Cardamonin (CDN, purity> 99.5%) was purchased from Lemeitian Medicine (Chengdu, China). The following compounds were obtained from MedChemExpress (Shanghai, China): 5-fluorouracil (5-FU, HY-90006), Ferrostatin-1 (Fer-1, HY-100579), Necrostatin-1 (Nec-1, HY-15760), 3-Methyladenine (3-MA, HY-19312), Z-VAD-FMK (HY-16658B), Stattic (HY-13818), Garcinone D (GarD, HY-N6953), and Upadacitinib (Upa, HY-19569). Assay kits for detecting aspartate aminotransferase (AST), alanine aminotransferase (ALT), blood urea nitrogen (BUN), and creatinine (CREA) were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). Primary antibodies against CDK1 (19532-1-AP), CDK4 (11026-1-AP), p21 (10355-1-AP), γ-H₂AX (83307-2-RR), E-cadherin (20874-1-AP), N-cadherin (22018-1-AP), vimentin (10366-1-AP), Ki-67 (28074-1-AP), Bax (50599-2-Ig), Bcl-2 (12789-1-AP), caspase-3 (19677-1-AP), caspase-9 (10380-1-AP), PARP (13371-1-AP), STAT3 (10253-2-AP), GAPDH (60004-1-Ig), and β-tubulin (10068-1-AP) were purchased from Proteintech (Wuhan, China). Antibodies against phospho-STAT3 (Tyr705), phospho-JAK1 (Tyr1022/Tyr1023), and phospho-JAK2 (Tyr1007/1008) were obtained from ZenBio (Chengdu, China).

Network pharmacology analysis was performed as follows. Potential targets associated with CRC were collected from the GeneCards (https://www.genecards.org/) (Stelzer et al., 2016) and the Online Mendelian Inheritance in Man (OMIM) database (https://omim.org/) (Amberger et al., 2015). Putative targets of the CDN were predicted using the SwissTargetPrediction (https://www.SwissTargetPrediction.ch/) (Daina et al., 2019) and BATMAN-TCM platforms (http://bionet.ncpsb.org.cn/batman-tcm/) (Kong et al., 2024). All acquired targets were combined, and duplicate entries were removed to create a unique target list. The overlapping targets between CDN and CRC were identified using Venny plot. These overlapping targets were then submitted to the STRING database (http://cn.string-db.org/) to construct a protein-protein interaction (PPI) network. The network was built with the following parameters: a minimum interaction score of 0.7, the organism limited to Homo sapiens, and disconnected nodes were hidden. The resulting PPI network was imported into Cytoscape (https://cytoscape.org/) for visualization and topological analysis (Shannon et al., 2003). Hub genes were identified using the CytoHubba plugin based on three centrality measures: degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC). Functional enrichment analysis was performed on the overlapping targets. Specifically, Gene Ontology (GO) biological process enrichment was conducted using Metascape (https://metascape.org/) (Zhou et al., 2019), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was carried out with the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://davidbioinformatics.nih.gov/) (Sherman et al., 2022). The top 15 significantly enriched biological processes and KEGG pathways were visualized using the online platform bioinformatics. com.cn (https://www.bioinformatics.com. cn/).

Three-dimensional (3D) crystal structures of JAK1 (PDB ID: 6AAH), JAK2 (PDB ID: 7F7W), and STAT3 (PDB ID: 6NUQ) were obtained from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (https://www.rcsb.org/). The molecular structure of CDN was retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). To prepare the proteins for docking, water molecules and co-crystallized ligands were removed from the protein structures. The grid box parameters, including the central coordinates and dimensions, were defined based on the predicted binding sites of each protein-ligand complex to encompass the potential interaction region. Molecular docking simulations were performed using AutoDock Vina (http://vina.scripps.edu/) (Trott and Olson, 2010) to dock CDN against JAK1, JAK2, and STAT3. The binding pose with the lowest predicted binding energy for each protein was selected for further analysis. Hydrogen bonding interactions between CDN and the key residues of the target proteins were analyzed using PyMOL to elucidate the binding mode and identify critical interaction sites.

