The primary bioactive compounds identified from the TCMSP database were cross-referenced with the PubChem database (https://pubchem.ncbi.nlm.nih.gov) to identify known protein targets and map protein names to their respective gene names (17). In addition, the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) was used to predict potential targets of the main bioactive constituents, with P>0 for target prediction (18). The potential targets obtained from the PubChem and Swiss Target Prediction databases were integrated, de-duplicated and regarded as the potential targets of the primary bioactive compounds of purslane.

The GeneCards database (https://www.genecards.org/) (19) and DisGeNET database (https://www.disgenet.org/) (20) were searched using the keyword ‘vitiligo’ to identify disease-related targets. The targets identified from the two databases were consolidated to obtain the potential targets associated with vitiligo.

The potential targets of the primary bioactive compounds of purslane were compared with the potential targets associated with vitiligo and entered into the Microscopic Letter platform (http://www.bioinformatics.com.cn/) for analysis. An intersection map was created to identify the potential targets of purslane for the management of vitiligo (21).

The bioactive constituents of purslane and their potential targets for the treatment of vitiligo were imported into Cytoscape version 3.7.2 software (22) to generate a drug component-potential target network for purslane in the context of vitiligo treatment. In this network, each node represents an active ingredient or a potential target, while each edge denotes the interaction between the bioactive compound and the potential target. Subsequently, the Cytoscape plug-in CytoNCA version 2.1.6 was used to analyze the topological properties of the network (23). Metrics such as betweenness centrality (BC), closeness centrality (CC), and degree centrality (DC) were calculated to identify the primary bioactive compounds of purslane that may be useful for vitiligo treatment. In network pharmacology, the calculation of BC, CC and DC values is primarily employed to identify key nodes within the network, assess the efficiency of information transfer among nodes and uncover the network's core regulatory mechanisms. This analytical approach facilitates drug target prediction, enhances the design of multi-target drugs and improves the overall comprehensiveness of network analysis, thereby providing a robust theoretical foundation for drug discovery and disease treatment (23).

The potential targets of purslane for the treatment of vitiligo were uploaded to the STRING database (https://string-db.org) (24) to generate a PPI network. The analysis was performed by choosing ‘Multiple proteins’ to integrate the overlapping genes of purslane and vitiligo, designating ‘Homo sapiens’ as the organism, configuring the ‘Minimum required interaction score’ to medium confidence (0.400), and maintaining default settings for other parameter options to produce the PPI network. The resulting data were imported into Cytoscape version 3.7.2 software for visualization of the PPI network. Subsequently, the topological properties of the network were analyzed using the Cytohubba 0.1 plug-in in Cytoscape. Key metrics, including Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness and Closeness, were calculated to identify potential key targets of purslane for the treatment of vitiligo (25).

The potential target genes associated with the purslane treatment of vitiligo were analyzed for GO enrichment and KEGG pathway enrichment using the DAVID database (https://david.ncifcrf.gov) (26). The analysis was performed by inputting the overlapping gene list between purslane and vitiligo, using ‘OFFICIAL_GENE_SYMBOL’ as the identifier, ‘Homo sapiens’ as the species and ‘Gene list’ as the list type, and submitting the list for annotation. P<0.002 was applied as the cutoff for GO annotation and KEGG pathway enrichment analysis (27).

Molecular docking of the key constituents of purslane with the key targets was performed. The 3-dimensional (3D) crystal structures of the target proteins were obtained from the RCSB Protein Data Bank (PDB) database (https://www.rcsb.org) to act as the receptors. Using PyMol version 2.2.0 software, the target proteins were dehydrated, non-relevant ligands were removed, and the results were saved in standard PDB format (28). The 3D structures of the compounds were acquired from the TCMSP database and saved in mol2 format. OpenBabel version 2.4.1 (http://openbabel.org) was used to convert the 3D structure file format to a PDB format file (29).

AutoDockTools version 1.5.7 was used to add hydrogen atoms to the target proteins and calculate their charges. Then AutoDockTools version 1.5.7 Vina software was applied to dock the receptor protein with the ligand and calculate the binding energy (30). Following this, PyMol version 2.2.0 was used to visualize the docking outcomes.

PTF solution of various concentrations (10, 20, 40, 80, 160, 320, 640 and 1,280 µg/ml) was mixed with 1.0 ml of 200 µM DPPH (cat. no. D273092; Shanghai Aladdin Biochemical Technology Co., Ltd.) in ethanol. The ethanolic DPPH solution was used as a control, and the reaction between PTF and DPPH was carried out at 37°C for 60 min, followed by colorimetric measurement at a wavelength of 517 nm. The DPPH radical scavenging activity was calculated using the following formula: DPPH (%)=1-[(A_blank-A_sample)/A_blank] ×100 (33).

