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Exploring the biological function of immune cell-related genes in human immunodeficiency virus (HIV)-1 infection based on weighted gene co-expression network analysis (WGCNA)
BACKGROUND: Acquired immunodeficiency syndrome (AIDS) is a chronic infectious disease characterized by consistent immune dysfunction. The objective of this study is to determine whether immune cell-related genes can be used as biomarkers for the occurrence of AIDS and potential molecular mechanisms....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9484082/ https://www.ncbi.nlm.nih.gov/pubmed/36123690 http://dx.doi.org/10.1186/s12920-022-01357-y |
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author | Bai, Ruojing Li, Zhen Lv, Shiyun Hua, Wei Dai, Lili Wu, Hao |
author_facet | Bai, Ruojing Li, Zhen Lv, Shiyun Hua, Wei Dai, Lili Wu, Hao |
author_sort | Bai, Ruojing |
collection | PubMed |
description | BACKGROUND: Acquired immunodeficiency syndrome (AIDS) is a chronic infectious disease characterized by consistent immune dysfunction. The objective of this study is to determine whether immune cell-related genes can be used as biomarkers for the occurrence of AIDS and potential molecular mechanisms. METHODS: A weighted gene co-expression network analysis was performed using the GSE6740 dataset from the Gene Expression Synthesis Database to identify the Hub gene, which contained microarray data from HIV-1 positive (HIV-1(+)) and HIV-1 negative (HIV-1(−)) individuals. The HIV-1(+)-related differentially expressed genes were then identified using the limma package. Subsequently, the characteristic immune cell-related genes were identified as diagnostic biomarkers for HIV-1(+) using the random forest model (RF), support vector machine model, and generalized linear model. RESULTS: MEdarkgreen exhibited the strongest correlation with HIV clinical features of any of these modules. As the best model for diagnosing HIV-1(±), RF was used to select four critical immune cell-related genes, namely, ARRB1, DPEP2, LTBP3, and RGCC, and a nomogram model was created to predict the occurrence of HIV-1 infection based on four key immune cell-related genes. Diagnostic genes were shown to be engaged in immune-related pathways, suggesting that immunological molecules, immune cells, and immune pathways all have a role in HIV-1 infection. The CTD database was explored for prospective medications or molecular compounds that might be utilized to treat HIV-1(+) patients. = Moreover, in HIV-1(+) individuals, the ceRNA network revealed that ARRB1, DPEP2, LTBP3, and RGCC could be regulated by lncRNAs through the corresponding miRNAs. Ultimately, RT-PCR results from clinical blood samples demonstrated that the four diagnostic genes were significantly downregulated in HIV-1(+) patients. CONCLUSION: We screened four immune cell-related genes, ARRB1, DPEP2, LTBP3, and RGCC, which may be considered as the diagnostic markers for HIV-1/AIDS. Our findings reveal that immune related genes and pathways involved in HIV-1 pathogenesis were regulated on both genetic and epigenetic levels by constructing a ceRNA network associated with lncRNA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01357-y. |
format | Online Article Text |
id | pubmed-9484082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-94840822022-09-20 Exploring the biological function of immune cell-related genes in human immunodeficiency virus (HIV)-1 infection based on weighted gene co-expression network analysis (WGCNA) Bai, Ruojing Li, Zhen Lv, Shiyun Hua, Wei Dai, Lili Wu, Hao BMC Med Genomics Research BACKGROUND: Acquired immunodeficiency syndrome (AIDS) is a chronic infectious disease characterized by consistent immune dysfunction. The objective of this study is to determine whether immune cell-related genes can be used as biomarkers for the occurrence of AIDS and potential molecular mechanisms. METHODS: A weighted gene co-expression network analysis was performed using the GSE6740 dataset from the Gene Expression Synthesis Database to identify the Hub gene, which contained microarray data from HIV-1 positive (HIV-1(+)) and HIV-1 negative (HIV-1(−)) individuals. The HIV-1(+)-related differentially expressed genes were then identified using the limma package. Subsequently, the characteristic immune cell-related genes were identified as diagnostic biomarkers for HIV-1(+) using the random forest model (RF), support vector machine model, and generalized linear model. RESULTS: MEdarkgreen exhibited the strongest correlation with HIV clinical features of any of these modules. As the best model for diagnosing HIV-1(±), RF was used to select four critical immune cell-related genes, namely, ARRB1, DPEP2, LTBP3, and RGCC, and a nomogram model was created to predict the occurrence of HIV-1 infection based on four key immune cell-related genes. Diagnostic genes were shown to be engaged in immune-related pathways, suggesting that immunological molecules, immune cells, and immune pathways all have a role in HIV-1 infection. The CTD database was explored for prospective medications or molecular compounds that might be utilized to treat HIV-1(+) patients. = Moreover, in HIV-1(+) individuals, the ceRNA network revealed that ARRB1, DPEP2, LTBP3, and RGCC could be regulated by lncRNAs through the corresponding miRNAs. Ultimately, RT-PCR results from clinical blood samples demonstrated that the four diagnostic genes were significantly downregulated in HIV-1(+) patients. CONCLUSION: We screened four immune cell-related genes, ARRB1, DPEP2, LTBP3, and RGCC, which may be considered as the diagnostic markers for HIV-1/AIDS. Our findings reveal that immune related genes and pathways involved in HIV-1 pathogenesis were regulated on both genetic and epigenetic levels by constructing a ceRNA network associated with lncRNA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01357-y. BioMed Central 2022-09-19 /pmc/articles/PMC9484082/ /pubmed/36123690 http://dx.doi.org/10.1186/s12920-022-01357-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Bai, Ruojing Li, Zhen Lv, Shiyun Hua, Wei Dai, Lili Wu, Hao Exploring the biological function of immune cell-related genes in human immunodeficiency virus (HIV)-1 infection based on weighted gene co-expression network analysis (WGCNA) |
title | Exploring the biological function of immune cell-related genes in human immunodeficiency virus (HIV)-1 infection based on weighted gene co-expression network analysis (WGCNA) |
title_full | Exploring the biological function of immune cell-related genes in human immunodeficiency virus (HIV)-1 infection based on weighted gene co-expression network analysis (WGCNA) |
title_fullStr | Exploring the biological function of immune cell-related genes in human immunodeficiency virus (HIV)-1 infection based on weighted gene co-expression network analysis (WGCNA) |
title_full_unstemmed | Exploring the biological function of immune cell-related genes in human immunodeficiency virus (HIV)-1 infection based on weighted gene co-expression network analysis (WGCNA) |
title_short | Exploring the biological function of immune cell-related genes in human immunodeficiency virus (HIV)-1 infection based on weighted gene co-expression network analysis (WGCNA) |
title_sort | exploring the biological function of immune cell-related genes in human immunodeficiency virus (hiv)-1 infection based on weighted gene co-expression network analysis (wgcna) |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9484082/ https://www.ncbi.nlm.nih.gov/pubmed/36123690 http://dx.doi.org/10.1186/s12920-022-01357-y |
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