Carvacrol attenuates mucosal barrier impairment and tumorigenesis by regulating gut microbiome

Highlights

• •TMT-based proteomics analysis highlighted the oxidative stress pathway and key proteins (ALB, ADAM10, APCDD1) in CAC development.
• •Carvacrol alleviates DSS-induced colitis and enhances colonic barrier integrity.
• •Carvacrol can regulate the gut microbiota.
• •Carvacrol inhibits the development of colitis-associated colorectal cancer.

Abstract

Colitis-associated colorectal cancer (CAC), which stems from inflammatory bowel disease (IBD), exhibits a high mortality rate. Chronic inflammation can drive the development of colorectal cancer via diverse mechanisms; however, proteomic-level studies in this regard are currently scarce. The chemical drugs conventionally employed for treating IBD carry significant side effects, accentuating the exigency for novel therapeutic agents. We focused on carvacrol, a traditional Chinese medicine-derived monoterpene phenol with anti-inflammatory and antioxidant traits, though its role in colitis and CAC was unclear. Employing TMT-based proteomics, we identified the oxidative stress pathway as crucial in CAC, with ALB, ADAM10, and APCDD1 (hub genes) being vital. Using DSS and AOM/DSS mouse models, carvacrol significantly restored colonic length (p < 0.01) and re-established key tight junction proteins like ZO-1. It also downregulated mRNA levels of inflammatory mediators such as iNOS and IL-6. Moreover, 16S rRNA sequencing and fluorescence in situ hybridization (FISH) assays indicated that the potential mechanism might be ascribed to carvacrol's modulation of the abundance of specific microbiota, such as Lactobacillus, Escherichia coli/Shigella, and Lachnoclostridium. In subsequent investigations, we ascertained that carvacrol exerted remarkable efficacy in the AOM/DSS models, as it markedly reduced the number of colonic tumors (p < 0.05) and concurrently suppressed the disease activity index scores (p < 0.05). These results jointly suggest its prospective role in thwarting the progression of colitis-associated colorectal cancer. Collectively, our study substantiates that carvacrol efficiently safeguards the mucosal barrier and curbs tumorigenesis, potentially via the modulation of gut microbiota.

Graphical abstract

Carvacrol ameliorates intestinal injury and inflammatory response in 3 % DSS-induced colitis and inhibits colitis-associated tumorigenesis in AOM/DSS models through modulation of gut microbiota composition.

Introduction

In 2024, approximately 152,810 individuals were estimated to have been diagnosed with colorectal cancer in the United States, with the disease claiming 53,010 lives that year [1]. Forecasts indicate that by 2040, a staggering 3.2 million new cases of colorectal cancer are expected to emerge globally [2]. Colitis-associated cancer (CAC) is a severe complication of long-standing inflammatory bowel disease (IBD), contributing to approximately 15 % of all-cause mortality in IBD patients [3]. The risk of developing CAC is 1.5–2.4 times higher in individuals with IBD compared to the general population [4]. However, the precise mechanisms by which chronic inflammation predisposes to the initiation and progression of colorectal cancer remain incompletely understood [5]. Recent advancements in surgical treatment, radiation therapy, chemotherapy, and immunotherapy have significantly improved outcomes for colorectal cancer patients. However, challenges such as postoperative recurrence and drug resistance in radiotherapy and chemotherapy negatively impact patient prognosis and mortality rates. Therefore, investigating the mechanisms of key regulatory factors and signaling pathway alterations in CAC is crucial for developing innovative strategies for prevention, diagnosis, and treatment.

Carvacrol (PubChem CID:10364), a phenolic monoterpenoid compound present in the essential oils of aromatic plants and orchids, exhibits various pharmacological properties, including antioxidant, anti-inflammatory, and anticancer effects. For instance, in interleukin 1β-induced osteoarthritis, carvacrol has been shown to suppress nitric oxide and prostaglandin E2 production [6]. Additionally, carvacrol improves memory deficits in Parkinson’s disease models through its antioxidant properties and exhibits anticancer activity through pro-apoptotic effects that inhibit cell proliferation. While some studies suggest the anti-inflammatory effects of carvacrol by inhibiting the TLR4/NF-κB signaling pathway [7], further research is needed to explore the in vivo antioxidant and anticancer effects of carvacrol, particularly in colitis and colon cancer models.