Cells in the logarithmic growth phase were harvested and counted after digestion with 0.25% trypsin solution (C25200056; Gibco; Thermo Fisher Scientific, Inc.) and the cells were centrifuged at 400 × g for 5 min using a high-speed refrigerated centrifuge (D3024R; Dalong Xingchuang Experimental Instrument (Beijing) Co., Ltd,). The cell concentration was adjusted to 5×10³ cells/100 µl, and 100 µl cell suspension was seeded into each well of a 96-well plate. The cells were then cultured for 24 h at 37°C in a 5% CO₂ incubator, after which the culture medium was removed. The control group was incubated with RPMI-1640 medium alone, while the drug intervention groups were exposed to RPMI-1640 medium supplemented with varying concentrations of PTF (40, 80, 160, 320 and 640 µg/ml) cultured in an incubator at 37°C with 5% CO₂ for 2, 4 and 6 h. Following this, 500 µM hydrogen peroxide (H₂O₂; cat. no. 20230726; Tianjin Damao Chemical Reagent Co., Ltd.) was added to each well and the plate was incubated cultured in an incubator at 37°C with 5% CO₂ for an additional 24 h (34). Subsequently, 10% CCK-8 reagent was added to each well to assess cell viability using the aforementioned protocol.

B16F10 cells were plated in 12-well plates at a density of 5×10⁵ cells per well. The cells were treated with PTF (40, 80 and 160 µg/ml) or extract of Ginkgo biloba (EGB; 10:1 purification, water extract; cat. no. G195711-25g; Shanghai Aladdin Biochemical Technology Co., Ltd.) for 2 h, followed by 500 µM hydrogen peroxide for 24 h as described above. In addition, in certain wells, the B16F10 cells were pretreated with the 20 µmol/l PI3K inhibitor LY294002 for 2 h (cat. no. S1737-1mg; Beyotime Institute of Biotechnology) prior to the 160 µg/ml PTF and H₂O₂ treatments. The medium was aspirated, and the DCFH-DA probe from a Reactive Oxygen Species (ROS) Detection kit (cat. no. S0033S; Beyotime Institute of Biotechnology) was applied at 1:1,000 dilution. The cells were then incubated with the probe for 20 min under standard culture conditions (37°C, 5% CO₂), rinsed with PBS and visualized using a 488-nm excitation long-wave microscope (35). The superoxide dismutase (SOD) and catalase (CAT) activity and melanin content in the cells were measured using specific kits, and the results were determined using a microplate reader. The kits were as follows: Total SOD Activity Detection Kit, WST-8 method (cat. no. S0101S; Beyotime Institute of Biotechnology), Catalase Detection Kit (cat. no. S0051; Beyotime Institute of Biotechnology) and Mouse Melanin (MC) ELISA Research Kit (cat. no. MM-0870M2; Shanghai Enzyme-linked Biotechnology Co., Ltd.) (36).

