Project description:Purpose: Chronic Hepatitis B virus (HBV) infection leads to liver fibrosis which is a major risk factor in Hepatocellular carcinoma (HCC) and an independent risk factor of recurrence after HCC tumor resection. HBV genome can be inserted into human genome, and chronic inflammation may trigger somatic mutations. Several studies characterized HBV integration sites in HCC patients with regard to frequently occurring hotspots. However, how HBV integration and other genomic changes contribute to the risk of tumor recurrence with regard to different degree of liver fibrosis is not clearly understood. In this study, we aim to find potential molecular mechanisms underlying tumor recurrence of HBV-associated HCC (HBV-HCC) with different degree of liver fibrosis. Methods: We performed RNA sequencing of 21 pairs of tumor and non-neoplastic liver tissues of HBV-HCC patients and performed comprehensive genomic analysis of our RNAseq data and public available sequencing data related to HBV-HCC. We developed a robust pipeline for sensitively identifying HBV integration sites based on sequencing data. Simulations with sequencing data showed that our method outperformed existing methods. We also compared SNPs of each sample with SNPs in cancer census database and inferred patient’s pathogenic SNP loads in tumor and non-neoplastic liver tissues. Conclusions: The HBV-integration and pathogenic SNP load patterns for HCC recurrence risk vary depending on liver fibrosis stage, suggesting potentially different tumorigenesis mechanisms for low and high liver fibrosis patients.
Project description:Tumor samples and matching healthy tissue from 23 human hepatocellular carcinoma (HCC) patients and one hepatocellular adenoma patient were collected after surgical resection. Total RNA was harvested and sequenced with a strand-specific single-end RNA-seq protocol.
Project description:To identify critical tumor-secreting factors that may contribute to immunotherapy efficacy against HCC, we subjected tumor samples from 10 HCC patients to whole-transcriptome sequencing, and categorized the patients into two groups according to clinical response to nivolumab. We then performed gene expression profiling analysis using data obtained from RNA-seq of Nivolumab reponders and non-reponsers.
Project description:We report the application of RNA sequencing technology for high-throughput profiling in HCC tissues. In the present study, to explore novel biomarkers of HCC, we firstly explored the expression profiles of mRNAs and miRNAs in three pairs of HCC patients (tumor tissues and non-tumorous tissues) by high-throughput RNA sequencing. A total of 1024 mRNAs were found to be significantly dysregulated (636 up-regulated and 388 down-regulated), 50 miRNAs were found to be significantly dysregulated (27 up-regulated and 23 down-regulated) in the HCC tissues compared with the non-tumorous tissues.Then validae the differentially expressed miRNAs by qRT-PCR in HCC tissues and serum. This study provides a framework for the application of miRNAs to be ideal biomarkers for HCC.
