Comprehensive network analysis of the molecular mechanisms associated with sorafenib resistance in hepatocellular carcinoma

Objective: Hepatocellular carcinoma (HCC) is an intractable disease because patients with HCC frequently develop sorafenib resistance after long-term chemotherapy. Although studies has demonstrated the availability of cumulative information on drug-resistant patients, little is known about the strategies and molecular mechanisms to reverse sorafenib resistance. Here, the present study identified critical mRNAs and transcription factors (TFs) associated with sorafenib resistance of HCC and evaluated the significance correlation between drug-resistant genes and TFs in comprehensive network for HCC xenografts mice. Methods: The expression profiles of mRNAs were compared between sorafenib-acquired resistant tissue and sorafenib sensitive tissue utilizing RNA-Seq data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Gene Ontology and KEGG pathway analysis were performed to investigate the biological function of significantly dysregulated mRNA. Furthermore, the Kaplan-Meier survival analyses were performed to evaluate the effect of mRNA on over survival. Subsequently, TFs were predicted using TRANSFAC and TF-mRNA regulatory networks were visualized using cytoscape software. Results: A total of 827 mRNAs were found to be differentially expressed in sorafenib-acquired resistant tissue compared with control. Thereafter, the results of functional enrichment analysis showed the dysregulated mRNAs involved in drug-resistant signaling pathway, including MAPK, JAK-STAT, TGF-beta and drug-metabolism cytochrome P450 signaling pathway. CDK1, CDKN1A and TAPBP might serve as prognostic signature of resistance of HCC to sorafenib according to the survival analysis. Furthermore, TF-mRNA networks were constructed. There were 18 TFs were predicted to regulate differentially expressed mRNAs, which play an essential role in the regulation of dysfunctional gene networks. NFKB1 was presented in the TF-mRNA networks as the node with the highest degree and MYC was predicted as prognostic TF in drug resistance of HCC Conclusions: Taken together, our findings showed that novel mRNAs and TFs, which served as critical biomarkers to predict survival and therapeutic targets of resistance to sorafenib in HCC. Furthermore, we constructed the TF-mRNA networks, which provides valuable theoretical references to further evaluate the molecular mechanisms of resistance to sorafenib in HCC. (C) 2020 Published by Elsevier Inc.