Human CRC cell lines (HCT116, RKO, and SW620) and the normal colonic mucosal epithelial cell line NCM460 were obtained from the Shanghai Cell Bank, Chinese Academy of Sciences (Shanghai, China). The CRC cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (Keygen Biotechnology Co., Ltd., Nanjing, China), supplemented with 10% fetal bovine serum (FBS, Gibco, Oklahoma, United States). NCM460 cells were maintained in RPMI 1640 medium containing 10% FBS. All cells were routinely cultured in a humidified incubator with 5% CO₂ at 37 °C.

Cell viability was assessed using the Cell Counting Kit-8 (CCK-8, Beyotime, China, Cat. No. C0037) according to the manufacturer’s instructions. Briefly, cells were seeded in 96-well plates at a density of 5 × 10³ cells per well. After 24 h, the cells were treated with a gradient of CDN concentrations (0, 1, 2, 4, 8, 16, 32, and 64 µM) for an additional 48 h. Subsequently, CCK-8 was added to each well, and the plates were incubated at 37 °C for 2 h. The optical density (OD) at 450 nm was measured using a microplate reader.

Cells were seeded into 6-well plates at a density of 1 × 10³ cells per well. After 24 h of incubation, the cells were treated with CDN (0, 4, and 8 μM) for 48 h. Subsequently, the medium was replaced with CDN-free fresh medium, and the cells were cultured for an additional 14 days to allow colony formation. The resulting colonies were fixed with 4% paraformaldehyde for 20 min and stained with 0.1% crystal violet for 20 min. The number of visible colonies was manually counted.

Cells were seeded in 24-well plates at a density of 5 × 10⁴ cells per well. After adherence, the cells were treated with CDN (0, 8, and 16 μM) for 48 h 5-ethynyl-2’-deoxyuridine (EdU) was then added to the culture medium, and incubation continued for additional 4 h. Thereafter, cells were fixed with 4% paraformaldehyde for 20 min and permeabilized with 0.1% Triton X-100. The Click-iT reaction was performed to detect incorporated EdU following the manufacturer’s protocol. Cell nuclei were counterstained with Hoechst 33,342 for 10 min. Fluorescence images were captured using an Olympus FV3000 confocal microscope (Olympus Corporation, Japan), and the ratio of EdU-positive cells to total Hoechst-positive cells was calculated.

Cell viability and mortality were assessed using a Calcein-AM/PI double staining kit (Beyotime, China, Cat. No. C2015) according to the manufacturer’s instructions. Cells were seeded in 35-mm dishes at a density of 1 × 10⁵ cells per plate. After adherences, the cells were treated with CDN (0, 8, and 16 μM) for 48 h. Following treatment, the cells were incubated with a Calcein AM/PI solution at 37 °C for 30 min in the dark. The cells were then gently washed with PBS to remove excess dye. Live cells and dead cells were observed and imaged using an Olympus FV3000 confocal microscopes.

Lactic dehydrogenase release levels were measured using the Lactic Dehydrogenase Release Assay Kit (Cat. No. C0016, Beyotime, China) according to the manufacturer’s instructions. Briefly, cells were seeded in 96-well plates at a density of 5 × 10³ cell per. After 24 h, the cells were treated with various concentrations of CDN for 48 h. After treatment, the supernatant from each well was carefully collected and transferred to a new 96-well plate. The LDH detection reagent was added to the supernatant, and the mixture was incubated on a shaker at room temperature for 30 min protected from light. The absorbance was measured at 490 nm using a microplate reader.

MMP assays were performed according to our previously described protocols (Liu et al., 2025). Cells were seeded in 6-well plates (1 × 10⁵ cells per well) for 24 h. After treatment with CDN for 48 h, cells were harvested, washed with PBS. The cell suspension was incubated with JC-1 staining solution at 37 °C for 20 min, washed twice, and analyzed by flow cytometry (Sony SA3800). The MMP was quantified by the ratio of red fluorescence to green fluorescence.

