Novel Gene Signatures as Prognostic Biomarkers for Predicting the Recurrence of Hepatocellular Carcinoma

Abstract

Hepatocellular carcinoma (HCC) has a high rate of cancer recurrence (up to 70%) in patients who undergo surgical resection. We investigated prognostic gene signatures for predicting HCC recurrence using in silico gene expression analysis. Recurrence-associated gene candidates were chosen by a comparative analysis of gene expression profiles from two independent whole-transcriptome datasets in patients with HCC who underwent surgical resection. Five promising candidate genes, CETN2, HMGA1, MPZL1, RACGAP1, and SNRPB were identified, and the expression of these genes was evaluated using quantitative reverse transcription PCR in the validation set (n = 57). The genes CETN2, HMGA1, RACGAP1, and SNRPB, but not MPZL1, were upregulated in patients with recurrent HCC. In addition, the combination of HMGA1 and MPZL1 demonstrated the best area under the curve (0.807, 95% confidence interval [CI] = 0.681–0.899) for predicting HCC recurrence. In terms of clinicopathological correlation, CETN2, MPZL1, RACGAP1, and SNRPB were upregulated in patients with microvascular invasion, and the expression of MPZL1 and SNRPB was increased in proportion to the Edmonson tumor differentiation grade. Additionally, overexpression of CETN2, HMGA1, and RACGAP1 correlated with poor overall survival (OS) and disease-free survival (DFS) in the validation set. Finally, Cox regression analysis showed that the expression of serum alpha-fetoprotein and RACGAP1 significantly affected OS, whereas platelet count, microvascular invasion, and HMGA1 expression significantly affected DFS. In conclusion, HMGA1 and RACGAP1 may be potential prognostic biomarkers for predicting the recurrence of HCC after surgical resection.

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

“…Additionally, a high score of the HMGA1-included risk model could predict an advanced tumor grade, TNM stage, tumor invasion, and dismal clinical outcome for HCC patients. There is another HMGA1-involved signature been proved perform well in distinguishing HCC patients at a high risk of tumor recurrence ( Son et al, 2022 ). In gastric cancer, HMGA1 overexpression enhances the migration and invasion abilities and promotes epithelial-to-mesenchymal transition of cancer cells ( Ouyang et al, 2022 ).…”

“…On the contrary, functional silencing of MAFG could repress the liver homing of tumor cells in colorectal cancer, indicating its promoting roles in metastasis and liver colonization in colorectal cancer ( Torres et al, 2018 ). There were contradictory views regarding the contributions of age, gender and cirrhosis to patient survival in HCC, excepting AFP which was an unfavorable factor ( Kitisin et al, 2011 ; Polychronidis et al, 2022 ; Son et al, 2022 ). The current study found no relationship of the HMGA1-MAFG signature with age, gender, fibrosis and AFP in HCC patients.…”

Identification of differentially methylated regions associated with both liver fibrosis and hepatocellular carcinoma

“…We investigated the transcriptional levels of the genes in the 12 DMRs and determined whether these DMRs affect transcriptional levels in fibrotic livers and HCC. We used the transcriptional levels from the RNA sequencing datasets GSE114564, which included 15 normal, 20 chronic hepatitis, 10 cirrhosis, 10 dysplastic nodules, 18 early HCC, and 45 advanced HCC liver samples [ 17 ]. Replication was performed using RNA sequencing data sets, GSE94660 (21 pairs of tumor and non-neoplastic liver tissues of patients with HBV and/or HCV) [ 18 ], and GSE142530 (12 normal, 10 alcoholic hepatitis, and 6 alcoholic cirrhotic liver samples) [ 19 ].…”

“…To confirm whether the 17 identified DMRs were associated with HCC, we examined the methylation levels of normal culture hepatocytes ( n = 17) and established liver cancer cell lines ( n = 8) reported in GSE60753) [ 14 ]. For further evaluation of these DMRs, we investigated the transcriptional levels using the following RNA sequencing datasets: GSE114564 (15 normal, 20 chronic hepatitis, 10 cirrhosis, 10 dysplastic nodules, 18 early HCC, and 45 advanced HCC liver samples) [ 17 ], GSE94660 (21 pairs of tumor and non-neoplastic liver tissues of patients with HBV-HCC) [ 18 ], and GSE142530 (12 normal, 10 alcoholic hepatitis, and 6 alcoholic cirrhotic liver samples) [ 19 ].…”

From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring

“…The evolution of technology has emerged as a pivotal catalyst, propelling HCC research into an era of unprecedented possibilities. Cutting-edge imaging techniques, coupled with advancements in genetic profiling, provide researchers with a comprehensive understanding of the tumor microenvironment[ 17 - 20 ]. These insights, combined with the computational progress of modern data analysis, paved the way for a new generation of predictive models.…”