Fig 1.
Workflow chart.
Table 1.
Detailed information for the GEO datasets.
Fig 2.
Identification of ILA targets via network pharmacology.
(A) The chemical structure of ILA. (B) Venn diagram showing ILA-related targets among the five databases. (C) ILA-targets interaction network.
Fig 3.
Integrated identification of CRC targets through multi-database mining and WGCNA.
(A) Venn diagram of CRC targets retrieved from four databases. (B) Volcano plot of DEGs in CRC vs. normal groups. Red points: 891 significantly upregulated genes; green points: 1000 downregulated genes. (C) Hierarchical clustering heatmap of the top 50 most significant DEGs. (D) Scale independence and average connectivity to determine soft thresholds in WGCNA. (E) Gene clustering dendrogram and highly correlated gene modules. (F) The heatmap of module–trait relationships for the 6 co-expression modules. MEturquoise module showed the strongest positive correlation with CRC (R = 0.97, p < 0.0001). (G) Intersection of CRC targets from databases (2122 genes), DEGs (1891 genes), and WGCNA (3314 genes), identifying 252 CRC key genes.
Fig 4.
PPI network and functional enrichment analysis of common targets associated with ILA in CRC.
(A) Venn diagram identifying 39 potential targets between ILA and CRC. (B) PPI network of 39 common targets. (C) Top 15 hub genes ranked by cytoHubba plugin using three topological algorithms. (D) Eleven intersection hub genes of the betweenness, closeness, and MCC algorithms. (E) Sankey bubble chart of the top 5 KEGG pathways. (F) Chord diagram of the top 5 biological processes.
Fig 5.
Machine learning-based identification of candidate targets associated with ILA in CRC.
(A) The coefficients and regularization plot of LASSO regression, the vertical dashed lines indicate the optimal lambda value. (B) Error rates in random forests and the top 14 genes with relative importance greater than 0.3. (C) Accuracy and error rate curves of SVM-REF algorithm. (D) Four hub genes (EPHA2, HMOX1, MMP3, PARP1) were identified by intersecting cytoHubba and machine learning selected targets.
Fig 6.
Expression analysis of hub genes in the normal and CRC groups.
(A) Differential expression of EPHA2, HMOX1, MMP3, and PARP1 between normal (n = 50) and CRC (n = 98) in the GSE44076 training set. (B) ROC curves demonstrating diagnostic performance of hub genes in GSE44076. AUC values: EPHA2 (0.939), HMOX1 (0.980), MMP3 (0.955), PARP1 (0.997). (C) Differential expression of hub genes between normal (n = 61) and CRC (n = 61) in the validation set. (D) ROC analysis of hub genes in the validation set. AUC values: EPHA2 (0.935), HMOX1 (0.883), MMP3 (0.935), PARP1 (0.804).
Fig 7.
GSEA analysis of the hub genes.
(A) GSEA up- and down-regulation pathways for EPHA2. (B) GSEA up- and down-regulation pathways for HMOX1. (C) GSEA up- and down-regulation pathways for MMP3. (D) GSEA up- and down-regulation pathways for PARP1.
Fig 8.
Immune cell infiltration analysis.
(A) Differential immune cell infiltration between normal (n = 50) and CRC (n = 98) in the GSE44076 training set. CIBERSORT analysis quantified 22 immune cell subtypes. (B) Spearman correlation heatmap between hub genes and immune cell subtypes. Asterisks denote statistical significance of correlation coefficients: *P < 0.05, **P < 0.01, ***P < 0.001.
Table 2.
Detailed parameters of molecular docking.
Fig 9.
Molecular docking analysis of ILA and four hub genes.
(A) Docking diagrams for ILA and EPHA2, binding affinity −6.247 kcal/mol. (B) Docking diagrams for ILA and HMOX1, binding affinity −5.876 kcal/mol. (C) Docking diagrams for ILA and MMP3, binding affinity −7.208 kcal/mol. (D) Docking diagrams for ILA and PARP1, binding affinity −5.857 kcal/mol.
Fig 10.
Molecular dynamics simulations of ILA with hub genes.
(A-D) Comparative RMSD of Cα Atoms for the Four Protein-ILA Complexes over a 10 ns Simulation. (E) RMSD plots for the Cα atoms of HMOX1 and the heavy atoms of the ligand ILA during a 100 ns simulation, confirming the dynamic stability of the complex. (F) RMSF analysis of the ILA-HMOX1 complex. The blue curve illustrates the fluctuation magnitude of individual amino acid residues; The green vertical bars highlight the specific residues involved in the interaction with ILA. (G) Protein-ligand interaction analysis of the ILA-HMOX1 complex. Interaction fraction of key residues with ILA, categorized by interaction type, including hydrogen bonds, hydrophobic contacts, ionic interactions, and water bridges. (H) The 2D schematic diagram illustrating the key persistent intermolecular interactions, including π-cation, salt bridges, and hydrogen bonds, that anchor ILA within the HMOX1 binding pocket throughout the 100 ns trajectory.
Fig 11.
Potential hub genes and pathways associated with ILA in CRC.