The active components of Ganoderma lucidum (Lingzhi) were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://old.tcmsp-e.com/tcmsp.php ) using “Ganoderma lucidum” as the search term. The screening criteria were set as oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 [1] [12] . OB ≥ 30% ensures adequate absorption and system exposure of the components, and DL ≥ 0.18 ensures screening of molecules with good druggability. This combination provides a reliable candidate molecule for subsequent network pharmacology studies. Potential target proteins for the identified active components were predicted using the Swiss Target Prediction database ( http://www.swisstargetprediction.ch/ ). The species was restricted to Homo sapiens, and only targets with a prediction probability greater than zero were retained. Finally, duplicate targets were removed to generate the list of Ganoderma lucidum active component-related targets.

Aging-related targets were identified by querying three databases: GeneCards ( https://www.genecards.org/ ), Online Mendelian Inheritance in Man (OMIM, http://www.omim.org/ ), and DisGeNET ( https://disgenet.com/ ), using “Aging” as the search keyword. From GeneCards, only genes with a relevance score greater than 10 were selected. After removing duplicates, the list of aging-related targets was compiled. The Ganoderma lucidum-related targets and the aging-related targets were then uploaded to the Venny 2.1 online tool ( https://bioinfogp.cnb.csic.es/tools/venny/ ) to identify their intersection (common targets). A Venn diagram was generated for visual representation of the overlapping targets.

The common targets identified in Section 2.2 were imported into the STRING database ( https://www.string-db.org/ ). The “Multiple proteins” option was selected, the species was set to Homo sapiens, and a PPI network was constructed. The interaction data in TSV format were downloaded and imported into Cytoscape software (version 3.8.2). The “Centiscape2.2” plug-in was used to automatically calculate the network features, and the thresholds of degree, betweenness centrality and closeness centrality were obtained, and the key targets of Ganoderma lucidum anti-aging were screened according to the thresholds.

The common targets identified in Section 2.2 were submitted to the DAVID database ( https://david.ncifcrf.gov/ ) for GO functional enrichment analysis (covering biological process, molecular function, and cellular component) and the Kyoto Encyclopedia of KEGG pathway enrichment analysis. The results were visualized as enrichment bubble plots using the Bioinformatics platform Weishengxin website ( http://www.bioinformatics.com.cn/ ).

Based on the preceding network analysis results, the top nine active components ranked by Degree value were selected as ligand molecules. Their 3D structures were downloaded in SDF format from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) and energy-minimized using Chem3D software (version 23.1.1). The optimized structures were saved in MOL2 format. The top three core targets ranked by Degree value (CYP19A1, NR3C1, HMGCR) were selected as receptor proteins. Their crystal structures were retrieved from the RCSB Protein Data Bank (PDB, https://www.rcsb.org/ ) in PDB format. Molecular docking simulations were performed using the SYBYL-X software suite (version 2.1.1). The procedure included: preparing the receptor proteins by adding hydrogen atoms and repairing side-chain defects; automatically identifying the binding pocket/active site; and performing the docking analysis using the “Dock Ligand” module.

Screening the TCMSP database yielded 61 Ganoderma lucidum active components that met the criteria outlined in Section 2.1. The SMILES structures of these 61 components were submitted to the Swiss Target Prediction database for target prediction, which identified 398 potential disease-related targets. Since some active ingredients did not have targets with a predictive probability greater than 0, they were eliminated, and then 29 active ingredients with key targets were screened out among the remaining active ingredients (e.g., Ganoderenic acid E, MOL011256). Details are presented in Table 1 .

Screening the three databases (GeneCards, OMIM, DisGeNET) as described in Section 2.2 resulted in 819 unique aging-related targets following duplicate removal. Intersection analysis of the 398 Ganoderma lucidum component targets and the 819 aging-related targets using the Venny 2.1 tool identified 98 common targets ( Figure 1 ).

The 98 common targets were imported into the STRING database to construct a PPI network ( Figure 2 ). Visualization and analysis using Cytoscape 3.8.2 revealed a network comprising 98 nodes and 1,427 edges. Key targets were identified based on network topology parameters (degree, betweenness centrality, and closeness centrality) using the CytoHubba plugin. Nodes exceeding the established thresholds for all three parameters (≥29.1224 for degree, ≥72.2653 for betweenness centrality, and ≥0.006024 for closeness centrality) were designated as key targets. This screening yielded 24 key targets implicated in the anti-aging effects of Ganoderma lucidum. Ranked in descending order of degree centrality, the top targets included Cytochrome P450 19A1 (CYP19A1), Nuclear Receptor Subfamily 3 Group C Member 1 (Glucocorticoid Receptor, NR3C1), and 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR) ( Figure 3 ).

The network relationship between the 29 active components of Ganoderma lucidum and the 24 key targets identified in Section 3.3 was visualized using Cytoscape 3.8.2. The resulting network consisted of 53 nodes and 145 edges ( Figure 4 ).

To elucidate the anti-aging mechanisms of Ganoderma lucidum active components, systematic GO functional and KEGG pathway enrichment analyses were performed on the 98 common targets using the DAVID database. For GO analysis, the top 10 significantly enriched terms (p-value < 0.05) in each category—Biological Process (BP), Cellular Component (CC), and Molecular Function (MF), were selected for detailed analysis ( Figure 5 ). Key enriched functions included identical protein binding (MF) and protein tyrosine kinase activity (MF). KEGG pathway enrichment analysis ( Figure 6 ) revealed significant enrichment in pathways such as the PI3K-Akt signaling pathway and the MAPK signaling pathway, suggesting their potential roles in mediating the anti-aging effects.

Based on degree centrality rankings from Section 3.3, the top 3 key targets (CYP19A1, NR3C1, HMGCR) and the top 9 core active components (MOL000279, MOL000358, MOL011125, MOL011160, MOL011189, MOL011225, MOL011235, MOL011241, MOL011256) were selected for molecular docking validation. The docking complexes exhibiting favorable binding poses are illustrated in Figure 7 . Docking scores (Total Score) for each ligand—receptor pair are presented in Figure 8 . Interactions with a Total Score ≥ 7.0 were considered indicative of stable binding, based on the scoring function within SYBYL-X. The results suggest that CYP19A1 and NR3C1 may play crucial roles in the anti-aging activity of Ganoderma lucidum.