Intracellular ROS levels were quantified using a Reactive Oxygen Species Assay Kit (Cat. No. S0033, Beyotime, China) following the manufacturer’s protocols. Briefly, cells were seeded into 6-well plates at a density of 1 × 10⁵ cells per well and allowed to adhere overnight. Subsequently, cells were treated with CDN at the specified concentrations for 48 h. After treatment, cells were incubated with 10 μM DCFH-DA at 37 °C in the dark for 20 min. Following two washes with serum-free medium to remove excess probe, the fluorescence intensity was measured using a Sony SA3800 spectral cell analyzer.

Apoptosis was assessed using an Annexin V-FITC assay kit (Beyotime, China, Cat. No. C1062) as described previously (Nong et al., 2022). Briefly, cells were seeded in 6-well plates at a density of 1 × 10⁵ cells per well and treated with CDN at various concentrations for 48 h. Thereafter, the cells were washed with PBS and resuspended in binding buffer. The suspensions were then incubated with Annexin V-FITC and propidium iodide (Keygen Biotech, Nanjing, China) for 15 min at the room temperature in the dark. Apoptosis was analyzed using a Sony SA3800 spectral cell analyzer.

Cell migration was assessed using the scratch assay according to our previously described protocols (Liu et al., 2025). Briefly, cells were seeded into 6-well plates at a density of 5 × 10⁵ cells per well and cultured until full confluence. A straight scratch was created in the cell monolayer using a sterile 200 µL pipette tip. After washing with PBS to remove detached cells, serum-free medium containing varying doses of CDN was added. The plates were incubated and images of the scratches were captured at predefined time points. The migration ability was evaluated by measuring the rate of wound closure.

Transwell assays were conducted according to our previously described protocols (Liu et al., 2025). Cells were resuspended in serum-free medium containing various concentrations of CDN and seeded into the upper chamber of Matrigel-precoated Transwell inserts at a density of 1 × 10⁵ cells per well. The lower chamber was filled with medium supplemented with 10% FBS as a chemoattractant. After 48 h of incubation, non-invading cells on the upper surface of the membrane were gently removed with a cotton swab. Invading cells on the lower surface were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and quantified by counting six random fields per membrane under a microscope.

Cell cycle assays were performed using a Cell Cycle Detection Kit (KGA512, Keygen, Nanjing, China) following the manufacturer’s protocols. Cells were seeded in 6-well plates at a density of 2 × 10⁵ cells per well for 24 h and then treated with CDN for 48 h. Cells were harvested, fixed in 70% cold ethanol, and stored at 4 °C overnight. Prior to analysis, the fixed cells were washed with PBS, and stained with PI/RNase A buffer for 30 min in the dark. Cell cycle distribution was determined by analyzing DNA content via flow cytometry (Sony SA3800).

Western blot assays were performed according to our previously described methods (Liu et al., 2025; Wu et al., 2025). Briefly, protein samples were homogenized and lysed in lysis buffer to extract total proteins. After quantifying the protein concentration, equal amounts of protein were separated by SDS-PAGE and transferred to a polyvinylidene fluoride (PVDF) membrane. The membrane was blocked with 5% skim milk for 1 h, followed by overnight incubation with primary antibodies specific to the target proteins at 4 °C with agitation. After thorough washing with TBST, horseradish peroxidase-conjugated secondary antibodies were added and incubated for 1 h at room temperature. Protein bands were visualized using an ECL detection system. Band intensities were quantified by analyzing grayscale values with ImageJ software, and relative protein expression levels were normalized to internal control proteins to correct for loading variations.