The gut microbiota interacts with a diverse array of microorganisms, forming a complex microecosystem recognized as a network of genes influencing tumor gene stability, metabolism, and immune response [8]. The impact of the microbiota on tumors is profound and extensive, and it has been reported in various types of cancers such as prostate cancer [9] and CRC. Pathogenic intestinal bacteria can directly interact with host epithelial or immune cells, leading to intestinal inflammation and potentially contributing to the progression of CAC [10]. Clinical research has revealed similarities in the microbiome composition of patients with IBD and colorectal cancer, such as reduced diversity, decreased levels of specific bacterial genera like Clostridium and Bacteroides, and elevated levels of Fusobacterium spp [11,12]. These findings suggest a potential link between IBD and colorectal cancer. While certain natural products have shown promise in modulating gut microbiota, reducing inflammation, and inhibiting tumor growth, there is currently no research on the impact of carvacrol on gut microecology and its potential therapeutic role in colitis and colon cancer.

In this study, we aimed to elucidate the changes in key regulators and important signaling pathways in CAC development through proteomics. By targeting the key pathway, the therapeutic effect of carvacrol on colitis are expected to be elucidated, and further exploring the preventive and therapeutic effect on CAC. Additionally, this study proposes that carvacrol regulates intestinal flora and its potential as a treatment for colitis and colon cancer.

Materials and methods

Reagents

Carvacrol(MedChemExpress, USA), AOM (Sigma-Aldrich, MO, USA), dextran sodium sulfate (DSS), bicinchoninic acid kit (Thermo Scientific), TMT labeling reagent (Thermo Scientific), Proteome Discoverer TM 2.4 (Thermo Fisher, USA), Alcian blue staining (Solarbio, China), MUC2 (Abcam), Ultrasensitive SAP IHC Kit and MAX-001 (FUZHOU MAIXIN BIOTECH, China), EUB338 (Guangzhou Exon Biotechology, China), Hieff UNICON® Universal Blue qPCR SYBR Green Master Mix and Hifair® AdvanceFast 1st Strand cDNA Synthesis Kit (YEASEN, China), PowerSoil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA), Hot Master PCR mixture (5Prime, Gaithersburg, MD, USA)

Animals

Male C57BL/6 J mice, each weighing 20 g, were acquired from Gempharmatech Co. (Jiangsu, China) and accommodated in a room devoid of specific pathogens (25 °C), experiencing a 12-hour cycle of light and darkness, with unrestricted access to food and water. Every aspect of mouse care and experimentation adhered strictly to the protocols set by Sichuan University for animal welfare and treatment. This study was approved by the Institutional Review Board of West China Hospital, Sichuan University.

AOM/DSS-induced mouse model of primary colon cancer

Following an initial administration of 10mg/kg AOM, the mice underwent a regimen where they drank water containing 2 % DSS for seven consecutive days, followed by fourteen days of regular water. This sequence was repeated twice. Post AOM injection, the animals were observed for nine weeks. High-resolution microendoscopy (Stoke, Germany) assessed CAC development. Upon sacrifice, the colon was collected and longitudinally dissected to quantify the number and size of tumors. (Approval No.20231127009)

Proteomics

Protein extraction and digestion

Method refer to this [13]. Intestinal tissue was collected at multiple time points. Each sample within each group was subjected to rigorous extraction utilizing radioimmunoprecipitation assay (RIPA) buffer, following which protein quantification was accurately performed employing the bicinchoninic acid kit.

TMT labeling

For TMT labeling, method refer to this [13].

Liquid chromatography–mass spectrometry/mass spectrometry (LC–MS/MS) analysis

Adhere to the methods described in this paper for conducting High Performance Liquid Chromatography fractionation (HPLC) and LC-MS/MS evaluations [13].

Identification of the differentially expressed proteins

The protein identification and quantification process employed the UniProt database, with the following parameter settings: TMT10-plex method was chosen for quantification. Only peptides with a confidence score above 99 % and proteins containing at least one unique peptide segment (specific peptides) were retained. A false discovery rate (FDR) cutoff of 1 % was applied to eliminate unreliable peptides and proteins. Automated normalization via the PD search software was carried out, and batch effects were addressed by dividing samples into different labeled groups using MIX. Significance was determined through a fold change (FC) threshold of 2 or less than 0.5, accompanied by a student's t-test p-value below 0.05. Proteins with FC greater than 2 were categorized as up-regulated, while those with FC below 0.5 were marked as down-regulated.

