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.

Keywords: MAF BZIP transcription factor G; hepatocellular carcinoma; high mobility group AT-hook protein 1; prognosis; therapeutic response; transcription factor.

FIGURE 1

Identification of prognosis-related TFs in HCC based on the TCGA cohort (A) Venn diagram of differentially expressed TFs between tumor and adjacent normal tissues shared in the TCGA and ICGC cohorts (B) Heatmap of the 25 overlapping TFs in the TCGA cohort (C) Forest plot of the prognosis-related TFs based on univariate Cox regression analysis (D) The correlation network of prognosis-related TFs. TFs, transcription factors; TCGA, The Cancer Genome Atlas; ICGC, International Cancer Genome Consortium.

FIGURE 2

Validated expressions of TFs in the TF signature in patient samples (A) The mRNA levels of HMGA1 and MAFG in tumor and adjacent non-tumorous tissues from our cohort. The immunohistochemical staining (B) and statistical significance (C) of HMGA1 and MAFG in HCC and normal liver tissues from our cohort. TFs, transcription factors.

FIGURE 3

Construction and validation of a 2-TF prognostic signature. The ROC curve, distributions of the risk score and survival status, heatmap of expression profiles, and Kaplan–Meier curve of the 2-TF signature in the training set of TCGA cohort (A), as well as in the validation sets of ICGC cohort (B) and GSE116174 cohort (C). TF, transcription factor; AUC, area under the curve; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; ICGC, International Cancer Genome Consortium.

FIGURE 4

The independent prognostic value of the 2-TF signature. Multivariate Cox regression analysis regarding overall survival in the training set of TCGA cohort (A) and the validation set of ICGC cohort (B). * Portal vein, vein or artery invasion; TCGA, The Cancer Genome Atlas; ICGC, International Cancer Genome Consortium.

FIGURE 5

The relationships between the TF signature and clinical indicators (A) The correlations of risk score with age, gender, grade, TNM stage, vascular invasion and AFP level in the TCGA cohort (B) The correlations of risk score with age, gender, TNM stage, invasion and fibrosis in the ICGC cohort. * Portal vein, vein or artery invasion; TF, transcription factor; AFP, alpha-fetoprotein; TCGA, The Cancer Genome Atlas; ICGC, International Cancer Genome Consortium.

FIGURE 6

Treatment efficacy based on the 2-TF signature (A) The correlation between response to TACE treatment and risk score in GSE104580 (B) The common agents estimated to have different IC₅₀ according to the risk score both in the TCGA and ICGC cohorts (C) The discrepancy of estimated IC₅₀ in the high- and low-risk groups in the TCGA and ICGC cohorts. *Chemotherapeutic agents; TF, transcription factor; IC₅₀: half-maximal inhibitory concentration; TACE: transarterial chemoembolization; TCGA, The Cancer Genome Atlas; ICGC, International Cancer Genome Consortium.

FIGURE 7

Enrichment analysis of DEGs related to the 2-TF signature. The most significant GO terms and KEGG pathways based on the DEGs between the high- and low-risk groups in the TCGA cohort (A) and ICGC cohort (B). DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; TCGA, The Cancer Genome Atlas; ICGC, International Cancer Genome Consortium.

FIGURE 8

Protein-protein interaction (PPI) network based on the DEGs (A) Venn diagram of DEGs between the high- and low-risk groups shared in the TCGA and ICGC cohorts (B) PPI network constructed with the common DEGs and functional modules (C) The top 10 hub genes identified based on the PPI network (D) KEGG pathway enrichment analysis of module one and module 2. DEGs, differentially expressed genes; TCGA, The Cancer Genome Atlas; ICGC, International Cancer Genome Consortium.