Construction and validation of a prognostic model based on metabolic characteristics of Candida albicans in colorectal cancer

Construction and validation of a prognostic model based on metabolic characteristics of Candida albicans in colorectal cancer | 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 Article Construction and validation of a prognostic model based on metabolic characteristics of Candida albicans in colorectal cancer HaoLing Zhang, Haolong Zhang, Weifang Chen, Yong Wang, Siti Nurfatimah Mohd Sapudin, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4555778/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There is evidence supporting the notion that Candida albicans ( C. albicans) indeed contributes to human cancers. Interestingly, the efficacy of C. albicans in improving Colorectal cancer (CRC) has been confirmed. This study primarily explores the paradox of whether C. albicans promotes or inhibits the development of CRC, focusing on its metabolites mixture for relevant arguments. This study identified a total of 214 differentially expressed genes. A prognostic model containing 5 specific mRNA markers, namely EHD4, LIME1, GADD45B, TIMP1 , and FDFT1 , was constructed. C. albicans metabolites mixture reduced CRC cell activity. qRT-PCR results showed that compared to normal colonic epithelial cells, LIME and EHD4 were downregulated in CRC cells, while FDFT1 expression was significantly upregulated. Notably, the TIMP1 gene was significantly upregulated in HT29 cells, while it was significantly downregulated in HCT116 cells. Furthermore, post-intervention analysis showed a significant decrease in gene expression levels in HT29 cells, while the expression of TIMP1, EHD4 , and GADD45B increased in HCT116 cells, with LIME and other CRC cells showing a corresponding decrease in expression. In NCM460 normal colonic epithelial cells, the expression levels of GADD45B, TIMP1 , and FDFT1 genes were significantly upregulated, while the expression levels of LIME and EHD4 showed a significant downward trend. After metabolite intervention, the invasion and migration capabilities of NCM460 cells, HT29 cells, and HCT116 cells decreased. Additionally, quantitative measurement of eATP levels after intervention showed a significant increase (P < 0.01) . This study's prognostic model opens up a new paradigm for prognostic assessment in CRC. The metabolites mixture of C. albicans play a protective role in the onset and progression of CRC, exhibiting dynamic interactions with cellular energetics. Candida albicans Colorectal cancer metabolic characteristics eATP prognostic model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction Currently, colorectal cancer (CRC) is the third most common cancer worldwide and ranks as the second leading cause of cancer-related deaths. As of 2020, the projected global incidence of CRC encompasses 1,993,590 newly diagnosed cases, with 935,173 resultant deaths, constituting 10.0% and 9.4% of the overall incidence and mortality rates, respectively [ 1 ] . The delineation of cancer patterns across different regions and time periods serves as a valuable compass for understanding risk factors, prevalence dynamics, and the formulation of comprehensive cancer control strategies. Our research delved into the CRC burden across 185 countries worldwide, spanning from 2020 into the envisioned landscape of 2040. In 2020 alone, an estimated 1.9 million novel instances of CRC emerged, culminating in 930,000 fatalities. Remarkably, the highest incidence rates were observed in Australia/New Zealand and Europe, peaking at 4.06×10 4 cases per 10 5 men, while the lowest rates were recorded in Africa and South Asia, registering at 4.4×10 3 cases per 10 5 women. Analogous trends were observed in mortality rates, with Eastern Europe demonstrating the highest rate at 2.02×10 4 deaths per 10 5 men, while South Asia exhibited the lowest at 2.5×10 3 deaths per 10 5 women. Forecasts suggest that the burden of CRC is poised to escalate to 3.2×10 6 new cases and 1.6×10 6 deaths by 2040, primarily concentrated in nations with high or very high human development indices [ 2 ] . Noteworthy is the status of CRC as the second most prevalent cancer in Malaysia, often diagnosed belatedly [ 3 ] . Prognostic models possess the capability to anticipate the likelihood of forthcoming events for an individual patient or a population, enabling the stratification of patients based on these prognostic risks. An exemplary model demonstrates the adeptness to judiciously and dependably categorize patients into distinct prognostic risk strata [ 4 ] . Furthermore, within the medical domain, prognostic models serve as instrumental tools for scrutinizing patient outcomes in correlation with both patient-specific and disease-related characteristics [ 5 ] . Adenosine triphosphate (ATP) is a high-energy phosphorylated compound that plays a crucial role in storing and releasing energy within cells, ensuring the energy supply required for various cellular activities by interconverting with ADP. Due to its ease of regeneration within cells, ATP can continuously provide