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

Abstract

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 independently predicted OS for HCC patients both in the training cohort of TCGA-LIHC dataset (HR = 2.498, p = 0.007) and in the testing cohort of ICGC-LIRI-JP dataset (HR = 5.411, p < 0.001). The risk score was also positively correlated to progressive characteristics regarding tumor grade, TNM stage and tumor invasion. Patients with a high-risk score were more resistant to transarterial chemoembolization (TACE) treatment and agents of lapatinib and erlotinib, but sensitive to chemotherapeutics. Further enrichment and PPI analyses demonstrated that the 2-TF signature distinguished tumors into 2 clusters with proliferative and metabolic features, with the hub genes belonging to the former cluster.Conclusion: Our study identified a 2-TF prognostic signature that indicated tumor heterogeneity with different clinical features and treatment preference, which help optimal therapeutic strategy and improved survival for HCC patients.

Transcription factors-related molecular subtypes and risk prognostic model: exploring the immunogenicity landscape and potential drug targets in hepatocellular carcinoma

“…Previous studies have identified TF-related gene signatures and risk score models in colorectal cancer, glioblastoma, breast cancer, endometrial cancer, ovarian cancer and gastric cancer, suggesting that TF-based signatures can predict prognosis and therapeutic efficacy for cancer patients [ 7 – 14 ]. In addition, a 2-TF prognostic signature with different clinical features and treatment preferences was constructed in HCC [ 15 ]. In the present study, we constructed a 9-TF prognostic signature and explored its potential to guide clinical treatment using completely different analytical strategies.…”

“…In the present study, we constructed a 9-TF prognostic signature and explored its potential to guide clinical treatment using completely different analytical strategies. The AUC values of the 2-TF prognostic signature were 0.59–0.74 for 1-, 2- and 3-year OS in different databases according to Yang’s study [ 15 ]. In contrast, the AUC values of the 9-TF prognostic signature in our study were 0.695–0.826 for 1-, 2- and 3-year OS, indicating that the 9-TF prognostic signature we established may have better performance in predicting OS in HCC patients.…”

“…Additionally, Yang et al did not investigate the relationship between TF-based signature and immune cell infiltration in HCC [ 15 ]. Here, we explored the potential associations between TFs and immune infiltration and immunotherapy.…”

“…Moreover, the prognostic potential of TFs and their effects on the tumor microenvironment and chemotherapy/immunotherapy in HCC need further exploration. Recently, Yang et al identified a 2-TF signature and constructed a prognostic model for HCC patients, but the relationship between TF-based signature and the tumor immune microenvironment remains unclear [ 15 ]. The aim of this study was to develop a TF-related prognostic risk model to predict the prognosis, uncover the effect of TF-related signature on immune cell infiltration, and guide the personalized treatment of HCC patients, including chemotherapy and immunotherapy.…”

“…We further developed a risk score model containing 9 genes for HCC patients based on the expression of TFs to explore the prognostic potential of TFs in HCC through Cox regression analysis. It is worth mentioning that our established 9-TF signature (1-year AUC = 0.792, 2-year AUC = 0.71 and 3-year AUC = 0.695) showed improved predictive capability for the survival rate of HCC patients compared to the previously identified 2-TF signature (1-year AUC = 0.73, 2-year AUC = 0.60 and 3-year AUC = 0.61) based on the TCGA database [ 15 ]. More importantly, we investigated the interaction between trametinib and core TFs by molecular docking.…”