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

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.

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).

Figure 1.

The total ion chromatogram of CCLE. (A) Positive ion mode. (B) Negative ion mode.

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.

Figure 2.

CCLE and UC intersection targets.

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.

No.	RT (min)	Compounds	Formula	Mass (Da)	Found at Mass (Da)	Mass Error (ppm)	Adduct	Product Ions	CAS	Area
CCLE1	0.605	Protocatechuic acid	C7H6O4	154.03	153.02	1.03	[M − H]−	115.02	99-50-3	822,607
CCLE2	0.843	Caffeic acid 3-(beta-1-glucoside)	C15H18O9	342.10	365.09	1.75	[M + Na]+	229.15, 104.11	24959-81-7	386,039
CCLE3	1.160	Glyceric acid	C3H6O4	106.03	165.04	1.54	[M + CH3COO]−	96.96, 78.96, 75.01	473-81-4	302,774
CCLE4	1.481	Gallic acid	C7H6O5	170.02	169.01	0.25	[M − H]−	161.05, 147.07, 125.02	149-91-7	261,446
CCLE5	1.586	2-Hydroxy-2-methylbutyric acid	C5H10O3	118.06	254.16	1.29	[2M + NH4]+	112.08, 97.03, 85.03	3739-30-8	84,680
CCLE6	1.726	Methyl-p-coumarate	C10H10O3	178.06	179.07	0.35	[M + H]+	166.05, 147.04, 137.06	19367-38-5	376,982
CCLE7	1.886	Paeonol	C9H10O3	166.06	167.07	2.96	[M + H]+	160.10, 155.07,136.06	552-41-0	477,646
CCLE8	1.966	L-Phenylalanine	C9H11NO2	165.08	166.09	0.49	[M + H]+	136.06, 120.08, 103.05	63-91-2	1,638,487
CCLE9	2.408	Paraxylic Acid	C9H10O2	150.07	151.07	0.62	[M + H]+	133.06, 105.07, 74.06	619-04-5	80,512
CCLE10	2.587	Quercetin-3-O-glucose-6″-acetate	C23H22O13	506.11	522.12	1.09	[M + NH4 − 2H]−	465.23, 216.98, 167.04	54542-51-7	43,235
CCLE11	3.132	Procyanidin B1	C30H26O12	578.14	577.13	3.15	[M − H]-	455.18, 395.05, 357.09	20315-25-7	83,608
CCLE12	3.294	Catechin	C15H14O6	290.08	289.07	0.48	[M − H]-	165.02, 137.02, 93.03	154-23-4	207,875
CCLE13	3.649	Caffeic acid	C9H8O4	180.04	179.03	0.18	[M − H]-	177.02, 167.04,152.01	501-16-6	57,799
CCLE14	3.786	Syringic acid	C9H10O5	198.05	197.05	1.76	[M − H]-	177.08, 165.06, 157.05	530-57-4	93,144
CCLE15	3.996	Methyl cinnamate	C10H10O2	162.07	163.08	0.82	[M + H]+	153.13, 133.06, 123.12	103-26-4	6,438,879
CCLE16	4.097	Eugenol	C10H12O2	164.08	233.08	5.01	[M + Na + HCOOH]+	149.10, 126.09, 95.05	97-53-0	336,947
CCLE17	4.347	p-Coumaric acid	C9H8O3	164.05	163.04	2.09	[M − H]−	137.05, 119.05, 107.05	501-98-4	285,238
CCLE18	4.602	Sinapic acid	C11H12O5	224.07	223.06	1.59	[M − H]−	221.05, 193.05, 179.03	7362-37-0	242,133
CCLE19	4.763	p-Coumaryl alcohol	C9H10O2	150.07	149.06	0.06	[M − H]−	137.02, 93.03	20649-40-5	159,374
CCLE20	4.905	Quercetin	C15H10O7	302.04	301.03	3.10	[M − H]−	243.00, 221.08, 187.10	117-39-5	99,293
CCLE21	5.047	3,4-Dimethoxycinnamic acid	C11H12O4	208.07	207.07	0.95	[M − H]−	183.10, 177.06, 161.02	14737-89-4	50,461
CCLE22	5.407	Coumarin	C9H6O2	146.04	147.04	0.15	[M + H]+	103.05, 91.05	91-64-5	11,631,077
CCLE23	5.568	Cinnamic acid	C9H8O2	148.05	149.06	0.64	[M + H]+	133.10, 131.05, 103.05	140-10-3	335,634
CCLE24	5.833	Naringenin	C15H12O5	272.07	317.07	1.42	[M + HCOO]−	239.02, 103.05	480-41-1	66,847
CCLE25	5.934	kaempferol 7-O-glucoside	C21H20O11	448.10	447.10	4.66	[M − H]−	391.11, 325.11, 259.10	16290-07-6	164,064
CCLE26	6.174	Quercetin 3,7-dimethyl ether	C17H14O7	330.07	329.07	1.89	[M − H]−	309.11, 295.10, 279.10	2068-02-2	59,547
CCLE27	6.633	Epicatechin gallate	C22H18O10	442.09	441.08	3.66	[M − H]−	371.14, 339.12, 309.11	1257-08-5	38,595
CCLE28	7.359	3′,4′-dimethoxyflavonol	C17H14O5	298.08	297.08	2.52	[M − H]−	275.10, 255.23, 209.10	6889-80-1	146,058
CCLE29	8.26	Neohesperidose	C12H22O10	326.12	371.12	2.92	[M + HCOO]−	297.05, 255.23	17074-02-1	147,094

Figure 3.