energy. The ATP cycle refers to the continuous utilization of energy through ATP hydrolysis and synthesis, cycling between energy-releasing and energy-absorbing reactions. As ATP is widely utilized as an energy carrier within cells, it is often referred to as the "currency of the cell." As a pivotal biochemical constituent within the tumor microenvironment (TME), ATP has significant effects on tumor progression, with its role in promoting or inhibiting tumors depending on its concentration and the expression of specific extracellular nucleotide enzymes and receptors on immune and cancer cells [ 6 ] . Remarkably, in rats with CRC subjected to chemotherapy drug treatment along with Lactobacillus plantarum and C. albicans , a notable reduction in cancer cell volume was noted, accompanied by the nucleus displaying heightened darkness, indicative of apoptosis. Notably, serum concentrations of IFN-γ, IL-4, and TGF-β were markedly diminished compared to the control cohort. Noteworthy benefits in CRC management were observed with the administration of Lactobacillus plantarum and C. albicans , sourced from the gastrointestinal microbiota of both elderly individuals and healthy subjects [ 7 ] . Recent discoveries suggest that C. albicans could potentially exert a favorable influence on CRC; however, the exact mechanism underlying this phenomenon remains elusive. Nevertheless, as of present, there is a dearth of studies delving into the impact of C. albicans metabolites mixture on CRC. Hence, the principal aim of this investigation is to scrutinize the potential prognostic implications of C. albicans metabolites mixture in CRC, along with an exploration of the interplay between C. albicans metabolites mixture and the mRNA associated with CRC prognosis-related genes. At the same time, the effects of C. albicans metabolites mixture on eATP content in CRC were investigated to evaluate cell energy homeostasis. The outcomes posit that the influence of C. albicans metabolites mixture on correlated mRNA could emerge as a novel focal point for both the diagnosis and therapeutic interventions in CRC. This discovery is poised to offer invaluable insights into the realm of precise treatment methodologies and the meticulous prognostic evaluation of CRC. There are some differences between studying metabolites and metabolic mixtures, and it is reasonable to choose to study metabolic mixtures rather than individual metabolites. The metabolic mixture may be more in line with the real biological environment, because in the body, cells are often affected by multiple metabolites at the same time. By studying metabolic mixtures, it is possible to gain a more complete understanding of how cells respond to complex environments, rather than just the effects of a single metabolite. In addition, metabolic mixtures may be more clinically relevant because cells tend to be affected by multiple metabolites in the body, rather than a single metabolite. Therefore, the selection of metabolic mixtures can better simulate the environment of real organisms in vivo and improve the biological reliability and clinical relevance of research results. In this investigation, a prognostic model for CRC was formulated employing C. albicans mRNA. Through meticulous in vitro experimentation, the repercussions of C. albicans metabolites mixture on the invasion and migration of CRC cells were elucidated. Does this influence contribute to the protective or detrimental aspects of CRC development? Effects of C. albicans metabolites mixture on eATP Content in CRC? Method 2.1 Source of dataset Using the Gene Expression Omnibus (GEO) dataset ( https://www.ncbi.nlm.nih.gov/geo/ ), this study obtained the gene expression profiles altered by C. albicans from GSE42606. The retrieval was conducted on August 16, 2023, encompassing 25 samples infected with C. albicans for 4 hours and 34 samples infected for 24 hours. Simultaneously, mRNA expression profiles and clinical data were sourced from The Cancer Genome Atlas CRC Dataset (TCGA-CRC; https://portal.gdc.cancer.gov/ ). This retrieval, conducted on August 16, 2023, involved 44 samples of both normal and tumor samples derived from a pool of 571. The dataset was randomly divided into a training set (70%) and an internal validation set (30%) in order to build and validate the model successfully. Furthermore, for external validation, GSE41258 (retrieved on August 16, 2023) was acquired from the GEO database, comprising 390 samples of CRC mRNA profiles along with corresponding clinical information. Throughout the analysis, meticulous attention was given to excluding samples with missing clinical data, as well as those with a survival duration of less than 10 days, ensuring the study's reliability and precision. 2.2 Differential gene analysis The limma package was used for the differential analysis of mRNA expression matrices between the 4-hour C. albicans samples and the 24-hour C. albicans infection samples [ 8 ] . The criteria for identifying significant mRNAs were set as follows: |log2 (fold change)| > 1 and a false discovery rate (FDR) < 0.05 [ 9 ] . 