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Article

Exploring the Treatment of Cinnamomum Cassia Leaf Extract in Ulcerative Colitis: Network Pharmacology and In Vitro Investigations

by
Zhuoya Zhang
,
Junrong Guo
,
Zurun Huang
,
Xiuyan Zheng
and
Ping Xiong
*
Department of Pharmaceutical Engineering, South China Agricultural University, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Plants 2026, 15(5), 706; https://doi.org/10.3390/plants15050706
Submission received: 9 January 2026 / Revised: 8 February 2026 / Accepted: 18 February 2026 / Published: 26 February 2026
(This article belongs to the Section Phytochemistry)
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Abstract

Cinnamomum cassia essential oil production generates substantial waste, and the therapeutic potential of non-volatile constituents from cinnamomum cassia leaves in ulcerative colitis (UC) has not been fully explored. This research focused on identifying the principal components of cinnamomum cassia leaf extract (CCLE) through ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS), and its anti-inflammatory potential was verified in vitro. A lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage model was employed, with assessments performed through cell viability assays, Griess assay, fluorescent probe detection, wound healing, and Transwell migration assays. Network pharmacology analysis combined with molecular docking revealed that CCLE exerts therapeutic effects against UC by targeting key molecules including TNF, TLR4, STAT3, SRC, PTGS2, NFKB1, MMP9, EGFR, BCL2, and AKT1, with high binding affinity between these targets and CCLE components (especially Quercetin, Catechin, Naringenin, 3′,4′-dimethoxyflavonol, Procyanidin Bl, and Caffeic acid). Enrichment analysis indicated that the therapeutic effect of CCLE on UC was significantly associated with the PI3K-Akt signaling pathway, B cell receptor signaling pathway, NF-κB signaling pathway, TNF signaling pathway, and JAK-STAT signaling pathway. The experimental results demonstrated that CCLE markedly reduced the production of nitric oxide (NO) and reactive oxygen species (ROS) (* p < 0.05) and inhibited macrophage migration (* p < 0.05). In conclusion, CCLE appears to ameliorate UC via a multi-target regulatory mechanism involving inflammatory signaling pathways. These outcomes offer a scientific foundation for the further development of CCLE.

Graphical Abstract

1. Introduction

Ulcerative colitis (UC) [1] is a chronic, relapsing inflammatory disorder characterized by continuous mucosal inflammation affecting the rectum and distal colon. Patients typically present with common symptoms including abdominal pain, rectal urgency, and bloody diarrhea, and long-term inflammation may lead to intestinal fibrosis, strictures, or colon cancer associated with colitis [2]. The etiology of UC is multifactorial, involving genetic susceptibility, environmental factors, dysbiosis, and immune system abnormalities [3,4]. Current therapeutic options include thiopurines, 5-aminosalicylic acid, corticosteroids, and biologics. Although these drugs can induce disease remission, more than 15% of patients still require surgery [5]. In recent years, despite some progress in drug therapy, its efficacy remains only 30% to 60% [6], and long-term use may gradually lead to drug resistance. Some patients may also experience allergic reactions, bloating, nausea, headaches, and other side effects [7]. In Traditional Chinese Medicine (TCM), UC falls under the categories of “diarrhea,” “chronic dysentery,” and “bloody stools.” TCM has a long history in the management of UC and is gaining recognition for its minimal side effects [8]. Increasing research is highlighting the treatment of UC as an adjunct or alternative to Western medicine in TCM, providing new perspectives and theoretical support for UC treatment. TCM focuses on holistic regulation in UC treatment, and cinnamon leaf, as a traditional medicinal plant, may alleviate UC inflammation through its non-volatile components acting on multiple targets, which aligns well with the principles of TCM.
Cinnamomum cassia (L.) D. Don is a Lauraceae plant whose bark and leaves hold significant medicinal values, and the bark is commonly utilized in TCM and as a spice [9]. The main components of cinnamon leaves include volatile oils, terpenes, phenolic glycosides, and flavonoids [10,11]. Numerous studies have demonstrated the broad biological activities of cinnamomum cassia essential oil, including antioxidant, anti-inflammatory, anticancer, and immunomodulatory effects, making it widely used in medicine and food [12]. As the demand for cinnamomum cassia essential oil continues to increase, the resulting cinnamomum cassia leaf residues and waste liquids from its extraction are also increasing [13]. Finding effective ways to utilize these waste materials and develop their potential value has become one of the current research hotspots. Yang et al. [14] identified 101 compounds in the water extract of cinnamomum cassia leaf residues, including phenolic acids, terpenoid compounds, glycosides, lactones, and flavanols, with 40 of these compounds showing potential antioxidant activity. Wu et al. [15] further studied the extract of cinnamomum cassia leaf residues and found that it exhibited excellent antioxidant and anti-inflammatory activities. Cinnamomum cassia leaves are mainly used for extracting cinnamomum cassia essential oil. While the volatile fraction of cinnamomum cassia leaves has been studied extensively, non-volatile components remain comparatively underexplored. Current studies have preliminarily demonstrated that the non-volatile substances have antioxidant and anti-inflammatory effects, providing theoretical support for their application in inflammatory diseases.
Network pharmacology is an emerging approach that integrates systems biology, genomics, proteomics, and other fields to elucidate multi-target drug mechanisms by analyzing large-scale biological data [16,17]. TCM prescriptions typically have characteristics such as multiple components, diverse targets, and multiple pathways. Due to their complex composition, traditional research methods struggle to clarify their specific mechanisms of action [18,19]. Network pharmacology aligns with TCM’s holistic concept, enabling systematic analysis of synergistic multi-target mechanisms, and has thus been widely applied in TCM research [20]. Through the method of network pharmacology, the potential pharmacological mechanisms of cinnamomum cassia leaf extract (CCLE) in the treatment of UC can be explored in depth.
This study aimed to systematically characterize the chemical composition of CCLE and to investigate its potential therapeutic mechanisms in UC, thereby providing a theoretical foundation for the high-value utilization of cinnamomum cassia leaf by-products. Specifically, ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was employed to identify the major components of CCLE and assess its effects on RAW264.7 macrophages in vitro. Furthermore, network pharmacology approaches were employed to elucidate the potential mechanisms underlying CCLE’s therapeutic effects on UC, offering a theoretical basis for its further development.

