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

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.

Keywords: HIV infection; bioinformatics analysis; hub genes; immune; regulatory networks.

Figure 1

Flowchart of bioinformatic analysis.

Figure 2

DEGs between HIV-positive patients and controls. (A) Volcano plot showed all DEGs of HIV-positive patients in contrast to controls. (B) The heatmap illustrates the top 50 up-regulated and 50 down-regulated DEGs in HIV-positive patients.

Figure 3

WGCNA and identification of highly related modules. (A) Determination of the soft-threshold power for HIV-positive samples. (B) Hierarchical clustering dendrogram of highly connected genes in key modules associated with HIV-positive status. (C) Correlations between gene modules and clinical traits in HIV-positive samples, with corresponding correlation coefficients and P-values displayed in each cell. (D) Scatter plot of module eigengenes associated with HIV-positive status in the red module. (E) Scatter plot of module eigengenes associated with HIV-positive status in the black module. (F) Scatter plot of module eigengenes associated with HIV-positive status in the turquoise module.

Figure 4

Identification of intersection genes and their positions on chromosome. (A) The Venn diagram for intersection genes. (B) positions of intersection genes on chromosome presented by ring heat map. (C) positions of intersection genes on chromosome presented by Manhattan diagram.

Figure 5

GO enrichment and KEGG pathway analysis. (A) The GO circle plot illustrates the scatter map of the logFC of the intersection genes. (B) The KEGG bar plot and bubble plot illustrate the scatter map of the logFC of the intersection genes.

Figure 6

PPI network. (A) Construction of the intersection gene network. (B) Identification of the hub genes.

Figure 7

Expression of hub genes in HIV-Positive patients and controls. (A–J) show the expression levels of TP53, AKT1, JUN, CTNNB1, PXDN, MAPK3, FOS, MMP9, FOXO1, and STAT1, respectively. **p < 0.01, ***p < 0.001.

Figure 8

ROC curves for diagnostic performance of hub genes in HIV-Positive patients. (A–J) show the ROC curves for TP53, AKT1, JUN, CTNNB1, PXDN, MAPK3, FOS, MMP9, FOXO1, and STAT1, respectively.

Figure 9

Correlations of the hub genes. (A) Correlation matrix of ten hub genes. (B) Correlation between TP53 and AKT1. (C) Correlation between PXDN and MAPK3. (D) Correlation between FOS and MMP9. (E) Correlation between FOS and MAPK3. (F) Correlation between PXDN and AKT1. (G) Correlation between TP53 and PXDN. (H) Correlation between FOXO1 and STAT1. (I) Correlation between TP53 and MAPK3. (J) Correlation between STAT1 and MAPK3. (K) Correlation between TP53 and FOS. (L) Correlation between FOXO1 and FOS.

Figure 10

KEGG pathway analysis of hub genes via GSEA identified signaling pathways associated with HIV-infected patients. (A) TP53. (B) AKT1. (C) JUN. (D) CTNNB1. (E) PXDN. (F) MAPK3. (G) FOS. (H) MMP9. (I) FOXO1. (J) STAT1.

Figure 11

Correlations between hub genes and immune cells in HIV-positive patients. (A–J) show the correlations of TP53, AKT1, JUN, CTNNB1, PXDN, MAPK3, FOS, MMP9, FOXO1, and STAT1 with immune cell components, respectively. P < 0.05 was highlighted.

Figure 12

Interaction between existing therapeutic drugs and hub genes. (A) MAPK3. (B) TP53. (C) MMP9. (D) FOS. (E) CTNNB1. (F) JUN. (G) AKT1. (H) STAT1 and FOXO1.

Figure 13

MiRNA-hub gene interaction networks.

Figure 14

Transcriptional factor (TF) regulatory network. The red ovals represent hub genes, the blue hexagons represent transcriptional factors.