2.3 Construction and validation of mRNAs related prognostic models In this study, R software version 4.1.0 was utilized for comprehensive data analysis. Initially, the glmnet (version 2.0.18) and survival (version 2.44.1.1) R packages were used to perform univariate Cox Proportional Hazards Model (Cox) regression, multifactor Cox regression analysis, and Less Absolute Shrinkage and Selection Operator (LASSO) regression. The evaluation of the relationship between mRNA expression levels and overall survival was made possible via univariate Cox regression, where P-values less than 0.05 were regarded as statistically significant [ 10 ] . The mRNAs that satisfied the criteria were subjected to LASSO regression analyses to refine their features. Subsequently, using multivariate Cox regression analysis, the prognostic effect and hazard ratio (HR) of the prediction model were assessed, and the 95% confidence intervals (CI) were computed [ 11 ] . Using the formula Σ (expmRNAn × βmRNA), the prognostic risk score was computed by summing the mRNA expression values and their respective coefficients. This risk score helped stratify samples into high- and low-risk groups. The prognostic significance of the risk scores in training, internal validation, whole cohort, and external validation were assessed using Kaplan-Meier analysis and bilateral log-rank testing with the "survminer" software package, with a significance level set at p < 0.05 [ 12 ] . In addition, time-dependent receiver operating characteristic (ROC) curves were created using the "timeROC" package to assess the accuracy of the prediction model. The AUC provides an estimate of the model's performance. All of the aforementioned indexes combined had p-values less than 0.05, which denotes significant differences. These thorough analyses contributed to the validation and evaluation of the built mRNA prognostic models' ability to predict survival and risk for CRC patients [ 13 ] . 2.4 Functional enrichment analysis In this study, microbial informatics analysis tools ( https://www.bioinformatics.com.cn/ , accessed on August 20 2023) were utilized to perform gene enrichment analysis, covering both Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A significance threshold of P < 0.05 was applied to identify functional items and pathways that showed significant enrichment in the GO and KEGG analyses. 2.5 Correlation analysis of immune infiltration in the mRNA prognostic model In this study, data from the Tumor Immune Analysis database ( http://timer.cistrome.org/ , accessed on August 20, 2023) was leveraged to examine the intricate relationship between gene expression patterns and the infiltration of various immune cell types within the prognostic model of colorectal cancer. Additionally, information from the Clinical Letters repository ( https://www.aclbi.com/static/index.html# , accessed on July 30, 2023) was utilized to analyze how gene expression profiles correlate with different classifications of immune cell infiltration, particularly within the context of colorectal cancer prognosis models. This comprehensive analysis facilitated the exploration of the correlation between differentially expressed genes and the tumor immune microenvironment within prognostic models, providing insights into potential interactions between these models and dynamics of immune cell infiltration. 2.6 Culture and metabolite acquisition of C. albicans The experiment utilized the C. albicans strain BNCC263676 (obtained from Beina Chuanglian Biotechnology Co., Ltd.). C .albicans strains are typically cultured on Yield Monitoring (YM) agar medium at 30°C. To induce the filamentous phase of C. albicans , colonies cultured on YM agar medium at 30°C for 24 hours were transferred to YM liquid medium and incubated on a constant temperature shaker at 37°C (250rpm) for 14–16 hours. Subsequently, C. albicans from the YM liquid medium was transferred to fresh YM liquid medium and incubated on a constant temperature shaker at 37°C (250rpm) for 3–8 hours to stabilize the results. Meanwhile, the absorbance values at 600nm wavelength of the C. albicans liquid culture medium were measured using an enzyme-linked immunosorbent assay (ELISA) reader, and C. albicans YM liquid culture medium with Optical Density (OD) values of 0.2, 0.3, and 0.4 were collected. Finally, C. albicans was killed by heating at 95°C in a water bath for 30 minutes to obtain its metabolites mixture. 2.7 Cell viability was measured by CCK-8 NCM460, HT29, and HCT 116 cells were harvested to prepare a cell suspension. Each well of the 96-well plate was seeded with 100µL of cell suspension, ensuring a cell concentration of 1×10 4 -10 5 cells per well. Sterile PBS buffer was added to the surrounding wells of each cell suspension well. The 96-well plates containing the cell suspensions were placed in a cell incubator (37℃, 5% CO 2 ) for standard incubation. Upon cell adhesion, C. albicans metabolites mixture with OD values of 0.2, 0.3, and 0.4 were applied for intervention, with exposure durations of 12 hours, 24 hours, 48 hours, and 72 hours to assess their impact on cell viability. After removing the surface medium, 10µL of CCK-8 solution was added directly to each well, followed by a 2-hour incubation in the incubator. The absorbance at 450nm was then measured using an enzyme-linked instrument. The ultimate intervention concentration and duration were selected through screening, with OD values serving as the criteria to differentiate between concentrations in this study. 