B16F10 cells from each experimental group were harvested, homogenized with RIPA Lysis and Extraction Buffer (cat. no. 89900; Thermo Fisher Scientific, Inc.) with phenylmethylsulfonyl fluoride (100 mM; cat. no. P0100-1ml; Beijing Solarbio Science & Technology Co., Ltd.), and then subjected to lysis on ice for 30 min. The supernatant was collected for subsequent analysis. The protein concentration was quantified using a BCA Protein Concentration Assay Kit (500X dilution; cat. no. P0012, Beyotime Institute of Biotechnology). After mixing with buffer, protein denaturation was carried out in a water bath at 100°C for 10 min. The precast gel (4–12% Bis-Tris prefabricated glue; cat. no. WBT41212BOX; Thermo Fisher Scientific, Inc.) was secured in an electrophoresis chamber, and electrophoresis buffer was added to the reservoir. The protein samples and marker (20–120 kDa marker, cat. no. M00521, GenScript Biotech Corporation; 10–250 kDa marker, cat. no. MF028-plus-01, Mei5 Biotechnology Co., Ltd.) were loaded into the wells at a total protein content of 40 µg/lane. The gel was subjected to electrophoresis at a constant voltage of 180 V until the bromophenol blue dye front reached the bottom of the gel, typically 40 min. Subsequently, the cells were transferred to a PVDF membrane (cat. no. ISEQ15150; Merck KGaA), which was then blocked with 5% skimmed milk powder for 2 h at room temperature. After washing with Tris-buffered saline with 0.1% Tween 20, the PVDF membranes were incubated with primary antibodies targeting β-actin (1:30,000; cat. no. 66009-1-Ig; Wuhan Sanying Biotechnology), GAPDH (1:30,000; cat. no. 60004-1-Ig; Wuhan Sanying Biotechnology), AKT (1:2,000; cat. no. 10176-2-AP; Wuhan Sanying Biotechnology), phosphorylated (p-)AKT (1:1,000; cat. no. 4060; Cell Signaling Technology, Inc.), Bax (1:8,000; cat. no, 50599-2-Ig; Wuhan Sanying Biotechnology) and Bcl-2 (1:2,000; cat. no, 26593-1-AP; Wuhan Sanying Biotechnology) overnight at 4°C. The following day, HRP was used to label sheep anti-rabbit or sheep anti-mouse secondary antibody (1:10,000; cat. nos. BA1054 or BA1051; Wuhan Boster Biological Technology, Ltd.) was applied and the membrane was incubated for 2 h at room temperature on a shaker. ECL reagent (cat. no. KF8003, Affinity Biosciences) was then used to treat the PVDF membrane, which was subjected to automatic exposure. If the bands were not clearly visible or did not appear, manual exposure was performed using chemiluminescence imaging system for color development (model SH-523; Hangzhou Shenhua Technology Co., Ltd.). Image J software (version 1.8.0, National Institutes of Health) was used to analyze the gray values of protein bands.

B16F10 cells were cultured in 24-well plates. After drug treatment, the culture medium was removed, and the cells were fixed with 4% paraformaldehyde at 25°C for 30 min and permeabilized with 5% Triton X-100 (cat. no. ST795; Beyotime Institute of Biotechnology). The cells were subsequently incubated at 37°C for 30 min and blocked with 10% goat serum at room temperature for 1 h (cat. no. AR1009; Boster Biological Technology). Primary antibodies against melan-A (1:100; cat no. SC56-02; HUABIO) were added and the samples were incubated overnight at 4°C. Melan-A is the most used indicator to detect the presence and delineate the distribution of melanocytes in skin pathology (37). The next day, sheep anti-rabbit secondary antibody (BA1032; Boster Biological Technology Co. Ltd.) was applied in the dark. Following 1 h incubation at room temperature, the cells were stained with 4′,6-diamidino-2′-phenylindole (C1002; Beyotime Institute of Biotechnology) in the dark for 5 min to visualize the nuclei. The stained samples were then observed under a fluorescence microscope.

Statistical analysis and graphical representation were performed using GraphPad Prism 9.0 software (Dotmatics). Data are presented as the mean ± standard error of the mean. All experiments were performed in triplicate at least to ensure the reproducibility of the experimental results. Statistical analysis was performed using one-way analysis of variance followed by Dunnett's or Tukey's post hoc tests to assess differences among multiple groups. When Tukey's test was performed, only some significant differences are shown for clarity. P≤0.05 was considered to indicate a statistically significant difference.

In total, 54 chemical components of purslane were extracted from the TCMSP database. Following screening, 10 candidate compounds were identified as the principal active constituents. These were kaempferol, hesperetin, luteolin, isobetanidin, isobetanin_qt, quercetin, arachidonic acid, cycloartenol, β-carotene and β-sitosterol (Table I).

Analysis using the Swiss Target Prediction database platform led to the identification of a total of 563 targets with P>0. Among these, 92 targets were associated with hesperetin, 100 with arachidonic acid, 44 with β-sitosterol, 1 with β-carotene, 25 with cycloartenol, 2 with isobetanidin, 12 with kaempferol, 100 with luteolin and 100 with quercetin. Through the integration of all targets obtained from the database screening process and the elimination of duplicate or invalid targets, a total of 255 targets associated with the primary active components of purslane were obtained (Table SI).