Tumor tissues harvested from mice were fixed in 10% formalin and subsequently embedded in paraffin. After embedded, tissue sections were deparaffinized and stained with hematoxylin and eosin (H&E). Immunohistochemical staining was then performed using previously established protocols (Liu et al., 2022a; Ruan et al., 2024).

Animal experiments were performed as described in our previous methods (Liu et al., 2025). Fifty female BALB/c nude mice, aged 4–5 weeks, were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). They were housed in a specific pathogen-free facility with controlled conditions (temperature: 25 °C ± 2 °C, humidity: 50% ± 5%), a 12-h light/dark cycle, and ad libitum access to food and water. The experimental procedures and animal care protocols were approved by the Animal Ethics Committee of North Sichuan Medical College (Approval No.: NSMC 2025062). After a 5-day acclimation period, HCT116 cells (5 × 10⁶ cells suspended in 0.2 mL PBS) were injected subcutaneously into the right axillary region of each mouse. Once the average tumor volume reached 50 mm³, mice were randomly assigned to four groups (n = 6 per group): control group (daily intraperitoneal injection of saline), low-dose CDN group (daily intraperitoneal injection of 10.0 mg/kg CDN), high-dose CDN group (daily intraperitoneal injection of 20.0 mg/kg CDN), and 5-FU group (intraperitoneal injection of 5-FU at 20.0 mg/kg every other day). Body weight and tumor volume were measured every 2 days. After 14 days of treatment, mice were anesthetized using 2% isoflurane inhalation. Blood samples were collected, and plasma was prepared by centrifugation at 4 °C for 10 min. Tumor tissues and vital organs (heart, liver, spleen, lung, and kidney) were excised and weighed. Tumor volume was calculated using the formula: Volume = (length × width²)/2. The biosafety assessment of CDN included evaluating hepatic and renal function through plasma levels of AST, ALT, CREA, and BUN. It also assessed CDN-induced pathological changes in the heart, liver, spleen, lung, and kidney using H&E staining.

Blood was collected from mice into EDTA-coated tubes, and plasma was subsequently prepared according to an established protocol (Liu et al., 2022b). For the hemolysis assay, whole blood samples were centrifuged at 3000×g for 10 min to isolate erythrocytes. After being washed twice with PBS, the erythrocytes were resuspended in PBS to prepare a 4% (v/v) suspension. Then, 40 µL of this erythrocyte suspension was mixed with 1 mL of serially diluted CDN solutions (25, 50, 100, 200, 400, 800, and 1600 μg/mL). For the control groups, 40 μL of the same erythrocyte suspension was mixed with 1 mL of normal saline (negative control) or distilled water (positive control), respectively. All mixtures were incubated at 37 °C for 2 h in a humidified incubator, followed by centrifugation at 3,000×g for 10 min. The optical density of the supernatant was measured at 545 nm using a spectrophotometer, and the hemolysis rate was calculated using the formula: Hemolysis (%) = [(OD_sample- OD_negative control)/(OD_positive control - OD_negative control)] × 100%.

Statistical analysis was completed using SPSS Statistics Software (v23.0). GraphPad Prism (v8.0.1) was used for data visualization. All data were presented as means ± standard error of the mean (SEM) from at least three independent experiments. The significance of differences between groups was determined using one-way analysis of variance and Student’s t-test. A p-value <0.05 was considered statistically significant.

Potential targets of CDN were predicted using the SwissTargetPrediction and BATMAN-TCM databases, yielding 171 candidates. Concurrently, 17,642 CRC-related targets were retrieved from the GeneCards and OMIM databases (Figure 1A). Intersection analysis identified 170 overlapping targets common to both CDN and CRC. A CDN-CRC target network was constructed using Cytoscape based on these 170 targets (Figure 1A). PPI network analysis was then conducted with the CytoHubba plugin, and 33 hub genes were identified based on topological features (Figure 1B). Among these, STAT3, JAK1, IL1B, AKT1, EGFR, ESR1, and NFKB1 showed high connectivity and centrality, suggesting their core regulatory roles.