2.8 Invasion and migration 2.8.1 Invasion The matrix glue was naturally melted overnight at 4°C, and the microinjection gun and its tip (blue and yellow tip) used in the experiment were pre-frozen at -20°C. The matrix glue was prepared by mixing 2.5µl of Matrigel per well with 97.5µl of medium (without bovine serum and pre-stored at 4°C), gently mixed at the end of the gun and added to the central upper chamber of each 24-well Transwell plate with 100µl of mixture per well. Incubate at 37°C for 30 minutes to 1 hour. After the matrix glue was dried, normal digestion group and C. albicans metabolite intervention group cells, washed (3 times), and 100µl HTR8 cells were suspended in the upper chamber, while 600µl medium containing 10% FBS was added to the lower chamber, and the culture was continued for 48 hours. After incubation, the liquid in the upper and lower chambers was discarded, washed 3 times with PBS, and then fixed with 4% paraformaldehyde for 30 minutes. After fixing, wash with PBS for 3 times again, add crystal violet and dye for 15 minutes. After staining, the chamber was cleaned with PBS and photographs were taken under a microscope. 2.8.2 Migration Digestive C. albicans metabolites mixture intervention in cells after treatment was compared with normal groups, washed three times, and then suspended in 100µl of medium in the upper chamber. Additionally, 600µl of medium containing 10% FBS was added to the lower chamber for a 48-hour incubation period. Following the completion of the incubation, the fluids from both the upper and lower chambers were discarded, and the chambers were rinsed three times with PBS. Subsequently, 4% paraformaldehyde was applied for fixation and allowed to incubate for 30 minutes. After the fixation period, the chambers were again rinsed three times with PBS before being stained with crystal violet for 15 minutes. Following staining, the chambers were rinsed with PBS and then imaged under a microscope. 2.9 PCR was used to detect gene expression in the model In this study, quantitative real-time polymerase chain reaction (qRT-PCR) was employed to assess the expression of model genes. Colorectal cancer cells were seeded in 6-well plates at a density of 5×10 4 cells per well. Following treatment with the metabolic mixture, total RNA was extracted from the cells using a RNA extraction kit. Subsequently, complementary DNA (cDNA) was synthesized using a reverse transcription kit. qRT-PCR assays were conducted utilizing the CFX96TM real-time PCR detection system as per the manufacturer's instructions. The expression levels of the target genes were normalized to the expression of GAPDH, serving as an internal control. Detailed information regarding the primer sequences utilized in the experiments is provided in Table 1 . Table 1 qRT-PCR primer sequence Gene (Rabbit) Prime Product length Login ID GAPDH F:GCAAAGTGGATGTTGTCGCC R:TGATGACCAGCTTCCCGTTC 132 NM_001082253.1 FDFT1 F: AGATTCGGAAAGGGCAAGCA R:AACGACAGGTAGATGGGGGA 223 NM_001287742.2 TIMP1 F:TCAACCAGACCACCTTATACC R: GCATTCCTCACAGCCAACAG 296 NM_003254.3 GADD45B F: GCCCTGCAAATCCACTTCAC R: GTTCGTGACCAGGAGACAAT 165 NM_015675.4 LIME1 F: GGAAGCGCAAGTCGGACAC R: CACGTTGGAATAGGTGGCCT 236 NM_001305654.2 EHD4 F: CTTCGAGAACAAGCCCATGA R: TGCCCTCAGTCTCTCCATACA 161 NM_139265.4 2.10 Extracellular ATP content detection In this investigation, eATP levels were assessed. Consequently, the culture medium was aspirated, approximately 0.1 mL of the medium was extracted, and 1mL of extraction solution was introduced. After thorough agitation, centrifugation was carried out at 10,000g for 10 minutes at 4°C. The resulting supernatant was then transferred to another EP tube, to which 500µL of chloroform was added and thoroughly mixed. Subsequent centrifugation at 10,000g for 3 minutes at 4°C was performed, followed by transferring the supernatant onto ice for measurement. The determination process involved the following steps: (1) Preheating the Ultraviolet (UV) spectrophotometer/ELISA for a minimum of 30 minutes, adjusting the wavelength to 340nm, and zeroing with distilled water. (2) Diluting the ATP standard solution of 10 µmol/mL by 16-fold with distilled water, resulting in a 0.625 µmol/mL standard solution. (3) Preparing the working solution according to the ratio of reagent 2 (mL) : reagent 3 (mL) : reagent 4 (mL) : reagent 5 (mL) : reagent 6 (mL) = 1:1:0.1:0.4:0.1 before usage. The procedure for adding the samples to the 96-well UV plate is as follows (Table 2 ): Table 2 Loading list Reagent name(µL) Measuring tube Standard tube sample 20 standard liquid 20 Reagent I 128 128 operating fluid 52 52 Following thorough mixing, the absorbance value A1 was promptly measured at 340nm for 10 seconds. Subsequently, the colorimetric