A total of 1,736 vitiligo-related targets were identified through searches in the DisGeNet and GeneCards databases. Following the elimination of duplicate targets, 1,487 unique vitiligo targets were obtained. By intersecting the targets of the active components in purslane with the known disease-related targets of vitiligo, 54 common targets were identified as potential targets for purslane in the treatment of vitiligo (Fig. 1A). Subsequently, a network illustrating the relationships between the active components of purslane and the associated target genes for the treatment of vitiligo was constructed, comprising 54 potential targets and seven active components (Fig. 1B). This network comprised 64 nodes and 137 edges, with an average BC of 118.59, CC of 0.355 and DC of 4.28. Notably, the seven active components kaempferol, hesperetin, luteolin, quercetin, arachidonic acid, cycloartenol and b-sitosterol had BC, CC and DC values exceeding the network average (Fig. 1C-E), suggesting their potential as key active components of purslane in the treatment of vitiligo and offering valuable insights for future studies. Moreover, a PPI network was generated, comprising 49 nodes and 230 edges. Cytohubba analysis highlighted six key targets, namely AKT1, tumor protein p53 (TP53), peroxisome proliferator-activated receptor γ (PPARG), estrogen receptor 1 (ESR1), prostaglandin-endoperoxidase synthase 2 (PTGS2) and mitogen-activated protein kinase 1 (MAPK1), which are suggested to be important as potential targets of purslane in the treatment of vitiligo (Fig. 1F-I).

GO functional enrichment analysis indicated that the use of purslane in vitiligo treatment may potentially influence ‘DNA binding’ and various cellular functions. This is potentially mediated via the effects of arachidonic acid, kaempferol, luteolin and quercetin on key genes, namely AKT1, ESR1, PPARG, PTGS2, MAPK1 and TP53, by modulating the processes ‘cellular response to hypoxia’, ‘enzyme binding’ and ‘zinc ion binding’, by affecting the cellular components ‘cytosol’ and ‘nucleus’, as well as ‘response to xenobiotic stimulus’ (Fig. 2A).

KEGG pathway enrichment analysis revealed that the targeting of ESR1 and PPARG by hesperetin could potentially influence the ‘thyroid hormone signaling pathway’, ‘PPAR signaling pathway’ and ‘longevity regulating pathway’ in the context of vitiligo treatment. Additionally, arachidonic acid was suggested to impact ‘melanoma’ and ‘VEGF signaling pathway’ by targeting ESR1, PTGS2, PPARG, MAPK1 and TP53. Furthermore, kaempferol and luteolin were indicated to act on ESR1, PTGS2 and AKT targets, demonstrating therapeutic effects on vitiligo through modulation of the ‘PI3K-Akt signaling pathway’, ‘p53 signaling pathway’ and ‘PPAR signaling pathway’ (Fig. 2B).

Overall, the screening process led to the identification of 7 principal components, namely kaempferol, hesperetin, luteolin, quercetin, arachidonic acid, cycloartenol and β-sitosterol, from 54 potential ingredients, in addition to 6 key targets, namely AKT1, TP53, PPARG, ESR1, PTGS2 and MAPK1, from 54 potential targets, and 8 pathways from 20 potential pathway for purslane-based vitiligo treatment.

Utilizing the findings from the PPI network and the purslane component-target gene network analyses, AKT1 (7nh5), TP53 (1ycq), PPARG (1fm6), ESR1 (1hcq), PTGS2 (P35354) and MAPK1 (P28482) were selected as candidate macromolecules for further investigation. The binding energy of these proteins with the corresponding active compounds was evaluated. The analysis suggested that b-sitosterol could form a hydrogen bond with the Asn-204 residue of AKT1 with a binding energy of −10.9 kcal/mol (Fig. 3A and B). In addition, it suggested that cycloartenol could form hydrogen bonds with the Gln-55, Tyr-51, Lys-24 and Leu-26 residues of TP53 with a binding energy of −7.9 kcal/mol (Fig. 3D and E); kaempferol could form hydrogen bonds with the Arg-316 and Ala-327 residues in PPARG with a binding energy of −8.8 kcal/mol (Fig. 3G and H); and luteolin could form hydrogen bonds with the Cys-21, His-24, Lys-454 and Arg-29 residues of PTGS2 with a binding energy of −8.5 kcal/mol (Fig. 3M and N). In addition, quercetin was predicted to form hydrogen bonds with the diacylglycerol (DG)-31, dicarboxylic acid (DC)-7, DG-5 and DC-33 residues of ESR1 with a binding energy of −8.3 kcal/mol (Fig. 3J and K), and with the Met-108, Lys-54, Ala-35 and Asn-154 residues of MAPK1 with a binding energy of −7.8 kcal/mol (Fig. 3P and Q). Hydrogen bonding and hydrophobic interactions between the purslane core components and the selected targets are presented in (Fig. 3C, F, I, L, O and R). These findings suggest that flavonoids are the predominant components of purslane that interact with vitiligo-associated targets.