2. Results

2.1. Analysis of CCLE Components

The chemical composition of CCLE was analyzed using UPLC-QTOF-MS (Figure 1). In total, 29 compounds were identified by combining relevant literature and databases, including thirteen phenolic acids, eight flavonoids, two organic acids, one proanthocyanidin, one lactone, one carbohydrate, one amino acid and its derivative, one benzene and its derivative, and one coumarin (Table 1).

2.2. Target Screening Results

The database was utilized to predict the targets of the 29 CCLE compounds, resulting in 380 active ingredient targets. The Gene Cards, OMIM, TTD, and DGT databases were searched using the keyword “Ulcerative colitis” to identify disease targets, yielding 1242, 8, 51, and 111 disease targets, respectively. After removing duplicates, 1326 disease targets remained. The overlap between the 380 CCLE targets and the 1242 UC targets identified 145 potential action targets, and a Venn diagram was created (Figure 2). These targets may be associated with the therapeutic effects of CCLE on UC.

2.3. PPI Network Construction Results

The network (Figure 3A) consisted of 144 nodes, representing different targets, and 2415 edges, representing the interactions between targets. After screening using Degree, Betweenness, and Closeness parameters, 31 core targets were selected, with Degree > 33.54, Betweenness > 122.89, and Closeness > 0.0038. The network consisted of 31 nodes and 396 edges. Further analysis of these core targets was identified via the MMC algorithm in Cytoscape, as illustrated in Figure 3B. Among the core targets, the top 10 by Degree value are TNF, TLR4, STAT3, SRC, PTPRC, PTGS2, PPARG, PDGFRA, PARP1, and NFKB1. The Degree value was proportional to the size of each node, with colors ranging from dark to light. This suggests that CCLE may exert its anti-UC effects through multiple targets.
Table 1. CCLE Compounds.
Table 1. CCLE Compounds.
No.RT (min)CompoundsFormulaMass (Da)Found at Mass (Da)Mass Error (ppm)AdductProduct IonsCASArea
CCLE10.605Protocatechuic acidC7H6O4154.03153.021.03[M − H]−115.0299-50-3822,607
CCLE20.843Caffeic acid 3-(beta-1-glucoside)C15H18O9342.10365.091.75[M + Na]+229.15, 104.1124959-81-7386,039
CCLE31.160Glyceric acidC3H6O4106.03165.041.54[M + CH3COO]−96.96, 78.96, 75.01473-81-4302,774
CCLE41.481Gallic acidC7H6O5170.02169.010.25[M − H]−161.05, 147.07, 125.02149-91-7261,446
CCLE51.5862-Hydroxy-2-methylbutyric acidC5H10O3118.06254.161.29[2M + NH4]+112.08, 97.03, 85.033739-30-884,680
CCLE61.726Methyl-p-coumarateC10H10O3178.06179.070.35[M + H]+166.05, 147.04, 137.0619367-38-5376,982
CCLE71.886PaeonolC9H10O3166.06167.072.96[M + H]+160.10, 155.07,136.06552-41-0477,646
CCLE81.966L-PhenylalanineC9H11NO2165.08166.090.49[M + H]+136.06, 120.08, 103.0563-91-21,638,487
CCLE92.408Paraxylic AcidC9H10O2150.07151.070.62[M + H]+133.06, 105.07, 74.06619-04-580,512
CCLE102.587Quercetin-3-O-glucose-6″-acetateC23H22O13506.11522.121.09[M + NH4 − 2H]−465.23, 216.98, 167.0454542-51-743,235
CCLE113.132Procyanidin B1C30H26O12578.14577.133.15[M − H]-455.18, 395.05, 357.0920315-25-783,608
CCLE123.294CatechinC15H14O6290.08289.070.48[M − H]-165.02, 137.02, 93.03154-23-4207,875
CCLE133.649Caffeic acidC9H8O4180.04179.030.18[M − H]-177.02, 167.04,152.01501-16-657,799
CCLE143.786Syringic acidC9H10O5198.05197.051.76[M − H]-177.08, 165.06, 157.05530-57-493,144
CCLE153.996Methyl cinnamateC10H10O2162.07163.080.82[M + H]+153.13, 133.06, 123.12103-26-46,438,879
CCLE164.097EugenolC10H12O2164.08233.085.01[M + Na + HCOOH]+149.10, 126.09, 95.0597-53-0336,947
CCLE174.347p-Coumaric acidC9H8O3164.05163.042.09[M − H]−137.05, 119.05, 107.05501-98-4285,238
CCLE184.602Sinapic acidC11H12O5224.07223.061.59[M − H]−221.05, 193.05, 179.037362-37-0242,133
CCLE194.763p-Coumaryl alcoholC9H10O2150.07149.060.06[M − H]−137.02, 93.0320649-40-5159,374
CCLE204.905QuercetinC15H10O7302.04301.033.10[M − H]−243.00, 221.08, 187.10117-39-599,293
CCLE215.0473,4-Dimethoxycinnamic acidC11H12O4208.07207.070.95[M − H]−183.10, 177.06, 161.0214737-89-450,461
CCLE225.407CoumarinC9H6O2146.04147.040.15[M + H]+103.05, 91.0591-64-511,631,077
CCLE235.568Cinnamic acidC9H8O2148.05149.060.64[M + H]+133.10, 131.05, 103.05140-10-3335,634
CCLE245.833NaringeninC15H12O5272.07317.071.42[M + HCOO]−239.02, 103.05480-41-166,847
CCLE255.934kaempferol 7-O-glucosideC21H20O11448.10447.104.66[M − H]−391.11, 325.11, 259.1016290-07-6164,064
CCLE266.174Quercetin 3,7-dimethyl etherC17H14O7330.07329.071.89[M − H]−309.11, 295.10, 279.102068-02-259,547
CCLE276.633Epicatechin gallateC22H18O10442.09441.083.66[M − H]−371.14, 339.12, 309.111257-08-538,595
CCLE287.3593′,4′-dimethoxyflavonolC17H14O5298.08297.082.52[M − H]−275.10, 255.23, 209.106889-80-1146,058
CCLE298.26NeohesperidoseC12H22O10326.12371.122.92[M + HCOO]−297.05, 255.2317074-02-1147,094