plate, along with the reaction solution, was immersed in a water bath set to 37°C (for mammals) or 25°C (for other species) for 3 minutes. At 3 minutes and 10 seconds, the absorbance value A2 was promptly determined. The 96-well plate was then incubated in a 37°C (for mammals) or 25°C (for other species) incubator. If the enzyme marker incorporates temperature control functionality, the temperature was adjusted accordingly. The ΔA measurement was calculated as A2 measurement tube - A1 measurement tube, and ΔA standard as A2 standard tube - A1 standard tube, respectively. The ATP content (µmol/mL) was determined using the formula: ATP content = ΔA determination / (ΔA standard / C standard)×(Vextraction + Vserum (pulp)) / V serum (pulp) = 6.875 × ΔA determination / ΔA standard. Result 3.1 Establishment of a Model Linking C. albicans Differential mRNA Features to Colorectal cancer Prognosis Under the thresholds of FDR 1, 213 differentially expressed genes were discovered, of which 135 were up-regulated and 78 down-regulated (Fig. 1 A). After doing univariate Cox proportional hazards regression analysis on the 214 mRNAs within the training cohort, it was observed that 7 of them had p-values less than 0.05 (Figs. 1 B). Following this, the selection process was enhanced using LASSO-Cox regression analysis, eventually choosing 5 mRNAs for inclusion in the training cohort's prognostic model (Figs. 1 C and D). Utilizing these chosen mRNA features, a novel risk scoring formula was formulated as follows: Risk score = 0.245a 1 -0.378a 2 + 0.318a 3 -0.357a 4 + 0.3184a 5 Tag:a 1 : LIME1 ; a 2 : EHD4 ; a 3 : GADD45B ; a 4 : FDFT1 ; a 5 : TIMP1 ; Using the risk score formula from Section 4.2, we computed the overall risk score for each patient (Fig. 2 A). Subsequently, the training cohort and the testing set cohort was used, the robustness of these traits was assessed (Figs. 2 B and 2 C). Patients in the high-risk group had considerably shorter OS than those in the low-risk group, according to Kaplan-Meier survival analysis (KM) and a two-sided log-rank test on the full dataset ( P < 0.001 ) (Fig. 3 A). In the testing and training set cohorts, additional validation of the prognostic effect of this risk score was carried out, and it was shown that the OS difference between the high-risk and low-risk groups was comparable to and significant ( P < 0.05 ) to the OS (Figs. 3 B and C). Furthermore, the AUC value derived from time-dependent ROC curve analysis for the overall population stood at 0.76 (Fig. 4 A). Regarding the 5-year OS predictions, the AUC values obtained from time-dependent ROC curve analysis were 0.76 for the training set and 0.70 for the test set cohort (Figs. 4 B and C). The risk ratings for each patient were calculated using the established technique, and the utility of these features in the GEO dataset's validation cohort was assessed (Fig. 2 D) was assessed. The GEO dataset showed a substantial difference in overall survival rates between high-risk and low-risk groups, which aligns with our findings in TCAG ( P < 0.001 ) (Fig. 3 D). Additionally, it was observed that the AUC values of the ROC curves varied over time, with values of 0.62, 0.67, and 0.66 at 1 year, 3 years, and 5 years respectively (Fig. 4 D), indicating the successful establishment of the model. 3.2 Differential gene expression mRNA and gene set enrichment analysis in high and low risk groups As shown in Fig. 5 A, compared with the low-risk group, 17 genes were up-regulated and 12 genes were down-regulated in the high-low-risk group (adjusted p value 0.05, and |log2 FC|>1). BP enrichment results show they are mainly involved in extracellular matrix organization, extracellular structure organization, antimicrobial humoral response and other signaling pathways (Fig. 5 B). MF enrichment results show that they are mainly involved in glycosaminoglycan binding, extracellular matrix structural constituen, Glycosaminoglycan binding, extracellular matrix structural constituen, oligosaccharide binding and other signaling pathways (Fig. 5 C), CC enrichment results showed that they were mainly involved in collagen − containing extracellular matrix,Golgi lumen,zymogen granule and other signaling pathways (Figure. 5D). KEGG enrichment results showed that they were mainly involved in Ascorbate and aldarate metabolism,Chemical carcinogenesis − DNA adducts and other signaling pathways (Fig. 5 F). 3.3 Immune infiltration analysis of prognostic model genes To estimate the immune infiltrating population by CIBERSORT algorithm, LIME1 is negatively correlated with Macrophage, NK cell, T cell CD4+. GADD45B is positively correlated with Endothelial cell, Macrophage and NK cell, and negatively correlated with T cell CD8 + and uncharacterized cell. FDFT1 is negatively correlated with Endothelial cell and Macrophage. EHD4 is positively correlated with B cell, Endothelial cell, Macrophage, NK cell and T cell CD4+(Fig. 6 ). 