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Introduction

Keloids, which are fibroproliferative, arise due to a disruption in the wound-healing process (1). Keloids exhibit cancer-like characteristics, and are raised complex lesions that grow rapidly and uncontrollably, infiltrate surrounding tissues, do not regress spontaneously and often recur after excision (2). These lesions can extend beyond the original incision site, leading to pain, functional impairment and physical distress (3). Despite significant progress in the prevention and treatment of keloids, the precise mechanism underlying their formation remains unknown. Thus, a comprehensive understanding of keloid pathogenesis is essential for the development of efficacious therapeutic interventions. Fructus arctii, a popular vegetable in China and Japan, is widely appreciated for its various health-promoting properties (4). Studies have demonstrated that β-sitosterol (SIT) possesses diverse pharmacological properties, including anti-tumor (5), anti-angiogenic (6) and anti-inflammatory effects (7). However, the exact pharmacological effects of SIT on keloids are not yet fully understood. The PI3K/AKT pathway is an intrinsic signal transduction pathway in mammalian cells that regulates angiogenesis, cell growth, proliferation and metabolism and plays a crucial role in keloid formation (8–10). PTEN serves as a negative regulator of the PI3K/AKT/mTOR pathway (11). Consequently, it governs a broad spectrum of cellular processes related to cell viability, proliferation and growth (12). Emerging evidence indicates the downregulation of PTEN in keloids (13). The hypothesis of the present study was that SIT exerts a therapeutic effect on keloids by inhibiting their growth through the PTEN/PI3K/AKT signaling pathway. The present study comprehensively investigated the potential mechanism underlying the therapeutic effects of SIT in keloid treatment through network pharmacology, molecular docking and in vitro and in vivo experiments. Additionally, it explored the potential association between SIT and the PTEN/PI3K/AKT signaling pathway. These findings may offer a potential remedy for keloids.

Materials and methods

F. arctii active ingredient acquisition and screening for F. arctii targets and keloids targets

The present study used The Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP; http://tcmsp-e.com/) for data analysis (14). The bioactive compounds isolated from F. arctii were identified via ‘Fructus Arctii’ as a keyword and the drug-likeness screening criterion had to have a minimum value of 0.18, while oral bioavailability screening had to have a minimum value of 30%. The number of targets for the active ingredients were recorded. The UniProt ID was acquired from the UniProt database for the target (https://www.uniprot.org/) (15). Using ‘keloid’ as a keyword, targets related to keloid is searched and obtained from the Therapeutic Target Database (TTD; http://db.idrblab.org/ttd/) (16), DrugBank (https://www.drugbank.ca/) (17), GeneCards (https://www.genecards.org/) (18) and Online Mendelian Inheritance in Man (OMIM; http://www.omim.org/) (19). Duplicates were removed from the merged data sets originating from each database. Afterward, a Venn diagram was created to illustrate the overlap between the targets of F. arctii and keloids (Fig. 1).

Figure 1. Flow chart of the present study. It was divided into four parts: Target prediction, network construction, molecular docking and in vitro verification. TCMSP, The Chinese Medicine Systems Pharmacology Database and Analysis Platform; TTD, Therapeutic Target Database; OMIM, Online Mendelian Inheritance in Man; PPI, protein-protein interaction; F. arctii, Fructus arctii; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; EdU, 5-Ethynyl-2′-deoxyuridine.

Construction of a drug-active ingredient-target-pathway network

Following the data entry requirements of Cytoscape software (version 3.7.0; http://cytoscape.org), the data from the previous steps were organized and imported into Cytoscape to generate a network depicting the relationships among active ingredients, targets and pathways (20). Following this, the outcomes of the analysis pertaining to F. arctii and common targets were saved from the network. Following this, visual representations of the connections between drug-component-targets were achieved by modifying the shape and color of each node.

Protein-protein interaction (PPI) network construction and analysis

The common targets of F. arctii and keloid were uploaded to the STRING database (21). Homo sapiens was designated as the species, a high confidence level of 0.700 was established as the minimum interaction threshold and all other parameters were maintained at their default values. This allowed the construction of a PPI core network of keloids, with F. arctii acting upon it (22). The Network Analyzer and cytoHubba plug-ins in Cytoscape 3.7.0 software were used to calculate the topological properties of the PPI network and the top 10 Hub genes were selected.

Pathway analysis of the target genes

The aforementioned predicted primary targets were entered into the Metascape database (http://metascape.org), with the species defined as Homo sapiens. Following database retrieval and transformation operations, enrichment analyses via Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were conducted. The significant GO functions and KEGG signaling pathways were screened on the basis of the P-value. This allowed for the prediction of the mechanism of action of F. arctii in the treatment of keloids.