2.4. Functional Enrichment Analysis Results

The 145 potential targets were analyzed using the DAVID database for GO and KEGG analysis to interpret the therapeutic process of CCLE in UC. The GO analysis (FDR < 0.01) identified 194 biological processes, 27 cellular components, and 51 molecular functions, totaling 272 significant biological results. The top 15 most significant results were visualized (Figure 4). The p-values gradually increase from top to bottom, while the right side represents the number of enriched genes. In biological processes, the main results included: apoptotic process, positive regulation of cell population proliferation, negative regulation of apoptotic process, inflammatory response, signal transduction, and others. In cellular components, the primary results included: plasma membrane, membrane, cytoplasm, cytosol, cell surface, and others. For molecular functions, the primary results included: kinase activity, enzyme binding, signaling receptor binding, protein binding, metal ion binding, and others.
KEGG pathway enrichment analysis (FDR < 0.01) was conducted on the intersecting targets, resulting in 166 enriched pathways. Among these, 40 pathways related to the UC process were screened and identified (Figure 5), including metabolic pathways, EGFR tyrosine kinase inhibitor resistance, calcium signaling pathways, PI3K-Akt, MAPK, HIF-1 signaling pathways, and others. Key genes involved in these pathways include TNF, NFKB1, SRC, STAT3, EGFR, and others.

2.5. Component–Target–Pathway Network Construction

Figure 6 shows the component–target–pathway networks of CCLE. In the diagram, the “diamond” shape represents CCLE, the “ellipse” shape represents chemical components, the “octagon” shape represents gene targets, and the “triangle” shape represents pathways. The component–target–pathway network includes 215 nodes and 1086 edges, with larger nodes corresponding to compounds such as CCLE28, CCLE26, CCLE20, CCLE24, CCLE21, CCLE18, CCLE12, CCLE13, CCLE11, and CCLE6 (Table 2).

2.6. Molecular Docking Results

The top 10 compounds based on the Degree values were selected in component–target–pathway networks. The top 10 core targets were selected: TNF, TLR4, STAT3, SRC, PTGS2, NFKB1, MMP9, EGFR, BCL2, and AKT1. Molecular docking was conducted between the 10 compounds and these 10 core targets, with binding energy used to assess the degree of interaction. Figure 7A shows the heatmap of the binding energies, all of which are below −5.0 kcal/mol, suggesting stable binding conformations and good interactions between these compounds and their core targets. Visualization of five molecules was performed using PyMOL 3.1 software, as shown in Figure 7B–F. In the figure, “silver” represents the target protein, “purple” represents the component, and “blue” represents the protein connected by hydrogen bonds. Procyanidin B1 in the PTGS2 protein forms 7 hydrogen bonds with HIS-122, LYS-532, GLN-372, ARG-44, and PHE-367, yielding a binding energy of −10.9 kcal/mol. Quercetin in AKT1 protein forms 2 hydrogen bonds with TYR-272 and ASN-204 and a binding energy of -10.4 kcal/mol. Naringenin in AKT1 protein forms 2 hydrogen bonds with ASN-53, SER-205, and ASN-204, and a binding energy of −9.6 kcal/mol. Quercetin in EGFR protein forms 3 hydrogen bonds with MET-793, THR-790, and ASP-855, yielding a binding energy of −9.5 kcal/mol. In the SRC protein, Catechin forms 4 hydrogen bonds with GLU-310, ASP-404, GLU-339, and MET-341, resulting in a binding energy of −9.0 kcal/mol.