3.4 The CCK8 method was used to assess the cell viability after intervention with different concentrations of C. albicans metabolites mixture and at different time points In this work, the cytotoxicity of C. albicans metabolites mixture on cells was evaluated by measuring cell viability using the CCK8 assay. As the concentration increased and the duration of exposure lengthened, cell viability decreased. For HT29 cells, under conditions of a concentration OD value of 0.4 and intervention time of 24 hours, the survival rate was approximately 53%, indicating that exposed cells had been affected by C. albicans metabolites mixture. Therefore, cells under the condition of an OD value of 0.4 and intervention time of 24 hours were used for subsequent experiments (Fig. 7 A). Similarly, for HCT116 cells, under conditions of a concentration OD value of 0.4 and intervention time of 24 hours, the survival rate was approximately 54%, suggesting that exposed cells had been affected by C. albicans metabolites mixture, and thus cells under the condition of an OD value of 0.4 and intervention time of 24 hours were selected for subsequent experiments (Fig. 7 B). Likewise, for NCM460 cells, under conditions of a concentration OD value of 0.3 and intervention time of 24 hours, the survival rate was approximately 57%, indicating that exposed cells had been affected by C. albicans metabolites mixture, and thus cells under the condition of an OD value of 0.3 and intervention time of 24 hours were used for subsequent experiments (Fig. 7 C). 3.5 Comparison of invasion and migration ability of C. albicans metabolites mixture in Colorectal cancer groups Relative to the control group, the experimental group exhibited a significant reduction in the number of invasive cells of HCT116 (P = 0.001) (Figs. 8 A.D.G), HT29 (P = 0.0044) (Figs. 8 B.E.H), and NCM460 cells (P = 0.00076) (Figs. 8 C.F.I) after 48 hours of intervention. Similarly, in comparison to the control group, the experimental group demonstrated a significant decrease in the number of migratory cells of HCT116 ( P = 0.001 ) (Figs. 9 A.D.G), HT29 ( P = 0.011) (Figs. 9 B.E.H), and NCM460 cells (P = 0.047) (Figs. 9 C.F.I) after 48 hours of intervention. 3.6 The effect of C. albicans metabolites mixture on Extracellular ATP levels in Colorectal cancer cells Compared to the control group, the eATP levels significantly increased in the experimental group after intervention for HCT116 (P = 0.0076) (Fig. 10 A), HT29 (P = 0.0013) (Fig. 10 B), and NCM460 (P = 0.0052) (Fig. 10 C). 3.7 Expression levels of FDFT1, TIMP1, GADD45B, LIME1 and EHD4 in cells of each group The expression levels of FDFT1 in CRC cells HT29 and HCT116 were considerably higher ( P < 0.001 ) than in normal colorectal epithelial cells (Fig. 11 A); the expression level of TIMP1 in HT29 cells was significantly increased (P < 0.05) (Fig. 11 B), while in HCT116 cells, it was significantly decreased (P < 0.05) (Fig. 11 B); the expression level of GADD45B in HCT116 cells was significantly decreased (P < 0.05) (Fig. 11 C); and the expression levels of EHD4 and LIME1 were significantly decreased (P 0.05) (Fig. 12 A); however, the expression levels of TIMP1 (P 0.05) (Fig. 12 C), LIME1 (P < 0.01) (Fig. 12 D), and EHD4 (P < 0.05) (Fig. 12 E) were increased. After the intervention, the expression levels of FDFT1 (Fig. 13 A), TIMP1 (Fig. 13 B), GADD45B (Fig. 13 C), LIME1 (Fig. 13 D), and EHD4 (Fig. 13 E) in HT29 cells were significantly decreased (P < 0.001) . After intervention, the expression levels of FDFT1 (P < 0.05) (Fig. 14 A), TIMP1 (P < 0.01) (Fig. 14 B), and GADD45B (P < 0.01) (Fig. 14 C) in NCM460 cells were significantly increased, while the expression levels of LIME1 (P < 0.01) (Fig. 14 D) and EHD4 (P < 0.01) (Fig. 14 E) were significantly decreased. Discussion The prognostic model's updated risk score algorithm was created as follows: Score for risk = 0.245a1-0.378a2 + 0.318a3-0.357a4 + 0.3184a5 Tag:a1:LIME1; a2:EHD4; a3:GADD45B; a4:FDFT1;a5:TIMP1; " EHD4 " represents Eps15 Homology Domain 4, which encodes a gene associated with the endogenous protein EHD4 [ 14 , 15 ] . EHD4 belongs to the Eps15 Homology Domain ( EHD ) family, whose proteins play important biological functions within cells, particularly involving activities related to endocytosis and cellular membrane trafficking. The C-terminal EHD proteins play crucial roles in regulating membrane transport during endocytosis. To put it simply, the amino acid sequences of the four EHD proteins ( EHD1-EHD4 ) that have been identified in mammals found to have a 70–86% sequence identity in common. Among them, actin filaments allow EHD2 , the least preserved a member of the family EHD , to interface with the plasma membrane. It has been demonstrated that EHD2 takes part in membrane resealing/fusion in muscle cells, which controls the actions of many cytoskeletal proteins in diverse cellular configurations. There is evidence linking EHD2 to the development of several malignant tumours. Prior research has demonstrated a positive correlation between increased malignancy and EHD2 downregulation. Tumor tissues have downregulated levels of EHD2 , particularly in cases of advanced and poorly differentiated malignancies. For patients with upregulated EHD2 levels, their survival rates are significantly correlated