Fifteen candidate target genes were associated with the HIF-1 signaling pathway

In the GSE10804 dataset, a total of 1,032 differentially expressed genes (DEGs) were identified, comprising 659 upregulated and 373 downregulated genes, between the four ED samples and eight healthy controls (HC) (Fig. 1A). Moreover, 16 candidate DEGs were obtained by intersecting these 1032 DEGs, 1124 OSRGs, and 564 FRGs, while 15 candidate target genes were obtained by intersecting these 16 candidate DEGs and 16,376 target genes for the chemically active ingredients of GuiZhiJiaLongGuMuLiTang. The 15 candidate target genes included ALDH3A3, AR, EGFR, IL1B, NDRG1, NGB, NOX5, NQO1, PGD, PPARG, SLC2A1, SLC7A11, SNCA, SRC, and TF (Fig. 1B, C).

Fig. 1

Fifteen candidate target genes were associated with HIF-1 signaling pathway. A The differential gene expression volcano plot between individuals with ED and healthy controls. B The differential gene expression heatmap between individuals with ED and healthy controls. C The Venn diagram of candidate differentially expressed genes. D The Venn diagram of candidate target genes. E The GO enrichment plot of candidate target genes. F The KEGG enrichment plot of candidate target genes. G The chromosomal location plot of candidate target genes

To elucidate the functions of these 15 candidate target genes, enrichment analysis was performed, revealing their involvement in pathways such as cellular response to oxidative stress, response to hypoxia, regulation of cellular ketone metabolism, among others. A total of 538 GO functions were identified, and the genes were linked to ferroptosis, the HIF-1 signaling pathway, and other processes. Moreover, 45 KEGG pathways (Fig. 1D, E) were identified based on which the network between key chemically active ingredients, candidate target genes, and KEGG pathways were constructed. The PPARG was identified as the potential key response target (Fig. 1F).

The analysis of tissue/organ-specific expression of the 15 candidate target genes indicated that transcripts mapping to individual organ systems exhibited > tenfold higher expression levels than the median value, whereas their expression in the second most abundant tissue did not exceed one-third of the median value. In total, there were five tissue-specific genes, including SLC2A1, SNCA, EGFR, IL1B, and PPARG (Fig. 1G, Table 1).

Table 1 Distribution of tissue/organ-specific expression genes identified by BioGPSEGFR, PPARG, SLC2A1, and SRC were defined as the key target genes of ED

The PPI network was constructed with 15 nodes and 22 protein–protein interaction relationship pairs. Four key target genes, including EGFR, PPARG, SLC2A1, and SRC, were obtained by five algorithms (Fig. 2A, B). A co-expression network was established for the key target genes and the top 20 genes exhibiting the highest correlation with these targets. Notably, EGFR, PPARG, and SRC emerged as potential hub genes, likely involved in regulating epithelial cell migration, protein kinase B (AKT) signaling, and the ERBB signaling pathway, among others. Additionally, PIK3CA, NR2F2, PIK2, and EGF were also implicated in these regulatory functions (Fig. 2C).

Fig. 2

EGFR, PPARG, SLC2A1, and SRC were defined as key target genes of ED. A The protein–protein interaction (PPI) network plot of candidate target genes. B The Venn diagram of target genes from 5 algorithms. C The related genes and associated pathways of key target genes. D The network plot of drug components–target genes–pathways

The functions of key target genes were related to oxidative phosphorylation, focal adhesion, and ECM-receptor interaction

The GSEA results for the four identified key target genes indicated that EGFR, PPARG, and SRC were significantly positively correlated with oxidative phosphorylation while showing a significant negative association with focal adhesion. Additionally, EGFR and SLC2A1 were significantly negatively correlated with cytokine–cytokine receptor interactions. SRC was significantly positively associated with ECM-ECM–receptor interaction, but EGFR and PPARG were significantly negatively associated with this interaction. Besides, SLC2A1 was significantly negatively associated with the chemokine signaling pathway (Fig. 3A–D).