Molecular docking verification

The structures of SIT and PTEN (PDB ID:7JVX) were obtained via the PubChem database (https://pubchem.ncbi.nlm.nih.gov) (23) and RCSB Protein Data Bank (RCSB PDB; http://www.rcsb.org/structure) (24) for molecular docking studies. They were then reconstructed using PyMOL software (25). The AutoDock Tools (version 1.5.6; http://autodock.scripps.edu) were used for protein-ligand docking analysis. The 3D interactions were generated using PyMOL software. The results of successful molecular docking were visualized with PyMOL 2.5.4 software. The binding affinity energy (kcal/mol) was computed via CB-Dock2 (https://cadd.labshare.cn/cb-dock2/php/index.php) to determine the optimal docking model with the lowest energy. Molecular visualization plays a crucial role in analyzing and communicating modeling studies. Therefore, the details of the ligand-receptor interactions were revealed via PyMol2 software (Free version; http://pymol.org) and BIOVIA Discovery Studio Visualizer software (version 2021; Dassault Systèmes) (26).

Cells and therapies

The KEL FIB (CRL-1762) human keloid fibroblast (KF) cell line was obtained from ATCC. The cells were cultured in Dulbecco's modified Eagle's medium (DMEM; Gibco; Thermo Fisher Scientific, Inc.) supplemented with 10% fetal bovine serum (FBS; Gibco; Thermo Fisher Scientific, Inc.) and 1% penicillin/streptomycin (Beyotime Institute of Biotechnology) at 37°C in a humidified atmosphere with 5% CO2. To investigate the effects of SIT, KF cells (KFs) were treated with various concentrations (15, 30, 60 and 120 µM) of SIT (MedChemExpress) prior to functional analyses. KFs were treated with YS-49 (10 µM) (cat. no. HY-15477; MedChemExpress), a PI3K/AKT pathway activator, for 24 h to assess cell replication (27). SIT and YS-49 were dissolved in dimethyl sulfoxide (DMSO) and the cells in the corresponding control groups were incubated with an equal volume of DMSO.

Cell counting kit-8 (CCK-8) assays

To establish an initial concentration range for SIT, 5×103 cells per well were seeded in a 96-well plate. The cells were subsequently exposed to a gradient of SIT concentrations (15, 30, 60 and 120 µM) for 12, 24 and 48 h. Cell viability was subsequently assessed via a CCK-8 assay. After the addition of the CCK-8 reagent, the cells were incubated for 2 h, after which the absorbance (OD value) at 450 nm was measured via a microplate reader (Molecular Devices, LLC). The obtained absorbance values were normalized to those of the control group.

5-Ethynyl-2′-deoxyuridine (EdU) assays

A Cell-Light EdU Apollo567 In Vitro Kit (C10310-1, RiboBio, Guangzhou, Guangdong, China) was used for EdU staining. Cell proliferation imaging experiments were conducted using 96-well cell culture plates. The manufacturer's guidelines were followed to detect EdU-labeled positive cells. Cells were incubated with Hoechst 33342 dye (10 µg/ml) for 30 min at room temperature in the dark. After staining, they were washed twice with PBS. EdU assays were used to assess the activity of DNA replication as an indicator of changes in the proliferative capacity of KFs.

Wound-healing assay

KFs were seeded in a six-well plate at a density of 1×105 cells per well and grown to 90% confluence. Subsequently, the cells were scraped with a 1-ml sterile pipette and the resulting cell pellets were rinsed with PBS three times. After incubation in serum-free medium, the cells were treated with different concentrations of SIT. The samples were observed under a microscope at 0 and 24 h after drug treatment. Cell migration was assessed by quantifying the percentage of the wound closure area in three independent experiments via ImageJ version 1.53t software (National Institutes of Health).

Transwell assay

In 24-well Transwell chambers, cell invasion and migration were evaluated. To analyze invasion, the plate was precoated with Matrigel (Beijing Solarbio Science & Technology Co., Ltd.) for 4 h at 37°C before cell seeding; for migration, chambers devoid of Matrigel were used. A total of 1×104 cells were seeded into the upper compartment of each Transwell chamber, which contained 100 µl of DMEM (Corning, Inc.). The lower chamber was filled with 500 µl of complete medium containing 10% FBS as a chemoattractant. The cells were fixed and incubated for 20 min with 0.1% crystal violet after 24 h of SIT treatment. A cotton swab was subsequently used to delicately remove the cells that were still present on the upper surface of the membrane insert. The quantity of cells that had successfully migrated to the lower surface was then determined.

Western blot analysis

Western blot analysis was used to determine protein expression in KFs in accordance with previously described procedures (28). Protein lysate was extracted using RIPA buffer (Beijing Solarbio Science & Technology Co., Ltd.) and protein concentration was determined using the BCA Protein Assay Kit (Beyotime Institute of Biotechnology). Proteins (30 µg/lane) were separated by SDS-PAGE on 8–10% gels, transferred onto PVDF membranes, blocked with 5% non-fat dry milk (MilliporeSigma) at room temperature for 1 h and were then incubated with primary antibodies overnight at 4°C. The primary antibodies used were as follows: anti-PI3K (4249T; 1:1,000; CST Biological Reagents Co., Ltd.), anti-phosphorylated (p-)PI3K (17366; 1:1,000; CST Biological Reagents Co., Ltd.), anti-PTEN (AF5447; 1:1,000; Affinity), anti-AKT (4691; 1:1,000; CST Biological Reagents Co., Ltd.), anti-p-AKT (4060; 1:1,000; CST Biological Reagents Co., Ltd.), anti-E-cadherin (3195; 1:1,000; CST Biological Reagents Co., Ltd.), anti-Vimentin (5741; 1:1,000; CST Biological Reagents Co., Ltd.), anti-Snail (3879; 1:1,000; CST Biological Reagents Co., Ltd.), anti-zonula occludens-1 (ZO-1; 8193; 1:1,000; CST Biological Reagents Co., Ltd.) and anti-β-actin (4967; 1:1,000; CST Biological Reagents Co., Ltd.). Subsequently, the blots were probed with HRP-conjugated anti-rabbit and anti-mouse secondary antibodies (1:5,000; cat. nos. RGAR001 and RGAM001; Proteintech Group, Inc.), which were diluted in TBS-0.1% Tween 20 solution, at room temperature for 2 h. The blots were observed with an enhanced chemiluminescence Western blot detection reagent (MilliporeSigma). ImageJ version 1.53t software (National Institutes of Health) was used to conduct a statistical analysis of the grayscale values of the proteins. After normalization to β-actin, the internal control, the relative expression of the target protein in each group of cells was computed.

