PO.IM03.01 · 免疫学

解析乙型肝炎病毒驱动的肝细胞癌中的circRNA-miRNA-mRNA调控网络

Decoding the circRNA-miRNA-mRNA regulatory network in hepatitis B Virus-driven hepatocellular carcinoma

海报缩略图:解析乙型肝炎病毒驱动的肝细胞癌中的circRNA-miRNA-mRNA调控网络
编号 207 展板 2 时间 4/19 02:00–05:00 区域 Section 10 主讲 Kainat Ahmed, MS
分会场 Virology and Cancer
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作者与单位 Authors & Affiliations

Kainat Ahmed1, Anwaruddin Mohammad2, Nan Chaiyariti3, Danya Sankaranarayanan1, Pankaj Kumar2, Sudhakar Jha1

1Oklahoma State University, Stillwater, OK,2University of Virginia, Charlottesville, OK,3Mahidol University, Bangkok, Thailand

摘要 Abstract

中文摘要
乙型肝炎病毒(HBV)基因组整合到感染患者的宿主染色体中,由于早期诊断困难和预后不良,对HBV相关肝细胞癌(HCC)患者构成威胁。已知circRNA在包括HBV-HCC在内的多种癌症中具有致癌和生物标志物潜力,其通过螯合抑癌miRNA发挥作用,而这些miRNA在游离状态下可沉默致癌mRNA的表达。因此,我们旨在鉴定HBV整合型HCC细胞系中的circRNA-miRNA-mRNA轴,以寻找HBV-HCC患者特异性的预后生物标志物。我们使用RNA-seq从HBV阴性和HBV整合型细胞中鉴定出失调的宿主circRNA和mRNA,随后采用DESeq进行差异基因表达分析,并使用GSEA进行通路分析。circRNA的连接序列通过对扩增产物进行Sanger测序验证。RT-qPCR进一步确认了从最高倍数变化和校正p值中随机选择的9个circRNA的失调。使用mirDB鉴定每个circRNA的miRNA配对。使用同一细胞公开可得的GEO数据库进行miRNA表达验证,并生成累积分布图以评估潜在结合miRNA配对中mRNA的倍数变化。对10个miRNA的ECDF图的mRNA靶点进行GO和KEGG通路分析,并使用STRING cytohubba蛋白质-蛋白质相互作用(PPI)分析鉴定枢纽基因。绘制枢纽基因的生存分析,并使用Cytoscape构建竞争性内源RNA(ceRNA)网络。我们在HBV整合型细胞中鉴定出494个失调的circRNA、311个失调的miRNA和10,419个失调的mRNA。circADGRL2(约25倍)显示出最高的上调,miR-361-5p作为多个circRNA(circADGRL2、circPROX1和circPALS2)的中心节点。miR-361-5p的靶mRNA BDNF被鉴定为HBV-HCC患者中风险比最高者,提示可能存在circADGRL2-miR-361-5p-BDNF轴。miRNA的靶mRNA被发现与多条癌症通路相关,如MAPK和RAS。我们的数据表明,HBV整合重编程了circRNA-miRNA-mRNA轴,导致HBV-HCC患者预后不良。
查看英文原文 English abstract
Integration of the hepatitis B virus (HBV) genome into the host chromosome of infected patients poses a threat to those with HBV-associated hepatocellular carcinoma (HCC) due to challenges in early diagnosis and poor prognosis. CircRNAs are known for their oncogenic and biomarker potential in various cancers, including HBV-HCC, by sequestering tumor suppressive miRNAs, which, when free, can silence the expression of oncogenic mRNAs. Therefore, we aimed to identify the circRNA-miRNA-mRNA axis in HBV-integrated HCC cell lines to find prognostic biomarkers specific to HBV-HCC patients. We identified dysregulated host circRNA and mRNA from HBV-negative and HBV-integrated cells using RNA-seq followed by differential gene expression analysis with DESeq and performed pathway analysis using GSEA. Junctional sequences of the circRNAs were validated by Sanger sequencing of the amplified products. RT-qPCR further confirmed the dysregulation of 9 randomly selected circRNAs chosen from those with the highest fold-change and adjusted p-values. The miRNA partners for each of the circRNA idenfied using mirDB. miRNA expression validation was performed using the publicly available GEO database of same cells and cumulative distribution plots were generated to assess the fold change of mRNAs in potential binding miRNA partners. The mRNA targets for 10 miRNA ECDF plots were subjected to GO and KEGG pathway analysis, and hub genes were identified using STRING cytohubba protein-protein interaction (PPI) analysis. Survival analysis of hub genes was plotted, and a competitive endogenous RNA (ceRNA) network was constructed using Cytoscape.We identified 494 dysregulated circRNAs, 311 dysregulated miRNAs and 10,419 dysregulated mRNA in HBV-integrated cells. circADGRL2 (~25-fold) showed the highest upregulation and miR-361-5p acted as a central node of multiple circRNAs: circADGRL2, circPROX1 and circPALS2. BDNF, a target mRNA of miR-361-5p, was identified as the highest risk ratio in HBV-HCC patients, suggesting a possible circADGRL2-miR-361-5p-BDNF axis. The target mRNAs of miRNAs were found to be associated with several cancer pathways, such as MAPK and RAS. Our data indicate that HBV integration reprograms the circRNA-miRNA-mRNA axis, leading to a poor prognosis for HBV-HCC patients.
利益披露 Disclosure
K. Ahmed, None.. A. Mohammad, None.. N. Chaiyariti, None.. D. Sankaranarayanan, None.. P. Kumar, None.. S. Jha, None.

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