综合生物信息学和机器学习分析将 VCAN 确定为乙型肝炎病毒

StephenW

综合生物信息学和机器学习分析将 VCAN 确定为乙型肝炎病毒相关肝纤维化的新型生物标志物
梦琴园 1 , 雪虎 1 , 李超瑶 1 , 刘平基 1 , 姜英干 1 , 李兰娟 1 2
隶属关系
隶属关系

    1
    【作者单位】： 武汉大学人民医院感染科;
    2
    浙江大学医学院附属第一医院传染病诊疗国家重点实验室，国家传染病临床研究中心，传染病诊疗协同创新中心，浙江杭州。

    PMID：36275632 PMCID：PMC9585216 DOI：10.3389/fmolb.2022.1010160

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抽象的

乙型肝炎病毒 (HBV) 感染仍然是全球肝纤维化 (LF) 的主要原因，尤其是在中国。需要确定 HBV 相关肝纤维化 (HBV-LF) 的决定性诊断生物标志物，以防止慢性乙型肝炎 (CHB) 进展为肝癌并更有效地选择最佳治疗策略。我们从没有 LF 的 CHB 患者中获得了 43 个样本，从有 LF 的 CHB 患者中获得了 81 个样本（GSE84044 数据集）。其中，鉴定出173个差异表达基因（DEGs）。功能分析表明，这些 DEG 主要参与免疫、细胞外基质和代谢相关的过程。随后，我们整合了四种算法（LASSO 回归、SVM-RFE、RF 和 WGCNA）来确定 HBV-LF 的诊断生物标志物。这些分析和接收操作特征曲线确定了 2C 型磷脂酸磷酸酶 (PPAP2C) 和 versican (VCAN) 基因作为 HBV-LF 潜在有价值的诊断生物标志物。单样本基因集富集分析 (ssGSEA) 进一步证实了 HBV-LF 的免疫情况。这两种诊断生物标志物也与浸润的免疫细胞显着相关。还分析了HBV-LF发生和发展的潜在VCAN调控机制。这些集体发现暗示 VCAN 作为 HBV-LF 的新型诊断生物标志物，免疫细胞的浸润可能对 HBV-LF 的发生和发展起关键作用。

关键词：生物信息学分析；诊断生物标志物；乙型肝炎病毒相关的肝纤维化；免疫细胞浸润；机器学习策略。

版权所有 © 2022 袁、胡、姚、刘、江、李。

StephenW

Comprehensive bioinformatics and machine learning analysis identify VCAN as a novel biomarker of hepatitis B virus-related liver fibrosis
Mengqin Yuan  1 , Xue Hu  1 , Lichao Yao  1 , Pingji Liu  1 , Yingan Jiang  1 , Lanjuan Li  1   2
Affiliations
Affiliations

    1
    Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
    2
    State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Centre for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

    PMID: 36275632 PMCID: PMC9585216 DOI: 10.3389/fmolb.2022.1010160

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Abstract

Hepatitis B virus (HBV) infection remains the leading cause of liver fibrosis (LF) worldwide, especially in China. Identification of decisive diagnostic biomarkers for HBV-associated liver fibrosis (HBV-LF) is required to prevent chronic hepatitis B (CHB) from progressing to liver cancer and to more effectively select the best treatment strategy. We obtained 43 samples from CHB patients without LF and 81 samples from CHB patients with LF (GSE84044 dataset). Among these, 173 differentially expressed genes (DEGs) were identified. Functional analysis revealed that these DEGs predominantly participated in immune-, extracellular matrix-, and metabolism-related processes. Subsequently, we integrated four algorithms (LASSO regression, SVM-RFE, RF, and WGCNA) to determine diagnostic biomarkers for HBV-LF. These analyses and receive operating characteristic curves identified the genes for phosphatidic acid phosphatase type 2C (PPAP2C) and versican (VCAN) as potentially valuable diagnostic biomarkers for HBV-LF. Single-sample gene set enrichment analysis (ssGSEA) further confirmed the immune landscape of HBV-LF. The two diagnostic biomarkers also significantly correlated with infiltrating immune cells. The potential regulatory mechanisms of VCAN underlying the occurrence and development of HBV-LF were also analyzed. These collective findings implicate VCAN as a novel diagnostic biomarker for HBV-LF, and infiltration of immune cells may critically contribute to the occurrence and development of HBV-LF.

Keywords: bioinformatic analysis; diagnostic biomarker; hepatitis B virus-related liver fibrosis; immune cell infiltration; machine-learning strategies.

Copyright © 2022 Yuan, Hu, Yao, Liu, Jiang and Li.

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https://www.frontiersin.org/arti ... lb.2022.1010160/pdf

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