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pmid:33859718
Transcriptome Classification Reveals Molecular Subgroups in Patients with Hepatitis B Virus.
Conghui Zhang · Jie Li · Lan Yang · Fengxia Xu · Huiyuan She · Xinghui Liu
Computational and mathematical methods in medicine · 2021 doi:10.1155/2021/5543747
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Experiment
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Data
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Analysis
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Execution
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Complete file
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Multiple studies indicated host genetic, virological, and immunological factors could affect the HBV infection. However, the underlying mechanism involved in HBV infection remained unclear. Based on the analysis of gene expression data of 124 HBV patients (GEO accession: GSE84044), molecular subgroups of patients infected with hepatitis B virus were identified in this study, including C1, C2, and C3 groups. The age, fiber, degree of chemical and inflammation, and gene expression difference were also compared among the three sampling groups. Furthermore, the liver index was calculated using 93 liver-specific genes. The liver-specific gene expression in different molecular subgroups of HBV patients was thoroughly analyzed and then was compared with fibrosis and inflammation levels. Results showed that the C2 group was the youngest and the C3 group had the highest degree of fibrosis and inflammation. Enrichment analysis showed that metabolism-related pathways were mainly expressed in the C1 and C2 groups, and inflammation-related pathways and proteoglycans in cancer were highly expressed in the C1 and C3 groups. The liver index was higher in the C2 group than in the C1 and C3 groups, and it was the lowest in the C3 group. Macrophage M1/M2 and neutrophils were significantly different in the three groups. M1 was mainly abundant in the C3 group, and M2 and neutrophils were mainly abundant in the C2 group. This study provides novel information to understand the mechanisms of HBV infection in chronic hepatitis B (CHB) patients.", "claims": [{"id": "claim-data-access", "statement": "Previously reported gene expression and clinical data were used to support this study and are available at Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/gds). 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