Aspirin Enhances Chemosensitivity of Colorectal Cancer Cells by Downregulating FOXP3 to Inhibit ABCB1 Expression

Aspirin Enhances Chemosensitivity of Colorectal Cancer Cells by Downregulating FOXP3 to Inhibit ABCB1 Expression | 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 Aspirin Enhances Chemosensitivity of Colorectal Cancer Cells by Downregulating FOXP3 to Inhibit ABCB1 Expression Yi-Xiao Lu, Jun-Jie Chen, Xiao-Dan Wang, Tai-Ran Wang, Bing-Sheng Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9334465/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract BACKGROUND The mechanisms underlying chemoresistance in Colorectal cancer (CRC) are complex and multifaceted, among which the overexpression of ATP-binding cassette (ABC) transporter family proteins is recognized as a key factor. Aspirin (acetylsalicylic acid, ASA), a classic non-steroidal anti-inflammatory drug, has been implicated in the regulation of tumor cells through various pathways, positioning it as a promising adjuvant anti-tumor agent. AIM To investigate the mechanism of ASA in enhancing CRC chemosensitivity and explore new therapeutic strategies. METHODS This study utilized the NHANES database to analyze the impact of ASA on the prognosis of CRC patients. Through in vivo and in vitro experiments, the synergistic inhibitory effects of ASA combined with doxorubicin (DOX) on tumor proliferation and apoptosis were validated. Furthermore, network pharmacology, molecular docking and dynamics simulations, and databases such as TCGA were integrated to screen for key targets. Mechanisms of ASA regulation of ABCB1 were investigated using doxorubicin uptake assays, immunofluorescence, and Western blot. RESULTS Through analysis of the NHANES cohort, in vitro and in vivo experiments, multi-database analysis, and molecular simulation, aspirin use was associated with reduced CRC-specific mortality; the combination of aspirin and doxorubicin exerted synergistic antitumor effects; FOXP3 was identified as a core target; and aspirin inhibited FOXP3 to downregulate ABCB1, thereby blocking doxorubicin efflux and enhancing chemosensitivity. CONCLUSION In conclusion, ASA enhances CRC chemosensitivity by targeting the FOXP3-ABCB1 axis to inhibit drug efflux and prolong DOX retention, positioning FOXP3 as a promising therapeutic target. Colorectal cancer Aspirin Chemotherapy sensitivity FOXP3 ABCB1 Doxorubicin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. INTRODUCTION Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, ranking as the third most commonly diagnosed cancer and the second leading cause of cancer-related deaths globally [ 1 ] . In cases of locally advanced or metastatic CRC, chemotherapy in combination with targeted therapies, such as anti-EGFR or anti-HER2 antibodies, has become the standard treatment approach [ 2 – 4 ] . However, chemotherapy resistance remains a major challenge in clinical practice. For instance, platinum-based agents like cisplatin and oxaliplatin, which are first-line treatments for CRC, often face diminished clinical efficacy due to drug resistance [ 5 , 6 ] . Similarly, doxorubicin (DOX), an anthracycline used in treating various cancers, also encounters significant resistance issues [ 7 ] . Consequently, tumor resistance to chemotherapeutic agents is a primary driver of treatment failure and disease progression. The mechanisms underlying chemoresistance in CRC cells are multifaceted, involving alterations in the tumor microenvironment, enhanced DNA repair mechanisms, dysregulation of apoptotic pathways, and abnormal epigenetic modifications [ 8 – 11 ] . The overexpression of ATP-binding cassette (ABC) transporter family proteins is a well-established factor in chemotherapy resistance. These membrane proteins, particularly ATP Binding Cassette Subfamily B Member 1 (ABCB1), actively pump chemotherapeutic drugs out of cells using energy from ATP hydrolysis. This process reduces the effective intracellular drug concentration, thereby diminishing the therapeutic efficacy of chemotherapy. Therefore, several studies suggest that combining ABC transporter inhibitors with chemotherapeutic agents may enhance treatment effectiveness [ 12 – 14 ] . Aspirin (acetylsalicylic acid, ASA) has increasingly garnered attention in cancer chemoprevention and therapy due to its widespread clinical use and potential anti-tumor effects. Meta-analyses of large cohort studies and randomized controlled trials have shown that long-term, regular use of low-dose ASA significantly reduces both the incidence and mortality of CRC [ 15 , 16 ] . Mechanistically, ASA affects tumor cells through various pathways: it inhibits the WNT/β-catenin and NF-κB signaling pathways, downregulates c-Myc expression, and induces G0/G1 phase cell cycle arrest [ 17 – 21 ] . In terms of apoptosis regulation, ASA promotes mitochondrial membrane potential collapse, increases the Bax/Bcl-2 ratio, and activates the caspase cascade [ 22 , 23 ] . These findings support ASA's potential as an adjunctive anti-tumor agent. Notably, ABCB1 is a critical protein in tumor chemotherapy resistance. However, the upstream transcriptional networks regulating ABCB1 gene expression and their associated mechanisms remain poorly understood. Specifically, it is unclear whether ASA can enhance the sensitivity of tumor cells to chemotherapeutic agents by targeting these regulatory pathways. This study, therefore, aims to systematically investigate the sensitizing effect of ASA in CRC chemotherapy, particularly its ability to enhance the anti-tumor efficacy of DOX. It is anticipated that this research will provide a theoretical foundation for ASA use in CRC treatment and offer novel strategies to overcome chemotherapy resistance in CRC. 2. MATERIALS AND METHODS 2.1 NHANES Database and Analysis Methods This study utilized patient data from four cycles of the National Health and Nutrition Examination Survey (NHANES) database spanning from 2011 to 2018 (n = 39,156). Patients with missing records on ASA use (n = 24,119) and those lacking data on age, sex, BMI, smoking status, hypertension, hyperlipidemia, diabetes, coronary heart disease, family income, education, and survival status (n = 2,477) were excluded. Subsequently, patients without cancer (n = 10,879) were also excluded, resulting in a final sample of 1,681 patients, which included 109 patients with CRC and others with various cancers. The data were categorized and stratified; patients were divided into two groups based on ASA use (users vs. non-users). Two regression models were used for analysis: 1) A partially adjusted model, accounting for age, gender, BMI, smoking, family income, and education as covariates; 2) A fully adjusted model, which additionally included hypertension, hyperlipidemia, diabetes, and coronary heart disease as covariates. ASA non-users served as the reference group, and Hazard Ratios (HRs) with corresponding P-values were calculated. 2.2 Cell Culture Materials and Methods 2.2.1 Reagents and Cell Culture ASA (99.0%) was sourced from Tianjin Huasheng Chemical Reagent Co., Ltd. Doxorubicin hydrochloride (98.0%) was obtained from Beijing Walkey Biological Technology Co., Ltd. The human CRC cell line HT-29 and the murine CRC cell line CT-26 were both procured from Laibaix Biotechnology Co., Ltd. (Shanghai, China). HT-29 cells were cultured in DMEM medium, and CT-26 cells were cultured in RPMI-1640 medium. All media were supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin-amphotericin B mixture. Cells were incubated at 37°C with 5% CO 2 . 2.2.2 Cell Viability Assay HT-29 cells were seeded into 96-well plates at a density of 5 × 10 4 cells per well and treated with various concentrations of ASA (0, 1, 2, 4, 6, 8 mmol/L) for different durations (24, 48, 72 hours), different concentrations of DOX (100, 1000, 10,000, 100,000 nmol/L) for 24 hours, or a combination of ASA (0.625, 1.25, 2.50 mmol/L) and DOX (150, 300, 600 nmol/L) for varying times (24, 48, 72 hours). Following treatment, 10 µL of CCK-8 reagent (Share-Bio, Shanghai Sheng'er Biotechnology Co., Ltd.) was added to each well. After a 4-hour incubation, absorbance at 450 nm was measured using a microplate reader. 2.2.3 Colony Formation Assay For colony formation assays, HT-29 cells in the logarithmic growth phase were digested with 0.25% trypsin containing EDTA to generate a single-cell suspension. Cells were then seeded into 6-well plates at 1,000 cells per well. After 24 hours of adherence, drugs were administered. The groups included: control (complete medium without drugs), ASA (2.5 mmol/L), DOX (600 nmol/L), and a combination of ASA (2.5 mmol/L) and DOX (600 nmol/L). After 24 hours of drug treatment, drugs were removed, and cells were cultured in complete medium for 14 days until visible colonies formed. Cells were fixed with 4% paraformaldehyde for 30 minutes, stained with crystal violet for 20 minutes, washed with Phosphate-Buffered Saline (PBS), and air-dried. Whole-well images were captured, and colony counts were quantified using ImageJ software. 2.2.4 Cell Proliferation Assay (EdU) Cell proliferation activity was assessed using the BeyoClick™ EdU-488 Cell Proliferation Assay Kit (Shanghai Beyotime Biotechnology Co., Ltd.). HT-29 cells in the logarithmic growth phase were seeded into 6-well plates at a density of 5 × 10 5 cells per well. After 24 hours of adherence, drug treatments were administered as described in section 2.2.3 . Following 24 hours of treatment, the medium was replaced with complete medium containing 10 µM 5-ethynyl-2′-deoxyuridine (EdU), 2 mL per well. Cells were incubated for an additional 2 hours at 37°C, fixed with 4% paraformaldehyde for 15 minutes, and permeabilized with 0.3% Triton X-100 for 15 minutes. A Click reaction mixture was added, and cells were incubated for 30 minutes in the dark. Hoechst 33342 was added for nuclear counterstaining for 10 minutes. Images were captured, and the proportion of EdU-positive cells was quantified using ImageJ software. 2.2.5 Live/Dead Cell Staining Cell viability and toxicity were assessed using the Sheng'er Animal Cell Viability/Toxicity Assay Kit (Shanghai Sheng'er Biotechnology Co., Ltd.). HT-29 cells in the logarithmic growth phase were seeded into 24-well plates at a density of 5 × 10 4 cells per well. After 24 hours of adherence, drugs were administered as outlined in section 2.2.3 . After 24 hours of treatment, the medium was discarded. Equal volumes of 2 µM Calcein acetoxymethyl ester (Calcein-AM) working solution and 4.5 µM propidium iodide (PI) working solution were mixed and added to each well, followed by incubation at 37°C for 20 minutes, protected from light. Images were captured and quantitatively analyzed using ImageJ software. 2.2.6 Apoptosis Assay Apoptosis was assessed using the FITC-Annexin V/PI Apoptosis Detection Kit (Shanghai Sheng'er Biotechnology Co., Ltd.). HT-29 cells in the logarithmic growth phase were seeded into 6-well plates at a density of 5 × 10 5 cells per well. After 24 hours of adherence, drugs were administered as described in section 2.2.3 . After 24 hours of treatment, the medium was discarded. Cells were digested with trypsin without EDTA, centrifuged, washed twice with pre-cooled PBS, and resuspended in 1 × Binding Buffer at a concentration of 1 × 10 6 cells/mL. A 100 µL aliquot of the cell suspension was transferred to a flow cytometry tube, and 5 µL of Fluorescein isothiocyanate-conjugated Annexin V (FITC-Annexin V) and 5 µL of PI staining solution were added. After 15 minutes of incubation at room temperature in the dark, 400 µL of 1 × Binding Buffer was added for dilution, and analysis was performed using a flow cytometer. 2.2.7 Cell Cycle Analysis The cell cycle was analyzed using a Cell Cycle Detection Kit (Shanghai Sheng'er Biotechnology Co., Ltd.). HT-29 cells were cultured in 6-well plates until they reached the logarithmic growth phase and divided into groups as described in section 2.2.3 , with a 24-hour treatment duration. Cells were collected, centrifuged at 1,000 rpm for 5 minutes, and washed twice with pre-cooled PBS. They were resuspended in 70% pre-cooled ethanol and fixed overnight at 4°C. After fixation, cells were washed twice with PBS, and 0.5 mL of staining working solution was added, followed by 30 minutes of incubation in the dark. Detection was performed using a flow cytometer, and the data were analyzed using FlowJo software. 2.2.8 Wound Healing Assay HT-29 cells in the logarithmic growth phase were seeded into 6-well plates at a density of 5 × 10 5 cells per well. Once a confluent cell layer was formed, a scratch was made vertically across the cell layer using a 200 µL pipette tip. Cells were washed three times with PBS, and the medium was replaced with serum-free medium under different treatment conditions: control group (serum-free medium without drugs), ASA group (2.5 mmol/L ASA), DOX group (600 nmol/L DOX), and combination group (ASA 2.5 mmol/L + DOX 600 nmol/L). After further incubation for 24 hours, images were captured under a microscope. The wound area was analyzed using ImageJ software. 2.2.9 Doxorubicin Uptake Assay HT-29 cells were cultured in 6-well plates until reaching the logarithmic growth phase. Cells were treated with DOX (600 nmol/L) for 2 hours, after which the drug was removed. The control group was cultured in complete medium for 6 hours and 12 hours, while the ASA group was cultured in medium containing 2.5 mmol/L ASA for 6 hours and 12 hours. Cell nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI) for 15 minutes. The fluorescence intensity of DOX was quantitatively analyzed using ImageJ software. 2.2.10 Immunofluorescence for Nuclear Translocation FOXP3 antibody and secondary antibody for immunofluorescence were purchased from Chengdu Zhengneng Biological Technology Co., Ltd. (Chengdu, China). HT-29 cells in the logarithmic growth phase were seeded onto coverslips in 12-well plates at a density of 1 × 10 5 cells per well. After 24 hours of culture for adherence, cells were divided into groups and treated as described in section 2.2.3 for 24 hours. Cells were fixed with 4% paraformaldehyde for 15 minutes, permeabilized with 0.5% Triton X-100 for 15 minutes, and blocked with 5% bovine serum albumin for 30 minutes. Cells were then incubated with the primary antibody (FOXP3, dilution 1:200) overnight at 4°C, followed by incubation with the secondary antibody for 2 hours in the dark. Nuclei were stained with DAPI for 15 minutes. Coverslips were mounted with an anti-fade mounting medium, and fluorescence signals were detected using a Leica upright fluorescence microscope. Nuclear fluorescence intensity was quantified using ImageJ software. 2.2.11 Cytoplasmic and Nuclear Protein Extraction Cells from different treatment groups were collected, and protein extraction was performed using a Nuclear and Cytoplasmic Protein Extraction Kit (Shanghai Sheng'er Biotechnology Co., Ltd.). Cells were incubated in CE buffer on ice, and the supernatant obtained after centrifugation contained the cytoplasmic protein fraction. The nuclear pellet was washed with CE wash buffer and then incubated in NE buffer on ice. After centrifugation, the supernatant obtained was the nuclear protein fraction. Proteins were stored at -80°C for subsequent analysis. 2.2.12 Western Blotting Antibodies used in this experiment, including FOXP3, ABCB1, BAX, BCL2, GAPDH, YY1, and secondary antibodies, were purchased from Samflex Biotech Co., Ltd. (Hefei, China). All antibodies were diluted according to the manufacturer's recommended ratios. Cells from different treatment groups were collected and lysed with a mixture of RIPA lysis buffer, protease inhibitor, and PMSF to extract proteins. The proteins from each group were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene fluoride (PVDF) membranes. Membranes were blocked with 5% skimmed milk powder and incubated with primary antibodies overnight at 4°C. Secondary antibody incubation was carried out for 2 hours at room temperature. Protein bands were visualized using enhanced chemiluminescence (ECL) reagent and detected with a gel imaging system. Gray values of the protein bands were quantified using ImageJ software. 