Identifying a Six-Gene Signature Predicting Response to TACE in Hepatocellular Carcinoma by Bioinformatics Analysis. (16th July 2021)

• Record Type: Journal Article
• Authors: Yao, Xin; Yin, Xin; Lu, Wei; Cao, Leitao
• Other Names: Tan Songwen Academic Editor.
• Is Part Of: Journal of nanomaterials. Volume 2021(2021)
• Publication Date: 2021-07-16
• Subjects: Nanostructured materials -- Periodicals; Nanotechnology -- Periodicals; Nanomatériaux; Nanostructured materials; Nanotechnology; Nanostructures; Nanotechnology; Periodicals; Fulltext; Internet Resources; Periodicals; 620.115
• DOI: 10.1155/2021/3249816
• Languages: English
• ISSNs: 1687-4110

Abstract

Abstract : Background and Aim . With regard to patients with intermediate-stage, irresectable hepatocellular carcinoma (HCC), transcatheter arterial chemoembolization (TACE) is the mainstay of treatment. There is an urgent clinical requirement to identify reliable biomarkers to predict the response of HCC patients to TACE treatment. We aimed to identify a gene signature for predicting TACE response in HCC patients based on bioinformatics analysis. Methods . We downloaded the gene expression profile GSE104580 based on 147 tumor samples from 81 responders to TACE and 66 nonresponders from the Gene Expression Omnibus (GEO) database. Then, we randomly divided the 147 tumor samples into a training set (n = 89 ) and a validation set (n = 58 ) and screened differentially expressed genes (DEGs) in the training set. Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to annotate functions of the DEGs. The DEGs were mapped into the STRING website for constructing protein-protein interaction (PPI). The predictive value of the candidate genes by receiver-operating characteristic (ROC) curves was further verified in the validation set. Results . We totally found 158 DEGs (92 upregulated genes and 66 downregulated genes) in the training set. The GO enrichment analysis revealed that DEGs were significantly enriched in metabolic and catabolic processes, such as drug metabolic process, fatty acid metabolic process, and small molecule catabolic process. The KEGG pathway analysis revealed that the DEGs were mainly concentrated in drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, and chemical carcinogenesis. We identified 6 candidate genes (CXCL8, AFP, CYP1A1, MMP9, CYP3A4, and SERPINC1) based on the PPI network of the DEGs, which had high predictive value in HCC response to TACE with an area under the curve (AUC) value of 0.875 and 0.897 for the training set and validation set, respectively. Conclusion . We identified a six-gene signature which might be biomarkers for predicting HCC response to TACE by a comprehensive bioinformatics analysis. However, the actual functions of these genes required verification.