Fig. 3

The functions of key target genes were related to oxidative phosphorylation, focal adhesion, and ECM-receptor interaction. A The KEGG significant enrichment pathway for the EGFR single gene. B The KEGG significant enrichment pathway for the SRC single gene. C The KEGG significant enrichment pathway for the PPARG single gene. D The KEGG significant enrichment pathway for the SLC2A1 single gene

Potential regulatory mechanisms of key target genes

A TF-mRNA regulatory network was established, comprising four key target genes and 47 transcription factors (TFs).Among them, there were 27 targeted TFs of EGFR, 13 targeted TFs of PPARG, three targeted TFs of SLC2A1, and only one targeted TF of SRC. Notably, EGR1 could regulate both EGFR and PPARG, TP53 could regulate both EGFR and SLC2A1, and SP1 could regulate both EGFR and SRC simultaneously (Fig. 4A).

Fig. 4

Potential regulatory mechanisms of key target genes. A The network plot of key target genes and transcription factors (TFs). B The miRNA-target gene network plot of key target genes. C The network plot of lncRNA–miRNA–key target genes

Additionally, an mRNA–miRNA regulatory network was constructed, encompassing four key target genes and 111 miRNAs. Among them, there were 46 targeted miRNAs of EGFR, seven targeted miRNAs of PPARG, 47 targeted miRNAs of SLC2A1, and seven targeted miRNAs of SRC. Notably, the hsa-miR-1-3p, hsa-miR-218-5p, hsa-miR-138-5p, and hsa-miR-27a-3p were the common miRNAs of EGFR and PPARG (Fig. 4B).

A comprehensive lncRNA–miRNA–mRNA regulatory network was established, involving four key target genes and 77 lncRNAs. This network yielded 38 lncRNA–miRNA–mRNA interaction pairs, comprising 20 lncRNAs, 14 miRNAs, and four target genes. Notably, the lncRNAs HCP5, ST7-AS1, PVT1, KCNQ1OT1, NEAT1, DNAJC3-AS1, and XIST7 were shared among the miRNAs associated with EGFR and PPARG (Fig. 4C).

Correlation analysis of key target genes and oxidative stress markers

The analysis of the correlation between key target genes and oxidative stress markers revealed that these genes were primarily associated with markers such as total oxidant status (TOS), reactive oxygen species (ROS), oxidized LDL, lipid peroxides, and total antioxidant capacity (TAOC). However, no significant association was found with markers related to anti-oxidative stress. The PPARG gene was positively correlated with five related oxidative stress pathways. EGFR was positively correlated with ROS, while SLC2A1 was positively correlated with TOS, ROS, TAOC, and oxidized LDL. The SRC gene was negatively correlated with TOS and TAOC (Fig. 5A, B).

Fig. 5

Correlation analysis of key target genes and oxidative stress markers. A The correlation heatmap of key target genes and oxidative stress-related biomarkers. B The network plot of key target genes and oxidative stress-related biomarkers

Molecular docking of key target genes and chemically active ingredients

Table 2 presents the docking affinity between the key target genes and quercetin, the primary bioactive compound of GuiZhiJiaLongGuMuLiTang. The findings demonstrated that the docking affinity between SLC2A1 and quercetin was the highest (− 8.234 kcal/mol). Besides, there were two hydrogen bonds between EGFR and quercetin (ASP-278 and ASP-289), four hydrogen bonds between PPARG and quercetin (ALA-15, GLU-2, GLU-7, and LYS-19), three hydrogen bonds between SLC2A1 and quercetin (ASN-403, ASN-407, and GLU-372), and four hydrogen bonds between SRC and quercetin (ARG-14, GLU-15, HIS-60, and VAL-58) (Fig. 6A–D).

Table 2 Statistical analysis of the binding affinity between key target genes and active compoundsFig. 6

Molecular docking of key target genes and chemically active ingredients. A The molecular docking results between EGFR and quercetin. B The molecular docking results between SRC and quercetin. C The molecular docking results between PPARG and quercetin. D The molecular docking results between SLC2A1 and quercetin