Identification of human genes potentially involved in the pathogenesis of viral hepatitis C based on multi-network bioinformatics analysis

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Abstract

Aim. The aim of this study was to search for human genes potentially involved in the pathogenesis of hepatitis C by multi-network bioinformatics linkage analysis of proteins involved in the stages of hepatitis C virus (HСV) attachment and entry.

Materials and methods. A number of web applications with complementary algorithms and databases were used to analyze genetic and protein-protein networks. The following genes were used as basic genes: CD81, CLDN1, LDLR, OCLN, SCARB1, the products of which are involved in interaction with viral glycoproteins E1 and E2 at the stage of HCV attachment and penetration into the cell. Data analysis was performed, including a two-stage scoring ranking of the identified candidate genes based on their interaction with basic genes and their presence in the results of network analysis of different web resources.

Results. Candidate genes were initially identified using three web resources: HumanNet – 100 candidate genes, GeneMania – 20, STRING – 98. Based on the intersection of the three web resources, the total number of candidate genes associated with basic genes was 170. The total number of genes with a rank higher than 4 points was 35. Candidate genes were grouped into functional sets: cellular barriers and intercellular contacts (17 genes, 48.6%); lipid metabolism and lipoproteins (9 genes, 25.7%); immune response and interaction with the virus (5 genes, 14.3%); signaling pathways, proteolysis and cytoskeleton (4 genes, 11.4%). The following candidate genes potentially involved in the pathogenesis of HCV have been identified: APOA1, CLDN3, APOE, LIPC, LRPAP1, CSNK1E, APOB, CD19, CLDN6, CLDN9, ESAM, F11R, IFITM1, LDLRAP1, PCSK9, TJP1, CD9, CLDN11, CLDN17, CLDN2, CLDN5, IGSF8, MMP2, PDZK1, ADAM10, APOA2, C3, CLDN12, DAB1, GJB1, ITGB1, MYLIP, NEDD4L, PTGFRN.

Conclusion. In the future, a detailed study of the functional features and polymorphic variants of the identified genes using bioinformatics and laboratory methods can significantly expand current understanding of the involvement of human genes in the development of HCV infection and discover new targets for the development of drugs and therapeutic strategies.

About the authors

Ekaterina V. Anufrieva

St. Petersburg Pasteur Institute

Author for correspondence.
Email: kate.an21@yandex.ru
ORCID iD: 0009-0002-1882-529X

Junior Researcher, Laboratory of Immunology and Virology HIV Infection, Postgraduate Student

Russian Federation, St Petersburg

Yulia V. Ostankova

St. Petersburg Pasteur Institute

Email: shenna1@yandex.ru
ORCID iD: 0000-0003-2270-8897

PhD, Senior Researcher at the Laboratory of Molecular Immunology, Head of the Laboratory of Immunology and Virology HIV Infection

Russian Federation, St Petersburg

Vladimir S. Davydenko

St. Petersburg Pasteur Institute

Email: vladimir_david@mail.ru
ORCID iD: 0000-0003-0078-9681

Junior Researcher, Laboratory of Immunology and Virology HIV Infection, Postgraduate Student

Russian Federation, St Petersburg

Alexandr N. Schemelev

St. Petersburg Pasteur Institute

Email: tvildorm@gmail.com
ORCID iD: 0000-0002-3139-3674

PhD, Junior Researcher, Laboratory of Immunology and Virology HIV Infection, Postgraduate Student

Russian Federation, St Petersburg

Areg A. Totolian

St. Petersburg Pasteur Institute

Email: totolian@pasteurorg.ru
ORCID iD: 0000-0003-4571-8799

Academician of the Russian Academy of Sciences, PhD, MD (Medicine), Professor, Head at the Laboratory of Molecular Immunology, Director, head Department of Immunology, First St. Petersburg State Medical University named after Academician I.P. Pavlov

Russian Federation, St Petersburg

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Supplementary files

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2. Fig. 1. AUROC prediction of the candidate genes involved in the pathogenesis of HCV infection to the level of false positive results of 1%.

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3. Fig. 2. Potential candidate genes involved in the infection and/or development of the HCV infection (HumanNetv3, threshold score > 2.6471).

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4. Fig. 3. Protein-protein interaction network based on the web resource STRING. Connections built based on databases are indicated in blue, while those based on experimental data are shown in pink.

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5. Fig. 4. Comprehensive protein-protein interaction network based on the web resource GeneMANIA. Basic genes are marked with shading. The size of the nodes reflects the number of connections of the specified protein/gene based on all interactions. Physical associations are indicated in red, genetic associations are indicated in green, co-expression is indicated in purple, participation in biological pathways is indicated in blue, predicted interactions are indicated in orange.

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Copyright (c) 2025 Anufrieva E.V., Ostankova Y.V., Davydenko V.S., Schemelev A.N., Totolian A.A.

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