Functional enrichment analysis of the 170 overlapping targets was performed. GO terms were significantly enriched in biological processes such as regulation of apoptotic signaling, cell proliferation, lipid metabolism, and response to reactive oxygen species (Figure 1C). KEGG pathway analysis revealed the top 15 enriched pathways, most of which were associated with tumor development. Among these, pathways in cancer, apoptosis, and the JAK-STAT signaling pathway showed the highest enrichment scores (Figure 1D). These results suggest that the anti-CRC effect of CDN is primarily mediated through modulation of the JAK/STAT signaling pathway and its influence on apoptosis.

To further investigate the interaction between CDN and key components of the JAK/STAT3 pathway, molecular docking simulations were carried out using AutoDock Vina. The results indicated that CDN exhibited strong binding affinity for JAK1, JAK2, and STAT3. The calculated binding free energy (ΔG) values were −6.86 ± 0.34 kcal/mol (pKi = 5.03 ± 0.26) for JAK1, -6.75 ± 0.46 kcal/mol (pKi = 4.95 ± 0.33) for JAK2, and -6.79 ± 0.55 kcal/mol (pKi = 4.98 ± 0.41) for STAT3, respectively (Figure 1E). Further analysis of the binding modes using PyMOL revealed that CDN forms hydrogen bonds with key residues in each protein: Arg-1002, Asp-1039, and Asp-1042 in JAK1; Glu-627 and Val-629 in JAK2; and Tyr-539, Trp-501, and Val-537 in STAT3 (Figure 1E).

We performed comparative transcriptomic analysis using mRNA expression profiles from 286 primary CRC tissues and 41 adjacent normal colon tissues obtained from The Cancer Genome Atlas (TCGA) (Figures 2A,B). The analysis revealed significant upregulation of IL6 and IL11 in CRC tissues compared with normal samples (Figure 2C). Overall survival (OS) analysis of the CRC cohort indicated that high expression of IL6, IL6R, IL11, IL11RA, JAK1, and STAT3 was associated with significantly poorer survival outcomes (Figure 2D). Gene Set Enrichment Analysis (GSEA) further confirmed strong activation of the JAK/STAT3 signaling pathway in CRC tissues (Figure 2E).

To further investigate the cellular-level expression patterns, we analyzed single-cell RNA sequencing (scRNA-seq) data from 12 CRC patients in the GSE108989 dataset (Figure 2F). The results showed significantly elevated expression of JAK1, STAT3, IL6ST, and IL6R, while JAK2 expression remained unchanged (Figures 2G–L). GSEA based on single-cell-derived gene signatures also indicated strong activation of the JAK/STAT3 signaling axis in CRC, consistent with the TCGA cohort results (Figure 2M).

Together, these multi-dimensional analyses provide compelling evidence that the JAK/STAT3 signaling pathway is aberrantly hyperactivated in CRC. These findings clarify the pathogenic role of this pathway in CRC progression and highlight its potential as a therapeutic target.

The chemical structure of CDN is shown in Figure 3A. We evaluated the anti-proliferative activity of CDN by treating three human CRC cell lines (HCT116, RKO, and SW620) with increasing concentrations (0–64 μM). CCK-8 assays showed that CDN inhibited cell viability in a dose- and time-dependent manner (Figure 3B). The half-maximal inhibitory concentration (IC₅₀) values at 24, 48, and 72 h were as follows: 22.6, 15.9, and 14.2 μM for HCT116; 17.0, 15.7, and 13.1 μM for RKO; and 19.3, 17.5, and 16.3 μM for SW620. Given their relatively higher sensitivity, HCT116 and RKO cells were selected for subsequent experiments. Consistent with the CCK-8 results, colony formation assays confirmed that CDN significantly reduced the clonogenic ability of both cell lines (Figure 3C). EdU incorporation assays further validated the anti-proliferative effect of CDN (Figure 3D).