2.3 Key Gene Screening and Bioinformatics Analysis In this study, the gene expression matrix of ASA-treated versus untreated CRC cells was obtained from the GSE76583 series in the Gene Expression Omnibus (GEO) database [ 24 ] . Differential expression analysis was performed using the R package limma (version 3.40.6) to identify differentially expressed genes (DEGs) between the treatment and control groups. Potential targets related to "DOX resistance" were obtained from the Genecards database and the Online Mendelian Inheritance in Man (OMIM) database. ABCB1-related transcription factors were predicted using the Human Transcription Factor Database (Human TFDB) and the Human Transcription Factor Targets (hTFtarget) database. The genes screened from GEO, Genecards, OMIM, hTFtarget, and Human TFDB were intersected and visualized using Venn diagrams. The intersecting genes were imported into the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein-protein interaction (PPI) network, which was then processed using Cytoscape software to generate a molecular interaction network diagram. For gene set functional enrichment analysis, Gene Ontology (GO) annotations for the genes were obtained using the R package org.Hs.eg.db (version 3.1.0). Kyoto Encyclopedia of Genes and Genomes (KEGG) gene annotations were retrieved using the KEGG rest API ( https://www.kegg.jp/kegg/rest/keggapi.html ). Enrichment analysis was conducted with the R package clusterProfiler (version 3.14.3) to obtain gene set enrichment results, which were subsequently visualized. RNA-seq data for the CRC project from The Cancer Genome Atlas (TCGA) database, consisting of 458 tumor tissue samples and 41 normal tissue samples, were downloaded from the National Cancer Institute's Center for Cancer Genomics website. The Sangerbox platform, an online tool for data analysis and visualization ( https://www.bioinformatics.com.cn ), was used to generate violin plots and a gene expression network diagram [ 25 ] . Immunohistochemistry images of CRC and normal colon tissues were obtained from the Human Protein Atlas (HPA) database. Survival analysis for patients with CRC was performed using the Kaplan-Meier Plotter database ( https://www.kmplot.com ) [ 26 ] . 2.4 Molecular Docking and Molecular Dynamics Simulations The 2D structures of small molecule ligands were sourced from the PubChem database ( http://pubchem.ncbi.nlm.nih.gov/ ) and converted to 3D structures using ChemOffice software, then exported in mol2 format. Protein targets were selected from the RCSB PDB database ( http://www.rcsb.org/ ), with preference given to crystal structures with higher resolution for use as receptors in docking studies. Pymol 2.6 software was then used for protein preprocessing, which involved removing water molecules and phosphate ions, followed by saving the structure in PDB format. Molecular docking was performed with AutoDock Vina software to assess the binding interactions between proteins and ligands. The optimal conformation for molecular simulation was chosen by comparing docking scores. Visualization of the docking results was carried out with Discovery Studio 2019 and Pymol 2.6 software, generating 2D and 3D interaction diagrams between the compound and key residues. Molecular dynamics simulations were conducted using Gromacs 2022 software under an NPT ensemble at a constant temperature of 310 K and constant pressure over a simulation time of 100 ns. During the simulation, tools such as g-rmsd, g-rmsf, and g-hbond were utilized to compute the Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and Hydrogen Bonds (HBonds), respectively. 2.5 Animal Experiment Materials and Methods 2.5.1 Mouse Subcutaneous Tumor Model A total of 20 male BALB/c mice, weighing between 18 and 22 grams and aged 6 to 8 weeks, were purchased from Hangzhou Ziyuan Experimental Animal Technology Co., Ltd. (Hangzhou, China). They were housed in a controlled environment with regulated temperature, humidity, and light cycles, with free access to food and water. The mice were randomly assigned to four groups. CT-26 cells were cultured to the logarithmic growth phase, digested with trypsin, washed with PBS, and resuspended. A 0.1 mL (5 × 10 6 cells) suspension of CT-26 cells was subcutaneously injected into the right hind limb root of each mouse. The control group received intragastric administration of normal saline and an intraperitoneal injection of normal saline. The ASA group was treated with ASA (100 mg/kg·1d, orally) and intraperitoneal injection of normal saline. The DOX group received DOX (5 mg/kg·1w, i.p.) and intragastric administration of normal saline. The ASA + DOX group was treated with ASA (100 mg/kg·1d, orally) and DOX (5 mg/kg·1w, i.p.). All mice were euthanized by cervical dislocation under anesthesia. Tumor tissues were excised, weighed, measured for volume, photographed, and stored at -80°C. This study was approved by the Animal Ethics Committee of Anhui Medical University (Approval Number: LLSC20242463). All animal procedures and related research protocols strictly adhered to internationally recognized guidelines for the use of laboratory animals. 2.5.2 Histopathological Examination of Tumor Tissues Hematoxylin and eosin (HE) staining was performed on tumor tissues. Ki-67 immunohistochemistry was carried out following antigen heat retrieval, followed by sequential incubation with primary and secondary antibodies, and color development using 3,3'-diaminobenzidine tetrahydrochloride. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining was performed by labeling dUTP with SpOrange-570, and nuclei were counterstained with DAPI. Fluorescence signals were quantified using ImageJ software. 2.6 Statistical Analysis Statistical analysis for databases and network pharmacology was performed using R software. Experimental data were analyzed using GraphPad Prism. Measurement data are expressed as mean ± standard deviation (x ± s). One-way analysis of variance (ANOVA) was used for comparisons among multiple groups, and comparisons between two groups were performed using Student's t-test. Each independent experiment was repeated three times. A p-value < 0.05 was considered statistically significant. 3. RESULTS 3.1 Aspirin Significantly Improves Survival Prognosis in Patients with Colorectal Cancer Tables 1 and 2 present the association between ASA use and survival in cancer individuals. In both the overall cancer population and the CRC subgroup, ASA use was associated with a reduced risk of death (HR < 1, P < 0.001). Patients with CRC showed a more significant benefit from ASA use. In the fully adjusted model, the risk of malignancy-specific death for patients with CRC (HR = 0.047, 95%CI = 0.046–0.048, P < 0.001) was significantly lower than that for the overall cancer population (HR = 0.102, 95%CI = 0.101–0.103, P < 0.001). This finding suggests that ASA may offer a protective effect, improving survival in patients with CRC, highlighting its potential value in CRC treatment. Table 1 Characteristics of aspirin users and non-users in the study population (n = 1681) Variable Study population all(n = 1681) aspirin nonusers(n = 745) aspirin users(n = 936) age(year) < 45 54 47 7 45 to 59 338 222 116 ≥ 60 1289 476 813 gender male 819 303 516 female 862 442 420 Educational level high school 1022 473 549 Family poverty income($) < 20000 401 195 206 20000 to 74999 877 356 521 ≥ 75000 403 194 209 Weight status, BMI 28 864 372 492 Smoking never 765 351 414 former 672 272 400 now 244 122 122 Hyperlipidemia No 747 407 340 Yes 934 338 596 Hypertension No 659 381 278 Yes 1022 364 658 Diabetes No 1274 632 642 Yes 407 113 294 Coronary heart disease No 1511 725 786 Yes 170 20 150 Table 2 Hazard ratios for all-cause and malignant neoplasms mortality by aspirin use in the cancer and colorectal cancer populations. Population Mortality outcome Aspirin use status Death/No. Hazard Ratio Model 1 P value Model 2 P value Cancer population All-cause mortality aspirin nonusers 126/745 1 < 0.001 1 < 0.001 aspirin users 182/936 0.763 0.691 Malignant neoplasms mortality aspirin nonusers 52/745 1 < 0.001 1 < 0.001 aspirin users 66/936 0.863 0.872 Colorectal cancer population All-cause mortality aspirin nonusers 20/55 1 < 0.001 1 < 0.001 aspirin users 11/54 0.261 0.102 Malignant neoplasms mortality aspirin nonusers 10/55 1 < 0.001 1 < 0.001 aspirin users 2/54 0.186 0.047 3.2 Aspirin and DOX Combination Exhibits Synergistic Anti-tumor Effects in Vitro To assess the efficacy of ASA and DOX in inhibiting HT-29 proliferation, CCK8 assays were performed to evaluate CRC cell viability after treatment with different concentrations of ASA, DOX, and their combination. The Half Maximal Inhibitory Concentration (IC 50 ) for ASA and DOX after 24-hour treatment was calculated. The results showed that both ASA and DOX inhibited CRC cell proliferation in a concentration-dependent manner. Based on these results, a 24-hour treatment duration with 2.5 mmol/L ASA, 600 nmol/L DOX, and their combination were selected for subsequent experiments (Fig. 2 A, B, C, and D ). Colony formation assay results indicated that compared to the control group, the number of colonies formed in the ASA, DOX, and combination treatment groups was significantly reduced (P < 0.05). Although the number of colonies in the ASA treatment group was lower than in the control group, it remained relatively high, whereas the colony count in the combination treatment group was markedly reduced and significantly lower than in either of the single-agent groups. These results suggest that the combination of ASA and DOX significantly inhibited the colony-forming ability of HT-29 cells (Fig. 2 G and L ). The EdU assay was used to evaluate cell proliferation activity. In the fluorescence images, EdU-positive signals (green) represent proliferating cells in the S-phase, and Hoechst staining (blue) shows the nuclei. Compared to the control group, the proliferation rate was significantly reduced in the DOX and ASA groups (P < 0.05), and the proliferation rate in the combination treatment group was further decreased, showing a highly significant difference compared to the NC group (P < 0.001). These results indicate that while DOX and ASA alone mildly inhibit HT-29 cell proliferation, their combination significantly enhances this inhibitory effect (Fig. 2 E and N ). The Calcein-AM/PI double staining method was used to assess the impact of different treatments on cell viability. Green fluorescence (Calcein-AM) labels live cells, while red fluorescence (PI) labels dead cells. Fluorescence images showed that compared to the control group, the proportion of dead cells was significantly increased in the DOX group (P < 0.01) and the ASA group (P < 0.05). Notably, the proportion of dead cells in the DOX and ASA combination group was significantly higher than in either single-agent group (P < 0.0001), suggesting a synergistic pro-apoptotic effect (Fig. 2 F and K ). Flow cytometry results revealed distinct differences in the effects of various treatments on apoptosis. Compared to the control group, ASA alone slightly promoted apoptosis (P < 0.05), although the effect was modest. In contrast, DOX alone significantly increased the apoptosis rate (P < 0.0001). Notably, the apoptosis rate was further enhanced with combination treatment (P < 0.0001), indicating that the combination of ASA and DOX synergistically induces apoptosis in CRC cells (Fig. 2 H and O ). Cell cycle analysis showed that neither ASA nor DOX treatment alone significantly altered the proportion of cells in the G0/G1 phase (P > 0.05). However, the combination treatment resulted in a significant increase in the proportion of cells in the G0/G1 phase (P < 0.0001), suggesting that the combination may more effectively inhibit tumor cell proliferation by promoting G0/G1 phase arrest, thereby reducing the number of cells entering the S phase for DNA replication (Fig. 2 I and M ). Wound healing assay results demonstrated that both DOX and ASA alone significantly inhibited HT-29 cell migration compared to the control group (P < 0.001), indicating that both treatments possess inhibitory effects on cell migration. In the combination treatment group, the wound area was nearly completely unfilled by cells, indicating that ASA and DOX significantly enhance the inhibition of HT-29 cell migration (Fig. 2 J and P ). The in vitro experiments above suggest that the combination of ASA and DOX has stronger anti-tumor effects than either treatment alone. 3.3 Aspirin Enhances the Anti-tumor Effect of Doxorubicin in Mice In the mouse subcutaneous tumor model, the body weight of mice in all treatment groups remained stable throughout the experiment, with no significant decrease, suggesting that the drug treatments did not induce substantial systemic toxicity (Fig. 3 B). Both the DOX and ASA treatment groups significantly inhibited tumor growth compared to the control group (NC). The combination treatment group exhibited the most significant inhibition of tumor growth, with significantly smaller tumor volume and mass compared to the single-agent treatment groups (Fig. 3 A, C, and D ). HE staining revealed varying degrees of cellular degeneration and necrosis in the tumor tissues of the DOX and ASA groups, with more pronounced structural disruption in the combination treatment group. Ki-67 immunohistochemistry staining indicated that the proportion of Ki-67-positive cells was reduced in the DOX and ASA groups compared to the control group, with a further reduction in the combination treatment group, demonstrating a stronger inhibitory effect on tumor cell proliferation (Fig. 3 G and E ). TUNEL staining showed a significantly higher number of TUNEL-positive cells in the combination treatment group, further suggesting a stronger pro-apoptotic effect on tumor tissue (Fig. 3 H and F ). These animal experiments confirm that, in vivo , the combination of ASA and DOX exerts a more significant anti-tumor effect compared to the single-agent treatments. 