To investigate the protective effects of PTF against oxidative stress, in vitro experiments were conducted. Initially, a CCK-8 assay was utilized to determine the optimal concentration of PTF. Results indicated that interventions of 10–640 µg/ml had no significant impact on B16F10 cell viability. However, an increase in concentration to 1,280 µg/ml resulted in a significant reduction in B16F10 cell viability and notable cytotoxicity (Fig. 4A).

The DPPH assay was then employed to evaluate the antioxidant effect of PTF. This assay assesses the ability of antioxidants to scavenge DPPH free radicals by monitoring changes in absorbance. The results revealed that antioxidant effect increased as the PTF concentration increased (Fig. 4B and C).

To investigate the antioxidative effect of PTF on H₂O₂-induced oxidative stress in B16F10 cells, the cells were treated with H₂O₂ (500 µM) to establish an oxidative stress model. The cells were pretreated with various concentrations of PTF (40, 80, 160, 320 and 640 µg/ml) prior to treatment with H₂O₂ for 24 h. Results revealed that following H₂O₂ treatment, the viability of the B16F10 cells significantly decreased compared with that of the control cells. However, preincubation with PTF at concentrations of 40, 80 and 160 µg/ml for 2, 4 and 6 h significantly increased cell viability compared with that of the model group. By contrast, the cell survival rate decreased significantly in cells preincubated with 320 and 640 µg/ml PTF for 4 and 6 h compared with that in the control group (Fig. 4D-F). Consequently, PTF concentrations of 40, 80 and 160 µg/ml were selected for use in subsequent experiments, in which they were referred to as the low, medium and high PTF, respectively.

To examine the protective effect of PTF against H₂O₂-induced oxidative damage, the B16F10 cells were incubated with various concentrations of PTF or 100 µg EBG for 2 h prior to treatment with 500 µM H₂O₂ for 24 h, and melan-A fluorescence levels, melanin content, SOD and CAT activity and ROS levels were then measured. The results demonstrate that the melan-A fluorescence intensity and melanin content were significantly reduced in the H₂O₂ model group compared with that in the control group. Conversely, the melan-A fluorescence intensity and melanin content were significantly elevated in the EGB and various PTF dose groups compared with those in the model group (Fig. 5A, C and D). The ROS level in the H₂O₂ model group was significantly elevated compared with that in the control group, whereas the CAT and SOD levels were significantly reduced. Following intervention with different doses of PTF and EGB, the ROS levels were significantly decreased compared with those in the H₂O₂ model group, with no significant difference observed among the low, medium and high PTF concentration groups. In addition, significant increases in the activity levels of CAT in the medium and high PTF and EGB groups, and of SOD in all PTF groups were observed compared with those in the model group. Furthermore, as the PTF concentration increased, the activity levels of CAT and SOD also increased. While ROS levels decreased and CAT activity levels increased in the EGB intervention group compared with those in the model group, the difference in SOD activity between these two groups was not statistically significant (Fig. 5B and E-G). These results suggest that flavonoids from purslane have the potential to ameliorate oxidative stress damage in vitiligo.

Western blot analysis demonstrated a significant reduction in the level of p-AKT/AKT protein in the H₂O₂ group compared with that in the control group. By contrast, the medium and high PTF groups and the EGB group exhibited elevated p-AKT/AKT protein levels compared with those in the H₂O₂ group. In particular, the high PTF group exhibited significantly higher p-AKT/AKT levels compared with those in the low PTF group. However, compared to the control group, the model group, the EGB intervention group and all PTF dose groups showed significant reductions (Fig. 6). No significant differences were observed in total AKT protein levels among the groups.

To investigate the involvement of the PI3K-AKT signaling pathway in the protective effect of PTF against H₂O₂-induced injury in B16F10 cells, the PI3K inhibitor LY294002 was used. Compared to the H₂O₂+PTF-H group, the LY294002 pretreatment group showed no significant difference in the expression level of Bcl-2, but a significant increase in Bax expression levels was observed (Fig. 7E-I). Furthermore, the PI3K inhibitor attenuated the protective effect of PTF against H₂O₂-induced oxidative damage in B16F10 cells. The ROS levels in the control and high PTF groups were significantly lower compared with those in the model group, whereas the SOD and CAT activities in the control and high PTF groups were significantly higher compared with those in the model group. The PI3K inhibitor significantly attenuated the changes in SOD and CAT activities compared with those in the high PTF group (Fig. 7A-D). These results suggest that PI3K/AKT signaling plays a key role in the protective effect of purslane on melanocytes subjected to oxidative stress.