with prolonged overall survival. According to all of these findings, EHD2 might represent a separate prognostic factor for CRC [ 16 ] . The potential mechanisms through which miR-4701-3p and miR-4793-3p trigger apoptosis in CRC cells were investigated. Screening using MirTarBase identified 62 shared targets for these miRNAs, such as SMARCA5, MBD3, VPS53 , and EHD4. These targets significantly influenced the endocytic cycle pathway and ACR. VPS53 and EHD4 are connected to endocytic cycling, whereas SMARCA5 and MBD3 are linked to ACR [ 17 ] . However, the role of EHD4 in the occurrence and development of rectal cancer and its prognostic factors remain unknown. Cell division, apoptosis, and DNA damage repair all depend on GADD45B , a member of the growth arrest and DNA damage-inducible gene family. In II stage CRC cohorts exhibiting elevated GADD45B expression, individuals subjected to adjuvant chemotherapy demonstrated significantly prolonged progression-free survival (PFS) compared to their counterparts who did not receive such treatment ( P = 0.008). Elevated expression levels of GADD45B serve as an autonomous prognostic determinant associated with diminished OS and PFS among patients diagnosed with stage II CRC. Therefore, adjuvant chemotherapy may be beneficial for patients with II stage CRC who express high levels of GADD45B [ 18 ] . Moreover, heightened expression of GADD45B correlates significantly with both recurrence and mortality rates in CRC patients (P < 0.05) . GADD45B overexpression in CRC patients is associated with a considerably lower DFS, according to KM survival curves. GADD45B overexpression and Tumor Node Metastasis (TNM) staging are significant factors impacting patient survival, as confirmed by Cox multivariate analysis [ 18 ] . A pathological diagnosis study involving 819 CRC patients demonstrated that the overall sensitivity, specificity, and diagnostic odds ratio DOR of TIMP-1 for diagnosing CRC were 0.65, 0.87, and 12.73, respectively. The area under the summary receiver operating characteristic curve was 0.77, indicating the potential diagnostic value of TIMP-1 in CRC patients. In patients with a 20% probability of CRC, the post-test probabilities for TIMP-1 positivity and negativity were 56% and 9%, respectively. According to the study's findings, TIMP-1 has a moderate to high level of sensitivity and specificity, making it a possible biomarker for CRC diagnosis. Therefore, the detection of TIMP-1 holds promise as a valuable non-invasive screening modality for CRC in clinical settings [ 19 ] . Another study, using 43 CRC patients and 24 healthy volunteers as controls, discovered that the serum content of TIMP-1 in CRC patients was considerably greater than in the healthy control group. Increased serum TIMP-1 levels were associated with female gender, tumor location in the colon, tumor dedifferentiation, and increased whole blood platelet count [ 20 ] . Additionally, the study results emphasized the overexpression of TIMP-1 in colorectal tumor tissues and lymph node metastasis specimens [ 21 ] . The downregulation of FDFT1 is intricately associated with the malignant progression and adverse prognosis of CRC. Furthermore, FDFT1 has been elucidated as a pivotal tumor suppressor in CRC. Mechanistically, FDFT1 exerts its tumor-suppressive effects by disrupting the AKT/mTOR/HIF1α signaling pathway [ 22 ] . Furthermore, numerous studies have elucidated the pervasive downregulation of FDFT1 expression in CRC [ 23 , 24 ] . As for LIME1 , its role in CRC has not been reported yet. Furthermore, a unique prognostic model based on four important genes, CXCL8 , IL13RA2 , MELK , and POP1 , has been created to accurately predict the survival of CRC patients. The finding might offer a new perspective on treating CRC associated with pyroptosis [ 25 ] . Yet another study devised a prognostic prognostication model comprising five genes ( RPX, CXCL13, MMP10, FABP4, CLDN23 ), creating another novel prognostic model for CRC [ 26 ] . This study successfully constructed a prognostic model for CRC associated with C. albicans , which accurately predicts the survival of CRC patients, while also providing a scientific basis for treatment. The research results indicated that with increasing concentration and prolonged exposure time, cell viability decreased. For HT29 cells, under the condition of a concentration with an OD value of 0.4 and an intervention time of 24 hours, the survival rate was approximately 53%, indicating that the exposed cells were affected by C. albicans metabolites mixture. Similarly, for HCT116 cells, under the conditions of a concentration with an OD 600 value of 0.4 and an intervention time of 24 hours, the survival rate was approximately 54%. For NCM460 cells, under the conditions of a concentration with an OD 600 value of 0.3 and an intervention time of 24 hours, the survival rate was approximately 57%. β-glucan was isolated from the cellular membrane of C. albicans . Within 48 hours, the viability of MSCs decreased significantly, but it was dose-dependent. The research indicated that treating MSC with β-glucan increased cancer