3.4 Screening of Differential Genes and Core Genes Regulated by Aspirin in Colorectal Cancer Differential expression analysis was performed between the ASA-treated CRC cell group and the control group from the GSE76583 series. Using |log 2 FC| ≥ 1.0 and P < 0.05 as the significance thresholds, a total of 4,309 significant DEGs were identified, including 1,864 up-regulated and 2,445 down-regulated genes (Fig. 4 C). Cluster analysis was performed on the 4,309 DEGs, and a heatmap was generated (Fig. 4 D). The results showed clear separation of samples from different treatment groups into two independent clusters, with high similarity in expression profiles within each group, indicating that gene expression differences between the treatment and control groups were group-specific. To identify the key biological processes involved in ASA treatment of CRC, GO enrichment analysis was performed on the identified DEGs. The results are presented as bar charts, with different colors representing Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). In terms of BP, the DEGs were primarily associated with viral processes, epithelial morphogenesis, glycolipid metabolism, and the non-canonical Wnt signaling pathway. For CC, enrichment was observed in various protein complexes and cellular substructures, suggesting that the DEGs may be involved in functions mediated by intracellular complexes. Regarding MF, DEGs were associated with diverse enzyme activities, receptor binding, and nucleotide binding, implying that these genes mainly exert their biological functions through enzyme catalysis and molecular binding (Fig. 4 H). KEGG enrichment analysis was performed on the DEGs, which indicated that pathways such as the Hippo signaling pathway, Wnt signaling pathway, Glucagon signaling pathway, viral infections, and cell cycle were closely associated with the DEGs. Among these, the Hippo signaling pathway exhibited the highest degree of enrichment, suggesting it may play a key role in the anti-CRC effect of ASA (Fig. 4 K). A total of 2,072 potential targets related to DOX resistance were obtained from the Genecards and OMIM databases. These targets were intersected with the DEGs from the GEO database using a Venn diagram, resulting in 224 intersecting genes (Fig. 4 A). The 224 intersecting genes were imported into the STRING database to construct a PPI network, which was then processed using Cytoscape software to generate the PPI network diagram. Genes with larger degree values were considered potential core targets (Fig. 4 E and I ). To identify genes associated with ABCB1 expression among the targets, 490 ABCB1-related transcription factors were predicted from the hTFtarget and Human TFDB databases. These transcription factors were intersected with the 224 intersecting genes using another Venn diagram, yielding 32 genes, including TP53, MYC, JUN, and others. A separate PPI network diagram was created based on these 32 genes (Fig. 4 B and F ). GO enrichment analysis was performed on the 32 genes. For BP, enrichment was observed in "RNA polymerase II-mediated pri-miRNA transcription regulation," "pri-miRNA transcription," and the "intracellular receptor signaling pathway." For CC, enrichment was seen in the "transcription regulator complex," "RNA polymerase II transcription regulator complex," and "transcription repressor complex." For MF, enrichment was found in "DNA-binding transcription activator activity," "RNA polymerase II-specific DNA-binding transcription factor activity," and "transcription coactivator binding" (Fig. 4 G). KEGG enrichment analysis revealed that these 32 genes were significantly enriched in oncogenic pathways, cancer-related transcriptional signaling pathways, and cell function regulatory pathways, with the chemical carcinogenesis (receptor activation), thyroid hormone signaling pathway, Wnt signaling pathway, and transcriptional dysregulation in cancer being the primary regulatory pathways (Fig. 4 J). A literature review identified that CDX2, NFE2L2, FOXP3, and ARNT are associated with multidrug resistance (MDR) in tumors. Thus, these four genes were considered potential core targets [ 27 – 30 ] . 3.5 Validation of the Molecular Binding Ability of Aspirin with Key Genes The binding activity of ASA with the four targets (CDX2, NFE2L2, FOXP3, ARNT) was evaluated through molecular docking (Fig. 5 A). The binding energies for ASA with CDX2, NFE2L2, FOXP3, and ARNT were − 5.6, -6.4, -5.8, and − 6.1 kcal/mol, respectively. Generally, a binding energy lower than − 5.0 kcal/mol indicates good binding activity between the two molecules; the lower the binding energy, the stronger the binding activity, the higher the affinity, and the more stable the conformation. The molecular docking results indicated that ASA demonstrated strong binding affinity with all four targets. To further assess the stability of the binding, molecular dynamics simulations were performed. RMSD is a reliable indicator of the conformational stability of protein-ligand complexes. The RMSD trajectories showed that the ASA molecule exhibited high stability when bound to the CDX2, NFE2L2, FOXP3, and ARNT target proteins. HBonds play a pivotal role in ligand-protein interactions, and the number of HBonds between ASA and the target proteins during molecular dynamics simulation is shown in the figures. In most cases, the ASA-target protein complexes had approximately 1–2 HBonds, suggesting good HBond interactions between ASA and the target proteins (Fig. 5 B, C, D, E, F, and G ). 3.6 Expression Characteristics and Clinical Significance of Key Genes The expression of CDX2, NFE2L2, FOXP3, and ARNT in tumor tissues versus normal tissues was analyzed using the TCGA database. The results indicated that CDX2, NFE2L2, and ARNT were downregulated in tumor tissues compared to normal tissues (P < 0.05), while FOXP3 was upregulated in tumor tissues (P < 0.05) (Fig. 6 C and D ). Immunohistochemistry sections from human CRC tissues retrieved from the HPA database showed results consistent with the TCGA analysis (Fig. 6 A). The impact of CDX2, NFE2L2, FOXP3, and ARNT expression on the survival of patients with CRC was evaluated using the public dataset integration analysis website ( https://www.kmplot.com ), developed by Balázs Győrffy et al. Patients with high CDX2 expression exhibited significantly improved overall survival (OS) and relapse-free survival (RFS) (P < 0.05), suggesting CDX2 as a potential protective factor. In contrast, patients with high ARNT expression showed significantly reduced OS and RFS (P < 0.05). High FOXP3 expression was associated with significantly reduced OS (P 0.05), suggesting that both FOXP3 and ARNT may serve as risk factors. Patients with high NFE2L2 expression had significantly reduced OS (P 0.05) (Fig. 6 B). 3.7 Aspirin Inhibits ABCB1 Expression by Downregulating FOXP3 to Enhance Intracellular Retention of Doxorubicin Intracellular retention of DOX in CRC cells was evaluated through DOX uptake experiments. The results revealed a clear red signal within the cells after 2 hours of DOX treatment, confirming the uptake of DOX. After removing DOX, the control group cultured in complete medium for 6 hours showed a significant reduction in the red fluorescence signal, which became extremely weak after 12 hours, indicating substantial efflux of DOX from the cells. In the ASA group, after 6 hours of ASA treatment, the red fluorescence signal intensity slightly decreased, but a certain intensity remained after 12 hours, suggesting that ASA reduces the efflux of DOX from colorectal tumor cells (Fig. 7 A and E ). Immunofluorescence results showed that DOX treatment alone did not significantly affect the nuclear expression of FOXP3 compared to the normal control group. However, the nuclear fluorescence intensity of FOXP3 was significantly reduced in both the ASA treatment and combination treatment groups (P < 0.0001), indicating that ASA inhibits the nuclear accumulation of FOXP3 protein in CRC cells, thus attenuating its transcriptional regulatory activity (Fig. 7 B and F ). Western blot analysis revealed that, compared to the normal control group, the expression levels of FOXP3 and ABCB1 did not significantly change in the DOX treatment group (P > 0.05). In contrast, the expression of both FOXP3 and ABCB1 was significantly reduced in the ASA and combination treatment groups (P < 0.05), indicating that ASA effectively inhibits the protein expression of FOXP3 and ABCB1. The BAX/BCL-2 ratio was significantly increased in the DOX treatment group (P < 0.05), suggesting activation of the mitochondrial apoptosis pathway. While the BAX/BCL-2 ratio did not change significantly in the ASA treatment group alone, it was further increased in the combination treatment group. These results suggest that the combination of ASA and DOX enhances the anti-tumor effect of DOX via the mitochondrial apoptosis pathway (Fig. 7 C, G, and H ). Cytoplasmic-nuclear protein separation followed by Western blotting was performed on CRC cells. GAPDH served as a cytoplasmic protein internal control, and YY1 as a nuclear protein internal control. The YY1 band was nearly undetectable in the cytoplasmic fraction, and the GAPDH band was nearly absent in the nuclear fraction, confirming successful separation of cytoplasmic and nuclear proteins. Compared to the control group, DOX treatment did not significantly alter the expression of FOXP3 in either the cytoplasm or nucleus. However, the expression levels of FOXP3 in both compartments were significantly reduced in the ASA and combination treatment groups, suggesting that ASA may exert its effects by influencing processes such as FOXP3 transcription or translation, rather than solely affecting its nuclear translocation (Fig. 7 D, I, and J ). 4. DISCUSSION FOXP3 is a key transcription factor involved in the differentiation of regulatory T cells (Tregs), with its primary function being the maintenance of immune tolerance by suppressing effector T cell activity to regulate immune responses. Most previous research has focused on the infiltration of FOXP3 + Treg cells within the tumor microenvironment. In contrast, this study shifts focus to the pro-cancerous role of FOXP3 expression within CRC cells themselves, revealing its mechanism in mediating chemotherapy resistance through the promotion of ABCB1 expression. This study also proposes a novel regulatory strategy targeting the FOXP3-ABCB1 axis. By downregulating FOXP3, it inhibits ABCB1 expression, circumventing the risks associated with traditional ABCB1 inhibitors and offering a new intervention pathway for reversing chemotherapy resistance in CRC. Within the tumor microenvironment, the extent of FOXP3 + Treg cell infiltration correlates closely with immune suppression. Modulating Treg cell function by depleting functional FOXP3 protein can temporarily and specifically disrupt Treg-mediated immunosuppression in the cancer environment [ 31 ] . However, the role of FOXP3 in tumors remains controversial. Previous studies have reported conflicting results regarding the association between FOXP3 + Treg cell infiltration in the CRC tumor microenvironment and patient prognosis. This discrepancy may stem from the heterogeneity of Treg subpopulations, which differ in their suppressive and pro-inflammatory functions [ 32 , 33 ] . In breast cancer, inadequate FOXP3 expression may promote cancer progression, with evidence suggesting that FOXP3 inhibits angiogenesis by downregulating VEGF expression [ 34 , 35 ] . These contradictions may arise from differences in the cell types expressing FOXP3 and an incomplete understanding of FOXP3's specific role within tumor cells. Emerging evidence indicates that FOXP3 is also expressed in tumor cells themselves. Studies have reported FOXP3 expression in various tumor types, including breast, prostate, lung, gastric, thyroid, and melanoma cells, suggesting that FOXP3 may play a broader role in tumorigenesis [ 36 ] . ABCB1, also known as P-glycoprotein (P-gp), is a transmembrane protein implicated in chemotherapy resistance. Numerous studies have shown increased ABCB1 expression in tumor cells following chemotherapy drug exposure. For instance, paclitaxel chemotherapy induces ABCB1 expression in tumor endothelial cells via enhanced IL-8 secretion, while cisplatin upregulates ABCB1 through activation of canonical Wnt signaling [ 37 , 38 ] . Therefore, ABCB1 induction upon chemotherapy exposure is a significant driver of MDR. Inhibiting ABCB1 transport has been a major focus in developing strategies to combat MDR. Several ABCB1 inhibitors have been developed, acting through various mechanisms, including binding to the substrate-binding site of ABCB1, altering its structure and function, interfering with ATP hydrolysis, and modifying the integrity of the cell membrane lipid bilayer [ 39 ] . However, the clinical application of these inhibitors is hindered by issues such as lack of specificity, drug-drug interactions, and toxicity [ 40 ] , highlighting the need for new MDR reversal strategies. Previous studies have shown that FOXP3 expression is upregulated during chemotherapy in various cancers, suggesting that chemotherapy induces FOXP3 expression, which is linked to poor prognosis and chemoresistance [ 41 – 43 ] . However, these studies did not fully explore the specific mechanisms by which FOXP3 contributes to poor prognosis and chemoresistance. This study aims to investigate these mechanisms further, potentially uncovering new therapeutic strategies for CRC treatment. This study initially confirmed within the NHANES cohort that ASA use is significantly associated with reduced CRC-specific mortality and improved patient prognosis. Notably, this benefit was more pronounced in the CRC population compared to the broader pan-cancer population, suggesting that the effect is not simply a "general anti-tumor effect," but likely involves a CRC-specific mechanism. This inference is supported by multiple studies demonstrating that ASA use improves survival outcomes in patients with CRC. For instance, a recent double-blind, randomized, placebo-controlled trial conducted by Anna Martling et al. found that ASA significantly reduces the risk of CRC recurrence [ 44 – 46 ] . In contrast, similar studies in non-colorectal cancers—such as esophageal, gastric, breast, prostate, ovarian, and endometrial cancers—did not observe significant improvements in survival with ASA use [ 47 – 51 ] . These findings align with our results and provide strong clinical evidence to support further exploration into the mechanisms by which ASA enhances chemosensitivity in CRC. At the in vitro level, this study confirmed that the combination of ASA and DOX exhibits stronger anti-tumor effects compared to either agent alone. Previous studies have shown that both ASA and DOX can inhibit tumor cell proliferation by inducing cell cycle arrest and promoting apoptosis [ 52 – 54 ] . However, under the experimental conditions of this study, no significant changes in the cell cycle were observed with single-agent treatments, possibly due to the chosen drug treatment time and dosages. Furthermore, some studies have reported that treatment with sublethal doses of DOX in breast cancer cells can enhance migration and invasion capabilities [ 55 ] . The present study did not observe similar results in CRC cells, likely due to differences in tumor type, drug dosages, and treatment durations. It is also possible that the combination treatment suppressed potential metastatic risk, although the specific mechanisms behind this effect require further investigation. In vivo experiments using the mouse subcutaneous tumor model further validated the anti-tumor activity of the combination regimen. Numerous studies have also reported synergistic anti-tumor effects of ASA combined with DOX in various tumor models in vivo [ 56 , 57 ] , which is consistent with our findings. These in vitro and in vivo results complement each other, demonstrating a clear synergistic anti-tumor effect of combining ASA and DOX in CRC. To investigate the underlying anti-tumor mechanisms of the combination therapy, 32 targets were identified through differential gene analysis from the GEO database and integrated network pharmacology predictions from multiple databases. A literature review revealed that CDX2, NFE2L2, FOXP3, and ARNT are linked to MDR in tumors. Further analysis showed that ASA treatment downregulated FOXP3 expression, and molecular docking and dynamics simulations indicated stable structural binding between ASA and FOXP3. TCGA/HPA database analyses also revealed high FOXP3 expression in CRC tumor tissues, while Kaplan-Meier survival analysis suggested that elevated FOXP3 expression was significantly associated with poor patient prognosis. In contrast, CDX2, NFE2L2, and ARNT displayed inconsistencies in their differential expression, expression patterns, and prognostic relevance, thus reducing their reliability as core targets. Based on the multi-dimensional evidence, FOXP3 was identified as the core target through which ASA modulates chemosensitivity in CRC. Although CDX2, NFE2L2, and ARNT were not ultimately identified