[A bioinformatics analysis of differentially expressed genes associated with liver cancer]

Objective: To investigate differentially expressed genes associated with liver cancer using bioinformatics methods, and to screen out molecular markers for early diagnosis of liver cancer and potential molecular targets for immunotherapy. Methods: The microarray data associated with liver cancer were downloaded from Gene Expression Omnibus. JMP software was used for correlation analysis of GSE datasets, Limma program in R language was used to screen out differentially expressed genes, and the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis were performed for differentially expressed genes. A protein-protein interaction (PPI) network was also established for analysis. An analysis of specific expression associated with liver cancer was performed with reference to RNA-seq transcriptome data for other tumors obtained from TCGA to further identify specific differentially expressed genes in liver cancer, and a survival curve analysis was performed for patients with liver cancer. Results: A total of 92 differentially expressed genes were identified, with 21 upregulated genes and 71 downregulated genes. Through the GO, KEGG, and PPI analyses, RNA-seq data verified that only glypican 3 (GPC3) was upregulated in liver cancer, and MBL2, SDS, SLCO1B3, TDO2, SAA4, and SPP2 were downregulated. Conclusions: GPC3 might act as a target for immunotherapy, and other molecular markers may become molecular markers for early detection of liver cancer and potential targets for immunotherapy.