Bioinformatic analysis

The Cluster analysis plot was generated using R software (version 2.4.3). Protein - protein interaction (PPI) networks were constructed and visualized using the igraph and ggraph packages. Proteins in such central positions usually have high connectivity, that is, they have co - expression relationships with many other proteins. These proteins are called “hub proteins”, which often play crucial roles in the physiological processes of cells. Since they can interact with multiple proteins, they may be key nodes in processes such as intracellular information transmission, substance metabolism, and signal transduction. When conducting enrichment analysis (GO) and pathway analysis (KEGG), it is considered to be statistically significant when the p-value is less than 0.05. Proteomics platform was provided by NOVOGENE (Beijing, China).

Histopathology

Colorectal tissue was fixed with 4 % paraformaldehyde, dehydrated in a gradient of 65 %, 75 %, 85 %, 95 %, and 100 % alcohol and embedded in paraffin. The slides underwent consecutive staining with hematoxylin and eosin (H&E) (Beyotime, Beijing, China).

Alcian blue staining

Paraffin sections of colon tissue are sequentially deparaffinized with xylene and alcohol. Alcian blue staining were performed in accordance with the manufacturer's instructions.

Immunohistochemical analysis

The expression of MUC2 in paraffin-embedded tissues was detected by immunohistochemistry (IHC). The MUC2 antibody was diluted at a ratio of 1:2000 and incubated overnight at 4 °C. A two-step detection kit (Ultrasensitive SAP IHC Kit and MAX-001) was used. The score is based on the percentage of positive cells.

Fluorescence in situ hybridization

EUB338 FISH was carried out following the Bacterial general in situ hybridization detection kit. The sections were incubated with a universal bacterial probe EUB33 at 60 °C for nearly 3 h. The sections were then washed, and the sections were counter-stained with DAPI. All images were obtained under a confocal microscope (Nikon, Japan).

Quantitative real-time PCR (qPCR)

Total RNA was isolated from cells using the TRizol reagent and used as template to synthesize cDNA with cDNA Synthesis Kit. qPCR was performed using the qPCR SYBR Green Master Mix according to the manufacturer’s instructions, using Step One Plus System. The data is calibrated based on the cDNA of the ACTB gene. The primers used in this study are detailed in Table 1.

Table 1.

qPCR primers.

Name	Primer sequence
IL-6-F	GCCTTCTTGGGACTGATGCT
IL-6-R	GACAGGTCTGTTGGGAGTGG
IL-1β-F	TGCCACCTTTTGACAGTGATG
IL-1β-R	TTCTTGTGACCCTGAGCGAC
IFN-γ-F	CGGCACAGTCATTGAAAGCC
IFN-γ-R	TGCATCCTTTTTCGCCTTGC
Claudin-1-F	AGCACCGGGCAGATACAGT
Claudin-1-R	GCCAATTACCATCAAGGCTCG
Claudin-3-F	TCATCGTGGTGTCCATCCTGCT
Claudin-3-R	AGAGCCGCCAACAGGAAAAGCA
IFN-gamma-F1	AGCAAGGCGAAAAAGGATGC
IFN-gamma-R1	ATTCAGAGCTGCAGTGACCC
Mucin-F	TGCCCACCTCCTCAAAGAC
Mucin-R	GTAGTTTCCGTTGGAACAGTGAA
COX2-F	GGTGCCTGGTCTGATGATG
COX-R	TGCTGGTTTGGAATAGTTGCT
iNOS-F	GTTCTCAGCCCAACAATACAAGA
iNOS-R	GTGGACGGGTCGATGTCAC

16S rRNA

Genomic DNA was isolated from mouse feces using a kinetic a PowerSoil DNA Isolation Kit. The 16S rRNA gene was then amplified using thermostat PCR mixture and a specific primer targeting V4 in the 16S rRNA region. Eventually, the amplified product was sent to Biotech (NOVOGENE, Beijing, China) for sequencing analysis.

Statistical analysis

The values were presented as mean ± standard deviation (SD). Significant differences were observed using student t-tests or one-way ANOVA (ANOVA) and GraphPad Prism (version 8.0). p < 0.05 was considered statistically significant.

Results

Proteomic changes in the progression of CAC

The AOM/DSS model is commonly utilized to replicate the development of CAC, mirroring the progression from human ulcerative colitis to cancer. Consequently, we established a primary colorectal cancer mouse model induced by AOM/DSS, as illustrated in Fig. 1A. Colonoscopy indicated that at T0, the mice’s colon mucosa exhibited a smooth surface with clear blood vessel textures. At T1, there was no apparent inflammation in the intestinal tissue, while at T2, severe colitis symptoms, including bloody stools, were observed. At T3, a significant number of solid colorectal tumors were identified (Fig. 1B). H&E staining revealed that at T2, the mice exhibited severe colitis, characterized by epithelial cell damage, inflammatory cell infiltration, and crypt loss. At T3, prominent tumor areas were detected in the colorectal region (Fig. 1C). To explore the changes in protein expression during CAC progression and identify key proteins, we collected colonic tissues at time points T0-T3 for proteomic analysis (Fig. 1D). The analysis revealed significant differences in protein expression levels at each time point, with the most notable changes observed at T3 compared to T0 and T2, including 100 downregulated and 433 upregulated proteins (Fig. 1E-G-F). Given the importance of subcellular localization in protein function, we conducted a statistical analysis of the subcellular localization of differentially expressed proteins (Fig. 1H-J). The results indicated that these proteins were predominantly located in the cell nucleus, suggesting a significant role for riboproteins in tumorigenesis.

Fig. 1.

Proteomic changes in the progression of colitis-associated colorectal cancer. (A) Schematic diagram of the establishment and sampling of the mouse colon cancer model induced by AOM and DSS. (B-C) Colonoscopy images and H&E staining images at time points T0, T1, T2, and T3. Scale bar, 100 μm. (D) Heatmap showing differential protein expression during colorectal cancer formation. (E) Heatmap comparing differential protein expression between time points T0 and T1. (F) Heatmap comparing differential protein expression between time points T1 and T2. (G) Heatmap comparing differential protein expression between time points T2 and T3. (H-J) Proportional statistics of subcellular localization of differentially expressed proteins for T1 vs T0, T2 vs T1, and T3 vs T2, respectively.

Subsequently, Protein-Protein Interaction (PPI) networks were constructed (Supplementary Figure 1A-C). A densely interconnected region was observed in the PPI network comparisons between T2 vs T1 and T3 vs T2 (hub genes), specifically associated with gene modules implicated in colitis and colorectal cancer, a feature absents in the T1 vs T0 comparison (Supplementary Figure 1B-C). KEGG analysis of the top 10 hub genes revealed that, compared to T0, hub genes in T1 were linked with spliceosome and endocrine resistance in gene transcription (Supplementary Figure 1D). In comparison to T1, hub genes in T2 were associated with immune cell phagosome maturation and the metabolism of valine, leucine, isoleucine degradation, pyruvate, and citrate salts (Supplementary Figure 1E). Additionally, compared to T2, hub genes in T3 were related to lysosomes and spliceosomes, the hippo signaling pathway, and monitoring mRNA during degradation processes (Supplementary Figure 1F).

The weighted gene co-expression network analysis of dysregulated proteins

The role of individual genes in the progression of colorectal cancer and their potential effects requires further investigation. Therefore, we utilized the expression values of differentially expressed proteins as input data and applied the K-means algorithm to classify these proteins into six clusters. These clusters are closely associated with the occurrence and development of CAC and may play regulatory roles in its progression (Supplementary Figure 2A-B). To identify modules significantly associated with the onset and progression of colorectal cancer, we incorporated temporal dynamics correlation as a key evaluation metric. The differential proteins, based on their predicted functions, were categorized into distinct gene modules (Fig. 2A). Our analysis revealed that eight modules exhibited strong temporal correlations across the stages of colorectal cancer development (temporal correlation > 0.6, P < 0.05) (Fig. 2B). Notably, proteins within the gray module were downregulated during the colitis phase (T2), whereas proteins in the pink, red, and blue modules were upregulated (Fig. 2C-D). To elucidate the pivotal proteins implicated in the initiation and progression of colorectal cancer, a differential protein co-expression network was constructed (Fig. 2E). Proteins such as ALB [14], ADAM10 [15], APCDD1 [16], CBFB [17] and TRAF6 [18] in the gray, pink, red, and blue modules may play critical roles in the progression of CAC as they are located at the central positions of the network and can interact with multiple other proteins. Additionally, KEGG enrichment analysis of genes within these modules showed significant enrichment in pathways linked to oxidative stress and apoptosis, indicating their potential importance in the advancement of colorectal cancer (Figs. 2F-I).

Fig. 2.

Weighted gene co-expression network analysis of differential proteins. (A) Tree of gene clusters of proteins at time points T0-T3. (B) The relationship between specific co-expressed gene modules and colon cancer formation. Input2: The number in each square represents the correlation between the module and colon cancer, along with the P-value for each correlation value. (C) Expression characteristics of differentially expressed genes in grey60, pink, red, and turquoise modules. (D) The expression of all genes in the grey, pink, red, and turquoise modules. (E) Co-expression network maps of differentially expressed proteins in the gray, pink, red, and blue modules, respectively. Each circle represents a protein. (F-I) KEGG functional analysis of differentially expressed proteins in the gray, pink, red, and blue modules, respectively.

Carvacrol has protective effect on DSS-induced colitis in mice

By targeting oxidative stress and inflammatory pathways, we screened the natural product carvacrol from the Chinese medicine bank. As shown in Fig. 3A, carvacrol was administered to a mouse model of DSS-induced colitis. The carvacrol group exhibited a lower disease activity index after seven days of treatment compared to the DSS group, including reduced mucus-like stools and occult blood (Fig. 3B). The shortening of colon length, indicative of colitis severity induced by DSS, was significantly alleviated in the carvacrol group (P < 0.01) (Fig. 3C-D). Additionally, carvacrol markedly reduced the histopathological damage in DSS-induced colitis (P < 0.0001) (Fig. 3E-F).

Fig. 3.

Carvacrol improves DSS-induced colitis disease activity index and colon length. (A) Schematic diagram of the 3 % DSS-induced colitis model. (B) Images of colonoscopy in small animals. Scale bar, 100 μm. (C) Colon length. (D) Statistical chart of colon length (n = 7, **p < 0.01). (E) Representative images of H&E staining (100×) and (F) statistical charts.

Fig. 4.

Carvacrol improves intestinal barrier function in DSS-induced mice. (A) Representative images of colon slices with MUC2 immunohistochemistry (100×) and MUC2 immunohistochemical scoring of colons. Scale bar, 100 μm. (B) Representative images and statistical charts of colon slices with Alcian Blue staining. Scale bar, 100 μm (100×, ****, p < 0.0001). (C-G) QPCR detection of ZO-1, Claudin3, Claudin1, Occludin, and Mucin expression levels (**, p < 0.01; *, p < 0.05; ***, p < 0.001).

Carvacrol improves intestinal barrier function

Maintaining intestinal barrier function is essential for overall intestinal health. DSS administration significantly reduced the thickness of the colon epithelial mucosa and mucin content, which was mitigated by carvacrol treatment, as evidenced by an increase in the number of goblet cells (P < 0.0001) (Fig. 4A). Tissue immunohistochemistry results showed a reduction in MUC2 expression in DSS-induced colitis mice, which was significantly restored by carvacrol treatment (P < 0.0001) (Fig. 4B). Additionally, qPCR analysis revealed that the expression levels of ZO-1, Claudin3, Claudin1, Occludin, and Mucin were significantly increased in the carvacrol group compared to the DSS group (Fig. 4C-G).

Carvacrol decreased the level of intestinal inflammatory factors

Next, we assessed the expression levels of intestinal inflammatory factors using qPCR. Carvacrol significantly inhibited the mRNA expression levels of iNOS, COX-2, Interferon-γ, IL-1β, and IL-6 in the intestinal tracts of colitis mice (Fig. 5A-E), suggesting its potential therapeutic effects in reducing oxidative damage and modulating intestinal inflammation.

Fig. 5.

Carvacrol decreased the levels of iNOS and COX-2 enzymes and inflammatory factors in colitis mice. (A-E) QPCR detection of iNOS, COX-2, IFN-γ, IL-β, and IL-6 expression levels (**, p < 0.01; ***, p < 0.001).

Carvacrol regulates intestinal microbial composition

We observed that carvacrol effectively protects the intestinal mucosa from pathogenic bacteria (Fig. 6A). To further investigate carvacrol’s role in regulating the diversity and structural composition of the gut microbiome, we performed high-throughput 16S rRNA gene sequencing of mouse fecal bacteria. In DSS-induced colitis, the alpha diversity was similar between the control group and the carvacrol group (Fig. 6B-D). However, there was a significant separation in the intestinal microbiota between the carvacrol-treated group and the DSS control group (Fig. 6E-F), indicating a distinct difference in microbial composition compared to the control group.

Fig. 6.

Carvacrol changes the composition of intestinal microbiota in DSS-induced colitis mice. (A) Representative images of bacterial in situ hybridization fluorescence in colon slices, using EUB338 probe. Scale bar, 100 μm. (B) Chao1 index. The Y-axis represents the values of the alpha diversity index. (C) Shannon index. The Y-axis represents the values of the alpha diversity index. (D) Simpson index. The Y-axis represents the values of the alpha diversity index. (E-F) PCoA diagram and NMDS2 diagram analyze the differences between samples. The orange dots represent the control group and the blue dots represent the carvacrol group.

Carvacrol regulates the abundance of intestinal microbiota

Next, we selected the top 10 most abundant species at the phylum level for visualization using stacked bar graphs. As shown in Fig. 7A-C, Firmicutes, Proteobacteria, and Bacteroidetes were the predominant members of the intestinal microbiota. Notably, carvacrol reduced the relative abundance of Proteus compared to the control group. At the genus level, the most abundant taxa included Lactobacillus, Escherichia coli/Shigella, Robinsonella, Rikenellaceae RC9 gut group, Lachnoclostridium, Proteus, Blautia, Alistipes, Mucispirillum, and Lachnospiraceae NK4A136 group.

Fig. 7.

Carvacrol regulates intestinal microbiota in DSS-induced colitis mice. (A) Analysis of community composition at gate level in DSS control group and carvacrol-treated group. (B) Heat maps showing the difference at the generic level between the DSS control group and the carvacrol group. (C) Box diagram showed the distribution characteristics of intestinal flora in DSS control group and carvacrol-treated group. (D-H) The relative abundance comparison of Escherichia coli Shigella, Lachnoclostridium, Lactobacillus, Alistipes and Bifidobacterium in the intestinal tract of mice in the carvacrol treatment group and DSS control group (*, p < 0.05).

Following carvacrol treatment, we observed a reduction in pathogenic E. coli and an increase in probiotic bacteria such as Lactobacillus and Bifidobacterium in the intestinal tracts of colitis mice. Additionally, the abundance of B. robinsonii was relatively reduced in the carvacrol treatment group. Our analysis revealed that carvacrol effectively reduced the population of intestinal Shigella E. coli, while decreasing the levels of intestinal Lactobacillus in DSS-induced colitis mice (Fig. 7D-F, p < 0.05). Furthermore, Alistipes and Bifidobacterium, known for producing short-chain fatty acids and modulating intestinal inflammation, showed increased abundance in the carvacrol group (Fig. 7G-H).

In conclusion, carvacrol treatment restored beneficial bacteria and reduced pathogenic bacteria in the intestines of colitis mice, improving the balance of the intestinal microflora.

Carvacrol regulates intestinal microbial function

Next, we predicted the metabolic capacity of the microbiota using KEGG analysis. The results indicated that the major functions of differentially expressed genes were related to cellular processes, genetic information processing, metabolism, and organismal systems. Most differentially expressed genes were primarily associated with biological metabolic processes, specifically carbohydrate metabolism, amino acid metabolism, cofactor metabolism, and vitamin metabolism (Fig. 8A). Functional difference analysis revealed significant variations in biotin metabolism, nitrogen metabolism, carbon 5 branch dicarboxylic acid metabolism, as well as cofactor and vitamin metabolism (Fig. 8B). Therefore, we concluded that carvacrol can regulate the development of colitis by modulating the metabolism of the intestinal microbiota.

Fig. 8.

Effect of carvacrol on intestinal microbial community function in DSS-induced colitis mice. (A) Classification of KEGG differentially expressed genes. (B) Box plots showing functional differences between DSS control and carvacrol-treated groups.

Carvacrol reduces AOM/DSS-induced tumor formation in mice

The promising therapeutic efficacy of carvacrol in the colitis model prompts further investigation into its potential preventive effects on colitis-associated colorectal cancer. To explore this, we utilized carvacrol in CAC treatment (Fig. 9A). Intestinal endoscopy revealed a significant reduction in both the number and size of tumors in the carvacrol group (Fig. 9B). Tumor numbers and sizes were significantly lower in the carvacrol group compared to the control (p < 0.05) (Fig. 9C-D). In the AOM/DSS model group, a large number of atypical hyperplastic surface tumors or tubular adenomas developed into adenocarcinoma or high-grade intraepithelial neoplasia, whereas the carvacrol treatment group mainly showed inflammatory mucosal damage with a few tubular adenomas (Supplementary Figure 3A). These findings suggest that carvacrol can reduce the incidence of primary colorectal cancer induced by AOM/DSS. Additionally, alcian blue staining confirmed that carvacrol restored mucin secretion in intestinal cells and protected the intestinal barrier (Supplementary Figure 3B). Given the important role of gut microbes in the development of colorectal cancer, bacterial in situ hybridization was used to assess the distribution of intestinal flora. The results indicated significant bacterial migration and colonization at the tumor site, except in the carvacrol group (Supplementary Figure 3C). Consequently, it is postulated that carvacrol may inhibit the invasion of pathogens into the intestine by impeding the migration of pathogenic bacteria to the intestinal mucosa, thereby potentially reducing tumor incidence.

Fig. 9.

The effect of carvacrol on reducing AOM/DSS-induced tumor load in primary colorectal cancer. (A) Carvacrol administration pattern diagram. (B) Endoscopic representation of the intestinal tract of mice treated with AOM/DSS for primary colon cancer and carvacrol. (C) Colorectal representation of mice treated with AOM/DSS for primary colon cancer and carvacrol. (D) Statistical map of tumor area in mice treated with AOM/DSS for primary colon cancer and carvacrol (n = 7, *, p < 0.05). (E) Statistical map of the number of tumors in mice treated with AOM/DSS for primary colon cancer and carvacrol (n = 7, *, p < 0.05).

Discussion

With the development of high-throughput sequencing technology, omics technology has made remarkable progress in recent years, which has greatly promoted the study of tumorigenesis mechanism of colon cancer. Transcriptome sequencing, CHIP sequencing, metabolome and other omics technologies have been widely used in the study of the occurrence and development of colon cancer. AOM/DSS-induced CAC animal models are important tools to explore the mechanism of inflammation-related cancer. Based on the CAC model, some researchers have carried out lncRNAs and mRNAs microarray detection to screen out the key long non-coding RNAs in the initiation process of colon cancer [19]. Some researchers have also determined the DNA methylation patterns at different time points during the occurrence and development of mouse CAC by whole genome methylation sequencing [20]. Different from other studies, we used the TMT quantitative proteome for the first time to conduct in-depth analysis of the occurrence of colitis-related colon cancer. We mapped the differential protein profiles in the colonic "inflammation-cancer" process, and used WGCNA to find a set of co-expressed proteins that are highly related to the formation of colorectal cancer, and then discovered the key proteins CTPX2, ALB, ADAM10, APCDD1, etc., which may be involved in the formation of colorectal cancer, and clarified the important role of signaling pathways such as oxidative stress in the process of colorectal cancer. Among them, it has been reported that ADAM10 can be used as a biomarker related to inflammation, metabolic disorders, and colorectal cancer [15]; APCDD1 may lead to colorectal tumorigenesis [21]. These evidences highlight the reliability and scientific research value of our analysis results.

Carvacrol, a naturally occurring monoterpene and isopropyl derivative of phenols, has been demonstrated in previous research to possess antioxidant and anti-inflammatory properties [22]. Somensi et al. discovered that carvacrol has the ability to modulate the molecular pathway involved in the pro-inflammatory activation of RAW 264.7 macrophages induced by endotoxin [23]. There is limited literature on the therapeutic use of carvacrol for colitis. In this investigation, the DSS-induced colitis mouse model was established to evaluate the efficacy of carvacrol treatment. The administration of carvacrol via intraperitoneal injection resulted in a significant decrease in the mRNA expression levels of iNOS and COX-2 enzymes in the intestinal tissues of the mice. Prior research has demonstrated that various polyphenols have the ability to modulate inflammatory responses through the regulation of enzymes involved in the metabolism of arachidonic acid. This indicates that carvacrol may potentially diminish the production of key inflammatory mediators, including prostaglandins, leukotrienes, and nitric oxide, by inhibiting iNOS and COX-2 enzymes, thus mitigating oxidative damage. In line with our findings, Arigesavan et al. also observed that carvacrol can suppress iNOS and pro-inflammatory cytokines, such as IL-1β [24]. Concurrently, our study revealed that carvacrol has the capacity to suppress the mRNA expression levels of IL-6 and IFN-γ in the intestinal tract of mice with DSS-induced colitis. IL-6 plays a pivotal role in the pathogenesis of IBD by modulating immune cell activity [25]. IFN-γ is implicated as a key instigator of the heightened immune response in IBD, resulting in extensive leukocyte infiltration and mucosal damage [25].

Currently, scholarly literature predominantly highlights carvacrol's anticancer properties in inducing apoptosis in cancer cells and causing cell cycle arrest. For instance, the Hedgehog signaling pathway in HPV-C33A cells is modulated by the induction of apoptosis and inhibition of cell cycle progression to impede the advancement of cervical cancer [26]. Additionally, the promotion of MCF-7 breast cancer cell cycle arrest and apoptosis is achieved through the PI3K/AKT signaling pathway [27], while the potential invasive capacity of PC3 prostate cancer cells is reduced in a dose-dependent manner [28]. The majority of existing research has focused on cellular mechanisms. In this study, we show that carvacrol effectively inhibits tumorigenesis and progression in AOM/DSS-induced CAC mouse models. Moreover, carvacrol effectively decreases the colonization of pathogenic bacteria and mitigates the presence of harmful bacteria on the intestinal mucosa in comparison to the control group with colon cancer. Given the significant effect of carvacrol on intestinal microbiome regulation in colitis mice, it is reasonable to suggest that this mechanism may contribute to the inhibition of primary intestinal cancer by carvacrol.

Phenolic compounds demonstrate promising potential in future tumor therapeutics due to their multimodal mechanisms. Their ability to simultaneously target inflammatory pathways, modulate gut microbiota composition, and induce cancer cell apoptosis positions them as multitarget agents against tumorigenesis. Natural phenolics' advantages in low systemic toxicity and microbiome-mediated efficacy enhancement further support their role in precision oncology. However, challenges remain in optimizing bioavailability and validating clinical translatability across diverse cancer subtypes. The study revealed carvacrol's modulatory effects on inflammatory mediators and gut microbiota, the specific molecular mechanisms and signaling pathways involved have not been fully elucidated. Furthermore, the research primarily relied on animal models and in vitro experiments, with insufficient pre-clinical or clinical data to substantiate its therapeutic potential in human applications.

Statement of ethics

The study was approved by the Ethics Committee of West China Hospital, Sichuan University. Animal experiments were approved by the Animal Ethics Committee at West China Hospital, Sichuan University.

CRediT authorship contribution statement

Yating Fan: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation. Ye Chen: Validation, Methodology, Investigation, Formal analysis, Data curation. Hua Yang: Visualization, Validation, Software, Resources, Methodology. Na Chen: Software, Resources, Methodology. Xiangshuai Gu: Software, Resources, Methodology. Xiaoliang Feng: Software, Resources. Chao Fang: Resources, Methodology. Yuan Yin: Writing – review & editing, Supervision, Project administration, Conceptualization. Hongxin Deng: Writing – review & editing, Writing – original draft, Project administration, Methodology, Funding acquisition, Conceptualization. Lei Dai: Writing – review & editing, Writing – original draft, Project administration, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.