Animal models

A total of 10 male SPF-grade BALB/c nude mice, aged six weeks and weighing 20±2 g, were procured from the Experimental Animal Center of Yanbian University (Yanji, China) and were randomly assigned to two groups (n=5 mice/group). Nude mice were housed in a specific pathogen-free environment with a temperature of 25°C and humidity of 30%. The mice were exposed to a 12-h light/dark cycle, and were given free access to adult mouse feed sterilized with cobalt-60 and water sterilized by autoclaving. Following a 1-week acclimatization period with standard feeding, a nude mouse keloid model was established. This was achieved by injecting a keloid fibroblast suspension with a density of 5.0×107 cells/ml diluted in Matrigel (Corning, Inc.) under aseptic conditions. The mixture was then kept on ice. Subsequently, 0.1 ml of the prepared keloid fibroblast suspension was subcutaneously injected into the axilla of the right forelimb of each nude mouse. The keloid volume, weight and growth of the mice were monitored every three days. The average tumor volume was calculated via the following formula: tumor volume=(length × width2)/2. After 7 days, keloid formation became visible under the skin in the right axilla of the nude mice. Upon reaching a size of approximately 50 mm3, the SIT-treated group of nude mice received intraperitoneal injections of SIT at a safe therapeutic dose of 50 mg/kg, as determined in a previous study (29). The treatments were repeated every two days for a total of 10 cycles. The control group received equivalent saline injections. Throughout the course of the experiment, the mice were closely monitored for any signs of poor health, including weight loss, abnormal posture, changes in activity levels, labored breathing and signs of distress. No signs of severe distress were observed in the animals prior to sacrifice. At the end of the experiment, all mice were sacrificed by cervical dislocation following anesthesia with an injection of sodium pentobarbital at a dose of 70 mg/kg body weight and death was confirmed by the absence of respiration and heartbeat for more than 5 min. The fibroproliferative tissues were collected for measurement and further experiments.

Hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC)

The subcutaneous tumors were fixed in 10% formalin at room temperature for 24 h, dehydrated in graded ethanol and embedded in paraffin. The paraffin-embedded tumors were then sectioned into 4-µm slices, which were oven-baked at 56°C overnight and stained using the H&E Stain kit (Beijing Solarbio Science & Technology Co., Ltd.), according to the manufacturer's protocol. For IHC, the slides were placed in 10 mM sodium citrate buffer (pH 6.0) and boiled, then simmered for 10 min, for antigen retrieval, before being cooled for 30 min. To remove endogenous peroxidase activity, slides were incubated with 3% hydrogen peroxide aqueous solution for 10 min at room temperature (20–25°C). Furthermore, blocking was performed using TBS-0.1% Tween with 5% normal goat serum for 1 h at room temperature (Cell Signaling Technology, Inc.). The sections were then incubated overnight at 4°C with primary antibodies against PTEN (cat. no. AF5447; 1:200 dilution; Affinity Biosciences). The next day, a biotinylated Goat Anti-Rabbit IgG H&L (Biotin) secondary antibody (1:100; cat. no. ab207995; Abcam) was added, and the slides were incubated at room temperature for 30 min. Streptavidin-horseradish peroxidase (cat. no. SA10001; Invitrogen; Thermo Fisher Scientific, Inc.) was then used to incubate the slides at room temperature for 30 min and 200 µl DAB was added to each section as the chromogen. Hematoxylin was used to counterstain the sections at room temperature for 1 min. Finally, images of the slides were captured using a Nikon Eclipse Ti-S/L100 inverted phase contrast fluorescence microscope with a 20× objective.

In vivo safety

Blood biochemical analysis was conducted to evaluate biosafety parameters, including creatinine (CREA), alanine aminotransferase (ALT), serum aspartate aminotransferase (AST) and blood urea nitrogen (BUN) (27). The serum was separated by centrifugation at 4,000 × g for 10 min at 4°C. ALT, AST, BUN and CREA levels were determined following the instructions provided with the kit (Nanjing Jiancheng Bioengineering Institute).

Statistical analysis

Data analysis was performed via GraphPad Prism 7.0 software (Dotmatics). Group variables were characterized by the number of cases, scores, measurements and percentages. Data that followed a normal distribution are presented as the means ± standard deviations. Multiple group comparisons were conducted via one-way ANOVA with either Tukey's or Dunnett's post hoc tests to determine significant differences between groups. P<0.05 was considered to indicate a statistically significant difference. Each experiment was performed in triplicate.

