcription factor G (MAFG), was constructed and validated to negatively related to the overall survival (OS) of HCC. The ROC curve showed good predictive efficiencies of the risk score regarding 1-year, 2-year and 3-year OS (mostly AUC >0.60). Additionally, the risk score independen...