as core targets in this study, previous research highlights their significant roles in CRC: they may serve as potential prognostic markers, contribute to chemoresistance, and participate in processes such as tumor microenvironment remodeling and gut microbiota regulation, warranting further validation and exploration in subsequent studies [ 58 – 61 ] . These findings guided the design of molecular mechanism validation experiments targeting the FOXP3-ABCB1 pathway. DOX uptake experiments demonstrated that ASA treatment significantly prolonged the intracellular retention of DOX in CRC cells, confirming a reduction in drug efflux efficiency. Immunofluorescence and Western blot analyses revealed reduced protein expression of both FOXP3 and ABCB1. These results confirm that ASA, by targeting the transcriptional regulator FOXP3, significantly inhibits its expression, leading to the concurrent downregulation of ABCB1 transcription. This disruption of the ABCB1-mediated DOX efflux pump enhances intracellular DOX concentrations, thereby improving the chemosensitivity of CRC cells (Fig. 7 K). Although this study elucidates the mechanism by which ASA sensitizes CRC chemotherapy via the FOXP3–ABCB1 axis, several limitations remain that warrant further investigation. First, while evidence such as the downregulation of FOXP3 and ABCB1 expression and the increased intracellular retention of DOX following ASA treatment was obtained, more comprehensive data are needed. For instance, chromatin immunoprecipitation assays could determine whether FOXP3 directly binds to the promoter region of the ABCB1 gene, providing critical evidence to establish a complete causal chain. Second, prior studies have indicated that ASA enhances chemotherapy sensitivity in breast cancer by upregulating SMAR1 expression and inhibiting ABCG2. Consequently, other resistance mechanisms, beyond ABCB1, may also contribute to the observed sensitizing effect [ 62 ] . Given these findings and limitations, future research could proceed in several directions. First, techniques like chromatin immunoprecipitation and RNA-seq can be employed to further explore the direct transcriptional regulation of ABCB1 by FOXP3 at the chromatin level. Second, the impact of ASA on the expression of other MDR-associated transporters should be investigated to more fully characterize the mechanisms by which ASA enhances chemotherapy sensitivity in CRC. Third, for clinical translation, patient-derived tumor xenograft models could be used to optimize the combination regimen of ASA and chemotherapeutic agents. Additionally, novel intervention strategies targeting FOXP3 should be explored. These efforts may ultimately yield clinically applicable approaches for reversing chemotherapy resistance in CRC. 5. CONCLUSION In conclusion, ASA enhances CRC chemosensitivity by targeting the FOXP3-ABCB1 axis to inhibit drug efflux and prolong DOX retention, positioning FOXP3 as a promising therapeutic target. Declarations 6. ACKNOWLEDGEMENTS The authors would like to acknowledge Wei-wei Sheng, Bo Chen and Zheng-yu Hu(First Affiliated Hospital of Anhui Medical University, MD) for their skillful technical assistance. We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript. Funding: This work was supported by the Anhui Provincial Natural Science Foundation [grant numbers 2408085QH271] Clinical trial number: not applicable. Consent to Participate declaration: not applicable Consent to Publish declaration: not applicable Animal ethics statement: This study was approved by the Animal Ethics Committee of Anhui Medical University (Approval Number: LLSC20242463). All animal procedures and related research protocols strictly adhered to internationally recognized guidelines for the use of laboratory animals. Data availability statement: The datasets used and analysed during the current study are available from the corresponding author on reasonable request. References PATEL S G, KARLITZ J J, YEN T, et al. The rising tide of early-onset colorectal cancer: a comprehensive review of epidemiology, clinical features, biology, risk factors, prevention, and early detection [J]. 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Aryl hydrocarbon receptor nuclear translocator limits the recruitment and function of regulatory neutrophils against colorectal cancer by regulating the gut microbiota [J]. Journal of experimental & clinical cancer research : CR, 2023, 42(1): 53. BHATTACHARYA A, MUKHERJEE S, KHAN P, et al. SMAR1 repression by pluripotency factors and consequent chemoresistance in breast cancer stem-like cells is reversed by aspirin [J]. Science signaling, 2020, 13(654). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 29 Apr, 2026 Editor invited by journal 20 Apr, 2026 Editor assigned by journal 18 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 16 Apr, 2026 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. 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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-9334465","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635576632,"identity":"f3feba9f-a738-4ee4-862e-67e9882cef70","order_by":0,"name":"Yi-Xiao Lu","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi-Xiao","middleName":"","lastName":"Lu","suffix":""},{"id":635576633,"identity":"dfce5df0-02f3-46b7-bf19-dacf0b038a9d","order_by":1,"name":"Jun-Jie Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun-Jie","middleName":"","lastName":"Chen","suffix":""},{"id":635576634,"identity":"41ffbc4d-2058-4329-bce8-d3b785a0185b","order_by":2,"name":"Xiao-Dan Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Dan","middleName":"","lastName":"Wang","suffix":""},{"id":635576637,"identity":"1d444796-cd00-409e-bc10-6c85b25db548","order_by":3,"name":"Tai-Ran Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tai-Ran","middleName":"","lastName":"Wang","suffix":""},{"id":635576638,"identity":"05b53470-5ad8-4551-8f8f-dbce79e33933","order_by":4,"name":"Bing-Sheng Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bing-Sheng","middleName":"","lastName":"Liu","suffix":""},{"id":635576639,"identity":"77a0453b-803c-48ca-8bfd-e738fc62cc17","order_by":5,"name":"Yi-Xian Cheng","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi-Xian","middleName":"","lastName":"Cheng","suffix":""},{"id":635576640,"identity":"be25270d-ea0a-4099-97f2-009713be69d3","order_by":6,"name":"Guo-Dong Cao","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guo-Dong","middleName":"","lastName":"Cao","suffix":""},{"id":635576641,"identity":"4c666a0f-4bf7-4a14-8b5e-950c8084bd66","order_by":7,"name":"Ting Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYDAC5gMMBxL/2PDwszc+AHIPgAUl8GphS2A88LEhTU6y57AB0VqYD85sOGxscCOZSC0Gx3gMDvPuOJy44eZjNgmGmjvRBgeYD97mYbDLw6/lTHrizNvJQC3HnuVuOMCWbM3DkFyMU8v9HoPDPGzWiX23849JMLAdBmrhMZPmAYZJAz5beNiYExtuHgba8g+khf8bQS0HZ7Y5GwvcYGaTYGwD28KGV4vkMbaCAx/OgAI5mdkise9w7szDbMaWcwyScWrhO8a8+UNCBSgqDzPe+PDtcG7f8eaHN95U2OHUonCAwwDGZpFIAFHMYAfjUA8E8g3sD2Bs5g+41Y2CUTAKRsFIBgBEVWQ7rBwLCQAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ting","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-04-06 13:39:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9334465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9334465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108947486,"identity":"a43fb84b-085c-4af4-9d51-f4e64274d1cd","added_by":"auto","created_at":"2026-05-11 06:29:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12194786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of Research Workflow for Aspirin-Reversing Chemotherapy Resistance\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9334465/v1/45d714b88e6a81f7cd68bc84.jpg"},{"id":108947418,"identity":"d9a72343-ef55-4457-bad1-1b9f040d9d61","added_by":"auto","created_at":"2026-05-11 06:28:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11580698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAspirin enhances the inhibitory effect of DOX on colorectal cancer cells and influences cellular phenotypes. (A) \u003c/strong\u003eCell viability changes after treatment with different concentrations of ASA for 24, 48, and 72 h. \u003cstrong\u003e(B)\u003c/strong\u003e IC50 of ASA. \u003cstrong\u003e(C)\u003c/strong\u003eIC50 of DOX. \u003cstrong\u003e(D)\u003c/strong\u003e Cell viability changes under combined ASA and DOX treatment. \u003cstrong\u003e(E)\u003c/strong\u003e EdU assay detecting cell proliferation. \u003cstrong\u003e(F)\u003c/strong\u003eCalcein-AM/PI double staining assessing cell viability and death. \u003cstrong\u003e(G)\u003c/strong\u003eColony formation assay. \u003cstrong\u003e(H)\u003c/strong\u003e Annexin V-FITC/PI flow cytometry detecting apoptosis. \u003cstrong\u003e(I)\u003c/strong\u003e Cell cycle distribution. \u003cstrong\u003e(J)\u003c/strong\u003e Wound healing assay. \u003cstrong\u003e(K)\u003c/strong\u003eBar chart showing the proportion of dead cells. \u003cstrong\u003e(L)\u003c/strong\u003e Bar chart showing the quantitative statistics of colony numbers. \u003cstrong\u003e(M)\u003c/strong\u003e Bar chart showing the proportion of cells in the G0/G1 phase. \u003cstrong\u003e(N)\u003c/strong\u003e Bar chart showing the proportion of cell proliferation. \u003cstrong\u003e(O)\u003c/strong\u003e Bar chart showing the proportion of apoptosis. \u003cstrong\u003e(P)\u003c/strong\u003e Bar chart showing the wound healing rate.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9334465/v1/3612f7fc378966e5007ed780.jpg"},{"id":108947490,"identity":"b9d887cc-d782-48aa-9f7b-0cac7a027663","added_by":"auto","created_at":"2026-05-11 06:29:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12308478,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eASA combined with DOX inhibits tumor growth and affects tumor proliferation and apoptosis in a mouse subcutaneous tumor-bearing model.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Images of excised xenograft tumors. \u003cstrong\u003e(B)\u003c/strong\u003e Mouse body weight change curves. \u003cstrong\u003e(C)\u003c/strong\u003eBar chart of tumor volume at the experimental endpoint. \u003cstrong\u003e(D)\u003c/strong\u003eBar chart of tumor weight at the experimental endpoint. \u003cstrong\u003e(E)\u003c/strong\u003eBar chart of the mean optical density of Ki-67 immunohistochemical staining. \u003cstrong\u003e(F)\u003c/strong\u003eBar chart of the proportion of TUNEL-positive cells. \u003cstrong\u003e(G)\u003c/strong\u003eRepresentative images of Ki-67 immunohistochemistry and HE staining of tumor tissues. \u003cstrong\u003e(H)\u003c/strong\u003e Representative images of TUNEL fluorescence staining of tumor tissues.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9334465/v1/60c2560f812c8e5aad923f93.jpg"},{"id":108947441,"identity":"0afaf91f-b7f0-4696-ac3d-7b4c75caa19c","added_by":"auto","created_at":"2026-05-11 06:29:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10218679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork pharmacology and bioinformatics analysis reveal potential key targets of aspirin in reversing DOX resistance in colorectal cancer.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003eVenn diagram showing the intersection of differentially expressed genes from GSE76583 and DOX resistance-related genes. \u003cstrong\u003e(B)\u003c/strong\u003e Venn diagram showing the further intersection with the ABCB1-related gene set. \u003cstrong\u003e(C)\u003c/strong\u003eVolcano plot of differentially expressed genes. \u003cstrong\u003e(D)\u003c/strong\u003e Heatmap of differentially expressed genes. \u003cstrong\u003e(E)\u003c/strong\u003e Protein-protein interaction (PPI) network diagram of 224 targets. \u003cstrong\u003e(F)\u003c/strong\u003e PPI network diagram of 32 targets. \u003cstrong\u003e(G)\u003c/strong\u003e GO enrichment analysis results of the 32 targets. \u003cstrong\u003e(H)\u003c/strong\u003e GO enrichment analysis results of the differentially expressed genes. \u003cstrong\u003e(I)\u003c/strong\u003e STRING database interaction network diagram of the 224 targets. \u003cstrong\u003e(J)\u003c/strong\u003e KEGG pathway enrichment analysis results of the 32 targets. \u003cstrong\u003e(K)\u003c/strong\u003eKEGG pathway enrichment analysis results of the differentially expressed genes.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9334465/v1/19b4c07457332506a2e61018.jpg"},{"id":108947475,"identity":"b119e8be-b274-4d17-b3ab-9bacdffac6f2","added_by":"auto","created_at":"2026-05-11 06:29:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9778479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking and molecular dynamics simulation results of aspirin with target proteins (CDX2, NFE2L2, FOXP3, ARNT).\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Schematic diagrams of the docking conformations of aspirin with the four proteins. \u003cstrong\u003e(B)\u003c/strong\u003e Root mean square fluctuation (RMSF) curves of residues in the CDX2–ASA complex. \u003cstrong\u003e(C)\u003c/strong\u003eRMSF curves of the NFE2L2–ASA complex. \u003cstrong\u003e(D)\u003c/strong\u003e RMSF curves of the FOXP3–ASA complex. \u003cstrong\u003e(E)\u003c/strong\u003e RMSF curves of the ARNT–ASA complex. \u003cstrong\u003e(F)\u003c/strong\u003eTime-dependent curves of the number of hydrogen bonds formed between aspirin and each protein complex. \u003cstrong\u003e(G)\u003c/strong\u003e Time-dependent curves of the root mean square deviation (RMSD) of the aspirin-protein complexes.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9334465/v1/4d21ffb53107320b0b50a0f2.jpg"},{"id":108947470,"identity":"aa80e2db-0d2c-4115-92f8-c037ba72c26d","added_by":"auto","created_at":"2026-05-11 06:29:17","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7569452,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrative analysis based on TCGA, HPA, and Kaplan–Meier Plotter databases reveals the expression differences and prognostic value of CDX2, NFE2L2, FOXP3, and ARNT in colorectal cancer.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003eImmunohistochemistry images from the HPA database. \u003cstrong\u003e(B)\u003c/strong\u003eSurvival analysis from the Kaplan–Meier Plotter database. \u003cstrong\u003e(C)\u003c/strong\u003eViolin plots showing differences in gene expression levels between normal and tumor tissues in the TCGA database. \u003cstrong\u003e(D)\u003c/strong\u003e Paired scatter plots showing gene expression changes between normal and tumor tissues in TCGA paired samples.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9334465/v1/f7ebc8b9fa371957153ac42e.jpg"},{"id":108947471,"identity":"86e7ba52-7237-4a91-a43c-3816f49412c0","added_by":"auto","created_at":"2026-05-11 06:29:17","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":11712901,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMechanistic validation of ASA inhibiting drug efflux, enhancing intracellular DOX retention, and promoting chemotherapy sensitivity in colorectal cancer cells by regulating the FOXP3–ABCB1 axis.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Evaluation of DOX uptake and retention using DOX spontaneous fluorescence imaging. \u003cstrong\u003e(B)\u003c/strong\u003e Detection of FOXP3 expression and nuclear translocation by immunofluorescence. \u003cstrong\u003e(C)\u003c/strong\u003eWestern blot analysis of FOXP3, ABCB1, BCL-2, and BAX protein expression levels. \u003cstrong\u003e(D)\u003c/strong\u003e Western blot results after cytoplasmic-nuclear fractionation. \u003cstrong\u003e(E)\u003c/strong\u003e Bar chart of intracellular DOX fluorescence intensity at different time points. \u003cstrong\u003e(F)\u003c/strong\u003e Bar chart of the mean fluorescence intensity of FOXP3 immunofluorescence. \u003cstrong\u003e(G)\u003c/strong\u003e Bar chart of the BAX/BCL-2 ratio. \u003cstrong\u003e(H)\u003c/strong\u003e Bar chart of the gray values of FOXP3 and ABCB1 protein bands. \u003cstrong\u003e(I)\u003c/strong\u003e Bar chart of the gray values of FOXP3 protein bands in the cytoplasm. \u003cstrong\u003e(J)\u003c/strong\u003e Bar chart of the gray values of FOXP3 protein bands in the nucleus. \u003cstrong\u003e(K)\u003c/strong\u003eSchematic diagram of the mechanism.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9334465/v1/63459fd19b789edda326daa9.jpg"},{"id":108947589,"identity":"9a409a4f-1f9d-4405-ad99-b1ba30306c72","added_by":"auto","created_at":"2026-05-11 06:30:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":42721065,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9334465/v1/e2ce7f9f-e409-4cbe-aa34-fec38aa7f6b5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Aspirin Enhances Chemosensitivity of Colorectal Cancer Cells by Downregulating FOXP3 to Inhibit ABCB1 Expression","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eColorectal cancer (CRC) is one of the most prevalent malignancies worldwide, ranking as the third most commonly diagnosed cancer and the second leading cause of cancer-related deaths globally \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In cases of locally advanced or metastatic CRC, chemotherapy in combination with targeted therapies, such as anti-EGFR or anti-HER2 antibodies, has become the standard treatment approach \u003csup\u003e[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, chemotherapy resistance remains a major challenge in clinical practice. For instance, platinum-based agents like cisplatin and oxaliplatin, which are first-line treatments for CRC, often face diminished clinical efficacy due to drug resistance \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Similarly, doxorubicin (DOX), an anthracycline used in treating various cancers, also encounters significant resistance issues \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Consequently, tumor resistance to chemotherapeutic agents is a primary driver of treatment failure and disease progression. The mechanisms underlying chemoresistance in CRC cells are multifaceted, involving alterations in the tumor microenvironment, enhanced DNA repair mechanisms, dysregulation of apoptotic pathways, and abnormal epigenetic modifications \u003csup\u003e[\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The overexpression of ATP-binding cassette (ABC) transporter family proteins is a well-established factor in chemotherapy resistance. These membrane proteins, particularly ATP Binding Cassette Subfamily B Member 1 (ABCB1), actively pump chemotherapeutic drugs out of cells using energy from ATP hydrolysis. This process reduces the effective intracellular drug concentration, thereby diminishing the therapeutic efficacy of chemotherapy. Therefore, several studies suggest that combining ABC transporter inhibitors with chemotherapeutic agents may enhance treatment effectiveness \u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAspirin (acetylsalicylic acid, ASA) has increasingly garnered attention in cancer chemoprevention and therapy due to its widespread clinical use and potential anti-tumor effects. Meta-analyses of large cohort studies and randomized controlled trials have shown that long-term, regular use of low-dose ASA significantly reduces both the incidence and mortality of CRC \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Mechanistically, ASA affects tumor cells through various pathways: it inhibits the WNT/β-catenin and NF-κB signaling pathways, downregulates c-Myc expression, and induces G0/G1 phase cell cycle arrest \u003csup\u003e[\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In terms of apoptosis regulation, ASA promotes mitochondrial membrane potential collapse, increases the Bax/Bcl-2 ratio, and activates the caspase cascade \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. These findings support ASA's potential as an adjunctive anti-tumor agent. Notably, ABCB1 is a critical protein in tumor chemotherapy resistance. However, the upstream transcriptional networks regulating ABCB1 gene expression and their associated mechanisms remain poorly understood. Specifically, it is unclear whether ASA can enhance the sensitivity of tumor cells to chemotherapeutic agents by targeting these regulatory pathways. This study, therefore, aims to systematically investigate the sensitizing effect of ASA in CRC chemotherapy, particularly its ability to enhance the anti-tumor efficacy of DOX. It is anticipated that this research will provide a theoretical foundation for ASA use in CRC treatment and offer novel strategies to overcome chemotherapy resistance in CRC.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 NHANES Database and Analysis Methods\u003c/h2\u003e \u003cp\u003eThis study utilized patient data from four cycles of the National Health and Nutrition Examination Survey (NHANES) database spanning from 2011 to 2018 (n\u0026thinsp;=\u0026thinsp;39,156). Patients with missing records on ASA use (n\u0026thinsp;=\u0026thinsp;24,119) and those lacking data on age, sex, BMI, smoking status, hypertension, hyperlipidemia, diabetes, coronary heart disease, family income, education, and survival status (n\u0026thinsp;=\u0026thinsp;2,477) were excluded. Subsequently, patients without cancer (n\u0026thinsp;=\u0026thinsp;10,879) were also excluded, resulting in a final sample of 1,681 patients, which included 109 patients with CRC and others with various cancers. The data were categorized and stratified; patients were divided into two groups based on ASA use (users vs. non-users). Two regression models were used for analysis: 1) A partially adjusted model, accounting for age, gender, BMI, smoking, family income, and education as covariates; 2) A fully adjusted model, which additionally included hypertension, hyperlipidemia, diabetes, and coronary heart disease as covariates. ASA non-users served as the reference group, and Hazard Ratios (HRs) with corresponding P-values were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cell Culture Materials and Methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Reagents and Cell Culture\u003c/h2\u003e \u003cp\u003eASA (99.0%) was sourced from Tianjin Huasheng Chemical Reagent Co., Ltd. Doxorubicin hydrochloride (98.0%) was obtained from Beijing Walkey Biological Technology Co., Ltd. The human CRC cell line HT-29 and the murine CRC cell line CT-26 were both procured from Laibaix Biotechnology Co., Ltd. (Shanghai, China). HT-29 cells were cultured in DMEM medium, and CT-26 cells were cultured in RPMI-1640 medium. All media were supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin-amphotericin B mixture. Cells were incubated at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Cell Viability Assay\u003c/h2\u003e \u003cp\u003eHT-29 cells were seeded into 96-well plates at a density of 5 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells per well and treated with various concentrations of ASA (0, 1, 2, 4, 6, 8 mmol/L) for different durations (24, 48, 72 hours), different concentrations of DOX (100, 1000, 10,000, 100,000 nmol/L) for 24 hours, or a combination of ASA (0.625, 1.25, 2.50 mmol/L) and DOX (150, 300, 600 nmol/L) for varying times (24, 48, 72 hours). Following treatment, 10 \u0026micro;L of CCK-8 reagent (Share-Bio, Shanghai Sheng'er Biotechnology Co., Ltd.) was added to each well. After a 4-hour incubation, absorbance at 450 nm was measured using a microplate reader.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Colony Formation Assay\u003c/h2\u003e \u003cp\u003eFor colony formation assays, HT-29 cells in the logarithmic growth phase were digested with 0.25% trypsin containing EDTA to generate a single-cell suspension. Cells were then seeded into 6-well plates at 1,000 cells per well. After 24 hours of adherence, drugs were administered. The groups included: control (complete medium without drugs), ASA (2.5 mmol/L), DOX (600 nmol/L), and a combination of ASA (2.5 mmol/L) and DOX (600 nmol/L). After 24 hours of drug treatment, drugs were removed, and cells were cultured in complete medium for 14 days until visible colonies formed. Cells were fixed with 4% paraformaldehyde for 30 minutes, stained with crystal violet for 20 minutes, washed with Phosphate-Buffered Saline (PBS), and air-dried. Whole-well images were captured, and colony counts were quantified using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Cell Proliferation Assay (EdU)\u003c/h2\u003e \u003cp\u003eCell proliferation activity was assessed using the BeyoClick\u0026trade; EdU-488 Cell Proliferation Assay Kit (Shanghai Beyotime Biotechnology Co., Ltd.). HT-29 cells in the logarithmic growth phase were seeded into 6-well plates at a density of 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well. After 24 hours of adherence, drug treatments were administered as described in section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.2.3\u003c/span\u003e. Following 24 hours of treatment, the medium was replaced with complete medium containing 10 \u0026micro;M 5-ethynyl-2\u0026prime;-deoxyuridine (EdU), 2 mL per well. Cells were incubated for an additional 2 hours at 37\u0026deg;C, fixed with 4% paraformaldehyde for 15 minutes, and permeabilized with 0.3% Triton X-100 for 15 minutes. A Click reaction mixture was added, and cells were incubated for 30 minutes in the dark. Hoechst 33342 was added for nuclear counterstaining for 10 minutes. Images were captured, and the proportion of EdU-positive cells was quantified using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Live/Dead Cell Staining\u003c/h2\u003e \u003cp\u003eCell viability and toxicity were assessed using the Sheng'er Animal Cell Viability/Toxicity Assay Kit (Shanghai Sheng'er Biotechnology Co., Ltd.). HT-29 cells in the logarithmic growth phase were seeded into 24-well plates at a density of 5 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells per well. After 24 hours of adherence, drugs were administered as outlined in section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.2.3\u003c/span\u003e. After 24 hours of treatment, the medium was discarded. Equal volumes of 2 \u0026micro;M Calcein acetoxymethyl ester (Calcein-AM) working solution and 4.5 \u0026micro;M propidium iodide (PI) working solution were mixed and added to each well, followed by incubation at 37\u0026deg;C for 20 minutes, protected from light. Images were captured and quantitatively analyzed using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Apoptosis Assay\u003c/h2\u003e \u003cp\u003eApoptosis was assessed using the FITC-Annexin V/PI Apoptosis Detection Kit (Shanghai Sheng'er Biotechnology Co., Ltd.). HT-29 cells in the logarithmic growth phase were seeded into 6-well plates at a density of 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well. After 24 hours of adherence, drugs were administered as described in section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.2.3\u003c/span\u003e. After 24 hours of treatment, the medium was discarded. Cells were digested with trypsin without EDTA, centrifuged, washed twice with pre-cooled PBS, and resuspended in 1 \u0026times; Binding Buffer at a concentration of 1 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e cells/mL. A 100 \u0026micro;L aliquot of the cell suspension was transferred to a flow cytometry tube, and 5 \u0026micro;L of Fluorescein isothiocyanate-conjugated Annexin V (FITC-Annexin V) and 5 \u0026micro;L of PI staining solution were added. After 15 minutes of incubation at room temperature in the dark, 400 \u0026micro;L of 1 \u0026times; Binding Buffer was added for dilution, and analysis was performed using a flow cytometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.7 Cell Cycle Analysis\u003c/h2\u003e \u003cp\u003eThe cell cycle was analyzed using a Cell Cycle Detection Kit (Shanghai Sheng'er Biotechnology Co., Ltd.). HT-29 cells were cultured in 6-well plates until they reached the logarithmic growth phase and divided into groups as described in section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.2.3\u003c/span\u003e, with a 24-hour treatment duration. Cells were collected, centrifuged at 1,000 rpm for 5 minutes, and washed twice with pre-cooled PBS. They were resuspended in 70% pre-cooled ethanol and fixed overnight at 4\u0026deg;C. After fixation, cells were washed twice with PBS, and 0.5 mL of staining working solution was added, followed by 30 minutes of incubation in the dark. Detection was performed using a flow cytometer, and the data were analyzed using FlowJo software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.8 Wound Healing Assay\u003c/h2\u003e \u003cp\u003eHT-29 cells in the logarithmic growth phase were seeded into 6-well plates at a density of 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well. Once a confluent cell layer was formed, a scratch was made vertically across the cell layer using a 200 \u0026micro;L pipette tip. Cells were washed three times with PBS, and the medium was replaced with serum-free medium under different treatment conditions: control group (serum-free medium without drugs), ASA group (2.5 mmol/L ASA), DOX group (600 nmol/L DOX), and combination group (ASA 2.5 mmol/L\u0026thinsp;+\u0026thinsp;DOX 600 nmol/L). After further incubation for 24 hours, images were captured under a microscope. The wound area was analyzed using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.9 Doxorubicin Uptake Assay\u003c/h2\u003e \u003cp\u003eHT-29 cells were cultured in 6-well plates until reaching the logarithmic growth phase. Cells were treated with DOX (600 nmol/L) for 2 hours, after which the drug was removed. The control group was cultured in complete medium for 6 hours and 12 hours, while the ASA group was cultured in medium containing 2.5 mmol/L ASA for 6 hours and 12 hours. Cell nuclei were stained with 4\u0026prime;,6-diamidino-2-phenylindole (DAPI) for 15 minutes. The fluorescence intensity of DOX was quantitatively analyzed using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.2.10 Immunofluorescence for Nuclear Translocation\u003c/h2\u003e \u003cp\u003eFOXP3 antibody and secondary antibody for immunofluorescence were purchased from Chengdu Zhengneng Biological Technology Co., Ltd. (Chengdu, China). HT-29 cells in the logarithmic growth phase were seeded onto coverslips in 12-well plates at a density of 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well. After 24 hours of culture for adherence, cells were divided into groups and treated as described in section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.2.3\u003c/span\u003e for 24 hours. Cells were fixed with 4% paraformaldehyde for 15 minutes, permeabilized with 0.5% Triton X-100 for 15 minutes, and blocked with 5% bovine serum albumin for 30 minutes. Cells were then incubated with the primary antibody (FOXP3, dilution 1:200) overnight at 4\u0026deg;C, followed by incubation with the secondary antibody for 2 hours in the dark. Nuclei were stained with DAPI for 15 minutes. Coverslips were mounted with an anti-fade mounting medium, and fluorescence signals were detected using a Leica upright fluorescence microscope. Nuclear fluorescence intensity was quantified using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.2.11 Cytoplasmic and Nuclear Protein Extraction\u003c/h2\u003e \u003cp\u003eCells from different treatment groups were collected, and protein extraction was performed using a Nuclear and Cytoplasmic Protein Extraction Kit (Shanghai Sheng'er Biotechnology Co., Ltd.). Cells were incubated in CE buffer on ice, and the supernatant obtained after centrifugation contained the cytoplasmic protein fraction. The nuclear pellet was washed with CE wash buffer and then incubated in NE buffer on ice. After centrifugation, the supernatant obtained was the nuclear protein fraction. Proteins were stored at -80\u0026deg;C for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.2.12 Western Blotting\u003c/h2\u003e \u003cp\u003eAntibodies used in this experiment, including FOXP3, ABCB1, BAX, BCL2, GAPDH, YY1, and secondary antibodies, were purchased from Samflex Biotech Co., Ltd. (Hefei, China). All antibodies were diluted according to the manufacturer's recommended ratios. Cells from different treatment groups were collected and lysed with a mixture of RIPA lysis buffer, protease inhibitor, and PMSF to extract proteins. The proteins from each group were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene fluoride (PVDF) membranes. Membranes were blocked with 5% skimmed milk powder and incubated with primary antibodies overnight at 4\u0026deg;C. Secondary antibody incubation was carried out for 2 hours at room temperature. Protein bands were visualized using enhanced chemiluminescence (ECL) reagent and detected with a gel imaging system. Gray values of the protein bands were quantified using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Key Gene Screening and Bioinformatics Analysis\u003c/h2\u003e \u003cp\u003eIn this study, the gene expression matrix of ASA-treated versus untreated CRC cells was obtained from the GSE76583 series in the Gene Expression Omnibus (GEO) database \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Differential expression analysis was performed using the R package limma (version 3.40.6) to identify differentially expressed genes (DEGs) between the treatment and control groups. Potential targets related to \"DOX resistance\" were obtained from the Genecards database and the Online Mendelian Inheritance in Man (OMIM) database. ABCB1-related transcription factors were predicted using the Human Transcription Factor Database (Human TFDB) and the Human Transcription Factor Targets (hTFtarget) database. The genes screened from GEO, Genecards, OMIM, hTFtarget, and Human TFDB were intersected and visualized using Venn diagrams. The intersecting genes were imported into the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein-protein interaction (PPI) network, which was then processed using Cytoscape software to generate a molecular interaction network diagram. For gene set functional enrichment analysis, Gene Ontology (GO) annotations for the genes were obtained using the R package org.Hs.eg.db (version 3.1.0). Kyoto Encyclopedia of Genes and Genomes (KEGG) gene annotations were retrieved using the KEGG rest API (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/kegg/rest/keggapi.html\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/kegg/rest/keggapi.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Enrichment analysis was conducted with the R package clusterProfiler (version 3.14.3) to obtain gene set enrichment results, which were subsequently visualized. RNA-seq data for the CRC project from The Cancer Genome Atlas (TCGA) database, consisting of 458 tumor tissue samples and 41 normal tissue samples, were downloaded from the National Cancer Institute's Center for Cancer Genomics website. The Sangerbox platform, an online tool for data analysis and visualization (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was used to generate violin plots and a gene expression network diagram \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Immunohistochemistry images of CRC and normal colon tissues were obtained from the Human Protein Atlas (HPA) database. Survival analysis for patients with CRC was performed using the Kaplan-Meier Plotter database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kmplot.com\u003c/span\u003e\u003cspan address=\"https://www.kmplot.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Molecular Docking and Molecular Dynamics Simulations\u003c/h2\u003e \u003cp\u003eThe 2D structures of small molecule ligands were sourced from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"http://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and converted to 3D structures using ChemOffice software, then exported in mol2 format. Protein targets were selected from the RCSB PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with preference given to crystal structures with higher resolution for use as receptors in docking studies. Pymol 2.6 software was then used for protein preprocessing, which involved removing water molecules and phosphate ions, followed by saving the structure in PDB format. Molecular docking was performed with AutoDock Vina software to assess the binding interactions between proteins and ligands. The optimal conformation for molecular simulation was chosen by comparing docking scores. Visualization of the docking results was carried out with Discovery Studio 2019 and Pymol 2.6 software, generating 2D and 3D interaction diagrams between the compound and key residues. Molecular dynamics simulations were conducted using Gromacs 2022 software under an NPT ensemble at a constant temperature of 310 K and constant pressure over a simulation time of 100 ns. During the simulation, tools such as g-rmsd, g-rmsf, and g-hbond were utilized to compute the Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and Hydrogen Bonds (HBonds), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Animal Experiment Materials and Methods\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Mouse Subcutaneous Tumor Model\u003c/h2\u003e \u003cp\u003eA total of 20 male BALB/c mice, weighing between 18 and 22 grams and aged 6 to 8 weeks, were purchased from Hangzhou Ziyuan Experimental Animal Technology Co., Ltd. (Hangzhou, China). They were housed in a controlled environment with regulated temperature, humidity, and light cycles, with free access to food and water. The mice were randomly assigned to four groups. CT-26 cells were cultured to the logarithmic growth phase, digested with trypsin, washed with PBS, and resuspended. A 0.1 mL (5 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e cells) suspension of CT-26 cells was subcutaneously injected into the right hind limb root of each mouse. The control group received intragastric administration of normal saline and an intraperitoneal injection of normal saline. The ASA group was treated with ASA (100 mg/kg\u0026middot;1d, orally) and intraperitoneal injection of normal saline. The DOX group received DOX (5 mg/kg\u0026middot;1w, i.p.) and intragastric administration of normal saline. The ASA\u0026thinsp;+\u0026thinsp;DOX group was treated with ASA (100 mg/kg\u0026middot;1d, orally) and DOX (5 mg/kg\u0026middot;1w, i.p.). All mice were euthanized by cervical dislocation under anesthesia. Tumor tissues were excised, weighed, measured for volume, photographed, and stored at -80\u0026deg;C. This study was approved by the Animal Ethics Committee of Anhui Medical University (Approval Number: LLSC20242463). All animal procedures and related research protocols strictly adhered to internationally recognized guidelines for the use of laboratory animals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Histopathological Examination of Tumor Tissues\u003c/h2\u003e \u003cp\u003eHematoxylin and eosin (HE) staining was performed on tumor tissues. Ki-67 immunohistochemistry was carried out following antigen heat retrieval, followed by sequential incubation with primary and secondary antibodies, and color development using 3,3'-diaminobenzidine tetrahydrochloride. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining was performed by labeling dUTP with SpOrange-570, and nuclei were counterstained with DAPI. Fluorescence signals were quantified using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis for databases and network pharmacology was performed using R software. Experimental data were analyzed using GraphPad Prism. Measurement data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s). One-way analysis of variance (ANOVA) was used for comparisons among multiple groups, and comparisons between two groups were performed using Student's t-test. Each independent experiment was repeated three times. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Aspirin Significantly Improves Survival Prognosis in Patients with Colorectal Cancer\u003c/h2\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e present the association between ASA use and survival in cancer individuals. In both the overall cancer population and the CRC subgroup, ASA use was associated with a reduced risk of death (HR\u0026thinsp;\u0026lt;\u0026thinsp;1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with CRC showed a more significant benefit from ASA use. In the fully adjusted model, the risk of malignancy-specific death for patients with CRC (HR\u0026thinsp;=\u0026thinsp;0.047, 95%CI\u0026thinsp;=\u0026thinsp;0.046\u0026ndash;0.048, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was significantly lower than that for the overall cancer population (HR\u0026thinsp;=\u0026thinsp;0.102, 95%CI\u0026thinsp;=\u0026thinsp;0.101\u0026ndash;0.103, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding suggests that ASA may offer a protective effect, improving survival in patients with CRC, highlighting its potential value in CRC treatment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of aspirin users and non-users in the study population (n\u0026thinsp;=\u0026thinsp;1681)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eStudy population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eall(n\u0026thinsp;=\u0026thinsp;1681)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003easpirin nonusers(n\u0026thinsp;=\u0026thinsp;745)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003easpirin users(n\u0026thinsp;=\u0026thinsp;936)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eage(year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 to 59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003egender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFamily poverty income($)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20000 to 74999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eWeight status, BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 to 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eHyperlipidemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCoronary heart disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratios for all-cause and malignant neoplasms mortality by aspirin use in the cancer and colorectal cancer populations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMortality outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAspirin use status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDeath/No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCancer population\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003easpirin nonusers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126/745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003easpirin users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182/936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalignant neoplasms mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003easpirin nonusers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52/745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003easpirin users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66/936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eColorectal cancer population\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003easpirin nonusers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20/55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003easpirin users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11/54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalignant neoplasms mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003easpirin nonusers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003easpirin users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Aspirin and DOX Combination Exhibits Synergistic Anti-tumor Effects \u003cem\u003ein Vitro\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo assess the efficacy of ASA and DOX in inhibiting HT-29 proliferation, CCK8 assays were performed to evaluate CRC cell viability after treatment with different concentrations of ASA, DOX, and their combination. The Half Maximal Inhibitory Concentration (IC\u003csub\u003e50\u003c/sub\u003e) for ASA and DOX after 24-hour treatment was calculated. The results showed that both ASA and DOX inhibited CRC cell proliferation in a concentration-dependent manner. Based on these results, a 24-hour treatment duration with 2.5 mmol/L ASA, 600 nmol/L DOX, and their combination were selected for subsequent experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B, C, \u003cb\u003eand D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eColony formation assay results indicated that compared to the control group, the number of colonies formed in the ASA, DOX, and combination treatment groups was significantly reduced (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although the number of colonies in the ASA treatment group was lower than in the control group, it remained relatively high, whereas the colony count in the combination treatment group was markedly reduced and significantly lower than in either of the single-agent groups. These results suggest that the combination of ASA and DOX significantly inhibited the colony-forming ability of HT-29 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG \u003cb\u003eand L\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe EdU assay was used to evaluate cell proliferation activity. In the fluorescence images, EdU-positive signals (green) represent proliferating cells in the S-phase, and Hoechst staining (blue) shows the nuclei. Compared to the control group, the proliferation rate was significantly reduced in the DOX and ASA groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the proliferation rate in the combination treatment group was further decreased, showing a highly significant difference compared to the NC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results indicate that while DOX and ASA alone mildly inhibit HT-29 cell proliferation, their combination significantly enhances this inhibitory effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE \u003cb\u003eand N\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe Calcein-AM/PI double staining method was used to assess the impact of different treatments on cell viability. Green fluorescence (Calcein-AM) labels live cells, while red fluorescence (PI) labels dead cells. Fluorescence images showed that compared to the control group, the proportion of dead cells was significantly increased in the DOX group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the ASA group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the proportion of dead cells in the DOX and ASA combination group was significantly higher than in either single-agent group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), suggesting a synergistic pro-apoptotic effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF \u003cb\u003eand K\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFlow cytometry results revealed distinct differences in the effects of various treatments on apoptosis. Compared to the control group, ASA alone slightly promoted apoptosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), although the effect was modest. In contrast, DOX alone significantly increased the apoptosis rate (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Notably, the apoptosis rate was further enhanced with combination treatment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that the combination of ASA and DOX synergistically induces apoptosis in CRC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH \u003cb\u003eand O\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eCell cycle analysis showed that neither ASA nor DOX treatment alone significantly altered the proportion of cells in the G0/G1 phase (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the combination treatment resulted in a significant increase in the proportion of cells in the G0/G1 phase (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), suggesting that the combination may more effectively inhibit tumor cell proliferation by promoting G0/G1 phase arrest, thereby reducing the number of cells entering the S phase for DNA replication (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI \u003cb\u003eand M\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWound healing assay results demonstrated that both DOX and ASA alone significantly inhibited HT-29 cell migration compared to the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that both treatments possess inhibitory effects on cell migration. In the combination treatment group, the wound area was nearly completely unfilled by cells, indicating that ASA and DOX significantly enhance the inhibition of HT-29 cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ \u003cb\u003eand P\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ein vitro\u003c/em\u003e experiments above suggest that the combination of ASA and DOX has stronger anti-tumor effects than either treatment alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Aspirin Enhances the Anti-tumor Effect of Doxorubicin in Mice\u003c/h2\u003e \u003cp\u003eIn the mouse subcutaneous tumor model, the body weight of mice in all treatment groups remained stable throughout the experiment, with no significant decrease, suggesting that the drug treatments did not induce substantial systemic toxicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Both the DOX and ASA treatment groups significantly inhibited tumor growth compared to the control group (NC). The combination treatment group exhibited the most significant inhibition of tumor growth, with significantly smaller tumor volume and mass compared to the single-agent treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, C, \u003cb\u003eand D\u003c/b\u003e). HE staining revealed varying degrees of cellular degeneration and necrosis in the tumor tissues of the DOX and ASA groups, with more pronounced structural disruption in the combination treatment group. Ki-67 immunohistochemistry staining indicated that the proportion of Ki-67-positive cells was reduced in the DOX and ASA groups compared to the control group, with a further reduction in the combination treatment group, demonstrating a stronger inhibitory effect on tumor cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG \u003cb\u003eand E\u003c/b\u003e). TUNEL staining showed a significantly higher number of TUNEL-positive cells in the combination treatment group, further suggesting a stronger pro-apoptotic effect on tumor tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH \u003cb\u003eand F\u003c/b\u003e). These animal experiments confirm that, \u003cem\u003ein vivo\u003c/em\u003e, the combination of ASA and DOX exerts a more significant anti-tumor effect compared to the single-agent treatments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Screening of Differential Genes and Core Genes Regulated by Aspirin in Colorectal Cancer\u003c/h2\u003e \u003cp\u003eDifferential expression analysis was performed between the ASA-treated CRC cell group and the control group from the GSE76583 series. Using |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 1.0 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the significance thresholds, a total of 4,309 significant DEGs were identified, including 1,864 up-regulated and 2,445 down-regulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Cluster analysis was performed on the 4,309 DEGs, and a heatmap was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The results showed clear separation of samples from different treatment groups into two independent clusters, with high similarity in expression profiles within each group, indicating that gene expression differences between the treatment and control groups were group-specific. To identify the key biological processes involved in ASA treatment of CRC, GO enrichment analysis was performed on the identified DEGs. The results are presented as bar charts, with different colors representing Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). In terms of BP, the DEGs were primarily associated with viral processes, epithelial morphogenesis, glycolipid metabolism, and the non-canonical Wnt signaling pathway. For CC, enrichment was observed in various protein complexes and cellular substructures, suggesting that the DEGs may be involved in functions mediated by intracellular complexes. Regarding MF, DEGs were associated with diverse enzyme activities, receptor binding, and nucleotide binding, implying that these genes mainly exert their biological functions through enzyme catalysis and molecular binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). KEGG enrichment analysis was performed on the DEGs, which indicated that pathways such as the Hippo signaling pathway, Wnt signaling pathway, Glucagon signaling pathway, viral infections, and cell cycle were closely associated with the DEGs. Among these, the Hippo signaling pathway exhibited the highest degree of enrichment, suggesting it may play a key role in the anti-CRC effect of ASA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 2,072 potential targets related to DOX resistance were obtained from the Genecards and OMIM databases. These targets were intersected with the DEGs from the GEO database using a Venn diagram, resulting in 224 intersecting genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The 224 intersecting genes were imported into the STRING database to construct a PPI network, which was then processed using Cytoscape software to generate the PPI network diagram. Genes with larger degree values were considered potential core targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE \u003cb\u003eand I\u003c/b\u003e). To identify genes associated with ABCB1 expression among the targets, 490 ABCB1-related transcription factors were predicted from the hTFtarget and Human TFDB databases. These transcription factors were intersected with the 224 intersecting genes using another Venn diagram, yielding 32 genes, including TP53, MYC, JUN, and others. A separate PPI network diagram was created based on these 32 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB \u003cb\u003eand F\u003c/b\u003e). GO enrichment analysis was performed on the 32 genes. For BP, enrichment was observed in \"RNA polymerase II-mediated pri-miRNA transcription regulation,\" \"pri-miRNA transcription,\" and the \"intracellular receptor signaling pathway.\" For CC, enrichment was seen in the \"transcription regulator complex,\" \"RNA polymerase II transcription regulator complex,\" and \"transcription repressor complex.\" For MF, enrichment was found in \"DNA-binding transcription activator activity,\" \"RNA polymerase II-specific DNA-binding transcription factor activity,\" and \"transcription coactivator binding\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). KEGG enrichment analysis revealed that these 32 genes were significantly enriched in oncogenic pathways, cancer-related transcriptional signaling pathways, and cell function regulatory pathways, with the chemical carcinogenesis (receptor activation), thyroid hormone signaling pathway, Wnt signaling pathway, and transcriptional dysregulation in cancer being the primary regulatory pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ). A literature review identified that CDX2, NFE2L2, FOXP3, and ARNT are associated with multidrug resistance (MDR) in tumors. Thus, these four genes were considered potential core targets \u003csup\u003e[\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Validation of the Molecular Binding Ability of Aspirin with Key Genes\u003c/h2\u003e \u003cp\u003eThe binding activity of ASA with the four targets (CDX2, NFE2L2, FOXP3, ARNT) was evaluated through molecular docking (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The binding energies for ASA with CDX2, NFE2L2, FOXP3, and ARNT were \u0026minus;\u0026thinsp;5.6, -6.4, -5.8, and \u0026minus;\u0026thinsp;6.1 kcal/mol, respectively. Generally, a binding energy lower than \u0026minus;\u0026thinsp;5.0 kcal/mol indicates good binding activity between the two molecules; the lower the binding energy, the stronger the binding activity, the higher the affinity, and the more stable the conformation. The molecular docking results indicated that ASA demonstrated strong binding affinity with all four targets. To further assess the stability of the binding, molecular dynamics simulations were performed. RMSD is a reliable indicator of the conformational stability of protein-ligand complexes. The RMSD trajectories showed that the ASA molecule exhibited high stability when bound to the CDX2, NFE2L2, FOXP3, and ARNT target proteins. HBonds play a pivotal role in ligand-protein interactions, and the number of HBonds between ASA and the target proteins during molecular dynamics simulation is shown in the figures. In most cases, the ASA-target protein complexes had approximately 1\u0026ndash;2 HBonds, suggesting good HBond interactions between ASA and the target proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, C, D, E, F, \u003cb\u003eand G\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Expression Characteristics and Clinical Significance of Key Genes\u003c/h2\u003e \u003cp\u003eThe expression of CDX2, NFE2L2, FOXP3, and ARNT in tumor tissues versus normal tissues was analyzed using the TCGA database. The results indicated that CDX2, NFE2L2, and ARNT were downregulated in tumor tissues compared to normal tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while FOXP3 was upregulated in tumor tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC \u003cb\u003eand D\u003c/b\u003e). Immunohistochemistry sections from human CRC tissues retrieved from the HPA database showed results consistent with the TCGA analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The impact of CDX2, NFE2L2, FOXP3, and ARNT expression on the survival of patients with CRC was evaluated using the public dataset integration analysis website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kmplot.com\u003c/span\u003e\u003cspan address=\"https://www.kmplot.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), developed by Bal\u0026aacute;zs Győrffy et al. Patients with high CDX2 expression exhibited significantly improved overall survival (OS) and relapse-free survival (RFS) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting CDX2 as a potential protective factor. In contrast, patients with high ARNT expression showed significantly reduced OS and RFS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). High FOXP3 expression was associated with significantly reduced OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a decrease in RFS as well, though not statistically significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that both FOXP3 and ARNT may serve as risk factors. Patients with high NFE2L2 expression had significantly reduced OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but RFS was significantly increased (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Aspirin Inhibits ABCB1 Expression by Downregulating FOXP3 to Enhance Intracellular Retention of Doxorubicin\u003c/h2\u003e \u003cp\u003eIntracellular retention of DOX in CRC cells was evaluated through DOX uptake experiments. The results revealed a clear red signal within the cells after 2 hours of DOX treatment, confirming the uptake of DOX. After removing DOX, the control group cultured in complete medium for 6 hours showed a significant reduction in the red fluorescence signal, which became extremely weak after 12 hours, indicating substantial efflux of DOX from the cells. In the ASA group, after 6 hours of ASA treatment, the red fluorescence signal intensity slightly decreased, but a certain intensity remained after 12 hours, suggesting that ASA reduces the efflux of DOX from colorectal tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA \u003cb\u003eand E\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eImmunofluorescence results showed that DOX treatment alone did not significantly affect the nuclear expression of FOXP3 compared to the normal control group. However, the nuclear fluorescence intensity of FOXP3 was significantly reduced in both the ASA treatment and combination treatment groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that ASA inhibits the nuclear accumulation of FOXP3 protein in CRC cells, thus attenuating its transcriptional regulatory activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB \u003cb\u003eand F\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWestern blot analysis revealed that, compared to the normal control group, the expression levels of FOXP3 and ABCB1 did not significantly change in the DOX treatment group (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, the expression of both FOXP3 and ABCB1 was significantly reduced in the ASA and combination treatment groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that ASA effectively inhibits the protein expression of FOXP3 and ABCB1. The BAX/BCL-2 ratio was significantly increased in the DOX treatment group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting activation of the mitochondrial apoptosis pathway. While the BAX/BCL-2 ratio did not change significantly in the ASA treatment group alone, it was further increased in the combination treatment group. These results suggest that the combination of ASA and DOX enhances the anti-tumor effect of DOX via the mitochondrial apoptosis pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, G, \u003cb\u003eand H\u003c/b\u003e). Cytoplasmic-nuclear protein separation followed by Western blotting was performed on CRC cells. GAPDH served as a cytoplasmic protein internal control, and YY1 as a nuclear protein internal control. The YY1 band was nearly undetectable in the cytoplasmic fraction, and the GAPDH band was nearly absent in the nuclear fraction, confirming successful separation of cytoplasmic and nuclear proteins. Compared to the control group, DOX treatment did not significantly alter the expression of FOXP3 in either the cytoplasm or nucleus. However, the expression levels of FOXP3 in both compartments were significantly reduced in the ASA and combination treatment groups, suggesting that ASA may exert its effects by influencing processes such as FOXP3 transcription or translation, rather than solely affecting its nuclear translocation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, I, \u003cb\u003eand J\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eFOXP3 is a key transcription factor involved in the differentiation of regulatory T cells (Tregs), with its primary function being the maintenance of immune tolerance by suppressing effector T cell activity to regulate immune responses. Most previous research has focused on the infiltration of FOXP3\u0026thinsp;+\u0026thinsp;Treg cells within the tumor microenvironment. In contrast, this study shifts focus to the pro-cancerous role of FOXP3 expression within CRC cells themselves, revealing its mechanism in mediating chemotherapy resistance through the promotion of ABCB1 expression. This study also proposes a novel regulatory strategy targeting the FOXP3-ABCB1 axis. By downregulating FOXP3, it inhibits ABCB1 expression, circumventing the risks associated with traditional ABCB1 inhibitors and offering a new intervention pathway for reversing chemotherapy resistance in CRC. Within the tumor microenvironment, the extent of FOXP3\u0026thinsp;+\u0026thinsp;Treg cell infiltration correlates closely with immune suppression. Modulating Treg cell function by depleting functional FOXP3 protein can temporarily and specifically disrupt Treg-mediated immunosuppression in the cancer environment \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. However, the role of FOXP3 in tumors remains controversial. Previous studies have reported conflicting results regarding the association between FOXP3\u0026thinsp;+\u0026thinsp;Treg cell infiltration in the CRC tumor microenvironment and patient prognosis. This discrepancy may stem from the heterogeneity of Treg subpopulations, which differ in their suppressive and pro-inflammatory functions \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In breast cancer, inadequate FOXP3 expression may promote cancer progression, with evidence suggesting that FOXP3 inhibits angiogenesis by downregulating VEGF expression \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. These contradictions may arise from differences in the cell types expressing FOXP3 and an incomplete understanding of FOXP3's specific role within tumor cells. Emerging evidence indicates that FOXP3 is also expressed in tumor cells themselves. Studies have reported FOXP3 expression in various tumor types, including breast, prostate, lung, gastric, thyroid, and melanoma cells, suggesting that FOXP3 may play a broader role in tumorigenesis \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. ABCB1, also known as P-glycoprotein (P-gp), is a transmembrane protein implicated in chemotherapy resistance. Numerous studies have shown increased ABCB1 expression in tumor cells following chemotherapy drug exposure. For instance, paclitaxel chemotherapy induces ABCB1 expression in tumor endothelial cells via enhanced IL-8 secretion, while cisplatin upregulates ABCB1 through activation of canonical Wnt signaling \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Therefore, ABCB1 induction upon chemotherapy exposure is a significant driver of MDR. Inhibiting ABCB1 transport has been a major focus in developing strategies to combat MDR. Several ABCB1 inhibitors have been developed, acting through various mechanisms, including binding to the substrate-binding site of ABCB1, altering its structure and function, interfering with ATP hydrolysis, and modifying the integrity of the cell membrane lipid bilayer \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. However, the clinical application of these inhibitors is hindered by issues such as lack of specificity, drug-drug interactions, and toxicity \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, highlighting the need for new MDR reversal strategies. Previous studies have shown that FOXP3 expression is upregulated during chemotherapy in various cancers, suggesting that chemotherapy induces FOXP3 expression, which is linked to poor prognosis and chemoresistance \u003csup\u003e[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. However, these studies did not fully explore the specific mechanisms by which FOXP3 contributes to poor prognosis and chemoresistance. This study aims to investigate these mechanisms further, potentially uncovering new therapeutic strategies for CRC treatment.\u003c/p\u003e \u003cp\u003eThis study initially confirmed within the NHANES cohort that ASA use is significantly associated with reduced CRC-specific mortality and improved patient prognosis. Notably, this benefit was more pronounced in the CRC population compared to the broader pan-cancer population, suggesting that the effect is not simply a \"general anti-tumor effect,\" but likely involves a CRC-specific mechanism. This inference is supported by multiple studies demonstrating that ASA use improves survival outcomes in patients with CRC. For instance, a recent double-blind, randomized, placebo-controlled trial conducted by Anna Martling et al. found that ASA significantly reduces the risk of CRC recurrence \u003csup\u003e[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. In contrast, similar studies in non-colorectal cancers\u0026mdash;such as esophageal, gastric, breast, prostate, ovarian, and endometrial cancers\u0026mdash;did not observe significant improvements in survival with ASA use \u003csup\u003e[\u003cspan additionalcitationids=\"CR48 CR49 CR50\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. These findings align with our results and provide strong clinical evidence to support further exploration into the mechanisms by which ASA enhances chemosensitivity in CRC.\u003c/p\u003e \u003cp\u003eAt the \u003cem\u003ein vitro\u003c/em\u003e level, this study confirmed that the combination of ASA and DOX exhibits stronger anti-tumor effects compared to either agent alone. Previous studies have shown that both ASA and DOX can inhibit tumor cell proliferation by inducing cell cycle arrest and promoting apoptosis \u003csup\u003e[\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. However, under the experimental conditions of this study, no significant changes in the cell cycle were observed with single-agent treatments, possibly due to the chosen drug treatment time and dosages. Furthermore, some studies have reported that treatment with sublethal doses of DOX in breast cancer cells can enhance migration and invasion capabilities \u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. The present study did not observe similar results in CRC cells, likely due to differences in tumor type, drug dosages, and treatment durations. It is also possible that the combination treatment suppressed potential metastatic risk, although the specific mechanisms behind this effect require further investigation. \u003cem\u003eIn vivo\u003c/em\u003e experiments using the mouse subcutaneous tumor model further validated the anti-tumor activity of the combination regimen. Numerous studies have also reported synergistic anti-tumor effects of ASA combined with DOX in various tumor models \u003cem\u003ein vivo\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e, which is consistent with our findings. These \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e results complement each other, demonstrating a clear synergistic anti-tumor effect of combining ASA and DOX in CRC.\u003c/p\u003e \u003cp\u003eTo investigate the underlying anti-tumor mechanisms of the combination therapy, 32 targets were identified through differential gene analysis from the GEO database and integrated network pharmacology predictions from multiple databases. A literature review revealed that CDX2, NFE2L2, FOXP3, and ARNT are linked to MDR in tumors. Further analysis showed that ASA treatment downregulated FOXP3 expression, and molecular docking and dynamics simulations indicated stable structural binding between ASA and FOXP3. TCGA/HPA database analyses also revealed high FOXP3 expression in CRC tumor tissues, while Kaplan-Meier survival analysis suggested that elevated FOXP3 expression was significantly associated with poor patient prognosis. In contrast, CDX2, NFE2L2, and ARNT displayed inconsistencies in their differential expression, expression patterns, and prognostic relevance, thus reducing their reliability as core targets. Based on the multi-dimensional evidence, FOXP3 was identified as the core target through which ASA modulates chemosensitivity in CRC. Although CDX2, NFE2L2, and ARNT were not ultimately identified as core targets in this study, previous research highlights their significant roles in CRC: they may serve as potential prognostic markers, contribute to chemoresistance, and participate in processes such as tumor microenvironment remodeling and gut microbiota regulation, warranting further validation and exploration in subsequent studies \u003csup\u003e[\u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese findings guided the design of molecular mechanism validation experiments targeting the FOXP3-ABCB1 pathway. DOX uptake experiments demonstrated that ASA treatment significantly prolonged the intracellular retention of DOX in CRC cells, confirming a reduction in drug efflux efficiency. Immunofluorescence and Western blot analyses revealed reduced protein expression of both FOXP3 and ABCB1. These results confirm that ASA, by targeting the transcriptional regulator FOXP3, significantly inhibits its expression, leading to the concurrent downregulation of ABCB1 transcription. This disruption of the ABCB1-mediated DOX efflux pump enhances intracellular DOX concentrations, thereby improving the chemosensitivity of CRC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eK).\u003c/p\u003e \u003cp\u003eAlthough this study elucidates the mechanism by which ASA sensitizes CRC chemotherapy via the FOXP3\u0026ndash;ABCB1 axis, several limitations remain that warrant further investigation. First, while evidence such as the downregulation of FOXP3 and ABCB1 expression and the increased intracellular retention of DOX following ASA treatment was obtained, more comprehensive data are needed. For instance, chromatin immunoprecipitation assays could determine whether FOXP3 directly binds to the promoter region of the ABCB1 gene, providing critical evidence to establish a complete causal chain. Second, prior studies have indicated that ASA enhances chemotherapy sensitivity in breast cancer by upregulating SMAR1 expression and inhibiting ABCG2. Consequently, other resistance mechanisms, beyond ABCB1, may also contribute to the observed sensitizing effect \u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. Given these findings and limitations, future research could proceed in several directions. First, techniques like chromatin immunoprecipitation and RNA-seq can be employed to further explore the direct transcriptional regulation of ABCB1 by FOXP3 at the chromatin level. Second, the impact of ASA on the expression of other MDR-associated transporters should be investigated to more fully characterize the mechanisms by which ASA enhances chemotherapy sensitivity in CRC. Third, for clinical translation, patient-derived tumor xenograft models could be used to optimize the combination regimen of ASA and chemotherapeutic agents. Additionally, novel intervention strategies targeting FOXP3 should be explored. These efforts may ultimately yield clinically applicable approaches for reversing chemotherapy resistance in CRC.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eIn conclusion, ASA enhances CRC chemosensitivity by targeting the FOXP3-ABCB1 axis to inhibit drug efflux and prolong DOX retention, positioning FOXP3 as a promising therapeutic target.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge Wei-wei Sheng, Bo Chen and Zheng-yu Hu(First Affiliated Hospital of Anhui Medical University, MD)\u0026nbsp;for their skillful\u0026nbsp;technical assistance.\u0026nbsp;We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.\u003c/p\u003e\n\u003cp\u003eFunding: This work was supported by the Anhui Provincial Natural Science Foundation [grant numbers 2408085QH271]\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eConsent to Participate declaration: not applicable\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable\u003c/p\u003e\n\u003cp\u003eAnimal ethics statement: This study was approved by the Animal Ethics Committee of Anhui Medical University (Approval Number: LLSC20242463). All animal procedures and related research protocols strictly adhered to internationally recognized guidelines for the use of laboratory animals.\u003c/p\u003e\n\u003cp\u003eData availability statement: The datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePATEL S G, KARLITZ J J, YEN T, et al. The rising tide of early-onset colorectal cancer: a comprehensive review of epidemiology, clinical features, biology, risk factors, prevention, and early detection [J]. The lancet Gastroenterology \u0026amp; hepatology, 2022, 7(3): 262-74.\u003c/li\u003e\n \u003cli\u003eDUTA-ION S G, JUGANARU I R, HOTINCEANU I A, et al. Redefining Therapeutic Approaches in Colorectal Cancer: Targeting Molecular Pathways and Overcoming Resistance [J]. 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Science signaling, 2020, 13(654).\u003c/li\u003e\n\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":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Aspirin, Chemotherapy sensitivity, FOXP3, ABCB1, Doxorubicin","lastPublishedDoi":"10.21203/rs.3.rs-9334465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9334465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eThe mechanisms underlying chemoresistance in Colorectal cancer (CRC) are complex and multifaceted, among which the overexpression of ATP-binding cassette (ABC) transporter family proteins is recognized as a key factor. Aspirin (acetylsalicylic acid, ASA), a classic non-steroidal anti-inflammatory drug, has been implicated in the regulation of tumor cells through various pathways, positioning it as a promising adjuvant anti-tumor agent.\u003c/p\u003e\u003ch2\u003eAIM\u003c/h2\u003e \u003cp\u003eTo investigate the mechanism of ASA in enhancing CRC chemosensitivity and explore new therapeutic strategies.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eThis study utilized the NHANES database to analyze the impact of ASA on the prognosis of CRC patients. Through in vivo and in vitro experiments, the synergistic inhibitory effects of ASA combined with doxorubicin (DOX) on tumor proliferation and apoptosis were validated. Furthermore, network pharmacology, molecular docking and dynamics simulations, and databases such as TCGA were integrated to screen for key targets. Mechanisms of ASA regulation of ABCB1 were investigated using doxorubicin uptake assays, immunofluorescence, and Western blot.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eThrough analysis of the NHANES cohort, in vitro and in vivo experiments, multi-database analysis, and molecular simulation, aspirin use was associated with reduced CRC-specific mortality; the combination of aspirin and doxorubicin exerted synergistic antitumor effects; FOXP3 was identified as a core target; and aspirin inhibited FOXP3 to downregulate ABCB1, thereby blocking doxorubicin efflux and enhancing chemosensitivity.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eIn conclusion, ASA enhances CRC chemosensitivity by targeting the FOXP3-ABCB1 axis to inhibit drug efflux and prolong DOX retention, positioning FOXP3 as a promising therapeutic target.\u003c/p\u003e","manuscriptTitle":"Aspirin Enhances Chemosensitivity of Colorectal Cancer Cells by Downregulating FOXP3 to Inhibit ABCB1 Expression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 06:26:47","doi":"10.21203/rs.3.rs-9334465/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-29T14:12:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-20T04:42:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-18T04:48:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T12:00:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-04-16T10:43:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22e93759-dee0-4565-94a5-873ccc16c421","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"40","date":"2026-04-29T14:12:28+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T06:26:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 06:26:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9334465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9334465","identity":"rs-9334465","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}