Results

Identification of active components in F. arctii and screening of shared targets between drugs and diseases

The present study used the TCMSP screening criteria to identify eight chemical components (Table I). Among these components were neoarctin A, arctiin, SIT, kaempferol, aupraene, β-carotene, (3R,4R)-3,4-bis[(3,4-dimethoxyphenyl)methyl]oxolan-2-one and cynarine. By setting the species to Homo sapiens, the genes of each target pair were cross-referenced with the UniProt protein database to obtain the standardized symbols. A total of 99 target genes associated with the active ingredients and 740 targets related to keloid formation were identified (Table SI). A Venn diagram depicting the overlapping targets between F. arctii and keloid formation showed 29 shared targets (Fig. 2). The details of these targets are provided in Table II.

Figure 2. Common targets of Fructus arctii and keloids.

Table I. A total of eight Fructus arctii active ingredients from the TCMSP database were screened according to oral bioavailability (OB) and drug-likeness (DL).

Table I.

A total of eight Fructus arctii active ingredients from the TCMSP database were screened according to oral bioavailability (OB) and drug-likeness (DL).

Mol ID	Molecule Name	MW	AlogP	Hdon	Hacc	OB, %	Caco-2	BBB	DL	FASA-	HL	Targets
MOL010868	Neoarctin A	742.88	7.44	1	12	39.99	0.32	−0.36	0.27	0.21	5.82	0
MOL000522	Arctiin	534.61	1.81	4	11	34.45	−0.73	−1.5	0.84	0.24	1.41	10
MOL000358	β-sitosterol	414.79	8.08	1	1	36.91	1.32	0.99	0.75	0.23	5.36	38
MOL000422	kaempferol	286.25	1.77	4	6	41.88	0.26	−0.55	0.24	0	14.74	63
MOL001506	Supraene	410.8	11.33	0	0	33.55	2.08	1.73	0.42	0.27	2.72	0
MOL002773	β-Carotene	536.96	12	0	0	37.18	2.25	1.52	0.58	0.33	4.36	22
MOL003290	(3R,4R)-3,4-bis[(3,4-dimethoxy-phenyl) methyl] oxolan-2-one	386.48	3.97	0	6	52.3	0.78	0.17	0.48	0.23	3.07	16
MOL007326	Cynarin(e)	516.49	1.56	7	12	31.76	−0.95	−2.37	0.68	0.37	3.13	0

[i] TCMSP, The Chinese Medicine Systems Pharmacology Database and Analysis Platform; OB, oral bioavailability; DL, drug-likeness.

Table II. Gene names overlapping targets between Fructus arctii and keloid formation.

Table II.

Gene names overlapping targets between Fructus arctii and keloid formation.

Gene number	Target intersection
1	PTGS2
2	PTGS1
3	PIK3CG
4	ADRA1A
5	ADRA1B
6	BAX
7	CASP9
8	JUN
9	CASP3
10	CASP8
11	TGFB1
12	NOS2
13	PTEN
14	DPP4
15	F2R
16	AKT1
17	MAP3K7
18	XDH
19	MMP1
20	CYP3A4
21	AKR1C3
22	SLPI
23	MMP2
24	CAV1
25	CTNNB1
26	MYC
27	GJA1
28	MMP10
29	ADRA1D

Network of drug-active ingredient-target-pathway interactions

The present study used Cytoscape 3.7.0 software to analyze the drug-active ingredient-target-pathway network for F. arctii and keloids. Fig. 3 shows the exported analytical data. A total of eight KEGG pathways linked to keloid development were selected to highlight the ones with targets from F. arctii. It was found that five primary active ingredients potentially interact with 99 targets across eight signaling pathways, suggesting that F. arctii may inhibit keloid development through multiple chemical components, targets and pathways. In the network, the light blue ovals denoted active ingredients, the green diamonds indicated pathways and the red circles represented common targets among components and keloids and between the pathways and keloids.

Figure 3. A drug-active components-common targets-pathways network diagram. The relationship between active ingredients, common targets and pathways was calculated and edges between eight active ingredients, eight pathways and 98 targets were obtained.

Creation of a PPI network and screening of core genes

A PPI network for common genes was constructed using the STRING database. The network comprised 29 nodes and 151 edges, with an average node degree of 10.4 (Fig. 4A; Table SII). Fig. 4B and Table III show the nodes, including MYC, CASP3, PTEN, PTGS2, NOS2, CASP9, CASP8, BAX, CTNNB1 and AKT1, arranged in descending order of their degrees. Research shows that SIT significantly inhibits the progression of various malignant tumors by regulating PTEN expression (30,31). However, investigations on the combined mechanism of action of SIT and PTEN in keloids have yet to be conducted. The authors aim to explore the potential roles and mechanisms of action of SIT and PTEN in keloids. More network connections indicate stronger associations between the proteins. The PPI network suggested that F. arctii treatment affected keloids via multiple pathways, components and targets.