To assess the selectivity of CDN, we compared its effects on CRC cells and the normal colonic epithelial cell line NCM460 using Calcein-AM/PI double staining. CDN exhibited strong cytotoxicity against CRC cells but had negligible effects on NCM460 cells (Figure 3E). The IC₅₀ values of CDN in NCM460 cells at 24, 48, and 72 h were 33.9, 24.6, and 24.3 μM, respectively (Figure 3F). The corresponding selectivity indices (SI) for CDN were calculated: for HCT116 cells, the SIs were 1.5 (24 h), 1.55 (48 h), and 1.71 (72 h); for RKO cells, 1.99 (24 h), 1.57 (48 h), and 1.85 (72 h); and for SW620 cells, 1.76 (24 h), 1.41 (48 h), and 1.49 (72 h). Collectively, these results demonstrate that CDN selectively inhibits the proliferation of CRC cells while showing minimal toxicity to normal colonic epithelial cells.

Cell migration and invasion are critical drivers of tumor progression and malignant transformation (Keleg et al., 2003; Novikov et al., 2021). To investigate whether CDN influences these processes in CRC, we performed wound-healing and Transwell invasion assays. The results showed that CDN treatment significantly suppressed the migration and invasion of HCT116 and RKO cells in a dose-dependent manner (Figures 4A,B). To explore the underlying mechanism, we examined the expression of EMT-related markers by Western blot. CDN treatment increased the expression of E-cadherin and decreased the levels of N-cadherin and vimentin in both cell lines (Figure 4C), indicating that CDN inhibits migration, invasion, and EMT in CRC cells.

Mitochondrial membrane potential (MMP) dysfunction, characterized by loss of MMP and accumulation of reactive oxygen species (ROS), is a hallmark of mitochondria-mediated apoptosis and a well-established mechanism of action for many antitumor agents (Liu et al., 2025; Yang et al., 2024; Yevale et al., 2025). To determine whether CDN affects mitochondrial function, we measured MMP in HCT116 and RKO cells using JC-1 staining. The results showed that CDN treatment induced a significant, dose-dependent reduction in MMP (Figure 5A). Quantitative analysis showed that compared with controls, 8 μM and 16 μM CDN decreased MMP by 41.4% and 56.8% in HCT116 cells, and by 54.6% and 62.1% in RKO cells, respectively. Given the consistency between these results and the apoptotic phenotypes observed in our Annexin V/PI staining assays (Figure 6A), we conclude that CDN-induced MMP loss is a key event in triggering apoptotic signaling in CRC cells.

ROS accumulation not only exacerbates mitochondrial impairment but also directly triggers apoptosis by damaging cellular components and activating pro-apoptotic signaling cascades (Wang Z. et al., 2021). Given the close reciprocal relationship between mitochondrial dysfunction and ROS generation, we next assessed intracellular ROS levels. Flow cytometry analysis showed a concentration-dependent increase in ROS production in both cell lines following CDN treatment (Figure 5B). This elevated ROS level, in turn, exacerbates mitochondrial damage and promotes apoptosis-a finding consistent with the observed MMP loss.

Many anticancer agents exert their anti-proliferative effects by disrupting the cell cycle (Li et al., 2025). Moreover, ROS overproduction is a well-documented cause of cell cycle arrest, as it modulates DNA damage responses and regulates key cycle-regulatory proteins (Mackova et al., 2024; Sahoo et al., 2022). To determine whether CDN causes cell cycle arrest, we investigated its effect on cell cycle distribution. Flow cytometric analysis showed that CDN induced cell cycle arrest with distinct patterns between the 2 cell lines: HCT116 cells were arrested primarily in the G₀/G₁ phase, whereas RKO cells accumulated in the G₂/M phase (Figure 5C). Corroborating these findings, Western blot analysis demonstrated that CDN treatment upregulated γ-H2AX and p21, and downregulated CDK1 and CDK4 (Figure 5D). These results suggest that CDN-induced ROS triggers DNA damage (as marked by γ-H2AX), leading to p21 upregulation, which subsequently mediates cell cycle arrest by inhibiting CDK1/4.