2.7. CCLE In Vitro Anti-Inflammatory Experimental Results

The MTT assay showed that the CCLE concentrations ranged from 100 to 6.25 μg/mL (Figure 8A), and mesalazine concentrations ranged from 25 to 1.563 mg/mL (Figure 8B). RAW264.7 macrophage viability was greater than 90%. Based on this, 100, 50, and 25 μg/mL of CCLE concentrations were selected for the LPS + CCLE-H, LPS + CCLE-M, and LPS + CCLE-L groups, with 12.5 mg/mL mesalazine as the LPS + Mesalazine group, for subsequent cell experiments. Compared to the LPS group, the LPS + CCLE-M and LPS + CCLE-H groups significantly inhibited nitric oxide (NO) production, although the inhibition was less effective than that of the LPS + Mesalazine group (Figure 8C). Figure 8D shows that higher reactive oxygen species (ROS) levels corresponded to stronger relative fluorescence intensity. The LPS + CCLE-M and LPS + CCLE-H groups showed lower relative fluorescence intensity compared to the LPS group, indicating significant inhibition of ROS production, although less effectively than the LPS + Mesalazine group. The inhibitory effects of CCLE on LPS-stimulated NO and ROS production in RAW264.7 macrophages.

2.8. Cell Migration Experiment Results

To evaluate the effect of CCLE on cell migration, both the cell scratch and Transwell experiments were conducted. In the scratch experiment, images were captured at 0 h, 12 h, and 24 h using a microscope (Figure 9A). As shown in the figure, the gap area of cells in each group gradually decreased from 0 h to 24 h, indicating cell migration. However, the migration was faster in cells treated with LPS. At 12 h and 24 h (Figure 9B), the migration rates in the LPS + CCLE-M, LPS + CCLE-H, and LPS + Mesalazine groups were significantly lower than in the LPS group, indicating that CCLE inhibited macrophage migration. In the Transwell experiment, cells in each group migrated, but the cells in the LPS-treated group migrated faster (Figure 9C). As shown in the figure (Figure 9D), the number of migrating cells was significantly lower in all treated groups than in the LPS group, indicating that CCLE inhibited cell macrophage migration. Both the scratch assay and Transwell experiment confirmed that CCLE reduced LPS-induced RAW264.7 macrophage migration.

3. Discussion

In recent years, the incidence and prevalence of UC have been increasing globally, while conventional Western medicine has been unable to fully meet the treatment demands for UC. Reports indicate that some UC patients in China have received combined treatment with TCM and Western medicine [21]. In this study, the main components of CCLE were identified using UPLC-QTOF-MS technology, and network pharmacology analysis combined with molecular docking techniques was employed to screen active compounds and core targets. Then, the anti-inflammatory and migration effects of CCLE were preliminarily verified through in vitro cell experiments. This research provides a theoretical foundation for elucidating the pharmacological mechanisms of CCLE and developing novel therapeutic agents.
Using UPLC-QTOF-MS technology, we identified a total of 29 compounds. Among these, p-Coumaric acid, Methyl cinnamate, and Methyl-p-coumarate are cinnamic acid derivatives. Cinnamic acid and its derivatives possess anti-inflammatory effects, inhibit oxidative stress, and regulate COX-1, COX-2, and NF-κB levels [22]. Various methylated cinnamic acid derivatives not only enhance cell vitality but also upregulate the expression of multiple endogenous antioxidant enzymes [23]. Sinapic acid has been reported to exhibit anti-inflammatory, antioxidant, anti-ulcer, antiviral, and neuroprotective effects [24]. Zhu et al. [25] found that Gallic acid effectively inhibits the NF-κB pathway in TNBS-induced UC, thereby alleviating UC symptoms. Protocatechuic acid exhibits anti-inflammatory, antibacterial, and antioxidant properties and may alleviate intestinal damage and regulate the gut microbiota [26]. Catechin, Quercetin, Naringenin, and Epicatechin gallate are flavonoids. Substantial evidence has demonstrated that flavonoids possess diverse protective functions within the gastrointestinal tract, including preservation of intestinal mucosal barrier integrity, attenuation of intestinal wall injury induced by pharmacological agents and dietary toxins, as well as modulation of intestinal immune responses [27]. Notably, Epicatechin gallate has been reported to suppress myeloperoxidase activity in colonic tissue, limit macrophage recruitment and neutrophil infiltration, enhance endogenous antioxidant enzyme activities, and reduce the secretion of pro-inflammatory cytokines [28]. Furthermore, coumarin activates the Nrf2/Keap1 pathway to exert intestinal anti-inflammatory effects and is recognized for its antioxidant and anti-inflammatory effects in the treatment of oxidative stress-related diseases [29]. Collectively, current research indicates that multiple bioactive components within CCLE may cooperatively alleviate UC-associated inflammation and oxidative stress.
Using the network pharmacology study, we identified a total of 31 core targets. Among them, NFKB1, as a key member of the nuclear NF-κB transcription factor family, plays a role in immune regulation, cell proliferation, stress response, and apoptosis [30]. TNF [31], STAT3 [32], and EGFR [33] are not only involved in regulating cell growth, proliferation, differentiation, stress response, and apoptosis, but are also closely associated with the onset and progression of various inflammatory diseases. The lower the molecular docking binding energy, the better the binding conformation and interaction. A binding energy below −5.0 kcal/mol is generally considered significant [34]. All molecular docking interactions showed binding energies < −5.0 kcal/mol, indicating stable binding. According to the analysis of GO and KEGG functional enrichment results, the potential targets were mainly involved in protein phosphorylation, inflammatory response, apoptotic process, cell differentiation, and other biological processes. Key pathways enriched in relation to UC include the PI3K-Akt signaling pathway [35], MAPK signaling pathway [36], HIF-1 signaling pathway [37], Th17 cell differentiation [38], and others. These studies collectively indicate that CCLE may offer therapeutic potential against UC through multiple mechanisms.
Macrophages, as innate immune cells, play a crucial role in the pathological processes of chronic inflammation, participating in the inflammatory response in UC [39]. Intestinal macrophages play a key role in mediating the inflammatory response, and their excessive activation can disrupt the regulation of inflammation, transforming normal physiological inflammation into pathological intestinal damage [40]. During macrophage activation, the production of NO and ROS, along with the secretion of pro-inflammatory cytokines, typically occurs [30]. Excessive NO can lead to cytotoxicity, triggering chronic inflammation, while excessive ROS causes oxidative stress, enzyme dysfunction, and other negative effects, contributing to various severe diseases, such as UC [41]. Through in vitro cell experiments, it has been demonstrated that CCLE has anti-inflammatory effects and inhibits macrophage migration, potentially exhibiting anti-UC activity. However, the in vivo efficacy of CCLE requires further validation using mouse models. Future studies should focus on validating these findings in animal models and further elucidating the detailed molecular mechanisms, thereby providing more robust experimental evidence and new perspectives for the development of CCLE as a therapeutic candidate for UC.

4. Materials and Methods

4.1. Materials and Reagents

Cinnamomum cassia leaves were sourced from the Cinnamon Planting Base in Tanbin Town, Luoding City, Guangdong Province, China. Chromatographic-grade methanol, acetonitrile, and formic acid were separately provided by Merck (NJ, USA), CINC (Shanghai, China), and Aladdin (Shanghai, China). RAW264.7 macrophages (catalog number TCM13) were provided by Professor Hu (South China Agricultural University, Guangzhou, China). Fetal bovine serum (FBS), penicillin–streptomycin, and Dulbecco’s Modified Eagle Medium (DMEM) were provided by Yeasen (Shanghai, China). Phosphate-Buffered Solution (PBS) was provided by Labgic (Beijing, China). NO detection kits were provided by Whenkilife (Wuhan, China). ROS assay kits were provided by Beyotime (Shanghai, China). Other reagents were provided by Macklin (Shanghai, China).

4.2. Preparation of CCLE

Weigh 5 g of the cinnamomum cassia leaf raw material, and place an appropriate amount of cinnamon leaves into a round-bottom flask. Add 650 mL of water at a material-to-liquid ratio of 1:13 (g/mL), shake well, and use steam distillation to distill for 2 h to separate the cinnamon essential oil. Filter the mixture in the round-bottom flask, collect the filtrate, and repeat the distillation process as described above. Filter and combine the two filtrates, then concentrate by rotary evaporation and freeze-dry to obtain the CCLE [42,43].

4.3. CCLE Chemical Composition Detection

The chemical composition of CCLE was identified using UPLC-QTOF-MS [44,45], based on precise molecular weight and database screening. Weigh 50 mg of the CCLE sample, which was dissolved in methanol by vortexing. After centrifugation (12,000 rpm for 3 min), the supernatant was aspirated, and the sample was filtered through a microporous membrane (0.22 μm) and stored in the injection vial for analysis.
Chromatographic separation was performed on a Waters ACQUITY UPLC HSS T3 Column (Waters Corp, Milford, MA, USA) (1.8 μm, 2.1 mm × 100 mm) maintained at 40 °C. The mobile phases consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B), with a flow rate of 0.40 mL/min and an injection volume of 4 μL. The gradient elution program was as follows: 0~5 min, 5~65% B; 5~7.5 min, 99% B; 7.6~10 min, 5% B.
The parameters of mass spectrometry are as follows (Table 3) [46].

4.4. Network Pharmacology Study

4.4.1. Identification of Active Components and Targets

Identify and select the chemical components of CCLE, combine them with the chemical components of cinnamomum cassia leaves from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) (Appendix A) database and relevant literature, and a CCLE component database was constructed [47]. The structures of the chemical components of CCLE were obtained from the PubChem database and imported into the Swiss Target Prediction database (Appendix A) to predict potential targets of its components [48]. Disease targets related to UC were retrieved from the Gene Cards, Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), and Disease Gene NET work (DGT) databases (Appendix A) using the keyword “Ulcerative colitis” [49]. Venny 2.1 software was employed to Venny 2.1 software was employed to generate a Venn diagram, identifying overlapping targets between CCLE components and UC-related targets [50].

4.4.2. Protein–Protein Interaction (PPI) Construction

The intersecting targets were imported into the STRING database (Appendix A) to determine the interaction relationships of potential targets. The PPI network is visualized using Cytoscape 3.10.0 software, and core target screening is performed [51].

4.4.3. Functional Enrichment Analysis

The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were conducted using the DAVID database (Appendix A) to identify the main pathways through which CCLE treats UC. The significantly enriched pathways and processes were visualized through the Microbioinformatics platform [52].

4.4.4. Component–Target–Pathway Construction

A comprehensive network connecting CCLE components, their corresponding targets, and relevant signaling pathways was constructed in Cytoscape 3.10.0 to identify critical compounds and key biological processes [53].

4.4.5. Molecular Docking

Core targets from the previous screening were used to obtain the 3D structure of their protein from the PDB database (Table 4). The structure of the major active ingredients of CCLE was obtained from the PubChem database. Preprocessing steps such as ligand removal, non-protein molecule removal, and surface charge optimization were performed using PyMOL 3.1 software. The protein and small molecules were processed using AutoDock 1.5.7 and AutoDock Vina for molecular docking to obtain binding energies. Molecular visualization was performed using PyMOL 3.1 software [54].

4.5. Cell Experiments

4.5.1. Cell Culture

RAW264.7 macrophages were maintained in DMEM supplemented with 10% FBS and 0.5% penicillin-streptomycin at 37 °C with 5% CO2. The medium was replaced daily. When the cells reached 80~90% for passaging the culture. Cells within 20 generations were used for experiments.

4.5.2. Cell Proliferation Assay

Cell viability was assessed using the Methyl Thiazolyl Tetrazolium (MTT) method. Each group consisted of 6 replicates. RAW264.7 macrophages were seeded in 96-well plates at 1.0 × 105 cells/mL. After overnight incubation, using various concentrations of CCLE (800, 400, 200, 100, 50, 25, 12.5, 6.25, 0 μg/mL) or various concentrations of mesalazine (50, 25, 12.5, 6.25, 3.125, 1.563, 0 mg/mL), the cells were treated. Put into a 37 °C with 5% CO2 incubator to continue to cultivate for 24 h, then wash with PBS twice. In a light-protected environment, each well was added to 200 µL of MTT. After the incubation for 4 h, the MTT solution was discarded, and each well was filled with 150 µL of dimethyl sulfoxide. The plates were then shaken for 10 min in the dark, and absorbance was measured using an enzyme labeling instrument at 570 nm [55]. Based on cell viability, select appropriate concentrations for subsequent cell experiments.

4.5.3. Cell Grouping

The normal group (Normal): cells were cultured without any intervention. The LPS model group (LPS): cells were treated with 1 µg/mL of LPS to induce an inflammation injury model. The CCLE low-, medium-, and high-dose groups (LPS + CCLE-L, LPS + CCLE-M, LPS + CCLE-H): cells were treated with 1 µg/mL of LPS and 25 μg/mL, 50 μg/mL, or 100 μg/mL of CCLE, respectively. The mesalazine group (LPS + Mesalazine): cells were treated with 1 µg/mL of LPS and 12.5 mg/mL of mesalazine [30].

4.5.4. NO Concentration and ROS Detection

RAW264.7 macrophages were seeded into 96-well plates at 1.0 × 105 cells/mL and cultured overnight. After 24 h of further culture with the corresponding culture medium, according to the groupings. Culture supernatants were collected and reacted with 50 µL of Griess reagent. Optical 560 nm was used to record the absorbance, and NO concentration in the samples was determined from a standard curve [56]. The cells were washed with PBS twice and incubated with an appropriate volume of DCFH-DA probe for 30 min at 37 °C. Excess probe was removed by PBS washing. ROS levels were measured using a fluorescence microplate reader (485/525 nm) [57].

4.5.5. Cell Scratching Assay

RAW264.7 macrophages were seeded into 6-well plates at a density of 1.0 × 106 cells/mL and cultured overnight. A straight line was drawn on the cell surface using a pipette tip, and images were taken. The corresponding culture medium was added according to the groupings, and images were taken again at 12 h and 24 h. The scratch area was measured using Image J8 software to assess the cell migration ability [58].

4.5.6. Transwell Assay

After 24 h of serum starvation, RAW264.7 macrophages were resuspended at a concentration of 2.5 × 105 cells/mL in serum-free medium. Then, a total of 100 μL of suspension was seeded into the upper chamber of the Transwell, while 600 µL of drug-containing medium supplemented with 10% FBS was placed in the lower chamber according to the groupings. After 24 h of incubation, cells were fixed with paraformaldehyde for 30 min and stained with crystal violet for 30 min. Afterward, the cells were washed with PBS, and images were captured using an inverted microscope. Cell counts were performed using Image J software [59]. After 24 h of incubation, cells were fixed with paraformaldehyde for 30 min and stained with crystal violet for 30 min. Afterward, the cells were washed with PBS, and images were captured under an inverted microscope. Cell counts were performed using Image J software.

4.6. Data Analysis

Raw UPLC-QTOF-MS data were converted to the mzML format using ProteoWizard 3.0 and processed with XCMS 3.7.0 for peak detection, alignment, retention-time correction, and filtering. Then, metabolite identification is performed by searching and integrating databases (Metlin, HMDB, MassBank, and Mona) and the MetDNA method, and target compounds are finally screened after merging data from positive and negative ionization modes. Statistical analysis was performed using SPSS 27, and graphs were generated using GraphPad Prism 8. Data are presented as mean ± standard. One-way analysis of variance was used for group comparisons, and a p-value below 0.05 was regarded as significant. Each experiment was repeated three times.

5. Conclusions

Network pharmacology and molecular docking revealed that the Quercetin, Catechin, Naringenin, 3′,4′-dimethoxyflavonol, Procyanidin Bl, and Caffeic acid in CCLE may be the core compounds in alleviating UC. Their therapeutic effects are likely mediated through the PI3K-Akt, B-cell receptor, NF-κB, TNF, and JAK-STAT signaling pathways. Furthermore, the study demonstrated that CCLE significantly reduced inflammation and antioxidant levels and inhibited cell migration. By integrating UPLC-QTOF-MS profiling, network pharmacology, and cell experiments, we systematically investigated the mechanisms underlying CCLE’s potential in treating UC, thereby providing a theoretical foundation for the high-value utilization of the cinnamomum cassia leaf.

Author Contributions

Conceptualization: P.X. and Z.Z.; methodology: Z.Z. and J.G.; software: Z.Z. and Z.H.; validation: Z.Z. and J.G.; formal analysis: Z.Z. and X.Z.; investigation: Z.Z. and X.Z.; resources: P.X. and Z.Z.; data curation: Z.Z. and Z.H.; writing—original draft preparation: Z.Z. and J.G.; writing—review and editing: Z.Z.; visualization: Z.Z. and Z.H.; funding acquisition: P.X. and Z.Z.; supervision: P.X. and Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Agricultural Science and Technology Co-construction Project of New Rural Development Research Institute of South China Agricultural University, Guangdong, China (Grant numbers 4900-E23032).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Acknowledgments

All authors of the manuscript would like to express their sincere gratitude to the project fund and reference authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
TNFTumor Necrosis Factor
TLR4Toll-Like Receptor 4
STAT3Signal Transducer and Activator of Transcription 3
SRCProto-oncogene tyrosine-protein kinase Src
PTGS2Prostaglandin-endoperoxide synthase 2 (Cyclooxygenase-2)
NFKB1Nuclear Factor Kappa B Subunit 1
MMP9Matrix Metallopeptidase 9
EGFREpidermal Growth Factor Receptor
BCL2B-cell lymphoma 2
AKT1AKT Serine/Threonine Kinase 1
PTPRCProtein Tyrosine Phosphatase Receptor Type C (CD45)
PPARGPeroxisome Proliferator-Activated Receptor Gamma
PDGFRAPlatelet-Derived Growth Factor Receptor Alpha
PARP1Poly (ADP-ribose) Polymerase 1
DAVIDDatabase for Annotation, Visualization and Integrated Discovery
NF-κBNuclear Factor Kappa B
JAKJanus Kinase
STATSignal Transducer and Activator of Transcription
COX-1Cyclooxygenase-1 (PTGS1)
COX-2Cyclooxygenase-2 (PTGS2)
Nrf2Nuclear factor erythroid 2-related factor 2
Keap1Kelch-like ECH-associated protein 1
TNBS2,4,6-Trinitrobenzenesulfonic acid

Appendix A. Network Pharmacology Related Database Websites

DatabaseURL (The Access Date)
DAVIDhttps://davidbioinformatics.nih.gov (6 February 2025)
DGThttps://ngdc.cncb.ac.cn (1 February 2025)
Gene Cardshttps://www.genecards.org (1 February 2025)
OMIMhttps://www.omim.org (2 February 2025)
PDBhttps://www.rcsb.org/ (2 February 2025)
PubChemhttps://pubmed.ncbi.nlm.nih.gov (7 February 2025)
STRINGhttps://cn.string-db.org (6 February 2025)
Swiss Target Predictionhttp://www.swisstargetprediction.ch (5 February 2025)
TCMSPhttps://www.tcmsp-e.com (2 February 2025)
TTDhttps://db.idrblab.net/ttd/ (2 February 2025)

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Plants 15 00706 g001
Figure 1. The total ion chromatogram of CCLE. (A) Positive ion mode. (B) Negative ion mode.
Figure 1. The total ion chromatogram of CCLE. (A) Positive ion mode. (B) Negative ion mode.
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Figure 2. CCLE and UC intersection targets.
Figure 2. CCLE and UC intersection targets.
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Figure 3. PPI network of target proteins. (A) Overlapping target PPI network. (B) Core target network.
Figure 3. PPI network of target proteins. (A) Overlapping target PPI network. (B) Core target network.
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Figure 4. Visualization of GO enrichment.
Figure 4. Visualization of GO enrichment.
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Figure 5. Visualization of KEGG pathway enrichment.
Figure 5. Visualization of KEGG pathway enrichment.
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Figure 6. Component–target–pathway networks.
Figure 6. Component–target–pathway networks.
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Figure 7. Molecular docking results. (A) Heat map of the binding energy. (B) Docking of PTGS2 and Procyanidin B1. (C) Docking of AKT1 and Quercetin. (D) Docking of AKT1 and Naringenin. (E) Docking of EGFR and Quercetin. (F) Docking of SRC and Catechin.
Figure 7. Molecular docking results. (A) Heat map of the binding energy. (B) Docking of PTGS2 and Procyanidin B1. (C) Docking of AKT1 and Quercetin. (D) Docking of AKT1 and Naringenin. (E) Docking of EGFR and Quercetin. (F) Docking of SRC and Catechin.
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Figure 8. In vitro anti-inflammatory results. (A) Cell viability of various CCLE concentrations (* p < 0.05, ** p < 0.01). (B) Cell viability of various mesalazine concentrations (* p < 0.05, ** p < 0.01). (C) NO concentration (* p < 0.05, ** p < 0.01). (D) Relative fluorescence intensity (* p < 0.05, ** p < 0.01).
Figure 8. In vitro anti-inflammatory results. (A) Cell viability of various CCLE concentrations (* p < 0.05, ** p < 0.01). (B) Cell viability of various mesalazine concentrations (* p < 0.05, ** p < 0.01). (C) NO concentration (* p < 0.05, ** p < 0.01). (D) Relative fluorescence intensity (* p < 0.05, ** p < 0.01).
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Figure 9. Results of cell migration experiments. (A) The scratch experiment images. (B) Relative migration rates (* p < 0.05, ** p < 0.01). (C) Transwell assay images. (D) Number of migrating cells (* p <0.05, ** p < 0.01).
Figure 9. Results of cell migration experiments. (A) The scratch experiment images. (B) Relative migration rates (* p < 0.05, ** p < 0.01). (C) Transwell assay images. (D) Number of migrating cells (* p <0.05, ** p < 0.01).
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Table 2. Topology parameters in the network.
Table 2. Topology parameters in the network.
ComponentDegreeBetweenness Centrality (×10−2)TargetDegreeBetweenness Centrality (×10−2)
CCLE28498.35MAPK1302.32
CCLE26456.58PIK3R1292.65
CCLE20446.20PIK3CA292.99
CCLE24419.34PIK3CB292.99
CCLE21367.82AKT1282.45
CCLE18366.02CA2266.53
CCLE12314.64EGFR244.08
CCLE13283.98RELA221.84
CCLE11241.76MAPK14221.44
CCLE6242.60NFKB1211.15
Table 3. Ms conditions.
Table 3. Ms conditions.
ParametersESI+ESI−
Duration (min)1010
Ion Spray Voltage (V)5000−4000
Temperature (°C)550550
Ion Source Gas 1 (psi)5050
Ion Source Gas 2 (psi)6060
Curtain Gas (psi)3535
Declustering Potential (V)80−80
MS1 Collision Energy (V)10−10
MS2 Collision Energy (V)30−30
Collision Energy Spread (V)1515
MS1 TOF Masses (Da)50~125050~1250
MS2 TOF Masses (Da)25~125025~1250
Table 4. Target protein information.
Table 4. Target protein information.
GenePDB IDResolution (Å)
AKT17nh51.9
BCL21r2d1.95
EGFR8a271.07
MMP98k5y1.52
NFKB18tqd2.02
PTGS25F192.04
SRC2bdj2.5
STAT36njs2.7
TLR42Z652.7
TNF5inl1.55
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Zhang, Z.; Guo, J.; Huang, Z.; Zheng, X.; Xiong, P. Exploring the Treatment of Cinnamomum Cassia Leaf Extract in Ulcerative Colitis: Network Pharmacology and In Vitro Investigations. Plants 2026, 15, 706. https://doi.org/10.3390/plants15050706

AMA Style

Zhang Z, Guo J, Huang Z, Zheng X, Xiong P. Exploring the Treatment of Cinnamomum Cassia Leaf Extract in Ulcerative Colitis: Network Pharmacology and In Vitro Investigations. Plants. 2026; 15(5):706. https://doi.org/10.3390/plants15050706

Chicago/Turabian Style

Zhang, Zhuoya, Junrong Guo, Zurun Huang, Xiuyan Zheng, and Ping Xiong. 2026. "Exploring the Treatment of Cinnamomum Cassia Leaf Extract in Ulcerative Colitis: Network Pharmacology and In Vitro Investigations" Plants 15, no. 5: 706. https://doi.org/10.3390/plants15050706

APA Style

Zhang, Z., Guo, J., Huang, Z., Zheng, X., & Xiong, P. (2026). Exploring the Treatment of Cinnamomum Cassia Leaf Extract in Ulcerative Colitis: Network Pharmacology and In Vitro Investigations. Plants, 15(5), 706. https://doi.org/10.3390/plants15050706

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