Figure 4. Core PPI network. (A) The diagram of PPI. The larger node degree value meant more connections and deeper color. (B) Top 10 genes according to the highest confidence scores. PPI, protein-protein interaction.

Table III. The top 10 genes with highest confidence scores.

Table III.

The top 10 genes with highest confidence scores.

Rank	Name	Score
1	MYC	0.57059
2	CASP3	0.477329
3	PTEN	0.475638
4	PTGS2	0.475492
5	NOS2	0.473661
5	CASP9	0.473661
5	CASP8	0.473661
5	BAX	0.473661
9	CTNNB1	0.453462
10	AKT1	0.395144

GO and KEGG functional analyses

The results of the GO functional enrichment analysis for molecular functions (MFs), cellular components (CCs) and biological processes (BPs) are illustrated in Fig. 5A and Table SIII. The interaction targets were associated with 198 BPs, 27 CCs and 27 MFs. A histogram depicting the first 10 GO terms with P<0.01 was prepared. The primary BP terms included ‘positive regulation of vasoconstriction’, ‘response to estradiol and the apoptotic process’, ‘response to lipopolysaccharide’, ‘response to xenobiotic stimuli’, ‘response to hypoxia’, ‘positive regulation of smooth muscle cell proliferation’, ‘angiogenesis’, ‘inflammatory response’ and ‘MAPK cascade’. The primary CC terms were ‘caveolae’, ‘caspase complex’, ‘cytoplasm’, ‘macromolecular complex’, ‘membrane raft’, ‘plasma membrane’, ‘extracellular matrix’, ‘nucleus’, ‘death-inducing signaling complex’ and ‘mitochondrion’. The enriched MF terms included ‘enzyme binding’, ‘alpha1-adrenergic receptor activity’, ‘identical protein binding’, ‘peptidase activity’, ‘heme binding’, ‘macromolecular complex binding’ and ‘serine-type endopeptidase activity’.

Figure 5. Functional analysis of GO and KEGG. (A) GO functional analysis of common genes. Abscissa represents the name of the target genes. (B) KEGG enrichment analysis of key genes. Abscissa represents the number of target genes. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological processes; CC, cellular components; MF, molecular functions.

The findings of pathway enrichment analysis indicated significant enrichment of the 28 core targets in 99 pathways (Table SIV). A bubble plot was prepared displaying the top 20 pathways with P<0.01 (Fig. 5B). The top 10 significant pathways included the PI3K-AKT signaling pathway, neurodegeneration pathways, the MAPK signaling pathway, the IL-17 signaling pathway, fluid shear stress and atherosclerosis, AGE-RAGE signaling in diabetic complications, the relaxin signaling pathway, the TNF signaling pathway, focal adhesion and apoptosis. Among these pathways, the PI3K-AKT signaling pathway may exhibit the closest association with keloids.

Molecular docking of the active ingredients of SIT target genes

The molecular model revealed that SIT strongly interacts with PTEN, as depicted in Fig. 6. The space-filling model clearly demonstrated the perfect embedding of SIT within the crystal pocket of PTEN. Similarly, both the ribbon model image and local 2D binding model revealed the specific interactions between SIT and the associated protein residues. After simulating the components using AutoDock tools, a high affinity of −8.3 kcal/mol was computed. The receptor-ligand docking hypothesis suggests an inverse relationship between the docking energy and binding affinity. A lower docking energy indicates stronger binding affinity. SIT engages in van der Waals interactions with the PTEN residues PHE279, VAL275, ILE280, LEU320, THR319, GLN149, ASP324 and ASN323. The stable binding of SIT to PTEN involves the formation of alkyl and pi-alkyl interaction bonds with residues LEU318, TYR176, TYR177, ARG172 and ARG173.

Figure 6. Molecular docking study of β-sitosterol binding to the inhibitory pocket of PTEN. The affinity was −8.3 kcal/mol, indicating a high binding activity.

SIT inhibits the physiological function of KFs

Fig. 7A displays the chemical structure of SIT. To assess the effect of SIT on KFs proliferation and determine the optimal drug concentration for cellular experiments, the changes in cellular activity at 12, 24 and 48 h post-SIT treatment were compared to that in the control group using the CCK-8 assay. Fig. 7B shows that SIT significantly reduced the viability of KFs in a concentration- and time-dependent manner. The treatment of KFs with SIT of various concentrations for 48 h decreased the cell viability from 70 to 5%. The IC50 values for KFs at 12, 24 and 48 h were 70.09, 33.78 and 33.75 µM, respectively. KFs treated with 30 µM SIT for 48 h showed a drug concentration of 33.75 µM, which was the 48 h IC50 value. The inhibitory effects observed at other drug concentrations and time points deviated significantly from the corresponding IC50 values. Subsequently, a drug gradient study on keloid cell cultures was conducted using 15 and 30 µM SIT as well as a drug reversion analysis using 30 µM SIT, both for 48 h.

Figure 7. SIT inhibits the physiological function of KFs. (A) Chemical structure of SIT. (B) The relative cell viability of KFs following treatment with different concentrations of SIT for 12, 24 and 48 h. (C and D) The migration was evaluated by wound healing assay (magnificaion, ×40). (E-H) The number of migrated and the number of invaded cells was evaluated by Transwell assay (scale bar, 110 µm; magnification, ×40). (I and J) The result of EdU staining was presented as the percentage of EdU-positive cells whose nuclei were stained in red (scale bar, 100 µm; magnification, ×100). The results are the mean ± SD (n=3). **P<0.01, ***P<0.001 vs. control. SIT, β-sitosterol; KFs, keloid fibroblast cells; EdU, 5-Ethynyl-2′-deoxyuridine.

SIT inhibits the migration and invasion of KFs

Evidence from previous studies has shown that SIT inhibits the migration of various cancer cells (32). To assess the effect of SIT on the behavior of KFs, scratch and Transwell assays were performed and cell migration and invasion evaluated. The results revealed that in the control group, the scratch gap gradually closed over 24 h. SIT effectively impeded KFs migration in a concentration- and time-dependent manner (Fig. 7C and D). Migration was significantly reduced in cells treated with 15 and 30 µM SIT compared with that in control cells. Furthermore, to assess the migratory and invasive potential of cells, they were treated with SIT at various concentrations over a 24 h treatment period (Fig. 7E and F). The Transwell assay results revealed that treatment with SIT at various concentrations effectively inhibited keloid cell migration and invasion (Fig. 7G and H).

SIT modulates DNA synthesis and proliferation in KFs

The present study assessed DNA synthesis by measuring the incorporation of EdU, a thymidine nucleotide analog, during the S phase of the cell cycle. The EdU incorporation rate indicates the DNA synthesis rate. Fig. 7I and J show the effective inhibition of KFs growth by SIT. Compared with the control treatment, treatment with 15 and 30 µM SIT significantly reduced the number of EdU-labeled cells (P<0.01).

SIT modulates the expression of EMT-associated proteins in a dose-dependent manner

E-cadherin, Vimentin and Snail are established key biomarkers of EMT (33). To determine the importance of EMT in cell migration and invasion (34), the present study evaluated the effect of SIT on EMT regulation in KFs. It was found that the inhibitory effect of SIT on migration is correlated with the attenuation of EMT. The morphological changes in KFs after 48 h of SIT treatment were assessed. Post treatment, the KFs transitioned from a multi-protruding spindle shape to a more regular triangular or short fusiform shape (Fig. 8A). The expression of EMT markers, including E-cadherin, ZO-1 and Vimentin, in SIT-treated KFs were analyzed using western blotting. The expression levels of E-cadherin and ZO-1 increased in a concentration-dependent manner in the SIT-treated groups, whereas the Snail and Vimentin levels decreased in a dose-dependent manner (Fig. 8B and C).

Figure 8. Effect of SIT on PTEN/Akt signaling pathway and EMT in KFs. KFs were untreated or treated with 15 and 30 µM SIT for 48 h. (A) Representative images of treated cells were shown (scale bar, 80 µm; magnification, ×100). (B and C) the expression of the EMT-related proteins in KFs following treatment with SIT by western blotting. (D and E) the expression of PI3K,p-PI3K, Akt, p-Akt and PTEN in KFs following treatment with SIT by western blotting. The results are the mean ± SD (n=3). **P<0.01, ***P<0.001. SIT, β-sitosterol; EMT, epithelial-mesenchymal transition; KFs, keloid fibroblast cells; p-, phosphorylated; ZO-1, zonula occludens-1.

SIT regulates KFs via the PTEN/PI3K/AKT pathway

After it was established that SIT regulates EMT and the expression of associated markers, the present study investigated its inhibitory mechanisms in KFs. SIT treatment significantly increased PTEN protein expression (Fig. 8D and E). Given that PI3K and AKT are crucial downstream targets of PTEN (35), the expression levels of PI3K, p-PI3K, p-AKT and AKT were assessed concurrently. Total cellular proteins were extracted from cells treated with 15 and 30 µM SIT and control cells. SIT treatment reduced p-PI3K and p-AKT expression; however, it did not alter the total PI3K and AKT protein levels. Moreover, the changes in the p-PI3K and p-AKT levels were inversely correlated with the PTEN levels. These findings suggest that SIT may suppress growth of KFs via the PTEN/PI3K/AKT pathway.

Reversing the SIT-mediated inhibition of KFs proliferation, migration, invasion and EMT through PI3K/AKT pathway activation

SIT may suppress KFs growth via the PTEN/PI3K/AKT pathway. To confirm this, rescue experiments were conducted with YS-49, a PI3K/AKT activator (27). Findings from CCK-8 assays indicated that YS-49 treatment significantly reversed the inhibition of proliferation in SIT-treated KFs (Fig. 9A). Additionally, the results of the EdU, scratch and Transwell assays indicated that YS-49 reversed the inhibitory effects on the proliferation, migration and invasion of cells caused by SIT (Fig. 9B-H). Following YS-49 treatment, the PTEN, E-cadherin and ZO-1 levels decreased in SIT-treated KFs, whereas the Snail, Vimentin, p-PI3K and p-AKT levels increased (Fig. 10A-D). Collectively, these findings confirmed that SIT effectively suppresses the proliferation, migration and invasion and EMT progression of KFs by inhibiting the PTEN/PI3K/AKT signaling pathway.

Figure 9. Counteraction of SIT's inhibitory effects on KFs cell proliferation, migration and invasion by PI3K/AKT pathway activation. (A) The effect of SIT on KFs viability was detected following treatment with YS-49 by CCK-8 assay. (B and C) The effect of SIT on KFs proliferation was evaluated following treatment with YS-49 by EdU staining (magnification, ×100). (D and E) The effect of SIT on the migration of KFs following treatment with YS-49 was observed by wound healing assay (scale bar, 50 µm; magnification, ×40). (F-I) The effect of SIT on the invasion and migration of KFs was evaluated following treatment with YS-49 by Transwell assay (scale bar, 110 µm; magnification, ×40). Statistical analyses were performed using one-way ANOVA with Tukey's post hoc test. **P<0.01, ***P<0.001 vs. Control group (without treatment with SIT). ##P<0.01, ###P<0.001 vs. SIT treatment group. SIT, β-sitosterol; KFs, keloid fibroblast cells; EdU, 5-Ethynyl-2′-deoxyuridine.

Figure 10. Counteraction of SIT's inhibitory effects on KFs' EMT and PTEN/PI3K/AKT pathway activation. (A and B) The effect of SIT on KFs' EMT-related proteins was evaluated following treatment with YS-49 by western blotting. (C and D) The effect of SIT on the expression of PI3K, p-PI3K, Akt, p-Akt and PTEN was evaluated following treatment with YS-49 by western blotting. (Experimental data are expressed as: mean ± SD (n=3). Statistical analyses were performed using one-way ANOVA with Tukey's post hoc test. **P<0.01 vs. Control group (without treatment with SIT). ##P<0.01 vs. SIT treatment group. SIT, β-sitosterol; KFs, keloid fibroblast cells; ZO-1, zonula occludens-1.

SIT suppresses KFs xenograft growth by inhibiting PTEN expression

After confirming the in vitro effects of SIT on cell proliferation, migration and invasion, its effect on keloid formation in vivo was assessed using a nude mice model with KFs xenografts. Fig. 11A shows the flowchart of the animal experiments, detailing the model establishment and treatment procedures. Keloid growth was markedly slower in the SIT group than in the saline group, which indicated the inhibitory potential of SIT (Fig. 11D). The results for keloid measurements (Fig. 11B), tumor weight (Fig. 11C) and nude mice weight were consistent (Fig. 11E). The saline group showed the lowest weight, whereas the treatment group showed normal growth trends. Nude mice in the SIT group began losing weight after the 15th day of treatment, probably due to keloid reduction. This indicated effective in vivo anti-keloid effects. To further elucidate the mechanism underlying the inhibition of keloid growth by SIT in vivo, PTEN expression in fibroproliferative tissues was evaluated using immunohistochemistry. SIT treatment markedly increased the PTEN protein levels (Fig. 11F). Notably, H&E staining revealed no significant effects on the heart, liver, kidney, or spleen, indicating the absence of adverse effects of SIT treatment on these vital organs (Fig. S1). No evidence was observed of myocardial fiber disarray, necrosis, or inflammatory infiltration. Cardiac muscle structure and cellular integrity remained intact. No signs of hepatocyte degeneration, necrosis, or inflammatory cell infiltration were observed. There was no indication of glomerular damage, tubular necrosis, or interstitial inflammation. No evidence of hemorrhage or structural disorganization was detected. These results, which are consistent with the in vitro findings, indicated that SIT inhibits keloid growth by modulating PTEN expression in vivo.

Figure 11. In vivo anti- keloid efficacy of SIT in keloid-bearing nude mice. (A) A schematic illustration of the process and timeline of keloid-bearing nude mice. (B) Image of isolated keloid tissues of nude mice. (C) Weight of isolated keloids (n=5; means ± SD), ***P<0.001. (D) Keloid volume of nude mice (n=5; means ± SD), **P<0.01 and ***P<0.001. (E) Body weight of nude mice. (F) Immunocytochemistry results of PTEN of isolated keloids (scale bar, 100 µm). The ×40 image is an enlargement of the dashed box in the ×20 image. SIT, β-sitosterol.

In vivo cytotoxicity

The present study assessed formulation safety via blood biochemical analyses, including those for BUN, CREA, ALT and AST. The levels of BUN, CREA, ALT and AST remained consistently low across groups (Fig. 12). The findings indicate that SIT has a favorable safety profile for keloid treatment in nude mouse.

Figure 12. In vivo safety assessment. (A) BUN, (B) CREA, (C) ALT and (D) AST levels in blood samples are isolated from nude mice following treatment with normal saline and 50 mg/kg SIT, respectively (n=5; means ± SD). BUN, blood urea nitrogen; CREA, creatinine; ALT, alanine aminotransferase; AST, aspartate aminotransferase; SIT, β-sitosterol.

Discussion

Keloids extend beyond the initial dermal lesion, where they proliferate (2). Keloids and tumors share key characteristics, including epigenetic methylation profiles, disease-related biological behaviors and cellular bioenergetics (36). EMT activation increases invasiveness by inducing microenvironmental and phenotypic changes. Transformation involves the downregulation of epithelial genes, such as E-cadherin and ZO-1 genes and the upregulation of mesenchymal genes, such as Vimentin and Snail genes (37). The current treatments available for keloids are unclear, partly due to ineffective outcomes and unavailability of sufficient evidence.