PPI network of target proteins. (A) Overlapping target PPI network. (B) Core target network.

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.

Figure 4.

Visualization of GO enrichment.

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.

Figure 5.

Visualization of KEGG pathway enrichment.

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).

Figure 6.

Component–target–pathway networks.

Table 2.

Topology parameters in the network.

Component	Degree	Betweenness Centrality (×10−2)	Target	Degree	Betweenness Centrality (×10−2)
CCLE28	49	8.35	MAPK1	30	2.32
CCLE26	45	6.58	PIK3R1	29	2.65
CCLE20	44	6.20	PIK3CA	29	2.99
CCLE24	41	9.34	PIK3CB	29	2.99
CCLE21	36	7.82	AKT1	28	2.45
CCLE18	36	6.02	CA2	26	6.53
CCLE12	31	4.64	EGFR	24	4.08
CCLE13	28	3.98	RELA	22	1.84
CCLE11	24	1.76	MAPK14	22	1.44
CCLE6	24	2.60	NFKB1	21	1.15

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.

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.

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.

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).

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.

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).

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].

Table 3.

Ms conditions.

Parameters	ESI+	ESI−
Duration (min)	10	10
Ion Spray Voltage (V)	5000	−4000
Temperature (°C)	550	550
Ion Source Gas 1 (psi)	50	50
Ion Source Gas 2 (psi)	60	60
Curtain Gas (psi)	35	35
Declustering Potential (V)	80	−80
MS1 Collision Energy (V)	10	−10
MS2 Collision Energy (V)	30	−30
Collision Energy Spread (V)	15	15
MS1 TOF Masses (Da)	50~1250	50~1250
MS2 TOF Masses (Da)	25~1250	25~1250

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].

Table 4.

Target protein information.

Gene	PDB ID	Resolution (Å)
AKT1	7nh5	1.9
BCL2	1r2d	1.95
EGFR	8a27	1.07
MMP9	8k5y	1.52
NFKB1	8tqd	2.02
PTGS2	5F19	2.04
SRC	2bdj	2.5
STAT3	6njs	2.7
TLR4	2Z65	2.7
TNF	5inl	1.55

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.

Abbreviations

The following abbreviations are used in this manuscript:

TNF	Tumor Necrosis Factor
TLR4	Toll-Like Receptor 4
STAT3	Signal Transducer and Activator of Transcription 3
SRC	Proto-oncogene tyrosine-protein kinase Src
PTGS2	Prostaglandin-endoperoxide synthase 2 (Cyclooxygenase-2)
NFKB1	Nuclear Factor Kappa B Subunit 1
MMP9	Matrix Metallopeptidase 9
EGFR	Epidermal Growth Factor Receptor
BCL2	B-cell lymphoma 2
AKT1	AKT Serine/Threonine Kinase 1
PTPRC	Protein Tyrosine Phosphatase Receptor Type C (CD45)
PPARG	Peroxisome Proliferator-Activated Receptor Gamma
PDGFRA	Platelet-Derived Growth Factor Receptor Alpha
PARP1	Poly (ADP-ribose) Polymerase 1
DAVID	Database for Annotation, Visualization and Integrated Discovery
NF-κB	Nuclear Factor Kappa B
JAK	Janus Kinase
STAT	Signal Transducer and Activator of Transcription
COX-1	Cyclooxygenase-1 (PTGS1)
COX-2	Cyclooxygenase-2 (PTGS2)
Nrf2	Nuclear factor erythroid 2-related factor 2
Keap1	Kelch-like ECH-associated protein 1
TNBS	2,4,6-Trinitrobenzenesulfonic acid

Appendix A. Network Pharmacology Related Database Websites

Database	URL (The Access Date)
DAVID	https://davidbioinformatics.nih.gov (6 February 2025)
DGT	https://ngdc.cncb.ac.cn (1 February 2025)
Gene Cards	https://www.genecards.org (1 February 2025)
OMIM	https://www.omim.org (2 February 2025)
PDB	https://www.rcsb.org/ (2 February 2025)
PubChem	https://pubmed.ncbi.nlm.nih.gov (7 February 2025)
STRING	https://cn.string-db.org (6 February 2025)
Swiss Target Prediction	http://www.swisstargetprediction.ch (5 February 2025)
TCMSP	https://www.tcmsp-e.com (2 February 2025)
TTD	https://db.idrblab.net/ttd/ (2 February 2025)

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.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

Funding Statement

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).