Apoptosis plays a critical role in cancer cell survival, making it a key target for novel anticancer drug discovery (An et al., 2019). Therefore, to elucidate the mechanism underlying the anti-CRC activity of CDN, we first examined apoptotic cell death using Annexin V/PI staining and flow cytometry. As shown in Figure 6A, CDN treatment resulted in a significant, dose-dependent increase in the percentage of apoptotic cells in both CRC cell lines. Consistent with these findings, the release of lactic dehydrogenase (LDH)-a marker of plasma membrane integrity loss-was also elevated in a concentration-dependent manner (Figure 6B). To identify the specific cell death pathway involved, HCT116 and RKO cells were pretreated with 10 μM of inhibitors targeting different death modalities: the pan-caspase inhibitor Z-VAD-FMK (apoptosis), Nec-1 (necroptosis), 3-MA (autophagy), and Fer-1 (ferroptosis). Strikingly, only Z-VAD significantly attenuated the inhibitory effect of both low- and high-dose CDN on the viability of HCT116 and RKO cells (Figure 6C), whereas the other inhibitors showed no significant effect. Moreover, Z-VAD effectively reversed CDN-induced apoptotic cell death, as shown by a marked reduction in the apoptotic population (Figure 6D).

We next examined the expression of key apoptotic regulators by Western blot. CDN treatment increased the expression of pro-apoptotic Bax, decreased anti-apoptotic Bcl-2, and enhanced the levels of cleaved caspase-3, cleaved caspase-9, and cleaved PARP in both cell lines (Figure 6E). Taken together, these results demonstrate that CDN induces cell death in CRC cells primarily through a caspase-dependent apoptotic pathway.

To further elucidate the mechanism of CDN-induced apoptosis, we performed proteomic profiling of HCT116 cells treated with CDN according to established protocols. A total of 7015 proteins were identified, each identified with at least two unique peptides at a 1% false discovery rate (FDR). Among these, 6203 proteins quantified in at least three of six biological replicates (Supplementary Table) were included in subsequent analysis. Proteomics data have been deposited at Figshare database under DOI: 10.6084/m9.figshare.30529295. Principal component analysis (PCA) showed clear separation between CDN-treated and control groups, with tight clustering within groups, indicating high reproducibility (Figure 7A). Volcano plot analysis identified 248 differentially expressed proteins (DEPs) (fold change >1.5, p < 0.05), including 108 upregulated (red) and 140 downregulated (blue) in the CDN-treated group compared to control (Figure 7B). Hierarchical clustering confirmed that the majority of DEPs exhibited reduced abundance following CDN treatment (Figure 7C). PPI network analysis using the STRING database revealed three major functional modules enriched in cell cycle regulation, JAK/STAT3 signaling, and apoptosis (Figure 7D). GO term enrichment analysis indicated that DEPs were associated with biological processes including cell cycle regulation, DNA damage response, and apoptotic processes (Figure 7E). KEGG pathway analysis highlighted apoptosis, programmed cell death, JAK/STAT3 signaling, and DNA damage response among the most significantly enriched pathways (Figure 7F). Consistent with these findings, quantitative analysis showed upregulation of apoptosis-related proteins (CASP8, CASP9, CASP3, BAX, BAD, TRADD, BIRC6) and downregulation of JAK/STAT3 signaling components (STAT3, JAK1, FOXC1, TCF12) in the CDN-treated group, notably, whereas ADAM17 and GFER were upregulated. Marked expression changes were also observed for cell cycle regulators (CDK1, CDK4, CDK6, CDKN1A) (Figure 7G). GSEA further confirmed enrichment of gene signatures related to apoptosis, p53 signaling, JAK/STAT3 signaling, and DNA damage response (Figure 7H). Together, these results demonstrated that CDN induces cytotoxicity in CRC cells primarily through JAK/STAT3 pathway-mediated apoptosis, consistent with our initial network pharmacology predictions.

