Dataset Information

Nine hub genes related to the prognosis of HBV-positive hepatocellular carcinoma identified by protein interaction analysis.

ABSTRACT: BackgroundHepatocellular carcinoma (HCC) represents the second highest cause of cancer-associated deaths worldwide, and hepatitis B virus (HBV) infection is a major risk factor. Here, we aimed to identify genetic signatures of HBV-positive (HBV+) HCC and uncover potential carcinogenic mechanisms.MethodsGene expression profiles of 124 HBV-positive samples, including tumor and non-tumor tissues were subjected to bioinformatics analysis. The expression levels of thymidylate synthase (TYMS) and CDC45 in patients’ samples were validated by immunohistochemistry (IHC) and their association with patient survival was assessed by the Kaplan-Meier method.ResultsA total of 666 differentially expressed genes (DEGs) were identified. The 137 upregulated genes were mainly enriched in the cell cycle, P53 signaling pathway, and extracellular matrix-receptor interaction, whereas the 529 downregulated genes were enriched in cytochrome P450 xenobiotic and drug metabolism, and cytokine-cytokine receptor interaction. A total of 15 hub genes were identified from the protein-protein interaction (PPI) network and 10 of them were strongly associated with HBV+ HCC. The expression of 9 hub genes (CDK1, NDC80, TYMS, AURKA, FOXM1, CDC45, ZWINT, PBK, and TPX2) was associated with poor overall survival. Validation of TYMS and CDC45 protein expression levels in clinical samples by IHC showed that they were higher in HBV+ HCC than in HBV- HCC or normal tissue and were associated with poor patient survival.ConclusionsHBV may induce HCC through regulation of host gene expression. Among the hub DEGs identified, 9 key genes could be used as new prognostic biomarkers and treatment targets for HBV+ HCC.

SUBMITTER: Xie W

PROVIDER: S-EPMC7210132 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Currently, overcoming the drug resistance in HCC is a critical challenge and ferroptosis has emerged as a promising therapeutic option for cancer. We aim to construct a new gene signature related to ferroptosis and drug resistance to predict the prognosis in HCC. The RNA-seq data of HCC patients was obtained from the Cancer Genome Atlas database. Using least absolute shrinkage and selection operator cox regression, Kaplan-Meier analysis, and differential analysis, we constructed a prognostic model consisting of six hub genes (TOP2A, BIRC5, VEGFA, HIF1A, FTH1, ACSL3) related to ferroptosis and drug resistance in HCC. Functional enrichment, pathway enrichment and GSEA analysis were performed to investigate the potential molecular mechanism, and construction of PPI, mRNA-miRNA, mRNA-RBP, mRNA-TF and mRNA-drugs interaction networks to predict its interaction with different molecules. Clinical prognostic characteristics were revealed by univariate, multivariate cox regression analysis and nomogram. We also analyzed the relationship between the signature, immune checkpoints, and drug sensitivity. The expression of the gene signature was detected in HCC cell lines and HPA database. Our prognostic model classified patients into high and low-risk groups based on the risk scores and found the expression level of the genes was higher in the high-risk group than the low-risk group, demonstrating that high expression of the hub genes was associated with poor prognosis in HCC. ROC analysis revealed its high diagnostic efficacy in both HCC and normal tissues. The proportional hazards model and calibration analysis confirmed that the model's prediction was most accurate for 1- and 3-years survival. QRT-PCR showed the high expression level of the gene signature in HCC. Our study built a novel gene signature with good potential to predict the prognosis of HCC, which may provide new therapeutic targets and molecular mechanism for HCC diagnosis and treatment.

Dataset Information

Nine hub genes related to the prognosis of HBV-positive hepatocellular carcinoma identified by protein interaction analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets