文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期，新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

综合生物信息学分析确定了HIV感染中的关键基因和免疫调节网络。

Integrated bioinformatics analysis identifies hub genes and immune regulatory networks in HIV infection.

作者信息

Pang Xiaoxia, Chen Xinghong, Jing Yuxin, Shi Feng, Chen Xiaoying, Huang Huatuo, Liu Chunhong

机构信息

Center for Medical Laboratory Science, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.

Key Laboratory of Research on Clinical Molecular Diagnosis for High Incidence Diseases in Western Guangxi of Guangxi Higher Education Institutions, Baise, China.

出版信息

Front Immunol. 2025 Jun 12;16:1600713. doi: 10.3389/fimmu.2025.1600713. eCollection 2025.

DOI:10.3389/fimmu.2025.1600713
PMID:40574839
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12197935/
Abstract

INTRODUCTION: Acquired Immune Deficiency Syndrome (AIDS) is a chronic and life-threatening condition caused by the human immunodeficiency virus (HIV), which severely weakens the immune system. Despite advances in treatment, AIDS remains incurable. Understanding the molecular mechanisms underlying AIDS progression is crucial for developing effective therapeutic strategies. Therefore, this study aims to identify hub genes associated with AIDS susceptibility and progression, as well as to elucidate potential molecular mechanisms involved. METHODS: We used the Gene Expression Omnibus (GEO) dataset GSE76246 for this study. Differentially expressed genes (DEGs) were screened, and Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct gene modules associated with HIV infection. Hub genes were identified using the CytoHubba plugin, and their expression profiles were assessed using box plots. The diagnostic potential of these genes was evaluated using receiver operating characteristic (ROC) analysis. Functional enrichment and Gene Set Enrichment Analysis (GSEA) were conducted to identify key biological pathways. Additionally, we analyzed immune cell infiltration and constructed drug-gene interaction, miRNA and transcription factor (TF) regulatory networks. RESULTS: 101 intersection genes were identified by combining DEGs, Oxidative stress genes and module genes from WGCNA. Functional enrichment analysis highlighted key pathways, including oxidative stress response and apoptotic signaling. A protein-protein interaction (PPI) network analysis identified 10 hub genes (TP53, AKT1, JUN, CTNNB1, PXDN, MAPK3, FOS, MMP9, FOXO1, STAT1), which showed strong diagnostic potential, as evidenced by ROC curve analysis. Immune infiltration analysis revealed significant associations between hub genes and various immune cell populations. Furthermore, drug-gene interaction analysis predicted several potential therapeutic compounds. Additionally, miRNA and TF regulatory networks were constructed, identifying critical regulatory elements influencing the expression of hub genes. CONCLUSION: This study identified ten hub genes (TP53, AKT1, JUN, CTNNB1, PXDN, MAPK3, FOS, MMP9, FOXO1, STAT1) that play crucial roles in HIV infection and progression. These genes serve as potential biomarkers for HIV diagnosis and therapeutic targets.

摘要

引言：获得性免疫缺陷综合征（AIDS）是一种由人类免疫缺陷病毒（HIV）引起的慢性且危及生命的疾病，它会严重削弱免疫系统。尽管治疗取得了进展，但艾滋病仍然无法治愈。了解艾滋病进展背后的分子机制对于制定有效的治疗策略至关重要。因此，本研究旨在确定与艾滋病易感性和进展相关的枢纽基因，并阐明其中涉及的潜在分子机制。 方法：我们在本研究中使用了基因表达综合数据库（GEO）中的GSE76246数据集。筛选差异表达基因（DEGs），并采用加权基因共表达网络分析（WGCNA）构建与HIV感染相关的基因模块。使用CytoHubba插件识别枢纽基因，并使用箱线图评估其表达谱。使用受试者工作特征（ROC）分析评估这些基因的诊断潜力。进行功能富集和基因集富集分析（GSEA）以确定关键生物学途径。此外，我们分析了免疫细胞浸润并构建了药物 - 基因相互作用、miRNA和转录因子（TF）调控网络。 结果：通过整合DEGs、氧化应激基因和来自WGCNA的模块基因，确定了101个交集基因。功能富集分析突出了关键途径，包括氧化应激反应和凋亡信号传导。蛋白质 - 蛋白质相互作用（PPI）网络分析确定了10个枢纽基因（TP53、AKT1、JUN、CTNNB1、PXDN、MAPK3、FOS、MMP9、FOXO1、STAT1），ROC曲线分析表明它们具有很强的诊断潜力。免疫浸润分析揭示了枢纽基因与各种免疫细胞群体之间的显著关联。此外，药物 - 基因相互作用分析预测了几种潜在的治疗化合物。此外，构建了miRNA和TF调控网络，确定了影响枢纽基因表达的关键调控元件。 结论：本研究确定了十个在HIV感染和进展中起关键作用的枢纽基因（TP53、AKT1、JUN、CTNNB1、PXDN、MAPK3、FOS、MMP9、FOXO1、STAT1）。这些基因可作为HIV诊断的潜在生物标志物和治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/f356b108e030/fimmu-16-1600713-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/cb63d6517a39/fimmu-16-1600713-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/8b74764b55c8/fimmu-16-1600713-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/bd98d70a9791/fimmu-16-1600713-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/39e113274927/fimmu-16-1600713-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/f249341817b8/fimmu-16-1600713-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/4c9fd05904bb/fimmu-16-1600713-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/ca9adbf903ed/fimmu-16-1600713-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/51900335f84b/fimmu-16-1600713-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/46ba90fa00d7/fimmu-16-1600713-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/475a8c87962e/fimmu-16-1600713-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/8f84c21c5c71/fimmu-16-1600713-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/5807a8f3f2a0/fimmu-16-1600713-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/43b1062f83a1/fimmu-16-1600713-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/f356b108e030/fimmu-16-1600713-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/cb63d6517a39/fimmu-16-1600713-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/8b74764b55c8/fimmu-16-1600713-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/bd98d70a9791/fimmu-16-1600713-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/39e113274927/fimmu-16-1600713-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/f249341817b8/fimmu-16-1600713-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/4c9fd05904bb/fimmu-16-1600713-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/ca9adbf903ed/fimmu-16-1600713-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/51900335f84b/fimmu-16-1600713-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/46ba90fa00d7/fimmu-16-1600713-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/475a8c87962e/fimmu-16-1600713-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/8f84c21c5c71/fimmu-16-1600713-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/5807a8f3f2a0/fimmu-16-1600713-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/43b1062f83a1/fimmu-16-1600713-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cadb/12197935/f356b108e030/fimmu-16-1600713-g014.jpg

相似文献

[1]
Integrated bioinformatics analysis identifies hub genes and immune regulatory networks in HIV infection.

Front Immunol. 2025-6-12

[2]
Deciphering Shared Gene Signatures and Immune Infiltration Characteristics Between Gestational Diabetes Mellitus and Preeclampsia by Integrated Bioinformatics Analysis and Machine Learning.

Reprod Sci. 2025-5-15

[3]
The role of senescence-related hub genes correlating with immune infiltration in type A aortic dissection: Novel insights based on bioinformatic analysis.

PLoS One. 2025-6-25

[4]
Exploring the shared molecular mechanisms of primary hypertension and IgA vasculitis through a case report and combining bioinformatics analysis.

Front Immunol. 2025-6-6

[5]
Integrative bioinformatics analysis reveals STAT1, ORC2, and GTF2B as critical biomarkers in lupus nephritis with Monkeypox virus infection.

Sci Rep. 2025-4-19

[6]
Integrating WGCNA and SVM-RFE identifies hub molecular biomarkers driving ischemic stroke progression.

Neurol Res. 2025-7

[7]
Identification of key genes in membranous nephropathy and non-alcoholic fatty liver disease by bioinformatics and machine learning.

Front Immunol. 2025-6-5

[8]
Bioinformatics-Driven Identification of Ferroptosis-Related Gene Signatures Distinguishing Active and Latent Tuberculosis.

Genes (Basel). 2025-6-18

[9]
Integrated analysis of uterine leiomyosarcoma and leiomyoma utilizing TCGA and GEO data: a WGCNA and machine learning approach.

Transl Cancer Res. 2025-5-30

[10]
Uncovering common disease mechanisms and critical biomarkers in Crohn's disease with concurrent psoriasis and exploring potential therapeutic agents.

PLoS One. 2025-6-20

本文引用的文献

[1]
A combination analysis based on bioinformatics tools reveals new signature genes related to maternal obesity and fetal programming.

Front Med (Lausanne). 2024-9-4

[2]
AKT1 and MAPK8: New Targets for Gestational Diabetes Mellitus?

Fetal Pediatr Pathol. 2024

[3]
Expression pattern analysis of the long non-coding RNAs (TINCR, RP11-573D15.8, RP11-156E8.1), and their target genes (AKT1, FOXO1 and MAPK3) in patients with HIV infection, and elite controllers.

Heliyon. 2024-5-10

[4]
c-Jun signaling during initial HSV-1 infection modulates latency to enhance later reactivation in addition to directly promoting the progression to full reactivation.

J Virol. 2024-2-20

[5]
Exploring the biomarkers and potential therapeutic drugs for sepsis via integrated bioinformatic analysis.

BMC Infect Dis. 2024-1-2

[6]
AP-1/c-Fos supports SIV and HIV-1 latency in CD4 T cells infected .

iScience. 2023-9-22

[7]
Transcriptional activation of Jun and Fos members of the AP-1 complex is a conserved signature of immune aging that contributes to inflammaging.

Aging Cell. 2023-4

[8]
Prediction and analysis of osteoarthritis hub genes with bioinformatics.

Ann Transl Med. 2023-1-31

[9]
The Role of in Adaptation and Evolution.

Cells. 2023-2-3

[10]
Identification of 5' upstream sequence involved in HSPBP1 gene transcription and its downregulation during HIV-1 infection.

Virus Res. 2023-1-15

文献AI研究员

20分钟写一篇综述，助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索