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Gene & Protein in Disease Drugs and immune infiltration in IPF

3.4. Identification of hub genes and construction of hsa-miR-29b-3p and hsa-miR-29c-3p were identified as
a PPI network in IPF regulators of both COL15A1 and COL6A3.
Using the STRING web tool, the CG PPI network was 3.6. Immune cell infiltration and correlation analysis
initially established and refined by removing disconnected in IPF
nodes (Supplementary File: Table S6). The final PPI
network consisted of 30 nodes and 105 edges and included The bar chart and heatmap display the proportion of
29 upregulated genes and one downregulated gene immune cells in each sample from the IPF datasets
(Figure 4A). The CytoHubba (v0.1) plugin was used to (Figure 6A and B). As shown in violin plots, the proportions
identify the top 10 hub genes in this network: COL15A1; of memory B cells, plasma cells, resting CD4 memory T
COL6A3; ASPN; COL14A1; FBN1; SULF1; VCAN; THBS2; cells, activated CD4 memory T cells, M0 macrophages,
FAP; LTBP1 (Supplementary File: Table S7). Notably, resting dendritic cells, and resting mast cells were higher,
COL15A1 and COL6A3 exhibited the highest centrality, whereas the proportions of CD8 T cells, resting natural
and all hub genes were upregulated in IPF (Figure 4B). killer (NK) cells, and monocytes were lower in the IPF
group than in the control group (Figure 6C). Correlation
3.5. Integration and analysis of miRNA–TF–mRNA analysis revealed a positive correlation between memory B
regulatory networks in hub genes cells and regulatory T cells (Tregs) (r = 0.5) and between M1
We predicted miRNA–mRNA and TF–mRNA networks macrophages and activated NK cells (r = 0.42). Conversely,
for the 10 hub genes using miRTarBase, Starbase, negative correlations were observed between activated
TargetScan, and Enrichr databases. By integrating the two and resting mast cells (r = −0.45), between monocytes and
data files datasets, we obtained regulatory relationship plasma cells (r = −0.43), between neutrophils and activated
data for miRNA–TF–mRNA, resulting in a network of NK cells (r = −0.53), and between resting CD4 memory T
28 miRNAs, 5 TFs, and 10 mRNA genes. This integrated cells and CD8 T cells (r = −0.50) (Figure 6D).
network was visualized using Cytoscape_v3.10.1 (Figure 5, 3.7. Screening of candidate drugs targeting hub
Supplementary File: Table S8). In addition, we predicted genes
miRNA–TF–mRNA relationships for the 40 CGs; the
results are presented in Appendix (Figure A2) and 3. To explore potential treatment methods, the DrugBank,
Further analysis revealed that COL15A1 is targeted by CTD, and DGIdb databases were used to predict drugs
the TFs mindbomb E3 ubiquitin protein ligase 2 (MIB2) targeting the hub genes (Figure 5). Based on DrugBank,
and runt-related transcription factor 2 (RUNX2). only hyaluronic acid was identified as a compound
Similarly, COL6A3 is targeted by the TFs high mobility targeting VCAN. Meanwhile, based on DGIdb, the
group AT-hook 1 (HMGA1) and RUNX2. Furthermore, following drugs or compounds targeting hub genes

A B

Figure 4. PPI network construction and hub gene selection. (A) Orange nodes (circles) represent upregulated genes, whereas green nodes (triangles)
represent downregulated genes. The size of the node indicates the gene’s importance and centrality in the network, with larger circles indicating higher
importance and centrality. (B) Selection of hub genes using the DMNC algorithm, where the depth of the color represents the gene’s score. Darker red
colors indicate higher scores.
Abbreviations: PPI: Protein–protein interaction; CGs: Common genes.

Volume 3 Issue 4 (2024) 7 doi: 10.36922/gpd.4101