Abstract
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Despite continuous development of treatment methods, overall survival rate of liver cancer is low. Transcatheter arterial chemoembolization (TACE) is a first-choice treatment for advanced liver cancer. Although it is generally effective, a number of patients do not benefit from it. Therefore, the present study was conducted to assess the response of patients following TACE. RNA-sequencing data and corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Models were constructed using weighted gene co-expression network analysis and least absolute shrinkage and selection operator-Cox regression analysis based on TCGA-LIHC and GSE104580 cohorts. The receiver operating characteristic curve was used for evaluation. Immunoassay, half-maximal inhibitory concentration analysis of risk groups, genomic enrichment analysis and nomogram construction were also performed. The predictive models were validated at the single-cell level using single-cell databases. Finally, the present study examined the expression of TACE refractoriness-related TFs (TRTs) in TACE-resistant and non-resistant cell lines in vitro. A risk categorization approach was created based on screening of four TRTs. The patients were split into high- and low-risk groups. There were significant variations in immune cell infiltration, medication sensitivity and overall survival (OS) between patients in the high-risk and low-risk groups. Multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for OS. In the single-cell gene set, risk score was a good indicator of tumor microenvironment (TME). Reverse transcription-quantitative PCR revealed that three high-risk TRTs were upregulated in TACE-resistant cells. Prognosis and TME status of liver cancer patients following TACE could be assessed using a predictive model based on transcription factor correlation. This predictive model provided a reliable and simplified method to guide the clinical treatment of HCC.