Integrative network pharmacology and multi-omics to investigate the potential mechanisms involved in Wumei Wan treatment of colorectal adenomas

Integrative network pharmacology and multi-omics to investigate the potential mechanisms involved in Wumei Wan treatment of colorectal adenomas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative network pharmacology and multi-omics to investigate the potential mechanisms involved in Wumei Wan treatment of colorectal adenomas Qingwan Yang, Song Zhou, Chunlan Chen, Yuemeng Luo, Junsong Cui, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6629738/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background Wumei Wan (WMW), a classical Traditional Chinese Medicine (TCM) formulation, has been employed for treating colorectal adenoma (CRA), yet its pharmacological mechanisms remain to be elucidated. This study investigated the protective effects of WMW on CRA through the regulation of the arachidonic acid (AA) metabolism pathway. Methods Blood components of WMW were analyzed, and network pharmacology was used to predict potential targets. The APC min/+ mouse model was utilized to assess the effects of WMW on intestinal tumor number and size, with histopathology evaluated by H&E staining. Immunohistochemistry was employed to analyze Ki67 and p53 expression. Multi-omics approaches, including fecal metagenomics, UHPLC-Q-TOF MS, transcriptomics, and 4D-label-free proteomics, were used to study fecal microbiota, serum metabolites, colon mRNA, and protein expression. Real-time quantitative PCR (RT-qPCR) was used to verify the multi-omics findings. Results UHPLC-MS identified 809 blood components in WMW. WMW significantly reduced tumor number and size in CRA mice. Multi-omics analysis revealed WMW’s regulation of the AA metabolism pathway, identifying key metabolites (8(S)-HETE, PGF2α, and 12-HETE), genes ( Cyp2e1 , Pla2g2a , Pla2g4c , Alox5 , Alox15 , and Ptgds ), and proteins (Alox15 and Pla2g4c). RT-qPCR confirmed consistent mRNA expression of Mmp9 , Il - 1a , Esr1 , Il - 13 , Cyp2e1 , Alox5 , Alox15 , Pla2g2a , Pla2g4c , and Ptgds . Conclusion WMW inhibits the development of colorectal adenoma by modulating the AA metabolism pathway, involving changes in intestinal microbiota, serum metabolites, and mRNA/protein expression in the colon. Colorectal adenoma Wumei Wan Network pharmacology Multi-omics Arachidonic acid metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Colorectal adenomas (CRAs) are benign tumors that develop from the glandular epithelial cells in the colorectal mucosa. These cells could aid digestive process by mediating nutrient absorption and intestinal immunity. While benign, CRAs can gain malignancy over time and are recognized as the primary precursor to colorectal cancer (CRC), a gastrointestinal malignant tumor with leading incidence. Moreover, CRC is the second leading cause of cancer-related deaths, characterized by high rates of local recurrence and propensity for distant metastasis 1 . Specifically, CRAs can develop into invasive cancers that penetrate the intestinal wall, which help them to enter abdomen and spread to other parts of the body. Among different types, the presence of multiple CRAs has been widely recognized as a predictive indicator for the development and prognosis of CRC 2 . Accordingly, effective management of CRAs can significantly reduce the burden of CRC, playing a crucial role in both prevention and treatment. The cornerstone of CRA management involves rigorous endoscopic surveillance, radical resection and scheduled follow-ups to monitor recurrence. Although preventive strategies—including increased physical activity, reduced red meat consumption, diets rich in fruits/vegetables, and pharmacological agents (e.g., aspirin, vitamin D)—have been explored, their efficacy remains inconclusive 3 . Currently, no pharmacological treatment has been confirmed reduce CRAs. Traditional Chinese Medicine (TCM) offers promising methods for treating CRAs and early-stage CRC, potentially addressing the gap in effective CRA-specific pharmaceutical options 4 . Notably, the adoption of Wumei Wan (WMW) in the Chinese guidelines for CRA prevention has attracted attention of clinicians. WMW, derived from the classical medical text Treatise on Febrile Diseases of the Han Dynasty, has been shown to reduce the recurrence rate of CRAs in clinical studies. Zhang Ran conducted a research using enhanced WMW, which demonstrated significantly higher overall efficacy in the treatment group compared to the control group (89.66% vs. 62.07%), as well as lower adenoma recurrence rates at 6 and 12 months post-treatment, confirmed by endoscopic evaluations 5 . Likewise, researchers such as Li Ye 6 and Liang Yifei 7 have reported substantial clinical effectiveness in managing CRAs with WMW. Preclinical evidence also supported the efficacy of WMW against CRC pathogenesis. Lu et al. 8 demonstrated that WMW suppressed colitis-associated carcinogenesis (CAC) by rectifying amino acid metabolism, inhibiting persistent PI3K/Akt activation, and regulating the dynamics of myeloid-derived suppressor cells (MDSCs). In another study, Wang et al. 9 identified that WMW attenuated CAC via suppression of S-adenosylhomocysteine hydrolase mediated Hedgehog signaling, thereby reducing inflammatory and oxidative stress. Current research has identified several bioactive compounds in WMW. Feng 10 initially identified some major active constituents including citric acid, berberine, and 6-gingerol - using high-performance liquid chromatography-mass spectrometry (HPLC-MS). Meanwhile, the composition was also characterized by Lu et al 8 through liquid chromatography-tandem mass spectrometry (LC-MS/MS), revealing similar bioactive compounds. While the chemical composition of WMW has been elucidated, critical pharmacological aspects remained unexplored. As a botanical drug, the therapeutic efficacy of MWM is inherently dependent on the bioavailability and tissue distribution of active components 11 . However, current research has predominantly focused on the anti-colitis mechanisms, neglecting its potential effects on the development from adenoma to carcinoma, which is the process of colorectal carcinogenesis. Furthermore, the pharmacokinetic profiles of WMW's bioactive compounds have yet to be systematically investigated. This knowledge gap significantly limits our understanding of WMW's pharmacodynamics, thereby hindering us from investigating clinical translatability in CRA prevention. To shed light on these unsolved questions, we employed a well-established mouse model relevant to CRA research: the APC min/+ mouse. The heterozygous transgenic model (on a C57BL/6J background) spontaneously develops intestinal adenomas and is widely used in studies on CRA and CRC 12 . Although WMW itself has not been studied in the APC min/+ model, several of its bioactive components such as 6-gingerol and berberine have been proved to reduce colorectal tumor burden. For instance, 6-gingerol suppressed tumor progression by inhibiting EGFR downstream signaling 13 , while berberine attenuated Vibrio vulnificus-induced immunomodulation and colorectal tumorigenesis in the same mouse model 14 . These findings support the feasibility of using APC min/+ mouse to investigate therapeutic mechanisms of WMW against CRA. Furthermore, TCM achieves its efficacy against CRA 15 and CRC 16 by modulating the gut microbiome. The gut microbiota, as well as its metabolites, engage with host physiological processes by modulating mRNA transcription and protein expression 17 . To delineate the action mode of WMW, we firstly characterized its systemic exposure profile using UHPLC-MS, and treated APC min/+ mice with WMW for 10 weeks to assess anti-adenoma effects. Multi-omics analyses of fecal, serum, and intestinal tissue samples were then performed to elucidate the impact of WMW on microbiota, metabolites, and host gene/protein networks. In addition, key predictions from these analyses were validated by Quantitative Real-time PCR (RT-qPCR). 2 Materials and Methods 2.1 Preparation of WMW WMW is composed of the following ingredients: Fructus Mume (Wu Mei) 30g, Asarum sieboldii (Xi Xin) 6g, Ramulus Cinnamomi (Gui Zhi) 9g, Coptis chinensis (Huang Lian) 6g, Phellodendron Chinense (Huang Bai) 15g, Angelica sinensis (Dang Gui) 6g, Panax ginseng (Ren Shen) 9g, Zanthoxylum bungeanum (Hua Jiao) 6g, Zingiber officinale (Gan Jiang) 9g, and Aconitum carmichaelii (Fu Zi) 15g. These ingredients were sourced from the TCM Pharmacy at the Second Affiliated Hospital of Guizhou University of TCM. The herbal mixture was immersed in fresh drinking water (weights ten times compared to ingredients) for 30 minutes, then boiled for additional 30 minutes. After draining the initial decoction, the remaining ingredients were reboiled in fresh drinking water (weights five times compared to ingredients) for another 30 minutes. The decoctions from both boiling processes were combined, with filtration carried out to remove solid residues. Subsequently, the mixture was gently concentrated over low heat until a paste-like consistency with relative density ranging from 1:1.1 to 1.2. This concentrated paste was frozen at -20°C overnight, freeze-dried for seven days, ground through a No. 3 sieve, and then subjected to an additional day of freeze-drying. The freeze-dried powder, which served as the final product, was produced from 2240g original WMW mixture with a yield of 463g, implying that each gram of freeze-dried powder corresponded to 4.838g raw herbs. And the powder was reconstituted in drinking water to form a suspension until administration. 2.2 Animals and Treatments 8-weeks-old male APCmin/+ and C57BL/6J mice were acquired from Cyagen Biosciences Inc., Suzhou (certification number SCXK (Su) 2022-0016) and maintained at the Animal Research Institute of Guizhou University of TCM under specific pathogen-free (SPF) conditions. The facility provided a controlled environment with a 12/12-hour light/dark cycle, a temperature of 22 ± 2°C, and relative humidity maintained at 55% ± 10%. The mice had unrestricted access to both food and a 60% high-fat diet supplied by Guizhou Huigu Biotechnology Co., Ltd. (product number 2310-18). Twenty-four male APC min/+ mice were randomly divided into three groups, each consisting of eight mice: the model group (MOD), the low-dose WMW (L-WMW) group, and the high-dose WMW (H-WMW) group. An additional group of eight age-matched male C57BL/6J mice served as the control group (CON). The L-WMW group received a daily oral administration of WMW at a dose of 17 g/kg, while the H-WMW group received 34 g/kg. These dosages were calculated based on the standard human clinical dose (112 g/60 kg ≈ 1.87 g/kg) and adjusted for differences in body surface area between humans and mice (112 g/60 kg * 9.1 ≈ 17 g/kg) according to results from Gou et al. 18 . Instead of WMW suspension, the CON and MOD groups were administered an equivalent volume of drinking water. The treatment duration for all groups was 10 weeks. To ensure animal welfare, predefined humane endpoints were used as criteria for euthanasia: (1) > 20% body weight loss within 48 hours, or (2) severe lethargy/inability to access food or water. No animals met these criteria during the study. Thirty minutes after the final WMW or drinking water administration, mice were anesthetized via intraperitoneal injection of 100 mg/kg pentobarbital sodium. Depth of anesthesia was confirmed by absence of toe-pinch reflex. Terminal blood collection was performed via cardiac puncture, followed by immediate cervical dislocation to ensure euthanasia. Death was confirmed by cessation of respiration, loss of corneal reflex, and no detectable heartbeat for > 1 minute. The experimental protocol adhered to ethical guidelines and was approved by the Animal Experiment Ethics Committee of Guizhou University of Traditional Chinese Medicine (Approval Number 20230136). 2.3 Analysis of Serum Chemical Composition of WMW C57BL/6J mice were maintained on a standard diet and randomly assigned to two groups: the WMW group, which received 34 g/kg/day via gavage, and the control group (n = 6). The serum samples were collected from mice after 14 consecutive days of oral gavage. The serum was isolated by allowing the blood to clot and then centrifuging it. The serum extracts were analyzed using a Vanquish UHPLC system equipped with an HSS-T3 column, maintained at 35°C. The mobile phases consisted of H2O with 0.1% formic acid and acetonitrile with 0.1% formic acid. The elution was performed at a flow rate of 0.3 mL/min using a specified gradient. Analyses were conducted using a Q-Exactive HFX mass spectrometer. Mass spectra were acquired in both ESI positive and negative modes, capturing MS/MS spectra from the top 10 most intense MS1 ions. Compound annotation was aligned with reference data from an in-house TCM standards database (Shanghai Applied Protein Technology Co., Ltd., Shanghai, China) and public databases such as GNPS 19 , ReSpect 20 , and Massbank 21 . 2.4 Morphological Examinations Colorectal tissues were collected from three randomly selected mice in each group, opened longitudinally, and rinsed with precooled PBS. The tissues were then mounted on black cardboard and photographed under uniform lighting conditions to assess the presence and measure the maximum diameter of any polyps in the colorectum. 2.5 Pathological Examinations Colorectal tissues from three mice in each group were preserved in 4% paraformaldehyde, subjected to a graded ethanol dehydration process, and embedded in paraffin. Sections were stained with hematoxylin and eosin (H&E) for histological assessment. Immunohistochemical (IHC) staining was performed to evaluate the prevalence of Ki67 and p53-positive cells, indicated by brown-yellow staining. The proportion of positive areas was quantified. 2.6 Network Pharmacology Analysis The TCMSP ( https://old.tcmsp-e.com/tcmsp.php ), TCMID ( http://www.megabionet.org/tcmid/ ), and CTD ( http://ctdbase.com/ ) databases were utilized to identify potential targets of blood components. Differentially expressed genes (DEGs) between the MOD and CON groups, identified through transcriptomic analysis, were classified as disease-related target genes. These key targets were integrated into the STRING database to construct a protein-protein interaction (PPI) network. Genes common to both the predicted component-target genes and the disease-related target genes were filtered based on their median gene degree. Topological analysis of the component-target-disease network was performed using the Python package NetworkX, which provided metrics such as degree, betweenness, and closeness centrality. Nodes were ranked based on their weighted importance in descending order, with the top 100 genes identified as core targets. Further functional annotations were carried out using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). 