HIF-1α activates the TGF-β/Smad signaling pathway in keloids (38), increasing collagen production by dermal fibroblasts (39). The inhibition of this pathway is a potential strategy for keloid treatment. TGF-β induces abnormal VEGF expression, leading to unusual blood vessel proliferation in the dermis (40). Inflammation and angiogenesis are essential for keloid progression. Additionally, these processes have been linked to the PI3K/AKT signaling pathway (41). Oral CDK12/13 degraders, which target this pathway, have been shown to be promising for therapy (42). TGF-β has been shown to activate the Wnt/β-catenin pathway, which subsequently leads to abnormal fibroblast proliferation and differentiation and impedes scar tissue repair (43). The present study used network pharmacology to determine whether SIT affects keloid progression through the PTEN/PI3K/AKT pathway. The findings suggested that exploring the interactions among the PTEN/PI3K/AKT, TGF-β/Smad and Wnt/β-catenin pathways can provide a novel research direction for SIT-mediated keloid treatment. Keloid pathogenesis is complex, with current treatments primarily including surgery and localized drug injections. These strategies are often associated with high recurrence rates, limited treatment options and adverse effects (44). SIT, a compound found in lipid-rich plants, effectively blocks cell migration and invasion in several cancers by modulating the PI3K/AKT pathway (45). SIT has been shown to regulate EMT in various tumors and fibrotic conditions. STRING analysis identified Caspases 3, 8 and 9 as well as Bax as key apoptotic factors in keloid formation (46,47). Activating these factors is crucial for inducing apoptosis and can potentially affect tissue repair and remodeling. MYC mediates keloid fibroblast proliferation and collagen deposition (48). These findings suggest that the effect of SIT on apoptotic factors could be crucial in treating keloids and represents a promising research direction. Additionally, pathway enrichment analysis revealed a strong link between keloid and the PTEN/PI3K/AKT signaling pathway (Fig. 5B). Findings from molecular docking studies also confirmed that SIT interacts with PTEN to form a stable complex.

Experiments with a PI3K/AKT activator revealed the role of SIT in keloid pathology. SIT treatment increases E-cadherin and ZO-1 levels while decreasing Snail and Vimentin levels, thus affecting EMT in keloids. The present study assessed the safety of the experimental dose by analyzing the levels of serum biochemical markers for liver and kidney function and performing H&E staining on vital organs in nude mice. The selected dose was deemed safe in the nude mouse model, which is consistent with findings from previous studies (49,50). The present study revealed that SIT suppresses keloid proliferation, migration, invasion and EMT via the PTEN/PI3K/AKT pathway. However, the present study had several limitations. First, it focused on the effects of SIT on keloid characteristics such as proliferation, migration, invasion and EMT mediated via the PTEN/PI3K/AKT pathway. A comprehensive understanding requires further investigation into additional pathways and molecules to elucidate fully the mechanism of action of SIT in keloid treatment. Second, it only used a subcutaneous keloid proliferation model in nude mouse for validation. Although this model is standard in keloid research and widely used across various biological disciplines, further studies with alternative models, such as pig skin and in vitro 3D models, are essential for establishing the minimum effective human dose of SIT. Pig skin and in vitro 3D keloid models might yield further information in the future. Finally, SIT may exert a synergistic effect by targeting multiple pathways. Addressing off-target effects, a significant challenge in drug discovery, will be a key focus of future research on this drug (51). Addressing these limitations will help provide crucial experimental evidence for the clinical applications of SIT. The present study aimed to introduce a new treatment for keloids in clinical practice, which could eventually improve the quality of life of patients with this condition. This significant finding has important implications for future innovative treatment strategies for keloids.

The present study was based on the results of network pharmacology research. The findings provided initial evidence that SIT suppresses keloid function by inhibiting the PTEN/PI3K/AKT signaling pathway, potentially impeding keloid growth and invasion. These conclusions were further supported by evidence from experiments conducted with PI3K/AKT activators. Overall, these findings highlighted the promising anti-keloid effects of SIT, indicating its strong safety profile and potential as a novel therapeutic agent for keloids. In the future, the authors aim to delve deeper into the anti-keloid mechanisms of SIT, which could help pave the way for its clinical application in keloid treatment.

Supplementary Material

Supporting Data

Supporting Data

Acknowledgements

Not applicable.

Funding

The present study was supported by National Natural Science Foundation of China (grant no. 82260617).

Availability of data and materials

The data generated in the present study are included in the figures and/or tables of this article.

Authors' contributions

PPH and ZHJ designed the project. PPH, ZNL and SJ performed the experiments. ZNL and SJW made substantial contributions to data analysis. PPH wrote this article. LHZ, SJW and YLL edited the manuscript and made substantial contributions to the bioinformatics analysis. SJ and LHZ supervised the project. ZHJ and LHZ confirm the authenticity of all the raw data. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

The present study was reviewed and approved by the Yanbian University Experimental Animal Welfare Ethics Committee (approval no. YD20230911009). All procedures adhered to the ethical guidelines for animal use set forth by the National Institutes of Health.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

References