Following a multi-dimensional analysis, subsequent in vivo experiments were performed to validate the therapeutic potential of targeting the JAK/STAT3 signaling pathway. Western blot analysis showed that CDN treatment caused a significant, dose-dependent decrease in the phosphorylation levels of JAK1 (at Tyr1022/Tyr1023), JAK2 (at Tyr1007/Tyr1008), and STAT3 (at Tyr705) (Figure 8A). These findings were corroborated by immunofluorescence assays, which yielded consistent results (Figure 8B). Notably, the combination of CDN and Upadacitinib (Upa), a JAK1/2 inhibitor, exhibited a synergistic effect: it markedly promoted apoptosis, reduced cell viability, and further decreased the expression of phosphorylated JAK1/2 (Figures 8C–E). Moreover, compared with CDN alone, the combination treatment led to increased expression of the pro-apoptotic proteins Bax and cleaved caspase-3/9, along with a significant reduction in the anti-apoptotic protein Bcl-2 (Figure 8E).

Subsequently, HCT116 and RKO cells were treated with both CDN and GarD, a STAT3-specific agonist. In comparison to CDN monotherapy, the combination resulted in a significant increase in cell viability and a marked decrease in apoptosis (Figures 8F,G), suggesting that STAT3 activation counteracts the pro-apoptotic effect of CDN. Consistent with these observations, Western blot analysis showed that GarD co-treatment reversed CDN-induced alterations in apoptosis-related proteins (Figure 8H). In contrast, combining CDN with Stattic, a STAT3-specific inhibitor, produced the opposite effect to that of GarD (Figures 8I–K). Collectively, these findings provide compelling evidence that CDN suppresses colorectal cancer by inhibiting the JAK/STAT3 signaling pathway.

The anti-tumor efficacy of CDN was evaluated in a subcutaneous HCT116 xenograft model (Figure 9A). CDN at 20.0 mg/kg significantly inhibited tumor growth, with an anti-tumor effect comparable to that of 5-FU (20.0 mg/kg). A trend toward decreased tumor weight and volume was also observed in the 10.0 mg/kg CDN group (Figures 9B–D). Immunohistochemical (IHC) analysis further demonstrated that CDN treatment resulted in a dose-dependent decrease in Ki-67-positive cells (Figure 9E). Biosafety assessments indicated a favorable toxicity profile for CDN. No significant differences in body weight (Figure 9F) or relative organ weights (heart, liver, spleen, lungs, and kidneys) were found between CDN-treated and model groups (Figure 9G). H&E staining confirmed the absence of pathological alterations in these organs (Figure 9H). Plasma levels of hepatic (ALT, AST) and renal (CREA, BUN) function markers in CDN-treated mice were comparable to those in the model group (Figure 9I). In contrast, 5-FU treatment induced marked toxicity, characterized by hepatic and renal vacuolation, widened renal septa (Figure 9H), significantly increased levels of ALT, AST, CREA, and BUN, and decreased red blood cell count and hemoglobin levels (Figure 9I). Furthermore, CDN showed negligible hemolytic activity, with a hemolysis rate of only 4.9% at the maximum concentration (1600 μg/mL), which is below the 5% safety threshold (Figure 9J). Consistent with our in vitro findings, Western blot analysis of tumor tissues revealed that CDN treatment downregulated p-JAK1/2 and p-STAT3 expression while upregulating the pro-apoptotic proteins Bax, cleaved caspase-3, and cleaved caspase-9. Additionally, CDN increased E-cadherin expression and decreased N-cadherin and vimentin levels, indicating that CDN suppresses EMT (Figure 9K). In summary, these in vivo results demonstrate that CDN effectively inhibits tumor growth by targeting the JAK/STAT3 pathway to promote apoptosis and suppress EMT, while maintaining a favorable biosafety profile.