2.7 Metagenomic Sequencing DNA was isolated from fecal samples using the Magnetic Soil and Stool DNA Kit (TIANGEN). Following quality assessment, sequencing libraries were prepared with the NEBNext® Ultra™ DNA Library Prep Kit for Illumina (NEB), and DNA was fragmented to an average size of 50 bp before undergoing end-repair. Sequencing was conducted on the NovaSeq 6000 platform (Biomarker Technologies Co., Beijing, China). Raw sequencing data were processed using fastp software ( https://github.com/OpenGene/fastp ) with default settings. The assembled sequences were handled using MEGAHIT, ORFs were predicted with Prodigal, and clustering was executed via CD-HIT. Quantification of sequences was performed using Bowtie2 software. DIAMOND software facilitated the alignment of unigenes to sequences from the NCBI NR database (Version: 2021.11) to classify species. The KEGG database was employed for functional annotation of microbial content. Microbial alpha (α) and beta (β) diversity at the species level were calculated. Linear discriminant analysis effect size (LEfSe) was applied, with microorganisms or functional pathways exhibiting a linear discriminant analysis score greater than 2 and a p-value less than 0.05 deemed notably different. 2.8 Non-Targeted Metabolomics Serum samples were analyzed using an Agilent 1290 Infinity LC ultrahigh-performance liquid chromatography (UHPLC) system equipped with an HILIC column. Mass spectrometric analysis was conducted utilizing a Triple TOF 6600 mass spectrometer (AB SCIEX) in both positive and negative electrospray ionization (ESI) modes. The protocols for detection, analysis, and quality control, including specific parameters, are comprehensively detailed in Supplementary Table 1. Metabolites were detected and subsequently classified according to their chemical taxonomy. To identify notable metabolic differences between groups, we carried out orthogonal partial least squares discriminant analysis (OPLS-DA) principal component analysis (PCA), applying thresholds of variable importance in projection (VIP) > 1.0 and P < 0.05. 2.9 Transcriptomics Total RNA was extracted using TRIzol® reagent (Magen, Waltham, MA, USA). RNA purity and quantification were assessed using a Nanodrop ND-2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). The procedures for library preparation, mRNA purification, and both first and second strand cDNA synthesis, along with sequencing parameters, are provided in Supplementary Material 1. Bioinformatics analysis of the sequencing data was performed, setting differential expression thresholds at an adjusted p-value 2 or FC < 0.5. 2.10 4D-Label-Free Proteomics Colorectal tissue was lysed in a 4% SDS aqueous solution containing protease inhibitors to extract total protein. Through a BCA assay kit, we quantified proteins. After overnight digestion with trypsin at 37°C, peptides were derived and analyzed using nano-LC-MS/MS. The analytical procedure and specific parameters are detailed in Supplementary Material 1. A protein was considered differentially expressed if the p-value between the two groups was < 0.05. 2.11 RT- qPCR Analysis Colorectal tissue was homogenized using a fast tissue homogenizer for 40 seconds in an appropriate volume of lysis buffer. Subsequently, 10 µL of proteinase K was added, and the mixture was incubated at room temperature for 5 minutes. Following centrifugation, the supernatant was collected, and RNA was extracted using a column-based centrifugation method. The integrity, concentration, and purity of the RNA were evaluated. The concentration of RNA was adjusted to 100 ng/µL, and reverse transcription was performed to synthesize complementary DNA (cDNA), which was then stored at − 20°C. Through SYBR GREEN qPCR mix, we carried out RT-qPCR, and data were collected using the Bio-Rad CFX96 Real-Time PCR Detection System. GAPDH served as an internal control. Through the 2 −ΔΔ C t method, we determined relative gene expression levels. The mRNA primers, used for this analysis, were synthesized by Shanghai Shenggong Biological Engineering. Table 1 lists the sequences of these primers. Table 1 Primer used in the current study Gene name Sequence(5’-3’) Mmp9 Forward: GCAGAGGCATACTTGTACCG Reverse: TGATGTTATGATGGTCCCACTTG Il-1a Forward: GGCATTGTTCTCTAATGTCTCCG Reverse: TGTCGAGCTTTGGGATGGTAG Esr1 Forward: CCCGCCTTCTACAGGTCTAAT Reverse: CTTTCTCGTTACTGCTGGACAG Il-13 Forward: CGGTGCCAAGATCTGTGTCT Reverse: CGTGGCGAAACAGTTGCTTT Alox5 Forward: TTGCTCTCACAGTATGACTGGT Reverse: AGTATCCACGATCTGCTCGAT Alox15 Forward: GAATACCTTGGGCCACTGCT Reverse: TTCCAGGAGTTTCGAACCCG Pla2g2a Forward: TGCTAGCAGCCTCGATCATG Reverse: TGGGCTTCTTCCCTTTGCAA Pla2g4c Forward: AGGAGCTGAAACATCGGTATGA Reverse: CTGCAAAGATGGGATAGGGC Ptgds Forward: GATGGGTTTGGTCCTCCTGG Reverse: ATGCACTTATCCGGTTGGGG Cyp2e1 Forward: TGGTCCTGCATGGCTACAAG Reverse: GACAGTCAGTCACATCCCGG Gapdh Forward: GGTTGTCTCCTGCGACTTCA Reverse: TGGTCCAGGGTTTCTTACTCC 2.12 Statistical Analysis Through SPSS version 26.0, we carried out data analysis, with results reported as . We used a one-way analysis of variance (ANOVA) for comparisons across multiple groups. In cases where data satisfied the criteria for normal distribution and homogeneity of variance, we used the Bonferroni test for post hoc pairwise comparisons. For datasets exhibiting unequal variances, the Welch test was initially utilized to compare overall means, followed by Dunnett's T3 test for pairwise comparisons. Graphical representations were created using GraphPad Prism version 9.0.1 (GraphPad Software, San Diego, CA). 3 Results 3.1 Identification of WNW Components in Serum We employed UHPLC-HRMS to analyze the active chemical constituents of WMW. This technique provided a comprehensive examination of the chemical profiles in each sample group. We analyzed and compared the base peak chromatograms (BPCs) in both positive and negative ion modes, which were crucial in identifying significant chromatographic peaks (Fig. 1 A-B). These peaks were systematically numbered after an extensive evaluation of their morphological and secondary spectral characteristics (Fig. 1 C-D). The identification process involved verifying compounds through searches in a local TCM high-resolution mass spectrometry database. The criteria for confirmation included a primary mass error below 25 ppm and a secondary fragmentation spectrum match score above 0.7, affirming high reliability in spectrum similarity and identification accuracy 22 . As a result, 2,641 chemical constituents in WMW and its post-administration serum were identified, with 809 constituents detected in the serum (Table S1 ). We classified these compounds using the NPClassifier method 23 , which categorized the serum-entering constituents into 19 pathways, 93 superclasses, and 293 classes. 3.2 WMW Ameliorates the Pathology of CRA After a 10-week regimen of oral gavage, the mice were euthanized, and their colorectal tissues were harvested for analysis. In the C57BL/6J control group, no colorectal tumors were found. However, the APC min/+ mice displayed multiple visible tumors within the colorectal tissues, with the largest tumor measuring approximately 6 mm in diameter. In the WMW-treated group, there were fewer intestinal tumors, and the largest tumor was significantly smaller in diameter than those in the MOD group (Fig. 2 A, C). Quantitatively, the MOD group developed an average of five CRAs, while the WMW-treated mice showed a substantial reduction, with only about one to two adenomas observed, as shown in Fig. 2 B. H&E staining revealed that in the CON group, the intestinal glands maintained normal architecture with well-ordered epithelial cells, uniform villi morphology and distribution, and no evident inflammation or cellular damage. Conversely, the MOD group's colon tissue exhibited severe structural disruptions, characterized by pronounced epithelial hyperplasia with a predominantly glandular appearance. Some areas displayed branching glands with atypical tissue features. The proliferative glandular areas were densely packed (↑), with minimal or absent stromal tissue, and goblet cells were sporadically present or absent altogether. The glandular lumina contained basophilic mucus (↑) and necrotic cellular debris. In some regions, the epithelium formed small papillary projections into the glandular lumina, presenting an irregular surface. The disordered hyperplastic epithelium showed pseudo-stratified or multilayered arrangements, featuring irregular nuclei, increased nuclear-to-cytoplasmic ratios, and active mitotic activity (↑). In the WMW-L group, there was mild mucosal disorganization with glandular-like epithelial hyperplasia, slight mucosal necrosis accompanied by inflammatory cell infiltration, and areas of indistinct intestinal gland structure interspersed with necrotic debris, lymphocytes, and neutrophils. In the WMW-H group, the layers of the colon mucosa, submucosa, and muscularis were distinctly layered and well-defined. The epithelial cells within the mucosal layer exhibited normal morphology without notable degeneration, necrosis, or desquamation. These observations suggest that WMW administration can mitigate atypical hyperplasia and reduce inflammatory cell infiltration in intestinal tumors (Fig. 2 D). IHC analysis demonstrated that Ki67 was primarily localized to the nuclei (Fig. 2 E), while p53 expression was predominantly observed in both the nuclei and cytoplasm of cells (Fig. 2 G). Compared to the CON group, the expression levels of Ki67 and p53 were notably elevated in the intestinal tissues of the MOD group (P < 0.01). In contrast, the WMW-L and WMW-H groups showed reduced expression of Ki67 and p53 compared to the MOD group (P < 0.05), as illustrated in Figs. 2 F and 2 H. 3.3 Network Pharmacology In this study, of the 809 components detected in the bloodstream, 536 were successfully annotated as detailed in Table S2 . A comprehensive database search identified 2,823 potential targets for these annotated components. Additionally, a total of 11,871 genes associated with CRA were extracted from the CTD. The intersection of the component-derived targets with the disease-associated genes resulted in the identification of 2,315 common targets, as shown in Fig. 3 A and listed in Table S2 . Among these common targets, 323 exhibited a gene degree higher than the median within the PPI network. Subsequent topological analysis, which included metrics such as Degree, Betweenness, and Closeness, facilitated the identification of the top 100 core targets, as depicted in Fig. 3 B and listed in Table S2 . These core targets underwent further analysis through GO and KEGG pathway analyses, elucidating the underlying mechanisms of action. A network diagram highlighting the interactions between key components and core targets was constructed, emphasizing components such as Genistein, Cycloheximide, and Luteolin, which displayed the highest degrees of interaction. Similarly, the core targets with the highest degrees were CASP3, MAPK3, and MAPK1, as shown in Fig. 3 C. The GO analysis revealed involvement in 421 molecular function terms, 220 cellular component terms, and 3,994 biological process terms, as listed in Table S2 . The top 10 terms prominently feature processes including positive regulation of apoptosis and cell proliferation, as depicted in Fig. 3 D. The KEGG analysis demonstrated the involvement of core targets in 202 pathway entries, encompassing a variety of pathways related to cancer, cell death, and inflammation. Notably, these pathways include CRC, pyroptosis, and the PI3K-Akt signaling pathway. The top 20 notably enriched pathways, potentially related to CRA, are presented in Fig. 3 E. 3.4 Regulation of Gut Microbiota by WMW Metagenomic sequencing revealed no notable differences in the fecal microbial α-diversity index among the three groups (Fig. 4 A). However, β-diversity indices at the species, genus, family, and phylum levels demonstrated intergroup variability. Notably, the β-diversity index of the WMW group was intermediate between those of the CON and MOD groups (Fig. 4 B-C). At the phylum level, the three predominant microorganisms in the CON were Firmicutes (66.31%), Actinobacteria (16.36%), and Bacteroidetes (10.20%); in the MOD group, they were Firmicutes (37.66%), Bacteroidetes (24.38%), and Verrucomicrobia (15.53%); in the WMW group, the predominant phyla were Firmicutes (43.46%), Bacteroidetes (37.42%), and Chlamydiae (11.94%) (Fig. 4 D). At the genus level, the most abundant genera in the control group were Dubosiella (29.91%), Bifidobacterium (14.96%), and Faecalibaculum (10.33%); in the MOD group, they were Akkermansia (19.17%), Chlamydia (16.02%), and Bacteroides (7.71%); and in the WMW group, they were Bacteroides (16.25%), Chlamydia (15.24%), and Blautia (10.71%) (Fig. 4 E). Functional enrichment analysis of the differential microbiota using the KEGG database revealed notable pathway distinctions among the groups. According to the LEfSe analysis at KEGG level C, the most notable pathways in the CON group were Starch and sucrose metabolism (LDA score 3.82), Phosphotransferase system (LDA score 3.77), and ABC transporters (LDA score 3.62). In the MOD group, the most prominent pathways were Lipopolysaccharide biosynthesis (LDA score 3.34), Fatty acid biosynthesis (LDA score 3.29), and Bacterial secretion system (LDA score 3.16). In the WMW group, the top pathways included Carbon fixation pathways in prokaryotes (LDA score 3.37), Folate biosynthesis (LDA score 3.33), and Other glycan degradation (LDA score 3.23) (Fig. 4 F). Comprehensive results are presented in Table S3 . 3.5 Regulation of Serum Metabolites by WMW Using UPLC-TOF/MS analysis following serum sample collection, we detected a total of 719 metabolites in the ESI(+) mode and 591 metabolites in the ESI(−) mode (Table S4 ). Principal coordinate analysis (PCoA) of the peaks from all experimental and QC samples demonstrated that QC samples clustered tightly in both ion modes, indicating high reproducibility of the experiment (Figs. 5 A-B). Subsequent orthogonal partial least squares discriminant analysis (OPLS-DA) revealed distinct separation of serum metabolite profiles among the CON, MOD, and WMW-treated groups (Figs. 5 C-F). We detected 67 differential metabolites between the WMW and MOD groups, comprising 20 upregulated and 47 downregulated metabolites (Figs. 5 G-H). KEGG pathway analysis of these differential metabolites highlighted their involvement in pathways including Arginine biosynthesis, amino acid biosynthesis, ABC transporters, 2-Oxocarboxylic acid metabolism, and Arachidonic acid (AA) metabolism (Fig. 5 I). 3.6 WMW Modulates the Colorectal Transcriptome Gene expression profiling revealed notable differences among the CON, MOD, and WMW-treated groups (Table S5 ). Compared to CON, the MOD group showed 820 downregulated and 1,448 upregulated DEGs (Fig. 6 A). In contrast, the WMW-treated group exhibited 627 downregulated and 376 upregulated DEGs relative to the MOD group (Fig. 6 B). Post-treatment with WMW, some gene expression alterations between the MOD and CON groups were partially reversed, including 162 downregulated and 438 upregulated DEGs observed in the MOD group relative to the CON group. Of these reversed genes, 87 coincided with predicted targets of WMW serum components (Fig. 6 C, Table S5 ). PPI network analysis pinpointed key genes among these 87 reversed genes, including Mmp9 , Alb , and Esr1 (Fig. 6 D). KEGG pathway analysis associated these genes with 20 pathways, primarily related to cancer (e.g., breast cancer, chemical carcinogenesis), inflammatory responses (e.g., TNF signaling pathway, IL-17 signaling pathway), and metabolic processes (e.g., glutathione metabolism, AA metabolism) (Fig. 6 E). 3.7 WMW Modulates the Colorectal Proteomic 4D label-free proteomic analysis detected 132 downregulated and 179 upregulated differentially expressed proteins (DEPs) between the MOD and CON groups (Fig. 7 A). In comparison, the WMW-treated group exhibited 21 downregulated and 48 upregulated DEPs relative to the MOD group (Fig. 7 B). Following WMW treatment, some protein expression alterations between the MOD and CON groups were partially restored, including 23 downregulated and 9 upregulated DEPs (Table S6 ). KEGG pathway analysis of the DEPs between the WMW-treated and MOD groups highlighted associations with 68 pathways, encompassing Linoleic acid metabolism, ovarian steroidogenesis, hepatitis C, choline metabolism in cancer, and AA metabolism (Fig. 7 C). Protein-protein interaction network analysis detected central hub DEPs, such as Isg15, Ifit1, and Oas3, in the WMW-treated and MOD groups (Fig. 7 D). 3.8 Association analysis Through KEGG pathway analysis, the AA metabolism pathway was detected as being enriched across all four omics datasets. It was also highlighted as one of the enriched pathways in the targets predicted by network pharmacology. Specific metabolites noted in the metabolomics analysis included 8(S)-hydroxy-5z,9e,11z,14z-eicosatetraenoic acid (8(S)-HETE), prostaglandin F2 alpha (PGF2α), and 12(S)-hydroxy-5z,8z,10e,14z-eicosatetraenoic acid (12-HETE). Additionally, genes such as Cyp2e1 , Pla2g2a , Pla2g4c , Alox5 , Alox15 , and Ptgds detected in the transcriptomics analysis, along with proteins including Alox15 and Pla2g4c found in the proteomics analysis, were all linked to the AA metabolism pathway, as depicted in Fig. 8 . 3.9 RT-qPCR We assessed the expression levels of genes associated with the AA metabolism pathway and those situated in the core regulatory regions for exploring the mechanistic role of WMW in treating CRA. The result indicated that the mRNA expression levels of Alox15, Pla2g2a, Ptgds, Cyp2e1, Mmp9, Il-1a, and Esr1 in the MOD group were notably higher as contrasted with those in the CON, WMW-L, and WMW-H groups. In contrast, the mRNA expression levels of Pla2g4c, Alox5, and Il-13 in the MOD group were notably lower than those in the CON, WMW-L, and WMW-H groups, aligning with the findings from the transcriptomics analysis. These results are presented in Fig. 9 . 4 Discussion CRA is the most common precursor to CRC 24 , 25 . Approximately 70%-90% of CRC cases arise from a sequence beginning with normal mucosa, progressing to intestinal polyps, and culminating in CRA. CRA has the potential to develop into a malignant form within an estimated 10–15 years. Consequently, preventing the recurrence and progression of CRA is a major concern. Currently, the available treatments for this condition are limited. Chinese guidelines recommend several TCM prescriptions for preventing CRA recurrence, including WMW 4 . Clinical studies have demonstrated that WMW can decrease the recurrence rate of CRA after its removal 5 , 7 , 10 and enhance the effectiveness of chemotherapy in advanced CRC 26 , 27 . In our study, we observed a significant reduction in both the number and size of intestinal tumors in APC mice treated with WMW, with no notable differences between the two doses tested. H&E staining revealed that the pathology of the WMW-treated group was markedly better than that of the MOD group, aligning with findings from other studies 8 , 9 . However, the mechanism by which WMW prevents CRA recurrence remains unclear. Network pharmacology, combined with multi-omics, is a method increasingly used in recent years to explore the efficacy and mechanisms of traditional compound formulations. After identifying the components of WMW that enter the bloodstream, we employed network pharmacology and multi-omics to systematically delineate the effects of low doses of WMW (equivalent to the usual human dose) on the intestinal microbiota, serum metabolites, and gene and protein expression in CRA mice. We identified 809 chemical components of WMW in serum, such as Rosmarinic acid, Phellodendrine, Magnoflorine, Codethyline, Berberrubine, Oxoglaucine, Coptisine, and Majarine. Many of these components have been shown to treat CRA or CRC effectively 28 – 30 . Network pharmacology indicates that these components target 112 genes intersecting with the DEGs in CRA mice. We hypothesize that these intersecting genes are associated with various cancers, cell death pathways, and inflammation, such as CRC, apoptosis, and the PI3K-Akt signaling pathway. Notably, abnormalities in AA metabolism have also been observed in other omics studies. The human gut microbiome plays a notable role in the development of various diseases. CRA and CRC are gut lesions influenced by gut microbiome changes 31 , 32 . Dysbiosis in the gut microbiome can affect intestinal epithelial permeability, disrupt immune tolerance, and activate local inflammatory responses. Our research found increased abundance of Firmicutes, Bacteroidetes, Chlamydiae, Proteobacteria, Verrucomicrobia, Actinobacteria, and Tenericutes in the MOD group. Other microbiomes except for Firmicutes and Bacteroidetes are less abundant in the WMW group than in the MOD group. In 533 CRC patients, the main phyla in colorectal tissues and feces were Proteobacteria (43.5%), Firmicutes (25.3%), and Actinobacteria (23.0%). Survival analysis showed that patients with higher abundance of Proteobacteria and lower abundance of Firmicutes and Actinobacteria in colorectal tissues had notably worse survival rates 33 . Other studies indicate that an increase in Proteobacteria relative abundance facilitates the progression from CRA to CRC and the occurrence of CRC 34 . In the AOM/DSS colitis CRC model, the abundance of Proteobacteria and Tenericutes was positively correlated with tumor load 35 . Chlamydiae, an infectious pathogen, is associated with breast cancer 36 and cervical cancer 37 . Chlamydia trachomatis infection in gut diseases can enhance the carcinogenic potential of HPV16 and the risk of high-grade anal intraepithelial neoplasia in HIV patients 38 . AA is a polyunsaturated fatty acid that forms part of cell membranes. It plays a crucial role in immune functions, inflammatory responses, and cancer 39 , 40 . AA is metabolized and transformed through three pathways: cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 41 . COX2 and Ptgds metabolize AA into substrates like prostaglandin D2 (PGD2), which are then converted into specific PGs such as PGF2α by PGFS 42 . We found that WMW treatment notably affected the AA metabolic pathway in the APC mouse model. Metabolites detected through metabolomics analysis, including 8(S)-HETE, PGF2α, and 12(S)-HETE, genes detected in transcriptomics analysis ( Cyp2e1 , Pla2g2a , Pla2g4c , Alox5 , Alox15 , and Ptgds ), and proteins detected in proteomics analysis (Alox15 and Pla2g4c) were involved in the AA metabolism pathway. In CRC tissues, PGFS protein and mRNA levels are higher than in adjacent tissues, leading to increased levels of PGF2α and resistance to the chemotherapy drug oxaliplatin 43 . PTGDS is a notable risk factor for the response to neoadjuvant chemoradiation therapy (NCRT) in rectal mucinous adenocarcinoma 44 and is associated with the development and prognosis of CRC 45 , 46 . Nonsteroidal anti-inflammatory drugs like aspirin and COX-2 selective inhibitors such as celecoxib may reduce recurrence and progression of CRA, possibly by modulating AA metabolism 47 , 48 . LOX enzymes insert oxygen into AA at various positions, forming four types of hydroperoxyeicosatetraenoic acids (HPETEs; 5-HPETE, 8-HPETE, 12-HPETE, and 15-HPETE), which are then reduced by peroxidases to hydroxyeicosatetraenoic acids (HETEs) or converted into bioactive compounds like leukotrienes, which regulate inflammation 41 , 49 . In CRC patients, the level of 8-HETE in the serum is lower than in non-CRC patients 50 and is linked to an increased risk of ovarian cancer 51 . 12-HETE disrupts cell-cell junctions, causing retraction of lymphatic and blood endothelial cell walls, which may facilitate CRC invasion and lymph node metastasis 52 , 53 . Elevated levels of 12-HETE in the serum of CRC and CRA patients compared to healthy individuals could be associated with higher proliferation rates in adenomas 54 . Exposure to 12-HETE or overexpression of ALOX12 stimulates CRC cell proliferation and migration 55 . Using ALOX12 inhibitors can reduce 12-HETE production in CRC cells (HT29) and suppress levels of EMT markers 56 . ALOX15 has tumor-suppressive effects, is underexpressed in CRA and early CRC, and can inhibit CRC cell proliferation and the development of colitis-associated CRC 57 . The cytochrome P450 (CYP) pathway is the third major pathway for AA metabolism. Many CYP enzymes function as both hydroxylases and epoxygenases, producing a range of products. For instance, the ω-hydroxylase activity of CYP enzymes converts AA to HETE, while their epoxygenase activity generates epoxyeicosatrienoic acids (EETs) 49 . Both HETE and EETs are involved in regulating cancer cell behaviors, including proliferation, survival, angiogenesis, invasion, and metastasis 58 . CYP2E1 is a phase I enzyme involved in xenobiotic metabolism, activating low molecular weight carcinogens like nitrosamines into active carcinogens that contribute to digestive tract tumors. Thus, higher CYP2E1 activity is associated with increased cancer progression 59 , 60 . Additionally, the release of AA from phospholipid membranes is regulated by phospholipase A2 (PLA2), which mediates the hydrolysis of phospholipid skeletons to directly produce free AA 61 . Research indicates that PLA2 and its variants, such as PLA2G4C and PLA2G2A, are directly involved in tumor development 62 , 63 . Studies have linked the A allele of the PLA2G4C SNP (rs1549637) with poorer prognosis in CRC patients 64 . Cell experiments have shown that targeting PLA2G4C can increase CRC sensitivity to irinotecan 65 . A retrospective study found that patients with tumors negative for PLA2G2A immunohistochemistry had a notably longer survival than those positive for it, suggesting a negative correlation between PLA2G2A and CRC prognosis 66 . In advanced and metastatic CRC, higher levels of PLA2G2A have been found in serum 62 . Recent research has shown that selenoprotein I (Selenoi) prevents intestinal epithelial cell ferroptosis by maintaining lipid homeostasis. Selenium deficiency leads to increased expression of PLA2G2A and ALOX15, excessive lipid peroxidation, and increased ferroptosis, thus promoting the progression of colitis and CRC 67 . Our study utilized metabolomics analysis to demonstrate that serum levels of PGF2α, 8-HETE, and 12-HETE were reduced in APC min/+ mice following treatment with WMW. Transcriptomic data revealed that, compared to the model group, the mRNA expression levels of Cyp2e1, Pla2g2a, Alox12, and Ptgds in the WMW group were significantly decreased, while those of Alox5 and Pla2g4c were significantly increased. Proteomic analysis further confirmed that the expression levels of Pla2g4c and Alox15 in the WMW group were notably higher than those in the model group. Using RT-qPCR, we verified the mRNA expression levels of Cyp2e1, Pla2g2a, Alox12, Alox5, Pla2g4c, and Ptgds. The results indicated that WMW therapy effectively restored the levels of Cyp2e1, Pla2g2a, Ptgds, and Alox15 in the colon, while inhibiting the expression of Alox5 and Pla2g4c, aligning with the findings from the transcriptome and proteome analyses. Through serum composition identification and network pharmacological predictions, we confirmed that compounds such as biotin, cyclohexanamide, quercetin, and genistein interact with these genes or proteins. These findings preliminarily elucidate the composition and mechanism of WMW in treating CRA and provide valuable insights for further research into WMW's therapeutic applications in CRA. Our study has several limitations. First, our experimental model involved APC min/+ mice, a spontaneous CRA model that closely mimics disease progression in CRA patients. Although this model shares similarities with human conditions, inherent species differences exist; however, our findings still offer some direction for applying WMW in treating CRA. Second, our conclusions are restricted to an animal model, necessitating further clinical validation. Third, while we employed various omics technologies to comprehensively describe the effects of WMW on the CRA model, our integration of multi-omics data was limited. For instance, there were numerous discrepancies between the differentially expressed genes identified in the transcriptome and the proteins detected in the proteome, potentially due to processes such as expression, transcription, and modification, which we did not fully explore. To our knowledge, this is the first report that employs a combined approach of network pharmacology and multi-omics to comprehensively describe the pharmacological basis and mechanisms of action of WMW treatment for CRA. We found that WMW and its components in the blood modulate the structure of the intestinal microbiota and influence the expression of metabolites, genes, and proteins related to the AA metabolic pathway, thereby exerting a therapeutic effect on CRA. Despite some limitations, our study has achieved its research objectives and can provide references for the use and study of WMW in both CRA and CRC. 5 Conclusions In this study, we demonstrated that WMW significantly reduced intestinal tumor burden and improved pathological outcomes in an APC min/+ mouse model of CRA. Utilizing a multi-omics approach, we revealed that WMW modulates the gut microbiota, serum metabolites, and the expression of genes and proteins linked to the arachidonic acid metabolic pathway. Specifically, WMW reduced levels of key AA metabolites, including PGF2α, 8-HETE, and 12-HETE, while regulating critical genes such as Cyp2e1 , Pla2g2a , Alox12, Ptgds , Alox5 , and Pla2g4c , as well as proteins like Alox15 and Pla2g4c. Furthermore, network pharmacology analysis identified bioactive components of WMW, such as Rosmarinic acid and Berberrubine, as key interactors with these molecular targets. These findings provide a mechanistic foundation for WMW’s efficacy in CRA, highlighting its potential as a therapeutic strategy for preventing CRA recurrence and progression. Abbreviations 8(S)-HETE 8(S)-hydroxy-5z,9e,11z,14z-eicosatetraenoic acid 12(S)-HETE 12(S)-hydroxy-5z,8z,10e,14z-eicosatetraenoic acid AA arachidonic acid BPC base peak chromatogram COX cyclooxygenase CRA colorectal adenoma CRC colorectal cancer CYP cytochrome P450 DEG differentially expressed gene H&E hematoxylin and eosin IHC immunohistochemical LOX lipoxygenase PGF2α prostaglandin F2 alpha PLA2 phospholipase A2 PPI protein-protein interaction RT-qPCR real-time quantitative polymerase chain reaction TCM Traditional Chinese Medicine UHPLC-MS ultra-high-performance liquid chromatography-mass spectrometry WMW Wumei Wan Declarations Funding This study was supported by Construction of Scientific and Technological Innovation Talent Team for the Prevention and Treatment of Digestive System Diseases with Integrated Traditional Chinese and Western Medicine at GUTCM（GUTCM TD Contract No. [2023] 001）, Key Laboratory of Translational Medicine for Combining Traditional Chinese and Western Medicines to Prevent and Control Diseases in Higher Education Institutions of Guizhou Province (Qian JiaoJi [2023] No. 017) and NATCM's Project of High‐level Construction of Key TCM Disciplines(zyyzdxk-2023187). Author information Authors and Affiliations The Second Clinical Medical College, Guizhou University of Traditional Chinese Medicine, Guiyang,550002, Guizhou, China: Qingwan Yang, Song Zhou , Chunlan Chen, Yuemeng Luo, Junsong Cui & Zhenghua Xiao. Department of Gastroenterology, The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang,550002, Guizhou, China: Junsong Cui & Zhenghua Xiao. Department of Oncology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou,510120, Guangdong, China: Haibo Zhang. State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou,510120, Guangdong, China:Haibo Zhang. C ontributions Qingwan Yang: Conceptualization, methodology, resources, data curation, formal analysis, supervision, writing—original draft. Song Zhou: Data curation,software, formal analysis, writing—review and editing. Chunlan Chen,Yuemeng Luo, and Junsong Cui: Data curation, validation, investigation, writing—review and editing. Haibo Zhang and Zhenghua Xiao:Project administration,writing—review and editing. Zhenghua Xiao: Conceptualization, supervision, funding acquisition, writing—review and editing. All authors have read and agreed to the published version of the manuscript. Corresponding authors Correspondence to Haibo Zhang and Zhenghua Xiao. Ethics declarations Ethics approval and consent to participate The animal operations were in accordance with the Guidelines of Care and Use of Laboratory Animals of Guizhou University of Traditional Chinese Medicine and the operations were approved by the Animal Experiment Ethics Committee of Guizhou University of Traditional Chinese Medicine (Approval Number 20230136). Consent for publication All authors declared to consent for publication this article. Availability of data and materials The raw metagenomics, metabolomics, transcriptomics, and proteomics data are deposited in GSA (CRA026470), MetaboLights (MTBLS12598), NCBI SRA (PRJNA1276508), and iProX (PXD064966), respectively, with public release scheduled for December 31, 2025. Competing interests The authors declare no competing interests. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71:209–49. 10.3322/caac.21660 . Løberg M, Kalager M, Holme Ø, Hoff G, Adami HO, Bretthauer M. Long-term colorectal-cancer mortality after adenoma removal. N Engl J Med. 2014;371:799–807. 10.1056/NEJMoa1315870 . 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Supplementary Files TableS1.xls TableS2.xls TableS3.xls TableS4.xlsx TableS5.xls TableS6.xls RNAIntegrity.tif Supplement1ExperimentalMethods.doc Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 Aug, 2025 Reviews received at journal 24 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviewers invited by journal 07 Jul, 2025 Editor assigned by journal 07 Jul, 2025 Editor invited by journal 26 Jun, 2025 Submission checks completed at journal 15 Jun, 2025 First submitted to journal 15 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6629738","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482958842,"identity":"4da029ec-cb5c-406b-9ad9-e36eb8546df2","order_by":0,"name":"Qingwan Yang","email":"","orcid":"","institution":"The Second Clinical Medical College of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qingwan","middleName":"","lastName":"Yang","suffix":""},{"id":482958847,"identity":"80b173e5-67d2-45ad-bb58-8b711ecc8993","order_by":1,"name":"Song Zhou","email":"","orcid":"","institution":"The Second Clinical Medical College of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Zhou","suffix":""},{"id":482958853,"identity":"64dad9f9-f4f5-4352-b639-f59b3671a8ba","order_by":2,"name":"Chunlan Chen","email":"","orcid":"","institution":"The Second Clinical Medical College of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chunlan","middleName":"","lastName":"Chen","suffix":""},{"id":482958855,"identity":"ac39515c-18f2-49e3-8642-e0d582f3efd2","order_by":3,"name":"Yuemeng Luo","email":"","orcid":"","institution":"The Second Clinical Medical College of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuemeng","middleName":"","lastName":"Luo","suffix":""},{"id":482958856,"identity":"58a46647-ef42-4550-aef7-e034e1eaa210","order_by":4,"name":"Junsong Cui","email":"","orcid":"","institution":"The Second Clinical Medical College of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Junsong","middleName":"","lastName":"Cui","suffix":""},{"id":482958857,"identity":"121ba4d9-6f7e-4fe8-883e-d40610cd62c2","order_by":5,"name":"Haibo Zhang","email":"","orcid":"","institution":"Guangdong Provincial Hospital of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haibo","middleName":"","lastName":"Zhang","suffix":""},{"id":482958858,"identity":"64bcb764-1191-4ad9-89e9-490a363d4fd9","order_by":6,"name":"Zhenghua Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYDACZiBmbAASEkCcUPFfjo29+QAJWj6cYTbm4zmWQNgmmBbGmW3MifMkchTwqjY4zvzw4c8dNnny0T2Gn3nb2NLbGHIYGH5UbMOpRbKZzdhA8kxaseGdM8bSPOd4ctsYzh5g7DlzG6cWfmYGMwnDtsOJG2fkGEjzlEnktjH2JTAztuHWwsbM/k0ise0/SIvxbx42g3Q2Zh4DvFr4mXnMJA62HUicL5FjJjmjLSGBjY2AFslmnmLDxrbkxA0SaWUWH84cMGzjYUs4iM8vBuePb3z4s80ucf6M5M03EioOyMvPf3zwwY8K3FoQeg8gcQ7gUIQK5BuIUjYKRsEoGAUjEQAAZzJXLv0JT50AAAAASUVORK5CYII=","orcid":"","institution":"The Second Clinical Medical College of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Zhenghua","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2025-05-09 14:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6629738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6629738/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86448331,"identity":"cd32b3d3-8414-43a6-9efa-d6577d7a098a","added_by":"auto","created_at":"2025-07-10 18:22:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8295063,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of WMW compounds in serum.\u003c/strong\u003e (A) BPC in positive ion mode for each sample group, presented sequentially from top to bottom: WMW sample, Blank group serum + WMW sample, Blank group serum (Blank), and WMW group serum. (B) BPC in negative ion mode for each sample group, arranged in the same order as above. (C) BPC in positive ion mode for the WMW group serum. (D) BPC in negative ion mode for the WMW group serum.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/d0abf847b2c4e2d63351d2c4.png"},{"id":86448321,"identity":"d4175bdf-84a0-4bb3-ba62-fc7fa273d431","added_by":"auto","created_at":"2025-07-10 18:22:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16150488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of WMW on the number and diameter of colorectal tumors, H\u0026amp;E staining, and expression levels of Ki67 and p53 in APC\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003emin/+ \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003emice\u003c/strong\u003e. (A)-(C): Anatomical examination indicates that WMW reduces both the number and size of colorectal tumors in APC\u003csup\u003emin/+\u003c/sup\u003e mice. (D): H\u0026amp;E staining demonstrates that the MOD group exhibits densely arranged colonic proliferative glands (yellow arrows), eosinophilic mucus (blue arrows), and necrosis of the mucosa and submucosa accompanied by inflammatory cell infiltration (green arrows). WMW effectively improves atypical hyperplasia and reduces inflammatory cell infiltration in the intestines of APC\u003csup\u003emin/+\u003c/sup\u003e mice. (E)-(F): Immunohistochemistry indicates that WMW downregulates Ki67-positive expression in the intestines of APC\u003csup\u003emin/+\u003c/sup\u003e mice. (G)-(H): Immunohistochemistry shows that WMW downregulates p53-positive expression in the intestines of APC\u003csup\u003emin/+\u003c/sup\u003e mice. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05,**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.005, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001: compared with the model group.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/6c3fe478fb938831456783bd.png"},{"id":86448322,"identity":"fbe4424f-a95d-4f2c-8ab4-c53bf1f96711","added_by":"auto","created_at":"2025-07-10 18:22:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14504334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted targets and pathways of WMW blood-absorbed components for CRA based on network pharmacology\u003c/strong\u003e. (A) Venn diagram illustrating the overlap of predicted targets from WMW blood-absorbed components and CRA-related disease genes. (B) PPI network depicting the top 100 common targets. (C) Network diagram displaying the relationships between WMW blood-absorbed components and the top 100 common targets. (D) Bubble chart summarizing the GO analysis for the common targets. (E) Bubble chart illustrating the KEGG pathway analysis for the common targets.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/a4d20d92dae70e42281070c3.png"},{"id":86449364,"identity":"bafe089c-2db4-488d-9d21-b24d43b75716","added_by":"auto","created_at":"2025-07-10 18:38:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8925773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetagenomic analysis of the impact of WMW on gut microbial diversity in the APC\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003emin/+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mice\u003c/strong\u003e. (A) The α-diversity indices of the three groups, (B) NMDS plot of β-diversity at the phylum level for the three groups, (C) NMDS plot of β-diversity at the genus level for the three groups, (D) Bar chart showing the relative abundance of the predominant microorganisms at the phylum level in the three groups, (E) Bar chart showing the relative abundance of the predominant microorganisms at the genus level in the three groups, (F) Distinct pathways at the KEGG C level in the three groups.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/aee3b2b7c7bdfb9f822bd90a.png"},{"id":86448854,"identity":"287019bf-ea36-44fa-a620-3e2360d7db66","added_by":"auto","created_at":"2025-07-10 18:30:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11228202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolomic analysis of the effects of WMW on serum metabolites in APC\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003emin/+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mice\u003c/strong\u003e. (A) PCA plot of anion mode among the three groups, (B) PCA plot of cation mode among the three groups, (C) Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) plot of anion mode between the control and model groups, (D) OPLS-DA plot of cation mode between the control and model groups, (E) OPLS-DA plot of anion mode between the model and WMW-treated groups, (F) OPLS-DA plot of cation mode between the model and WMW-treated groups, (G) Detected metabolites in anion mode between the model and WMW-treated groups, (H) Detected metabolites in cation mode between the model and WMW-treated groups, (I) Differential KEGG pathway enrichment between the model and WMW-treated groups.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/79e941f5f7879df72aee1c27.png"},{"id":86448332,"identity":"cdd2e2f5-d72d-4161-947d-0cb1f07b9b42","added_by":"auto","created_at":"2025-07-10 18:22:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11755379,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of WMW on colorectal transcriptomic in APC\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003emin/+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mice\u003c/strong\u003e. Volcano plot of RNA-seq data between the MOD and CON groups, (B) Volcano plot of RNA-seq data between the WMW-treated and MOD groups, (C) Venn diagram showing the overlap between DEGs with reversed expression patterns after WMW treatment and predicted targets of WMW serum components, (D) PPI network of the 87 overlapping genes between network pharmacology targets and restored targets, (E) KEGG pathway enrichment analysis of the 87 overlapping genes between network pharmacology targets and restored targets.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/2bf6148b7a25574e8b0aa08d.png"},{"id":86448341,"identity":"aeab6ccf-c709-4c2d-a073-763b81d3c41b","added_by":"auto","created_at":"2025-07-10 18:22:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":8084243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic analysis of the effects of WMW on intestinal proteins in APC\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003emin/+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mice.\u003c/strong\u003e (A) Volcano plot of protein expression between the MOD and CON groups, (B) Volcano plot of protein expression between the WMW-treated and MOD groups, (C) KEGG pathway enrichment analysis of DEPs between the WMW-treated and MOD groups, (D) PPI network of DEPs between the WMW-treated and MOD groups.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/78586d6b4e1982cf489a22b1.png"},{"id":86448354,"identity":"6dcb931a-102e-4a1e-9d1e-912ec0be9953","added_by":"auto","created_at":"2025-07-10 18:22:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1686116,"visible":true,"origin":"","legend":"\u003cp\u003eBrief relationship diagram of WMW components-targets and multi-omics results. Red italics indicate the blood-absorbed components of WMW, while the underlined terms represent DEMs, DEGs, or DEPs.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/35cd41c025c1feca2b585709.png"},{"id":86448862,"identity":"5519e545-bb64-4526-ae90-778063533182","added_by":"auto","created_at":"2025-07-10 18:30:40","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4128300,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative mRNA levels of key differential genes analyzed by RT-qPCR (n = 4). *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05,**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.005, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001: compared with the model group.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/d2730d80e518f22be60e5508.png"},{"id":86450185,"identity":"5f2fffed-4240-49e9-a342-c56d823575ba","added_by":"auto","created_at":"2025-07-10 18:55:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":87311742,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/bb2ee6d4-937b-4c96-b76c-a1c36987728b.pdf"},{"id":86449362,"identity":"08a0becb-657c-41bf-997c-6fcb6d48b25d","added_by":"auto","created_at":"2025-07-10 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18:22:40","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":558080,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xls","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/429eec19f57bde22e379566a.xls"},{"id":86448338,"identity":"f54bfe64-63df-4435-9a01-104e49696e36","added_by":"auto","created_at":"2025-07-10 18:22:40","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10633887,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/6ad720914f457f3dee71d117.xlsx"},{"id":86449370,"identity":"a3edcc34-ee80-45cd-ac2d-62a547cce082","added_by":"auto","created_at":"2025-07-10 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18:38:41","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":11975282,"visible":true,"origin":"","legend":"","description":"","filename":"RNAIntegrity.tif","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/790e4cb5b67678aebb853fae.tif"},{"id":86448853,"identity":"a41ddd5c-9baa-4f74-a6bf-0bcc9c9b6fd1","added_by":"auto","created_at":"2025-07-10 18:30:40","extension":"doc","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":63488,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1ExperimentalMethods.doc","url":"https://assets-eu.researchsquare.com/files/rs-6629738/v1/77e546a4453bebf8858628de.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative network pharmacology and multi-omics to investigate the potential mechanisms involved in Wumei Wan treatment of colorectal adenomas","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eColorectal adenomas (CRAs) are benign tumors that develop from the glandular epithelial cells in the colorectal mucosa. These cells could aid digestive process by mediating nutrient absorption and intestinal immunity. While benign, CRAs can gain malignancy over time and are recognized as the primary precursor to colorectal cancer (CRC), a gastrointestinal malignant tumor with leading incidence. Moreover, CRC is the second leading cause of cancer-related deaths, characterized by high rates of local recurrence and propensity for distant metastasis\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Specifically, CRAs can develop into invasive cancers that penetrate the intestinal wall, which help them to enter abdomen and spread to other parts of the body. Among different types, the presence of multiple CRAs has been widely recognized as a predictive indicator for the development and prognosis of CRC \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Accordingly, effective management of CRAs can significantly reduce the burden of CRC, playing a crucial role in both prevention and treatment.\u003c/p\u003e\u003cp\u003eThe cornerstone of CRA management involves rigorous endoscopic surveillance, radical resection and scheduled follow-ups to monitor recurrence. Although preventive strategies\u0026mdash;including increased physical activity, reduced red meat consumption, diets rich in fruits/vegetables, and pharmacological agents (e.g., aspirin, vitamin D)\u0026mdash;have been explored, their efficacy remains inconclusive\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Currently, no pharmacological treatment has been confirmed reduce CRAs. Traditional Chinese Medicine (TCM) offers promising methods for treating CRAs and early-stage CRC, potentially addressing the gap in effective CRA-specific pharmaceutical options \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Notably, the adoption of Wumei Wan (WMW) in the Chinese guidelines for CRA prevention has attracted attention of clinicians.\u003c/p\u003e\u003cp\u003eWMW, derived from the classical medical text \u003cem\u003eTreatise on Febrile Diseases\u003c/em\u003e of the Han Dynasty, has been shown to reduce the recurrence rate of CRAs in clinical studies. Zhang Ran conducted a research using enhanced WMW, which demonstrated significantly higher overall efficacy in the treatment group compared to the control group (89.66% vs. 62.07%), as well as lower adenoma recurrence rates at 6 and 12 months post-treatment, confirmed by endoscopic evaluations \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Likewise, researchers such as Li Ye \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and Liang Yifei \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e have reported substantial clinical effectiveness in managing CRAs with WMW. Preclinical evidence also supported the efficacy of WMW against CRC pathogenesis. Lu et al.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e demonstrated that WMW suppressed colitis-associated carcinogenesis (CAC) by rectifying amino acid metabolism, inhibiting persistent PI3K/Akt activation, and regulating the dynamics of myeloid-derived suppressor cells (MDSCs). In another study, Wang et al.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e identified that WMW attenuated CAC via suppression of S-adenosylhomocysteine hydrolase mediated Hedgehog signaling, thereby reducing inflammatory and oxidative stress.\u003c/p\u003e\u003cp\u003eCurrent research has identified several bioactive compounds in WMW. Feng \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e initially identified some major active constituents including citric acid, berberine, and 6-gingerol - using high-performance liquid chromatography-mass spectrometry (HPLC-MS). Meanwhile, the composition was also characterized by Lu et al\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e through liquid chromatography-tandem mass spectrometry (LC-MS/MS), revealing similar bioactive compounds. While the chemical composition of WMW has been elucidated, critical pharmacological aspects remained unexplored. As a botanical drug, the therapeutic efficacy of MWM is inherently dependent on the bioavailability and tissue distribution of active components \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, current research has predominantly focused on the anti-colitis mechanisms, neglecting its potential effects on the development from adenoma to carcinoma, which is the process of colorectal carcinogenesis. Furthermore, the pharmacokinetic profiles of WMW's bioactive compounds have yet to be systematically investigated. This knowledge gap significantly limits our understanding of WMW's pharmacodynamics, thereby hindering us from investigating clinical translatability in CRA prevention.\u003c/p\u003e\u003cp\u003eTo shed light on these unsolved questions, we employed a well-established mouse model relevant to CRA research: the APC\u003csup\u003emin/+\u003c/sup\u003e mouse. The heterozygous transgenic model (on a C57BL/6J background) spontaneously develops intestinal adenomas and is widely used in studies on CRA and CRC \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Although WMW itself has not been studied in the APC \u003csup\u003emin/+\u003c/sup\u003e model, several of its bioactive components such as 6-gingerol and berberine have been proved to reduce colorectal tumor burden. For instance, 6-gingerol suppressed tumor progression by inhibiting EGFR downstream signaling \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, while berberine attenuated Vibrio vulnificus-induced immunomodulation and colorectal tumorigenesis in the same mouse model\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These findings support the feasibility of using APC \u003csup\u003emin/+\u003c/sup\u003e mouse to investigate therapeutic mechanisms of WMW against CRA.\u003c/p\u003e\u003cp\u003eFurthermore, TCM achieves its efficacy against CRA \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and CRC \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e by modulating the gut microbiome. The gut microbiota, as well as its metabolites, engage with host physiological processes by modulating mRNA transcription and protein expression \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. To delineate the action mode of WMW, we firstly characterized its systemic exposure profile using UHPLC-MS, and treated APC\u003csup\u003emin/+\u003c/sup\u003e mice with WMW for 10 weeks to assess anti-adenoma effects. Multi-omics analyses of fecal, serum, and intestinal tissue samples were then performed to elucidate the impact of WMW on microbiota, metabolites, and host gene/protein networks. In addition, key predictions from these analyses were validated by Quantitative Real-time PCR (RT-qPCR).\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Preparation of WMW\u003c/h2\u003e\n \u003cp\u003eWMW is composed of the following ingredients: Fructus Mume (Wu Mei) 30g, Asarum sieboldii (Xi Xin) 6g, Ramulus Cinnamomi (Gui Zhi) 9g, Coptis chinensis (Huang Lian) 6g, Phellodendron Chinense (Huang Bai) 15g, Angelica sinensis (Dang Gui) 6g, Panax ginseng (Ren Shen) 9g, Zanthoxylum bungeanum (Hua Jiao) 6g, Zingiber officinale (Gan Jiang) 9g, and Aconitum carmichaelii (Fu Zi) 15g. These ingredients were sourced from the TCM Pharmacy at the Second Affiliated Hospital of Guizhou University of TCM. The herbal mixture was immersed in fresh drinking water (weights ten times compared to ingredients) for 30 minutes, then boiled for additional 30 minutes. After draining the initial decoction, the remaining ingredients were reboiled in fresh drinking water (weights five times compared to ingredients) for another 30 minutes. The decoctions from both boiling processes were combined, with filtration carried out to remove solid residues. Subsequently, the mixture was gently concentrated over low heat until a paste-like consistency with relative density ranging from 1:1.1 to 1.2. This concentrated paste was frozen at -20\u0026deg;C overnight, freeze-dried for seven days, ground through a No. 3 sieve, and then subjected to an additional day of freeze-drying. The freeze-dried powder, which served as the final product, was produced from 2240g original WMW mixture with a yield of 463g, implying that each gram of freeze-dried powder corresponded to 4.838g raw herbs. And the powder was reconstituted in drinking water to form a suspension until administration.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Animals and Treatments\u003c/h2\u003e\n \u003cp\u003e8-weeks-old male APCmin/+ and C57BL/6J mice were acquired from Cyagen Biosciences Inc., Suzhou (certification number SCXK (Su) 2022-0016) and maintained at the Animal Research Institute of Guizhou University of TCM under specific pathogen-free (SPF) conditions. The facility provided a controlled environment with a 12/12-hour light/dark cycle, a temperature of 22\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, and relative humidity maintained at 55% \u0026plusmn; 10%. The mice had unrestricted access to both food and a 60% high-fat diet supplied by Guizhou Huigu Biotechnology Co., Ltd. (product number 2310-18). Twenty-four male APC\u003csup\u003emin/+\u003c/sup\u003e mice were randomly divided into three groups, each consisting of eight mice: the model group (MOD), the low-dose WMW (L-WMW) group, and the high-dose WMW (H-WMW) group. An additional group of eight age-matched male C57BL/6J mice served as the control group (CON). The L-WMW group received a daily oral administration of WMW at a dose of 17 g/kg, while the H-WMW group received 34 g/kg. These dosages were calculated based on the standard human clinical dose (112 g/60 kg\u0026thinsp;\u0026asymp;\u0026thinsp;1.87 g/kg) and adjusted for differences in body surface area between humans and mice (112 g/60 kg * 9.1\u0026thinsp;\u0026asymp;\u0026thinsp;17 g/kg) according to results from Gou et al. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Instead of WMW suspension, the CON and MOD groups were administered an equivalent volume of drinking water. The treatment duration for all groups was 10 weeks. To ensure animal welfare, predefined humane endpoints were used as criteria for euthanasia: (1)\u0026thinsp;\u0026gt;\u0026thinsp;20% body weight loss within 48 hours, or (2) severe lethargy/inability to access food or water. No animals met these criteria during the study. Thirty minutes after the final WMW or drinking water administration, mice were anesthetized via intraperitoneal injection of 100 mg/kg pentobarbital sodium. Depth of anesthesia was confirmed by absence of toe-pinch reflex. Terminal blood collection was performed via cardiac puncture, followed by immediate cervical dislocation to ensure euthanasia. Death was confirmed by cessation of respiration, loss of corneal reflex, and no detectable heartbeat for \u0026gt;\u0026thinsp;1 minute. The experimental protocol adhered to ethical guidelines and was approved by the Animal Experiment Ethics Committee of Guizhou University of Traditional Chinese Medicine (Approval Number 20230136).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Analysis of Serum Chemical Composition of WMW\u003c/h2\u003e\n \u003cp\u003eC57BL/6J mice were maintained on a standard diet and randomly assigned to two groups: the WMW group, which received 34 g/kg/day via gavage, and the control group (n\u0026thinsp;=\u0026thinsp;6). The serum samples were collected from mice after 14 consecutive days of oral gavage. The serum was isolated by allowing the blood to clot and then centrifuging it. The serum extracts were analyzed using a Vanquish UHPLC system equipped with an HSS-T3 column, maintained at 35\u0026deg;C. The mobile phases consisted of H2O with 0.1% formic acid and acetonitrile with 0.1% formic acid. The elution was performed at a flow rate of 0.3 mL/min using a specified gradient. Analyses were conducted using a Q-Exactive HFX mass spectrometer. Mass spectra were acquired in both ESI positive and negative modes, capturing MS/MS spectra from the top 10 most intense MS1 ions. Compound annotation was aligned with reference data from an in-house TCM standards database (Shanghai Applied Protein Technology Co., Ltd., Shanghai, China) and public databases such as GNPS \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, ReSpect \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and Massbank \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Morphological Examinations\u003c/h2\u003e\n \u003cp\u003eColorectal tissues were collected from three randomly selected mice in each group, opened longitudinally, and rinsed with precooled PBS. The tissues were then mounted on black cardboard and photographed under uniform lighting conditions to assess the presence and measure the maximum diameter of any polyps in the colorectum.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Pathological Examinations\u003c/h2\u003e\n \u003cp\u003eColorectal tissues from three mice in each group were preserved in 4% paraformaldehyde, subjected to a graded ethanol dehydration process, and embedded in paraffin. Sections were stained with hematoxylin and eosin (H\u0026amp;E) for histological assessment. Immunohistochemical (IHC) staining was performed to evaluate the prevalence of Ki67 and p53-positive cells, indicated by brown-yellow staining. The proportion of positive areas was quantified.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Network Pharmacology Analysis\u003c/h2\u003e\n \u003cp\u003eThe TCMSP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://old.tcmsp-e.com/tcmsp.php\u003c/span\u003e\u003c/span\u003e), TCMID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.megabionet.org/tcmid/\u003c/span\u003e\u003c/span\u003e), and CTD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ctdbase.com/\u003c/span\u003e\u003c/span\u003e) databases were utilized to identify potential targets of blood components. Differentially expressed genes (DEGs) between the MOD and CON groups, identified through transcriptomic analysis, were classified as disease-related target genes. These key targets were integrated into the STRING database to construct a protein-protein interaction (PPI) network. Genes common to both the predicted component-target genes and the disease-related target genes were filtered based on their median gene degree. Topological analysis of the component-target-disease network was performed using the Python package NetworkX, which provided metrics such as degree, betweenness, and closeness centrality. Nodes were ranked based on their weighted importance in descending order, with the top 100 genes identified as core targets. Further functional annotations were carried out using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Metagenomic Sequencing\u003c/h2\u003e\n \u003cp\u003eDNA was isolated from fecal samples using the Magnetic Soil and Stool DNA Kit (TIANGEN). Following quality assessment, sequencing libraries were prepared with the NEBNext\u0026reg; Ultra\u0026trade; DNA Library Prep Kit for Illumina (NEB), and DNA was fragmented to an average size of 50 bp before undergoing end-repair. Sequencing was conducted on the NovaSeq 6000 platform (Biomarker Technologies Co., Beijing, China). Raw sequencing data were processed using fastp software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003c/span\u003e) with default settings. The assembled sequences were handled using MEGAHIT, ORFs were predicted with Prodigal, and clustering was executed via CD-HIT. Quantification of sequences was performed using Bowtie2 software. DIAMOND software facilitated the alignment of unigenes to sequences from the NCBI NR database (Version: 2021.11) to classify species. The KEGG database was employed for functional annotation of microbial content. Microbial alpha (\u0026alpha;) and beta (\u0026beta;) diversity at the species level were calculated. Linear discriminant analysis effect size (LEfSe) was applied, with microorganisms or functional pathways exhibiting a linear discriminant analysis score greater than 2 and a p-value less than 0.05 deemed notably different.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Non-Targeted Metabolomics\u003c/h2\u003e\n \u003cp\u003eSerum samples were analyzed using an Agilent 1290 Infinity LC ultrahigh-performance liquid chromatography (UHPLC) system equipped with an HILIC column. Mass spectrometric analysis was conducted utilizing a Triple TOF 6600 mass spectrometer (AB SCIEX) in both positive and negative electrospray ionization (ESI) modes. The protocols for detection, analysis, and quality control, including specific parameters, are comprehensively detailed in Supplementary Table\u0026nbsp;1. Metabolites were detected and subsequently classified according to their chemical taxonomy. To identify notable metabolic differences between groups, we carried out orthogonal partial least squares discriminant analysis (OPLS-DA) principal component analysis (PCA), applying thresholds of variable importance in projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1.0 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9 Transcriptomics\u003c/h2\u003e\n \u003cp\u003eTotal RNA was extracted using TRIzol\u0026reg; reagent (Magen, Waltham, MA, USA). RNA purity and quantification were assessed using a Nanodrop ND-2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). The procedures for library preparation, mRNA purification, and both first and second strand cDNA synthesis, along with sequencing parameters, are provided in Supplementary Material 1. Bioinformatics analysis of the sequencing data was performed, setting differential expression thresholds at an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;2 or FC\u0026thinsp;\u0026lt;\u0026thinsp;0.5.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.10 4D-Label-Free Proteomics\u003c/h2\u003e\n \u003cp\u003eColorectal tissue was lysed in a 4% SDS aqueous solution containing protease inhibitors to extract total protein. Through a BCA assay kit, we quantified proteins. After overnight digestion with trypsin at 37\u0026deg;C, peptides were derived and analyzed using nano-LC-MS/MS. The analytical procedure and specific parameters are detailed in Supplementary Material 1. A protein was considered differentially expressed if the p-value between the two groups was \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.11 RT- qPCR Analysis\u003c/h2\u003e\n \u003cp\u003eColorectal tissue was homogenized using a fast tissue homogenizer for 40 seconds in an appropriate volume of lysis buffer. Subsequently, 10 \u0026micro;L of proteinase K was added, and the mixture was incubated at room temperature for 5 minutes. Following centrifugation, the supernatant was collected, and RNA was extracted using a column-based centrifugation method. The integrity, concentration, and purity of the RNA were evaluated. The concentration of RNA was adjusted to 100 ng/\u0026micro;L, and reverse transcription was performed to synthesize complementary DNA (cDNA), which was then stored at \u0026minus;\u0026thinsp;20\u0026deg;C. Through SYBR GREEN qPCR mix, we carried out RT-qPCR, and data were collected using the Bio-Rad CFX96 Real-Time PCR Detection System. GAPDH served as an internal control. Through the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;\u003cem\u003eC\u003c/em\u003et\u003c/sup\u003e method, we determined relative gene expression levels. The mRNA primers, used for this analysis, were synthesized by Shanghai Shenggong Biological Engineering. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e lists the sequences of these primers.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrimer used in the current study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSequence(5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eMmp9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: GCAGAGGCATACTTGTACCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: TGATGTTATGATGGTCCCACTTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eIl-1a\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: GGCATTGTTCTCTAATGTCTCCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: TGTCGAGCTTTGGGATGGTAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eEsr1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: CCCGCCTTCTACAGGTCTAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: CTTTCTCGTTACTGCTGGACAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eIl-13\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: CGGTGCCAAGATCTGTGTCT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: CGTGGCGAAACAGTTGCTTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eAlox5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: TTGCTCTCACAGTATGACTGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: AGTATCCACGATCTGCTCGAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eAlox15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: GAATACCTTGGGCCACTGCT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: TTCCAGGAGTTTCGAACCCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ePla2g2a\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: TGCTAGCAGCCTCGATCATG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: TGGGCTTCTTCCCTTTGCAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ePla2g4c\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: AGGAGCTGAAACATCGGTATGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: CTGCAAAGATGGGATAGGGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ePtgds\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: GATGGGTTTGGTCCTCCTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: ATGCACTTATCCGGTTGGGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eCyp2e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: TGGTCCTGCATGGCTACAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: GACAGTCAGTCACATCCCGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eGapdh\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward: GGTTGTCTCCTGCGACTTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReverse: TGGTCCAGGGTTTCTTACTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.12 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eThrough SPSS version 26.0, we carried out data analysis, with results reported as . We used a one-way analysis of variance (ANOVA) for comparisons across multiple groups. In cases where data satisfied the criteria for normal distribution and homogeneity of variance, we used the Bonferroni test for post hoc pairwise comparisons. For datasets exhibiting unequal variances, the Welch test was initially utilized to compare overall means, followed by Dunnett\u0026apos;s T3 test for pairwise comparisons. Graphical representations were created using GraphPad Prism version 9.0.1 (GraphPad Software, San Diego, CA).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Identification of WNW Components in Serum\u003c/h2\u003e\u003cp\u003eWe employed UHPLC-HRMS to analyze the active chemical constituents of WMW. This technique provided a comprehensive examination of the chemical profiles in each sample group. We analyzed and compared the base peak chromatograms (BPCs) in both positive and negative ion modes, which were crucial in identifying significant chromatographic peaks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). These peaks were systematically numbered after an extensive evaluation of their morphological and secondary spectral characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D). The identification process involved verifying compounds through searches in a local TCM high-resolution mass spectrometry database. The criteria for confirmation included a primary mass error below 25 ppm and a secondary fragmentation spectrum match score above 0.7, affirming high reliability in spectrum similarity and identification accuracy \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. As a result, 2,641 chemical constituents in WMW and its post-administration serum were identified, with 809 constituents detected in the serum (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We classified these compounds using the NPClassifier method \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, which categorized the serum-entering constituents into 19 pathways, 93 superclasses, and 293 classes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.2 WMW Ameliorates the Pathology of CRA\u003c/h2\u003e\u003cp\u003eAfter a 10-week regimen of oral gavage, the mice were euthanized, and their colorectal tissues were harvested for analysis. In the C57BL/6J control group, no colorectal tumors were found. However, the APC\u003csup\u003emin/+\u003c/sup\u003e mice displayed multiple visible tumors within the colorectal tissues, with the largest tumor measuring approximately 6 mm in diameter. In the WMW-treated group, there were fewer intestinal tumors, and the largest tumor was significantly smaller in diameter than those in the MOD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, C). Quantitatively, the MOD group developed an average of five CRAs, while the WMW-treated mice showed a substantial reduction, with only about one to two adenomas observed, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB.\u003c/p\u003e\u003cp\u003eH\u0026amp;E staining revealed that in the CON group, the intestinal glands maintained normal architecture with well-ordered epithelial cells, uniform villi morphology and distribution, and no evident inflammation or cellular damage. Conversely, the MOD group's colon tissue exhibited severe structural disruptions, characterized by pronounced epithelial hyperplasia with a predominantly glandular appearance. Some areas displayed branching glands with atypical tissue features. The proliferative glandular areas were densely packed (\u0026uarr;), with minimal or absent stromal tissue, and goblet cells were sporadically present or absent altogether. The glandular lumina contained basophilic mucus (\u0026uarr;) and necrotic cellular debris. In some regions, the epithelium formed small papillary projections into the glandular lumina, presenting an irregular surface. The disordered hyperplastic epithelium showed pseudo-stratified or multilayered arrangements, featuring irregular nuclei, increased nuclear-to-cytoplasmic ratios, and active mitotic activity (\u0026uarr;). In the WMW-L group, there was mild mucosal disorganization with glandular-like epithelial hyperplasia, slight mucosal necrosis accompanied by inflammatory cell infiltration, and areas of indistinct intestinal gland structure interspersed with necrotic debris, lymphocytes, and neutrophils. In the WMW-H group, the layers of the colon mucosa, submucosa, and muscularis were distinctly layered and well-defined. The epithelial cells within the mucosal layer exhibited normal morphology without notable degeneration, necrosis, or desquamation. These observations suggest that WMW administration can mitigate atypical hyperplasia and reduce inflammatory cell infiltration in intestinal tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eIHC analysis demonstrated that Ki67 was primarily localized to the nuclei (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), while p53 expression was predominantly observed in both the nuclei and cytoplasm of cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Compared to the CON group, the expression levels of Ki67 and p53 were notably elevated in the intestinal tissues of the MOD group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, the WMW-L and WMW-H groups showed reduced expression of Ki67 and p53 compared to the MOD group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eH.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Network Pharmacology\u003c/h2\u003e\u003cp\u003eIn this study, of the 809 components detected in the bloodstream, 536 were successfully annotated as detailed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. A comprehensive database search identified 2,823 potential targets for these annotated components. Additionally, a total of 11,871 genes associated with CRA were extracted from the CTD. The intersection of the component-derived targets with the disease-associated genes resulted in the identification of 2,315 common targets, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and listed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Among these common targets, 323 exhibited a gene degree higher than the median within the PPI network. Subsequent topological analysis, which included metrics such as Degree, Betweenness, and Closeness, facilitated the identification of the top 100 core targets, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and listed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. These core targets underwent further analysis through GO and KEGG pathway analyses, elucidating the underlying mechanisms of action. A network diagram highlighting the interactions between key components and core targets was constructed, emphasizing components such as Genistein, Cycloheximide, and Luteolin, which displayed the highest degrees of interaction. Similarly, the core targets with the highest degrees were CASP3, MAPK3, and MAPK1, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC. The GO analysis revealed involvement in 421 molecular function terms, 220 cellular component terms, and 3,994 biological process terms, as listed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. The top 10 terms prominently feature processes including positive regulation of apoptosis and cell proliferation, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eD. The KEGG analysis demonstrated the involvement of core targets in 202 pathway entries, encompassing a variety of pathways related to cancer, cell death, and inflammation. Notably, these pathways include CRC, pyroptosis, and the PI3K-Akt signaling pathway. The top 20 notably enriched pathways, potentially related to CRA, are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Regulation of Gut Microbiota by WMW\u003c/h2\u003e\u003cp\u003eMetagenomic sequencing revealed no notable differences in the fecal microbial α-diversity index among the three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). However, β-diversity indices at the species, genus, family, and phylum levels demonstrated intergroup variability. Notably, the β-diversity index of the WMW group was intermediate between those of the CON and MOD groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). At the phylum level, the three predominant microorganisms in the CON were Firmicutes (66.31%), Actinobacteria (16.36%), and Bacteroidetes (10.20%); in the MOD group, they were Firmicutes (37.66%), Bacteroidetes (24.38%), and Verrucomicrobia (15.53%); in the WMW group, the predominant phyla were Firmicutes (43.46%), Bacteroidetes (37.42%), and Chlamydiae (11.94%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). At the genus level, the most abundant genera in the control group were Dubosiella (29.91%), Bifidobacterium (14.96%), and Faecalibaculum (10.33%); in the MOD group, they were Akkermansia (19.17%), Chlamydia (16.02%), and Bacteroides (7.71%); and in the WMW group, they were Bacteroides (16.25%), Chlamydia (15.24%), and Blautia (10.71%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Functional enrichment analysis of the differential microbiota using the KEGG database revealed notable pathway distinctions among the groups. According to the LEfSe analysis at KEGG level C, the most notable pathways in the CON group were Starch and sucrose metabolism (LDA score 3.82), Phosphotransferase system (LDA score 3.77), and ABC transporters (LDA score 3.62). In the MOD group, the most prominent pathways were Lipopolysaccharide biosynthesis (LDA score 3.34), Fatty acid biosynthesis (LDA score 3.29), and Bacterial secretion system (LDA score 3.16). In the WMW group, the top pathways included Carbon fixation pathways in prokaryotes (LDA score 3.37), Folate biosynthesis (LDA score 3.33), and Other glycan degradation (LDA score 3.23) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Comprehensive results are presented in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Regulation of Serum Metabolites by WMW\u003c/h2\u003e\u003cp\u003eUsing UPLC-TOF/MS analysis following serum sample collection, we detected a total of 719 metabolites in the ESI(+) mode and 591 metabolites in the ESI(\u0026minus;) mode (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Principal coordinate analysis (PCoA) of the peaks from all experimental and QC samples demonstrated that QC samples clustered tightly in both ion modes, indicating high reproducibility of the experiment (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). Subsequent orthogonal partial least squares discriminant analysis (OPLS-DA) revealed distinct separation of serum metabolite profiles among the CON, MOD, and WMW-treated groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-F). We detected 67 differential metabolites between the WMW and MOD groups, comprising 20 upregulated and 47 downregulated metabolites (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eG-H). KEGG pathway analysis of these differential metabolites highlighted their involvement in pathways including Arginine biosynthesis, amino acid biosynthesis, ABC transporters, 2-Oxocarboxylic acid metabolism, and Arachidonic acid (AA) metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eI).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.6 WMW Modulates the Colorectal Transcriptome\u003c/h2\u003e\u003cp\u003eGene expression profiling revealed notable differences among the CON, MOD, and WMW-treated groups (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Compared to CON, the MOD group showed 820 downregulated and 1,448 upregulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In contrast, the WMW-treated group exhibited 627 downregulated and 376 upregulated DEGs relative to the MOD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Post-treatment with WMW, some gene expression alterations between the MOD and CON groups were partially reversed, including 162 downregulated and 438 upregulated DEGs observed in the MOD group relative to the CON group. Of these reversed genes, 87 coincided with predicted targets of WMW serum components (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). PPI network analysis pinpointed key genes among these 87 reversed genes, including \u003cem\u003eMmp9\u003c/em\u003e, \u003cem\u003eAlb\u003c/em\u003e, and \u003cem\u003eEsr1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). KEGG pathway analysis associated these genes with 20 pathways, primarily related to cancer (e.g., breast cancer, chemical carcinogenesis), inflammatory responses (e.g., TNF signaling pathway, IL-17 signaling pathway), and metabolic processes (e.g., glutathione metabolism, AA metabolism) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.7 WMW Modulates the Colorectal Proteomic\u003c/h2\u003e\u003cp\u003e4D label-free proteomic analysis detected 132 downregulated and 179 upregulated differentially expressed proteins (DEPs) between the MOD and CON groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In comparison, the WMW-treated group exhibited 21 downregulated and 48 upregulated DEPs relative to the MOD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Following WMW treatment, some protein expression alterations between the MOD and CON groups were partially restored, including 23 downregulated and 9 upregulated DEPs (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). KEGG pathway analysis of the DEPs between the WMW-treated and MOD groups highlighted associations with 68 pathways, encompassing Linoleic acid metabolism, ovarian steroidogenesis, hepatitis C, choline metabolism in cancer, and AA metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Protein-protein interaction network analysis detected central hub DEPs, such as Isg15, Ifit1, and Oas3, in the WMW-treated and MOD groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Association analysis\u003c/h2\u003e\u003cp\u003eThrough KEGG pathway analysis, the AA metabolism pathway was detected as being enriched across all four omics datasets. It was also highlighted as one of the enriched pathways in the targets predicted by network pharmacology. Specific metabolites noted in the metabolomics analysis included 8(S)-hydroxy-5z,9e,11z,14z-eicosatetraenoic acid (8(S)-HETE), prostaglandin F2 alpha (PGF2α), and 12(S)-hydroxy-5z,8z,10e,14z-eicosatetraenoic acid (12-HETE). Additionally, genes such as \u003cem\u003eCyp2e1\u003c/em\u003e, \u003cem\u003ePla2g2a\u003c/em\u003e, \u003cem\u003ePla2g4c\u003c/em\u003e, \u003cem\u003eAlox5\u003c/em\u003e, \u003cem\u003eAlox15\u003c/em\u003e, and \u003cem\u003ePtgds\u003c/em\u003e detected in the transcriptomics analysis, along with proteins including Alox15 and Pla2g4c found in the proteomics analysis, were all linked to the AA metabolism pathway, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.9 RT-qPCR\u003c/h2\u003e\u003cp\u003eWe assessed the expression levels of genes associated with the AA metabolism pathway and those situated in the core regulatory regions for exploring the mechanistic role of WMW in treating CRA. The result indicated that the mRNA expression levels of Alox15, Pla2g2a, Ptgds, Cyp2e1, Mmp9, Il-1a, and Esr1 in the MOD group were notably higher as contrasted with those in the CON, WMW-L, and WMW-H groups. In contrast, the mRNA expression levels of Pla2g4c, Alox5, and Il-13 in the MOD group were notably lower than those in the CON, WMW-L, and WMW-H groups, aligning with the findings from the transcriptomics analysis. These results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eCRA is the most common precursor to CRC \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Approximately 70%-90% of CRC cases arise from a sequence beginning with normal mucosa, progressing to intestinal polyps, and culminating in CRA. CRA has the potential to develop into a malignant form within an estimated 10\u0026ndash;15 years. Consequently, preventing the recurrence and progression of CRA is a major concern. Currently, the available treatments for this condition are limited. Chinese guidelines recommend several TCM prescriptions for preventing CRA recurrence, including WMW \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Clinical studies have demonstrated that WMW can decrease the recurrence rate of CRA after its removal \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and enhance the effectiveness of chemotherapy in advanced CRC \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In our study, we observed a significant reduction in both the number and size of intestinal tumors in APC mice treated with WMW, with no notable differences between the two doses tested. H\u0026amp;E staining revealed that the pathology of the WMW-treated group was markedly better than that of the MOD group, aligning with findings from other studies \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, the mechanism by which WMW prevents CRA recurrence remains unclear. Network pharmacology, combined with multi-omics, is a method increasingly used in recent years to explore the efficacy and mechanisms of traditional compound formulations. After identifying the components of WMW that enter the bloodstream, we employed network pharmacology and multi-omics to systematically delineate the effects of low doses of WMW (equivalent to the usual human dose) on the intestinal microbiota, serum metabolites, and gene and protein expression in CRA mice.\u003c/p\u003e\u003cp\u003eWe identified 809 chemical components of WMW in serum, such as Rosmarinic acid, Phellodendrine, Magnoflorine, Codethyline, Berberrubine, Oxoglaucine, Coptisine, and Majarine. Many of these components have been shown to treat CRA or CRC effectively \u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Network pharmacology indicates that these components target 112 genes intersecting with the DEGs in CRA mice. We hypothesize that these intersecting genes are associated with various cancers, cell death pathways, and inflammation, such as CRC, apoptosis, and the PI3K-Akt signaling pathway. Notably, abnormalities in AA metabolism have also been observed in other omics studies.\u003c/p\u003e\u003cp\u003eThe human gut microbiome plays a notable role in the development of various diseases. CRA and CRC are gut lesions influenced by gut microbiome changes \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Dysbiosis in the gut microbiome can affect intestinal epithelial permeability, disrupt immune tolerance, and activate local inflammatory responses. Our research found increased abundance of Firmicutes, Bacteroidetes, Chlamydiae, Proteobacteria, Verrucomicrobia, Actinobacteria, and Tenericutes in the MOD group. Other microbiomes except for Firmicutes and Bacteroidetes are less abundant in the WMW group than in the MOD group. In 533 CRC patients, the main phyla in colorectal tissues and feces were Proteobacteria (43.5%), Firmicutes (25.3%), and Actinobacteria (23.0%). Survival analysis showed that patients with higher abundance of Proteobacteria and lower abundance of Firmicutes and Actinobacteria in colorectal tissues had notably worse survival rates \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Other studies indicate that an increase in Proteobacteria relative abundance facilitates the progression from CRA to CRC and the occurrence of CRC \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In the AOM/DSS colitis CRC model, the abundance of Proteobacteria and Tenericutes was positively correlated with tumor load \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Chlamydiae, an infectious pathogen, is associated with breast cancer \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and cervical cancer \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Chlamydia trachomatis infection in gut diseases can enhance the carcinogenic potential of HPV16 and the risk of high-grade anal intraepithelial neoplasia in HIV patients \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAA is a polyunsaturated fatty acid that forms part of cell membranes. It plays a crucial role in immune functions, inflammatory responses, and cancer \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. AA is metabolized and transformed through three pathways: cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 \u003csup\u003e41\u003c/sup\u003e. COX2 and \u003cem\u003ePtgds\u003c/em\u003e metabolize AA into substrates like prostaglandin D2 (PGD2), which are then converted into specific PGs such as PGF2α by PGFS \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. We found that WMW treatment notably affected the AA metabolic pathway in the APC mouse model. Metabolites detected through metabolomics analysis, including 8(S)-HETE, PGF2α, and 12(S)-HETE, genes detected in transcriptomics analysis (\u003cem\u003eCyp2e1\u003c/em\u003e, \u003cem\u003ePla2g2a\u003c/em\u003e, \u003cem\u003ePla2g4c\u003c/em\u003e, \u003cem\u003eAlox5\u003c/em\u003e, \u003cem\u003eAlox15\u003c/em\u003e, and \u003cem\u003ePtgds\u003c/em\u003e), and proteins detected in proteomics analysis (Alox15 and Pla2g4c) were involved in the AA metabolism pathway. In CRC tissues, PGFS protein and mRNA levels are higher than in adjacent tissues, leading to increased levels of PGF2α and resistance to the chemotherapy drug oxaliplatin \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. PTGDS is a notable risk factor for the response to neoadjuvant chemoradiation therapy (NCRT) in rectal mucinous adenocarcinoma \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e and is associated with the development and prognosis of CRC \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Nonsteroidal anti-inflammatory drugs like aspirin and COX-2 selective inhibitors such as celecoxib may reduce recurrence and progression of CRA, possibly by modulating AA metabolism \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLOX enzymes insert oxygen into AA at various positions, forming four types of hydroperoxyeicosatetraenoic acids (HPETEs; 5-HPETE, 8-HPETE, 12-HPETE, and 15-HPETE), which are then reduced by peroxidases to hydroxyeicosatetraenoic acids (HETEs) or converted into bioactive compounds like leukotrienes, which regulate inflammation \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. In CRC patients, the level of 8-HETE in the serum is lower than in non-CRC patients \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and is linked to an increased risk of ovarian cancer \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. 12-HETE disrupts cell-cell junctions, causing retraction of lymphatic and blood endothelial cell walls, which may facilitate CRC invasion and lymph node metastasis \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Elevated levels of 12-HETE in the serum of CRC and CRA patients compared to healthy individuals could be associated with higher proliferation rates in adenomas \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Exposure to 12-HETE or overexpression of ALOX12 stimulates CRC cell proliferation and migration \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Using ALOX12 inhibitors can reduce 12-HETE production in CRC cells (HT29) and suppress levels of EMT markers \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. ALOX15 has tumor-suppressive effects, is underexpressed in CRA and early CRC, and can inhibit CRC cell proliferation and the development of colitis-associated CRC \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe cytochrome P450 (CYP) pathway is the third major pathway for AA metabolism. Many CYP enzymes function as both hydroxylases and epoxygenases, producing a range of products. For instance, the ω-hydroxylase activity of CYP enzymes converts AA to HETE, while their epoxygenase activity generates epoxyeicosatrienoic acids (EETs) \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Both HETE and EETs are involved in regulating cancer cell behaviors, including proliferation, survival, angiogenesis, invasion, and metastasis \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. CYP2E1 is a phase I enzyme involved in xenobiotic metabolism, activating low molecular weight carcinogens like nitrosamines into active carcinogens that contribute to digestive tract tumors. Thus, higher CYP2E1 activity is associated with increased cancer progression \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAdditionally, the release of AA from phospholipid membranes is regulated by phospholipase A2 (PLA2), which mediates the hydrolysis of phospholipid skeletons to directly produce free AA \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Research indicates that PLA2 and its variants, such as PLA2G4C and PLA2G2A, are directly involved in tumor development \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Studies have linked the A allele of the PLA2G4C SNP (rs1549637) with poorer prognosis in CRC patients \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Cell experiments have shown that targeting PLA2G4C can increase CRC sensitivity to irinotecan \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. A retrospective study found that patients with tumors negative for PLA2G2A immunohistochemistry had a notably longer survival than those positive for it, suggesting a negative correlation between PLA2G2A and CRC prognosis \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. In advanced and metastatic CRC, higher levels of PLA2G2A have been found in serum \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Recent research has shown that selenoprotein I (Selenoi) prevents intestinal epithelial cell ferroptosis by maintaining lipid homeostasis. Selenium deficiency leads to increased expression of PLA2G2A and ALOX15, excessive lipid peroxidation, and increased ferroptosis, thus promoting the progression of colitis and CRC \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur study utilized metabolomics analysis to demonstrate that serum levels of PGF2α, 8-HETE, and 12-HETE were reduced in APC\u003csup\u003emin/+\u003c/sup\u003e mice following treatment with WMW. Transcriptomic data revealed that, compared to the model group, the mRNA expression levels of Cyp2e1, Pla2g2a, Alox12, and Ptgds in the WMW group were significantly decreased, while those of Alox5 and Pla2g4c were significantly increased. Proteomic analysis further confirmed that the expression levels of Pla2g4c and Alox15 in the WMW group were notably higher than those in the model group. Using RT-qPCR, we verified the mRNA expression levels of Cyp2e1, Pla2g2a, Alox12, Alox5, Pla2g4c, and Ptgds. The results indicated that WMW therapy effectively restored the levels of Cyp2e1, Pla2g2a, Ptgds, and Alox15 in the colon, while inhibiting the expression of Alox5 and Pla2g4c, aligning with the findings from the transcriptome and proteome analyses. Through serum composition identification and network pharmacological predictions, we confirmed that compounds such as biotin, cyclohexanamide, quercetin, and genistein interact with these genes or proteins. These findings preliminarily elucidate the composition and mechanism of WMW in treating CRA and provide valuable insights for further research into WMW's therapeutic applications in CRA.\u003c/p\u003e\u003cp\u003eOur study has several limitations. First, our experimental model involved APC\u003csup\u003emin/+\u003c/sup\u003e mice, a spontaneous CRA model that closely mimics disease progression in CRA patients. Although this model shares similarities with human conditions, inherent species differences exist; however, our findings still offer some direction for applying WMW in treating CRA. Second, our conclusions are restricted to an animal model, necessitating further clinical validation. Third, while we employed various omics technologies to comprehensively describe the effects of WMW on the CRA model, our integration of multi-omics data was limited. For instance, there were numerous discrepancies between the differentially expressed genes identified in the transcriptome and the proteins detected in the proteome, potentially due to processes such as expression, transcription, and modification, which we did not fully explore.\u003c/p\u003e\u003cp\u003eTo our knowledge, this is the first report that employs a combined approach of network pharmacology and multi-omics to comprehensively describe the pharmacological basis and mechanisms of action of WMW treatment for CRA. We found that WMW and its components in the blood modulate the structure of the intestinal microbiota and influence the expression of metabolites, genes, and proteins related to the AA metabolic pathway, thereby exerting a therapeutic effect on CRA. Despite some limitations, our study has achieved its research objectives and can provide references for the use and study of WMW in both CRA and CRC.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn this study, we demonstrated that WMW significantly reduced intestinal tumor burden and improved pathological outcomes in an APC\u003csup\u003emin/+\u003c/sup\u003e mouse model of CRA. Utilizing a multi-omics approach, we revealed that WMW modulates the gut microbiota, serum metabolites, and the expression of genes and proteins linked to the arachidonic acid metabolic pathway. Specifically, WMW reduced levels of key AA metabolites, including PGF2α, 8-HETE, and 12-HETE, while regulating critical genes such as \u003cem\u003eCyp2e1\u003c/em\u003e, \u003cem\u003ePla2g2a\u003c/em\u003e, \u003cem\u003eAlox12, Ptgds\u003c/em\u003e, \u003cem\u003eAlox5\u003c/em\u003e, and \u003cem\u003ePla2g4c\u003c/em\u003e, as well as proteins like Alox15 and Pla2g4c. Furthermore, network pharmacology analysis identified bioactive components of WMW, such as Rosmarinic acid and Berberrubine, as key interactors with these molecular targets. These findings provide a mechanistic foundation for WMW\u0026rsquo;s efficacy in CRA, highlighting its potential as a therapeutic strategy for preventing CRA recurrence and progression.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e8(S)-HETE\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;8(S)-hydroxy-5z,9e,11z,14z-eicosatetraenoic acid\u003c/p\u003e\n\u003cp\u003e12(S)-HETE\u0026nbsp; \u0026nbsp;\u0026nbsp;12(S)-hydroxy-5z,8z,10e,14z-eicosatetraenoic acid\u003c/p\u003e\n\u003cp\u003eAA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;arachidonic acid\u003c/p\u003e\n\u003cp\u003eBPC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;base peak chromatogram\u003c/p\u003e\n\u003cp\u003eCOX\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;cyclooxygenase\u003c/p\u003e\n\u003cp\u003eCRA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;colorectal adenoma\u003c/p\u003e\n\u003cp\u003eCRC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;colorectal cancer\u003c/p\u003e\n\u003cp\u003eCYP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;cytochrome P450\u003c/p\u003e\n\u003cp\u003eDEG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;differentially expressed gene\u003c/p\u003e\n\u003cp\u003eH\u0026amp;E\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;hematoxylin and eosin\u003c/p\u003e\n\u003cp\u003eIHC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;immunohistochemical\u003c/p\u003e\n\u003cp\u003eLOX\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;lipoxygenase\u003c/p\u003e\n\u003cp\u003ePGF2α\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;prostaglandin F2 alpha\u003c/p\u003e\n\u003cp\u003ePLA2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;phospholipase A2\u003c/p\u003e\n\u003cp\u003ePPI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;protein-protein interaction\u003c/p\u003e\n\u003cp\u003eRT-qPCR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;real-time quantitative polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eTCM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Traditional Chinese Medicine\u003c/p\u003e\n\u003cp\u003eUHPLC-MS\u0026nbsp; \u0026nbsp;\u0026nbsp;ultra-high-performance liquid chromatography-mass spectrometry\u003c/p\u003e\n\u003cp\u003eWMW\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Wumei Wan\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Construction of Scientific and Technological Innovation Talent Team for the Prevention and Treatment of Digestive System Diseases with Integrated Traditional Chinese and Western Medicine at GUTCM（GUTCM TD Contract No. [2023] 001）, Key Laboratory of Translational Medicine for Combining Traditional Chinese and Western Medicines to Prevent and Control Diseases in Higher Education Institutions of Guizhou Province (Qian JiaoJi [2023] No. 017) and NATCM's Project of High‐level Construction of Key TCM Disciplines(zyyzdxk-2023187).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Second Clinical Medical College, Guizhou University of Traditional Chinese Medicine, Guiyang,550002, Guizhou, China: Qingwan Yang, Song Zhou , Chunlan Chen, Yuemeng Luo, Junsong Cui \u0026amp; Zhenghua Xiao.\u003c/p\u003e\n\u003cp\u003eDepartment of Gastroenterology, The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang,550002, Guizhou, China: Junsong Cui \u0026amp; Zhenghua Xiao.\u003c/p\u003e\n\u003cp\u003eDepartment of Oncology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou,510120, Guangdong, China: Haibo Zhang.\u003c/p\u003e\n\u003cp\u003eState Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou,510120, Guangdong, China:Haibo Zhang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eontributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQingwan Yang: Conceptualization, methodology, resources, data curation, formal analysis, supervision, writing—original draft. Song Zhou: Data curation,software, formal analysis, writing—review and editing. Chunlan Chen,Yuemeng Luo, and Junsong Cui: Data curation, validation, investigation, writing—review and editing. Haibo Zhang and Zhenghua Xiao:Project administration,writing—review and editing. Zhenghua Xiao: Conceptualization, supervision, funding acquisition, writing—review and editing. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Haibo Zhang and Zhenghua Xiao.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal operations were in accordance with the Guidelines of Care and Use of Laboratory Animals of Guizhou University of Traditional Chinese Medicine and the operations were approved by the Animal Experiment Ethics Committee of Guizhou University of Traditional Chinese Medicine (Approval Number 20230136).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declared to consent for publication this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw metagenomics, metabolomics, transcriptomics, and proteomics data are deposited in GSA (CRA026470), MetaboLights (MTBLS12598), NCBI SRA (PRJNA1276508), and iProX (PXD064966), respectively, with public release scheduled for December 31, 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. 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SELENOI Functions as a Key Modulator of Ferroptosis Pathway in Colitis and Colorectal Cancer. \u003cem\u003eAdv Sci (Weinh)\u003c/em\u003e. 2024;e2404073. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/advs.202404073\u003c/span\u003e\u003cspan address=\"10.1002/advs.202404073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-complementary-medicine-and-therapies","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcam","sideBox":"Learn more about [BMC Complementary Medicine and Therapies](https://bmccomplementmedtherapies.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Complementary Medicine and Therapies","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Colorectal adenoma, Wumei Wan, Network pharmacology, Multi-omics, Arachidonic acid metabolism","lastPublishedDoi":"10.21203/rs.3.rs-6629738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6629738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWumei Wan (WMW), a classical Traditional Chinese Medicine (TCM) formulation, has been employed for treating colorectal adenoma (CRA), yet its pharmacological mechanisms remain to be elucidated. This study investigated the protective effects of WMW on CRA through the regulation of the arachidonic acid (AA) metabolism pathway.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eBlood components of WMW were analyzed, and network pharmacology was used to predict potential targets. The APC\u003csup\u003emin/+\u003c/sup\u003e mouse model was utilized to assess the effects of WMW on intestinal tumor number and size, with histopathology evaluated by H\u0026amp;E staining. Immunohistochemistry was employed to analyze Ki67 and p53 expression. Multi-omics approaches, including fecal metagenomics, UHPLC-Q-TOF MS, transcriptomics, and 4D-label-free proteomics, were used to study fecal microbiota, serum metabolites, colon mRNA, and protein expression. Real-time quantitative PCR (RT-qPCR) was used to verify the multi-omics findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eUHPLC-MS identified 809 blood components in WMW. WMW significantly reduced tumor number and size in CRA mice. Multi-omics analysis revealed WMW\u0026rsquo;s regulation of the AA metabolism pathway, identifying key metabolites (8(S)-HETE, PGF2α, and 12-HETE), genes (\u003cem\u003eCyp2e1\u003c/em\u003e, \u003cem\u003ePla2g2a\u003c/em\u003e, \u003cem\u003ePla2g4c\u003c/em\u003e, \u003cem\u003eAlox5\u003c/em\u003e, \u003cem\u003eAlox15\u003c/em\u003e, and \u003cem\u003ePtgds\u003c/em\u003e), and proteins (Alox15 and Pla2g4c). RT-qPCR confirmed consistent mRNA expression of \u003cem\u003eMmp9\u003c/em\u003e, \u003cem\u003eIl\u003c/em\u003e-\u003cem\u003e1a\u003c/em\u003e, \u003cem\u003eEsr1\u003c/em\u003e, \u003cem\u003eIl\u003c/em\u003e-\u003cem\u003e13\u003c/em\u003e, \u003cem\u003eCyp2e1\u003c/em\u003e, \u003cem\u003eAlox5\u003c/em\u003e, \u003cem\u003eAlox15\u003c/em\u003e, \u003cem\u003ePla2g2a\u003c/em\u003e, \u003cem\u003ePla2g4c\u003c/em\u003e, and \u003cem\u003ePtgds\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eWMW inhibits the development of colorectal adenoma by modulating the AA metabolism pathway, involving changes in intestinal microbiota, serum metabolites, and mRNA/protein expression in the colon.\u003c/p\u003e","manuscriptTitle":"Integrative network pharmacology and multi-omics to investigate the potential mechanisms involved in Wumei Wan treatment of colorectal adenomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 18:22:34","doi":"10.21203/rs.3.rs-6629738/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-18T15:19:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T09:05:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T08:40:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57359921170614283542017003061401267143","date":"2025-07-09T12:30:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278648785796416800359324993062052140926","date":"2025-07-07T09:43:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-07T07:42:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-07T07:34:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-26T11:05:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-15T07:59:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Complementary Medicine and Therapies","date":"2025-06-15T07:55:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-complementary-medicine-and-therapies","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcam","sideBox":"Learn more about [BMC Complementary Medicine and Therapies](https://bmccomplementmedtherapies.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Complementary Medicine and Therapies","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6bdeaf64-a96c-4cc1-af4b-81e9a38fb58d","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2025-12-23T09:38:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 18:22:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6629738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6629738","identity":"rs-6629738","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}