Introduction

Although the incidence of colorectal cancer (CRC) has decreased among patients of average onset age in high-income countries, CRC remains the third most commonly diagnosed cancer worldwide, with a rising incidence in emerging economies1. The development of CRC involves multiple pathophysiological processes, including abnormal cell proliferation, differentiation, resistance to apoptosis, local invasion of surrounding tissues, and distant metastasis2. Surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy remain the main treatment options for CRC3. Despite advances in therapy, the mortality rate of CRC remains high, and survival outcomes have shown limited improvement4. Therefore, further investigation into the molecular mechanisms of CRC is urgently needed to develop more effective treatment strategies.

Traditional Chinese Medicine (TCM) has been used for centuries as an integral part of complementary and alternative medicine for a variety of diseases5. Study on TCM formulas and herbs demonstrate their unique holistic approach, characterized by multi-component and multi-target actions6. Moreover, TCM prescriptions, proprietary medicines, monomers, and compounds have shown promising anti-tumor effects7. In CRC treatment, TCM is widely applied to promote tumor cell apoptosis, inhibit metastasis, and reduce drug resistance and side effects8. Shancigu was first recorded in Ben Cao Shi Yi (Supplements to Chinese Materia Medica)9 and is traditionally known for its properties such as clearing heat, detoxification, resolving phlegm, and dissipating masses10. However, the anti-cancer mechanism of Shancigu, particularly in CRC, remains poorly understood and requires further study.

Previous research explored the active components of Danggui Buxue Decoction in metastatic colon cancer through network pharmacology and identified stigmasterol and JUN as key targets11. However, that study centered on a different herbal formula and lacked experimental validation. In contrast, the potential role and mechanisms of Shancigu in CRC have not been systematically studied and remain to be clarified.

TCM has achieved favorable clinical outcomes in complex diseases due to its multi-component, multi-target, and multi-pathway therapeutic approach. There is an increasing need for new methods to elucidate the complex interactions between TCM and diseases12. Network pharmacology, which integrates systems biology and bioinformatics, has become a powerful tool in TCM research13. As a systematic strategy, it aims to predict drug actions and their interactions with multiple targets14. Molecular docking, widely used in drug discovery, is an established in-silico approach for predicting interactions between small molecules and biological targets15. It not only facilitates virtual screening of potential ligands but also provides valuable insights for structure-based optimization and mechanism studies16. The integration of network pharmacology and molecular docking has proven effective in investigating the material basis and mechanisms of TCM in disease treatment. Therefore, this study aimed to explore the potential therapeutic effects and underlying mechanisms of Shancigu against CRC based on network pharmacology.

In this research, we combined network pharmacology analysis with molecular docking, as well as in vitro and in vivo experiments, to investigate the potential targets and molecular mechanisms of Shancigu in CRC treatment, providing a valuable reference for future studies in this field.

Materials and methods

Shancigu target identification and network establishment

The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (https://tcmspw.com/tcmsp.php) was adopted to obtain the Absorption, Distribution, Metabolism, and Excretion (ADME) parameters of Shancigu’s active ingredients. Compounds with drug-likeness (DL) scores ≥ 0.18 were selected. Target prediction for the selected compounds was performed using the SwissTargetPrediction database (http://www.swisstargetprediction.ch/), and targets with a probability greater than zero were retained. The Shancigu active ingredient-target network was then constructed using Cytoscape 3.7.2 software.

Screening of differentially expressed genes in CRC

The original expression matrix of the colon and rectal adenocarcinoma (COAD-READ) dataset was downloaded from The Cancer Genome Atlas (TCGA) database, and the clinical data were processed using R software. Differentially expressed mRNAs were identified based on a false discovery rate (FDR) < 0.05 and fold change ≥ 1.5 (|log₂FC| ≥ 0.585). Volcano plots and heatmaps of the differentially expressed genes were generated.

Intersection and functional analysis of related genes and Shancigu targets

The intersecting genes between those associated with CRC metastasis and invasion and the predicted targets of Shancigu’s active ingredients were identified using the Venn diagram tool jvenn (http://jvenn.toulouse.inra.fr/app/example.html). The Bioconductor package, along with the clusterProfiler and DOSE packages in R, was applied to perform Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses17,18,19.

Construction of intersection gene protein interaction network and analysis of core genes

The intersecting genes were analyzed using the STRING database, and the TSV file was exported and imported into Cytoscape 3.7.2 to construct the protein–protein interaction (PPI) network. Core genes were identified using the CytoHubba plugin, with Degree, Stress, and Betweenness algorithms applied to analyze the PPI network, and distribution maps of each algorithm were generated. The final core genes were determined by taking the intersection of the results from the three algorithms. To further assess the impact of the core genes on CRC patient survival, the Kaplan–Meier Plotter was used for survival analysis.

Molecular docking

To further explore the interactions between Shancigu and key survival-related genes AKT1, AR, FN1, HRAS, ITGB1, and JUN, AutoDock Vina was used for molecular docking. A semi-empirical force field was applied to predict the binding energy between each receptor and ligand. A heatmap of the molecular docking scores was generated. Subsequently, visual analysis using PyMOL confirmed that the compounds could stably bind within the protein binding pockets and interact with surrounding amino acid residues.

Cell culture and treatment

CRC cell lines HCT116 (CTCC-001-0002, Meisen) and Caco-2 (CTCC-001-0020, Meisen) were cultured under standard conditions at 37 °C with 5% CO₂ and saturated humidity. HCT116 cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM, C11965500BT, Gibco) supplemented with 10% fetal bovine serum (FBS, 16000-044, Gibco) and 1% penicillin-streptomycin solution (BL505A, Biosharp). Caco-2 cells were cultured in DMEM supplemented with 20% FBS, 1% Non-Essential Amino Acids (NEAA), and 1% penicillin-streptomycin. Following enzymatic digestion, HCT116 and Caco-2 cells were counted, and 5 × 10⁵ cells were seeded into each well of a 6-well plate. Cells were incubated overnight at 37 °C in a 5% CO2 atmosphere to allow attachment and reach approximately 70–90% confluence the next day. The culture medium was then replaced with fresh medium containing different concentrations of Stigmasterol (0, 1, 10, 30, 100, and 500 µM; 25200-072, Gibco) and incubated for 24–48 h.

Cell counting kit 8 (CCK-8) assay

The CCK-8 assay was performed to evaluate the viability of HCT116 and Caco-2 cells. After washing with PBS, cells were digested into a single-cell suspension using 0.25% trypsin (25200-072, Gibco). Digestion was terminated with complete medium, and the cells were resuspended and counted. The cell concentration was adjusted to 3 × 10⁴ cells/mL. Cells were then seeded into 96-well plates at 100 µL per well, resulting in 3 × 10³ cells per well. Each group was set up in triplicate, and 100 µL of complete medium was added to each well. The plates were incubated at 37 °C with 5% CO₂. After 24–48 h of incubation, CCK-8 reagent was added to each well. Following a 2-h incubation at 37 °C, the absorbance was measured at 450 nm using a microplate reader.

Colony formation assay

HCT116 and Caco-2 cells were harvested, counted, and seeded into 6-well plates at a density of 500 cells/well. After allowing the cells to adhere overnight, they were exposed to various concentrations of Stigmasterol (0, 1, 10, 30, and 100 µM) for 48 h. Following treatment, the culture medium was replaced with fresh complete medium, and the cells were maintained under standard conditions (37 °C, 5% CO₂) for 14 days. Once visible colonies formed, cells were fixed with 4% paraformaldehyde for 20 min and stained with 0.1% crystal violet for 15 min. Excess dye was gently rinsed off with water, and the plates were air-dried. Colonies consisting of more than 50 cells were counted under a light microscope.

Flow cytometry

Flow cytometry was used to assess apoptosis in HCT116 and Caco-2 cells using the Annexin V-APC/PI Apoptosis Kit (E-CK-A217, Elabscience). After treatment, cells were harvested and centrifuged at 300 g for 5 min. The supernatant was discarded, and the cell pellet was collected. A total of 1–5 × 10⁵ cells were resuspended in 500 µL of diluted 1× Annexin V Binding Buffer after centrifugation under the same conditions. Subsequently, 5 µL of Annexin V-APC reagent and 5 µL of PI solution (50 µg/mL) were added to the cell suspension. After gentle mixing, the cells were incubated for 15–20 min in the dark. Apoptosis was immediately analyzed using flow cytometry.

Transwell assays

Cells in the logarithmic growth phase were harvested, washed once with PBS, and enzymatically dissociated into a single-cell suspension using 0.25% trypsin. The cell suspension was centrifuged at 800 rpm for 5 min. After discarding the supernatant, cells were resuspended in basal medium, and the concentration was adjusted to 1 × 10⁶ cells/mL. For the migration assay, 100 µL of the cell suspension was seeded into the upper chamber of the Transwell insert, while 600 µL of complete medium was added to the lower chamber. After incubation for 48 h, non-migrated cells on the upper surface of the membrane were gently removed with a cotton swab. Cells that migrated to the lower surface were fixed with 4% paraformaldehyde. The Transwell staining kit (BL710A, Biosharp) was used to stain the cells for 10 min. After rinsing once with PBS, the migrated cells were observed under a microscope to assess their ability to pass through the membrane pores. For the invasion assay, Matrigel was thawed at 4 °C overnight and then mixed with pre-cooled basal medium at a ratio of 1:3 (Matrigel: medium). A total of 40 µL of the mixture was added to the pre-cooled Transwell insert and allowed to solidify by incubation for 2 h. The subsequent steps were performed following the same procedure as the migration assay.

Western blot

The expression levels of Stigmasterol-binding proteins ITGB1 and JUN in cells and tissues were determined by Western blot analysis. Total proteins were extracted using RIPA lysis buffer (R0010, Solarbio Life Science), and protein concentrations were measured with a BCA protein assay kit (BL521A, Biosharp). After extraction and quantification, 20 µg of protein samples were separated on 12% polyacrylamide gels by SDS-PAGE and transferred onto PVDF membranes. The membranes were blocked with 5% bovine serum albumin (BSA) and then incubated with the appropriate primary antibodies, including ITGB1 (12594-1-AP, 1:1000, Proteintech), JUN (24909-1-AP, 1:1000, Proteintech), and GAPDH (60004-1-Ig, 1:50000, Proteintech) as a loading control. Following overnight incubation at 4 °C, the membranes were washed and then incubated with HRP-conjugated secondary antibodies—either anti-rabbit IgG (BL003A, 1:50000, Biosharp) or anti-mouse IgG (BL001A, 1:50000, Biosharp)—for 1 h at room temperature. After washing five times with TBST (5 min each), the protein bands were visualized using enhanced chemiluminescence (ECL). GAPDH served as the internal reference to normalize the protein levels, and the relative expression of ITGB1 and JUN was compared among different treatment groups.

Organoid culture and treatment and adenosine triphosphate (ATP) detection

CRC tissues were obtained from three patients diagnosed with CRC based on imaging, serology, or histopathological examination at Foshan Fosun Chanchen Hospital. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Medical Ethics Committee of Foshan Fosun Chanchen Hospital (Approval No. CYEC-LCYJ-2024081-PJ-20240926). Fresh tumor tissues from the three CRC patients were collected and immediately preserved in tissue preservation solution at 4 °C for subsequent experiments. The samples were transferred into 35 mm culture dishes containing PBS supplemented with 1% penicillin-streptomycin. After removing adjacent normal and necrotic tissues, the CRC samples were minced into 1–2 mm2 fragments. The tissue fragments were then placed into 10 mL centrifuge tubes, allowed to settle by gravity, and the supernatant was discarded. An appropriate volume of CRC organoid digestion solution was added based on tissue volume, and the mixture was incubated at 37 °C on a shaker for 1–2 h. After initial digestion, the tissue suspension was pipetted up and down 10 times using a 1 mL pipette for further dissociation. The cell suspension was filtered through a 100 μm cell strainer, centrifuged at 300 g for 5 min, and the supernatant was discarded. The pellet was resuspended in 5 mL of adDMEM/F12+++ medium, centrifuged again under the same conditions, and the supernatant was removed. According to the pellet volume, CRC organoid culture medium and Matrigel were mixed at a 1:1 ratio to resuspend the cells. Pre-warmed 24-well plates (incubated at 37 °C for 2 h) were removed from the incubator, and 50 µL of the mixture was added to each well. The plates were placed in the incubator at 37 °C for 5 min and then inverted for solidification. After the Matrigel solidified, CRC organoid culture medium was added. Organoid growth was observed daily, and the medium was refreshed every 2–3 days20. For treatment, CRC organoids from three patients were exposed to varying concentrations of Stigmasterol (0, 1, 10, 30, 100, and 500 µM) and cultured for 48 h. ATP levels were then measured using the CellCounting-Lite 3D assay. The reagent was added to each well, and organoid masses were thoroughly lysed by vigorous shaking for 5 min. After incubation for 25 min to stabilize the luminescent signal, ATP levels were detected.

Ethics and inclusion statement for animal experiment

All experimental procedures were conducted in accordance with relevant guidelines and regulations. All animal protocols were approved by the Animal Ethics Committee of Guangzhou Forevergen Medical Laboratory Animal Center (Approval No. IACUC-AEWC-F2309001). Animal experiments were performed following the ARRIVE guidelines, the IACUC Handbook (Third Edition), and the American Veterinary Medical Association (AVMA) Guidelines for the Euthanasia of Animals (2020).

Animals

Twenty specific-pathogen-free (SPF) BALB/c nude mice, aged 4 weeks and weighing 14–16 g, were purchased from Guangzhou Rigor Bio-Technology Co., Ltd (Guangzhou, China). A subcutaneous tumor model was established using HCT116 cells. After digestion and counting, the cells were resuspended in 100 µL PBS at a concentration of 2 × 10⁶ cells per 100 µL. The skin under the rib cage of each mouse was disinfected with 70% ethanol, and the cell suspension was injected subcutaneously using a 1 mL syringe. The needle was inserted approximately 1 cm deep at an angle of about 15°. After the injection, a sterile cotton swab was gently pressed on the site to prevent cell leakage. Once the subcutaneous tumors reached an average volume of 85 mm³, the mice were randomly assigned to three groups: Control, SCG, and Stigmasterol groups. The Control group received an equal volume of distilled water daily for 20 days. The SCG group was administered Shancigu orally at 600 mg/kg once daily for 20 days. The Stigmasterol group received Stigmasterol at 50 mg/kg once daily for 20 days. Tumor volumes were measured every 3 days throughout the treatment period. At the end of the experiment, tumor tissues were harvested for subsequent analysis. Mice were euthanized using CO₂. CO₂ gas was introduced into the euthanasia chamber at a flow rate of 10–30% of the chamber volume per minute until the mice became immobile, ceased breathing, and exhibited pupil dilation. CO₂ flow was then stopped, and the mice were observed for an additional 2 min to confirm death. The in vivo experiments were carried out in triplicate under identical conditions, with 6 mice assigned to each group per replicate.

Haematoxylin and eosin (H&E) staining

H&E staining was performed to evaluate tumor histopathology and inflammatory cell infiltration. After fixation, the fresh tissue samples were embedded in paraffin and sectioned at a thickness of 3–8 μm. The sections were deparaffinized twice in xylene for 5–10 min each time and rehydrated through a graded ethanol series. The sections were stained with hematoxylin solution (G1100, Solarbio) and rinsed with distilled water to remove excess stain. Differentiation was carried out using a differentiation solution, followed by eosin staining. After removing excess eosin, the sections were quickly dehydrated. The sections were sequentially immersed in 75%, 85%, 95%, and 100% ethanol (I) for 2–3 s each, then transferred to 100% ethanol (II), cleared in xylene, sealed with neutral resin, and examined under a light microscope.

Immunohistochemistry (IHC)

Ki67 expression was assessed by IHC. Tissue sections were baked at 60 °C, deparaffinized, and rehydrated. Antigen retrieval was performed using heat-induced epitope retrieval. Endogenous peroxidase activity was blocked with 3% H2O2 for 10–15 min. Sections were then incubated with 5% bovine serum albumin (BSA) for 15 min to block nonspecific binding. Next, the sections were incubated with the primary antibody against Ki67 (27309-1-AP, 1:100, Proteintech) overnight at 4 °C. After washing, an HRP-conjugated secondary antibody was applied and incubated for 1 h. The staining was visualized using DAB. Subsequently, the sections were counterstained with hematoxylin, differentiated in 1% hydrochloric acid alcohol, and blued for 10 min. Finally, the sections were dehydrated through a graded ethanol series (75%, 95%, and 100%), cleared in xylene, sealed with neutral resin, and examined under a light microscope.

Statistical analysis

Statistical analysis was conducted utilizing GraphPad Prism 8.0 software. The measurement data were presented as mean ± standard deviation (SD) from at least three independent experiments. Differences between multiple groups were analyzed using one-way analysis of variance (ANOVA), followed by Dunnett’s multiple comparisons test. P < 0.05 was deemed statistically significant.

Results

Active ingredients and corresponding targets of Shancigu

The potential targets of Shancigu’s active ingredients were initially retrieved from the TCMSP and SwissTargetPrediction databases. A total of 18 active compounds were identified (Table 1). Based on these results, a network of Shancigu’s active ingredients and their corresponding targets was constructed. In total, 366 potential targets were predicted for the 18 actives compounds (Fig. 1).

Table 1 The main chemical components of shancigu.
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Fig. 1
figure 1

Shancigu-active ingredients-target network. Blue represents Shancigu, purple represents active ingredients, and green represents targets.

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Differentially expressed genes related to metastasis and invasion of CRC

The original expression matrix of the COAD-READ dataset was downloaded from TCGA database. Clinical data were organized and processed using R software to extract the expression matrix related to CRC metastasis and invasion. A total of 19,331 differentially expressed mRNAs (DEmRNAs) were identified, including 8102 upregulated and 11,229 downregulated mRNAs. As shown in Fig. 2A, a volcano plot was generated to visualize the genes associated with CRC metastasis and invasion, with red indicating upregulated genes and blue indicating downregulated genes. Figure 2B presents the heatmap of the gene expression matrix related to CRC metastasis and invasion.

Fig. 2
figure 2

Differentially expressed genes related to metastasis and invasion of CRC. A Volcano map of genes associated with metastasis and invasion of CRC. Red indicates upregulated genes, while blue indicates downregulated genes. B Clustering heat map of genes associated with CRC metastasis and invasion.

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Intersection of gene associated with CRC metastasis and invasion and Shancigu target and functional analysis

Next, a Venn diagram was used to identify the overlap between genes associated with CRC metastasis and invasion and the predicted targets of Shancigu’s active ingredients, resulting in 365 intersecting genes (Fig. 3A). DO analysis indicated that 58 of these genes were enriched in CRC, including PTGS2, STAT1, SRC, BCHE, MAT2A, CASP3, PIK3CG, BCL2/BAX, CASP9, CASP8, TGFB1, MMP3, ESR2, DPP4, MAPK14, GSK3B, ACHE, HDAC1, EGFR, CES2, SHH, VDR, and others (Fig. 3B). GO enrichment analysis revealed that these intersecting genes were mainly involved in the following Biological Processes (BP): regulation of membrane potential, regulation of body fluid levels, response to alcohol, adenylate cyclase-modulating G protein-coupled receptor signaling pathway, regulation of postsynaptic membrane potential, and chemical synaptic transmission. Cellular Component (CC) enrichment included synaptic membrane, membrane raft, and postsynaptic membrane. Molecular Function (MF) analysis showed enrichment in neurotransmitter receptor activity, extracellular ligand-gated ion channel activity, and postsynaptic neurotransmitter receptor activity (Fig. 3C). KEGG pathway analysis further demonstrated that the intersecting genes were primarily enriched in neuroactive ligand–receptor interaction, calcium signaling pathway, proteoglycans in cancer, sphingolipid signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and nitrogen metabolism (Fig. 3D).

Fig. 3
figure 3

Intersection of gene associated with CRC metastasis and invasion and Shancigu target and functional analysis. A Intersection genes between CRC metastasis and invasion and the relevant action targets of the active ingredients of Shancigu. B–D DO, GO, and KEGG enrichment analysis of intersection genes.

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Construction of intersection gene protein interaction network and analysis of core genes

Next, the intersection genes were analyzed using the STRING database, and the protein–protein interaction (PPI) network of these genes was visualized (Fig. 4A). The cytoHubba plug-in was then employed to identify core genes. Degree, Stress, and Betweenness algorithms were applied to analyze the PPI network, and the distribution of scores from each algorithm was visualized (Fig. 4B, D). The results of the three algorithms were intersected, yielding 18 core genes (Fig. 4E). To further investigate the prognostic significance of these core genes in CRC, survival analysis was performed using the Kaplan–Meier Plotter. The analysis revealed that AKT1, AR, FN1, HRAS, ITGB1, and JUN were significantly associated with CRC patient survival and prognosis (P < 0.01, Fig. 4F, K).

Fig. 4
figure 4

Construction of intersection gene protein interaction network and analysis of core genes. A Intersection gene protein interaction network. B–D. Degree, stress and betweenness algorithms were selected to analyze intersection gene protein interaction network. E Intersection genes of three algorithms (degree, stress and betweenness). F Survival analysis of AKT1. G Survival analysis of AR. H Survival analysis of FN1. I Survival analysis of HRAS. J Survival analysis of ITGB1. K Survival analysis of JUN.

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Molecular docking of significant survival related genes with monomer of Shancigu

The autodock vina was used to study the interaction between monomer of Shancigu and significant survival related genes AKT1, AR, FN1, HRAS, ITGB1 and JUN. The PDB ID of AKT1 was 1UNQ, the PDB ID of AR was 1E3G, the PDB ID of FN1 was 1FNA, the PDB ID of HRAS was 2CE2, the PDB ID of ITGB1 was 3G9W, and the PDB ID of JUN was 5FV8. Figure 5A showed that the main chemical components of Shancigu were basically combined with significant survival related genes AKT1, AR, FN1, HRAS, ITGB1 and JUN. Among them, AKT1 and AR were most closely bound to the compound MOL007991 (2-methoxy-9,10-Dihydrophenanthren-4,5-diol) with energy scores of − 6.2 kcal mol−1 and − 5.8 kcal mol−1, respectively. FN1 was most closely bound to the compound MOL007990 (Militarin_qt) with an energy fraction of − 6.2 kcal mol−1. HRAS was most closely bound to MOL000358 (beta-sitosterol) with an energy score of − 8.8 kcal mol−1. ITGB1 and JUN were most closely bound to the compound MOL000449 (Stigmasterol), with energy scores of − 6.6 kcal mol−1 and − 7.2 kcal mol−1, respectively. Figure 5B and G further visualized the docking results. Among them, ITGB1 and JUN were most closely bound to the compound Stigmasterol and scored the highest. Therefore, Stigmasterol and Stigmasterol-binding proteins ITGB1 and JUN were subsequently selected for research. Considering the docking results comprehensively, the selection of Stigmasterol was further supported by its simultaneous strong binding to two survival-related core genes, which was not observed with other compounds. Although beta-sitosterol showed the strongest binding to HRAS, it was excluded from further validation due to extensive prior studies. Based on both binding affinity and biological relevance, Stigmasterol was considered the most suitable candidate for subsequent experimental verification.

Fig. 5
figure 5

Molecular docking of significant survival related genes with monomer of Shancigu. A Heat map of molecular docking score. B Molecular docking of AKT1 with compound MOL007991. C Molecular docking of AR with compound MOL007991. D Molecular docking of FN1 with compound MOL007990. E Molecular docking of HRAS with compound MOL000358. F Molecular docking of ITGB1 with compound MOL000449. G Molecular docking of JUN with compound MOL000449.

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Stigmasterol inhibited CRC cell viability, proliferation, migration, and invasion

To investigate the effects of Stigmasterol on CRC cells, we performed a series of in vitro experiments. The CCK-8 assay revealed that Stigmasterol significantly reduced the viability of HCT116 and Caco-2 cells in a concentration-dependent manner (Fig. 6A). Furthermore, colony formation assays showed that Stigmasterol suppressed the long-term proliferative capacity of both cell lines, as indicated by the decreased number and size of colonies with increasing concentrations of Stigmasterol (Fig. 6B). Flow cytometry analysis demonstrated that Stigmasterol promoted apoptosis in HCT116 and Caco-2 cells, with apoptotic rates increasing along with Stigmasterol concentrations (Fig. 6C). Transwell assays further confirmed that Stigmasterol inhibited the migration and invasion abilities of CRC cells in a dose-dependent manner (Fig. 6D). Western blot analysis showed that Stigmasterol reduced the protein expression levels of ITGB1 and JUN in both HCT116 and Caco-2 cells, consistent with its inhibitory effects on CRC cell functions (Fig. 6E).

Fig. 6
figure 6

Stigmasterol regulated CRC cell viability, proliferation, apoptosis, migration, and invasion. A CCK-8 assay detection of HCT116 and Caco-2 cell viability. B Colony formation assay assessed the long-term proliferative capacity of HCT116 and Caco-2 cells following Stigmasterol treatment. C Flow cytometry determination of HCT116 and Caco-2 cell apoptosis. D Transwell assays detection of HCT116 and Caco-2 cell migration and invasion abilities. E Western blot analysis of Stigmasterol-binding proteins ITGB1 and JUN expressions. Data represent the mean ± SD of three independent biological replicates. *P < 0.05, **P < 0001, ***P < 0.001.

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Stigmasterol regulated the viability of organoids in CRC

In addition, we explored the role of Stigmasterol at the organoid level. CRC organoids (3 cases) were treated with increasing concentrations of Stigmasterol (0, 1, 10, 30, 100, and 500 µM). Compared with 0 h, after 48 h of treatment, the viability of CRC organoids in all three cases decreased, accompanied by abnormal morphological changes and even cell death. These effects became more pronounced as the Stigmasterol concentration increased (Fig. 7A). Moreover, ATP activity in the three CRC organoid samples gradually decreased following treatment with 1, 10, 30, 100, and 500 µM Stigmasterol compared to the 0 µM group (Fig. 7B). Our results indicated that Stigmasterol regulated the viability of CRC organoids.

Fig. 7
figure 7

Stigmasterol regulated the viability of organoids in CRC. A Changes in morphology and viability of CRC organoids after Stigmasterol concentration gradients (0, 1, 10, 30, 100, and 500 µM) treatment. B ATP activity detection of CRC organoid. Data represent the mean ± SD of three independent biological replicates. *P < 0.05, ***P < 0.001.

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Shancigu and its monomer stigmasterol regulated the development of CRC in animal experiment

Finally, the effects of Shancigu and its monomer Stigmasterol on CRC were further investigated through animal experiments. Tumor images are shown in Fig. 8A. Tumor weight was markedly reduced in the SCG and Stigmasterol groups compared to the Control group (Fig. 8B). H&E staining revealed that, compared to the Control group, necrotic areas in tumor tissue and inflammatory cell infiltration were reduced in the SCG and Stigmasterol groups (Fig. 8C). Additionally, the expression of the tumor proliferation marker Ki67 was decreased in both the SCG and Stigmasterol groups, along with downregulation of the Stigmasterol-binding proteins ITGB1 and JUN (Fig. 8D, E). These results demonstrated that Shancigu and its monomer Stigmasterol regulated CRC progression in vivo.

Fig. 8
figure 8

Shancigu and its monomer Stigmasterol regulated the development of CRC in animal experiment. A Tumor images. B Tumor weight. C H&E staining of tumor histopathology and inflammatory cell infiltration. D IHC staining of the changes of tumor proliferation factor Ki67. E Western blot analysis of Stigmasterol-binding proteins ITGB1 and JUN expressions. All statistical data represent the mean ± SD from three independent biological experiments (n = 6 per group in each replicate). *P < 0.05, **P < 0.01, ***P < 0.001.

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Discussion

CRC is a highly heterogeneous malignancy, and the search for new therapeutic strategies remains an urgent challenge21. Shancigu has shown potential anti-cancer effects against CRC, yet its underlying molecular mechanisms are not fully understood. In this study, we investigated the mechanism of Shancigu in CRC using a network pharmacology approach, combined with validation at the cellular, organoid, and animal levels, focusing particularly on the key CRC-related genes JUN and ITGB1. Our findings suggested that Stigmasterol, an active component of Shancigu, may participate in regulating CRC cell proliferation and invasion by targeting JUN and ITGB1. The selection of Stigmasterol for further validation was driven by both molecular docking results and biological pathway relevance. Although beta-sitosterol exhibited the strongest binding affinity in docking studies, it was excluded due to extensive prior research. By contrast, Stigmasterol demonstrated strong binding to both ITGB1 and JUN, two critical genes implicated in CRC progression, making it a more promising candidate for subsequent experimental validation.

Network pharmacology is a research method based on multidisciplinary holistic analysis of biological systems, aligning with the holistic approach of Chinese medicine22. The action mechanism of TCM prescriptions exhibits the characteristics of multitarget and multilevel effects. This mechanism aligns with holistic, systematic, and comprehensive nature of network pharmacology, making network pharmacology well-suited for studying pharmacological mechanisms of TCM compounds23. In this study, a total of 18 active ingredients of Shancigu were identified through network pharmacology analysis, corresponding to 366 potential target genes. Additionally, 19,331 differentially expressed mRNAs were screened from the COAD-READ database, including 8102 upregulated and 11,229 downregulated mRNAs. Venn diagram analysis revealed 365 overlapping genes between the CRC metastasis- and invasion-related genes and the predicted targets of Shancigu’s active compounds. DO analysis further identified 58 genes enriched in CRC. GO enrichment analysis indicated that biological processes such as regulation of membrane potential, regulation of body fluid levels, response to alcohol, adenylate cyclase-modulating G protein-coupled receptor signaling, regulation of postsynaptic membrane potential, chemical synaptic transmission, membrane raft formation, neurotransmitter receptor activity, and extracellular ligand-gated ion channel activity may be involved in CRC progression. Furthermore, KEGG pathway analysis showed that the main enriched pathways included neuroactive ligand–receptor interaction, calcium signaling, proteoglycans in cancer, sphingolipid signaling, AGE-RAGE signaling in diabetic complications, and nitrogen metabolism. These pathways may play key roles in mediating the therapeutic effects of Shancigu against CRC. However, further experimental validation is warranted.

Based on the STRING database and the Degree, Stress and Betweenness algorithms, we screened 18 core genes. Survival and prognosis analysis showed that AKT1, AR, FN1, HRAS, ITGB1 and JUN had significant effects on survival and prognosis of CRC. At present, molecular docking is becoming an important tool for drug discovery and molecular modeling applications24, which aims to predict binding conformation of small molecular ligands with suitable target binding sites25. We then studied the interaction between Shancigu and significant survival related genes AKT1, AR, FN1, HRAS, ITGB1 and JUN through molecular docking. Among them, ITGB1 and JUN were most closely bound to the compound Stigmasterol and scored the highest. Since JUN and ITGB1 expressions were negatively correlated with CRC survival, Stigmasterol was closely bound to JUN and ITGB1 and scored the highest. Therefore, JUN and ITGB1 may serve as key targets for Stigmasterol’s involvement in CRC treatment. A previous study identified stigmasterol and JUN as key components in Danggui Buxue Decoction for metastatic colon cancer through network pharmacology analysis11. However, their research focused primarily on computational predictions without experimental validation. In comparison, our study investigated Shancigu, a different herbal medicine, and combined network pharmacology with experimental validation across cell, organoid, and animal models. Notably, we also identified ITGB1 as an additional target involved in CRC progression, which was not addressed in the previous work. These findings expand current knowledge on the potential mechanisms of Stigmasterol and support its role in regulating JUN and ITGB1 in CRC.

Stigmasterol is an unsaturated phytosterol belonging to tetracyclic triterpenoids26. Research has shown that Stigmasterol had anti-cancer, anti-inflammatory, anti-diabetes, immunomodulatory, anti-fungal, antibacterial, antioxidant and neuroprotective properties27. A rodent model study suggested that phytosterols might prevent the development of CRC by inhibiting dysregulated cell cycle processes and inducing apoptosis28. Stigmasterol was detected as the main active substance in the study of mechanisms of Ardisia gigantifolia Stapf., Xiao-Chai-Hu-Tang, Yiyi Fuzi Baijiang powder in the treatment of CRC29,30,31, indicating Stigmasterol might play a positive role treating CRC. ITGB1 is an important member of the transmembrane glycoprotein signaling receptor family and the core of the integrin family32. Previous research has demonstrated Ropivacaine inhibited CRC cell proliferation and migration via ITGB133. Yin Yang 1 improved the prognosis of CRC by regulating ITGAV and ITGB134. In addition, p53-induced miR-30e-5p inhibited CRC invasion and metastasis through targeting ITGA6 and ITGB135. Therefore, targeting ITGB1 is a strategy to treat CRC. Activator protein-1 (AP-1) is a model of extracellular signaling transcription response, and its components include JUN36. Research has shown that silencing NOB1 could affect the proliferation and apoptosis of CRC cells through c-Jun N-terminal kinase pathway37. Moreover, silencing COPB2 gene repressed CRC cell proliferation and elevated apoptosis via JNK/c-Jun signaling pathway38. However, the mechanism of JUN in CRC remains to be further explored. CRC organoids, a three-dimensional in vitro model, have become a valuable tool for replicating tumor biology, showing promise in scientific research, disease modeling, drug screening, and personalized medicine39. Therefore, in this study, through cell and organoid experiments, we found that Stigmasterol could affect CRC cell proliferation and invasion abilities, and decrease viability of CRC organoids, abnormal morphological changes and even death. Through animal experiments, we found that the weight and volume of tumors were reduced, the necrotic area of tumor tissue decreased, and the expression of the tumor proliferation marker Ki67 was suppressed, suggesting that Shancigu and its monomer Stigmasterol could be involved in regulating CRC development. The results of both cell and animal assays further demonstrated that Stigmasterol treatment downregulated JUN and ITGB1 expression. Therefore, we speculated that Stigmasterol might participate in CRC proliferation and invasion by targeting these key CRC-related genes. In this study, HCT116 cells were chosen for the xenograft model due to their high tumorigenic potential and stable growth in nude mice, which allowed for reliable evaluation of tumor progression and therapeutic effects. In contrast, Caco-2 cells are known to have low tumorigenicity and slow growth in vivo, making it challenging to obtain comparable or statistically meaningful results under the same experimental conditions. This limitation has also been reported in previous study40. In future work, we plan to include the Caco-2 xenograft model to further validate our findings and explore the differential in vivo behavior of CRC cell lines. This is also the first study to report the potential role of Stigmasterol in regulating CRC key genes JUN and ITGB1.

Conclusion

Based on network pharmacology and experimental validation, our study demonstrated that Stigmasterol may exert inhibitory effects on CRC proliferation and invasion by targeting the key genes JUN and ITGB1 across cellular, organoid, and animal models. These findings provide new insights into the potential mechanisms of Stigmasterol in CRC progression and suggest promising molecular targets and therapeutic strategies for CRC treatment.