cell apoptosis [ 27 ] . C. albicans promoted proliferation of WSU-HN4, WSU-HN6, and CAL27 cells in oral squamous cell carcinoma (OSCC) via the TLR2/MyD88 pathway, as demonstrated by CCK-8 experiments. OSCC cells treated with zymosan exhibited a significantly elevated density of C. albicans cells per field compared to the control group [ 27 ] . Physical obstruction of Sanguisorba officinalis by C. albicans β-glucan may weaken the antifungal activity of its derivative, sodium sanguinarine. Exposure to β-glucan induced by sodium sanguinarine significantly enhanced the phagocytic capacity of C. albicans and inhibited its growth [ 28 ] . KEGG enrichment results showed that they were mainly involved in Ascorbate and aldarate metabolism,Chemical carcinogenesis − DNA adducts and other signaling pathways. Chemical carcinogenesis is a common mechanism of cancer that involves in vivo or in vitro exposure to specific chemicals that interact with DNA to form DNA adducts. These DNA adducts may interfere with DNA replication and repair processes, leading to the accumulation of DNA damage and error repair, which may eventually lead to mutations in the cell's genetic information. These mutations may affect the growth regulation, apoptosis and repair ability of cells, thus promoting the occurrence and development of cancer [ 29 ] . In human tissues, there is a positive association between the highest levels of DNA adducts in blood cells or target organs and the highest risk of cancer; The magnitude of the increased risk was similar when DNA adducts were measured in blood cell DNA or target tissue DNA; In individuals with the highest levels of DNA adducts, the increase in cancer risk is generally less than 10, however, an increase in a second strong cancer risk factor may increase these numbers exponentially [ 30 ] . In addition, studies have shown that DNA adducts in colorectal cancer tissues are significantly higher than those in the control group and up-regulation of ascorbic acid and uronate metabolism and fatty acid degradation may enhance the immunosuppression of Tregs [ 31 ] . Enhanced metabolism of alpha-linolenic acid, linoleic acid and arachidonic acid may inhibit the pro-inflammatory function of CD4 + tcm and CD8 + TEMs in PSO and PSA, and protect the immunosuppression of Tregs in PSA [ 32 ] . This signaling pathway is consistent with the results of immunoinfiltration in this study. Cell migration, sometimes referred to as cell motility, cell crawling, or cell movement, and the term used to describe how cells migrate in response to migratory signals or specific chemical gradients. Cell migration involves the extension of pseudopodia at the forefront of cellular protrusion, the establishment of new adhesions, and the contraction of the cell body at the trailing edge in a spatiotemporal manner. Cell invasion is a type of cell migration and is inseparable from it. It refers to the process in which cells break through the basement membrane in situ, then infiltrate into blood vessels or lymphatic vessels, namely the invasion of cells (such as malignant tumor cells), which penetrate through the extracellular matrix or basement membrane extracellular matrix from one area to another. After a 48-hour intervention, the experimental group's HCT116, HT29, and NCM460 invasive cell count was considerably lower than that of the control group, which inhibited CRC cells from migrating and invading. C. albicans , a common human fungal infection, colonizes the cutaneous and mucosal surfaces of the majority of healthy individuals [ 33 ] . This study presents novel findings on the influence of C. albicans metabolites mixture on the invasion and migration of CRC cells, contributing to the expanding body of scientific knowledge elucidating the dual roles of C. albicans in promoting and ameliorating CRC. Previous research has shown that 786-O cells' ability to proliferate, migrate, and invade can be inhibited by upregulating the expression of FDFT1 , with FDFT1 inhibition leading to reduced cell movement [ 34 , 35 ] . In HT29 and HCT116 CRC cells, FDFT1 expression levels were significantly increased, which may be associated with enhanced invasive and migratory abilities. However, after metabolite intervention, FDFT1 expression levels were significantly decreased in HT29 and HCT116 cells, while significantly increased in NCM460 cells, which may be correlated with decreased invasive and migratory abilities, as confirmed by related studies [ 36 ] . In HT29 cells, TIMP1 expression levels were significantly increased, while significantly decreased in HCT116 cells compared to NCM460 cells. TIMP1 is typically associated with tissue metalloproteinase inhibition, with its upregulation possibly inhibiting cell invasion and migration, and its downregulation possibly promoting cell invasion and migration [ 24 ] . Nevertheless, studies have elucidated that heightened TIMP1 expression fosters the in vivo proliferation of human HCT116 and HT29 colon cancer cells [ 37 ] . TIMP1 deficiency can suppress the proliferation, migration, and invasion of colon cancer cells. The relationship between TIMP1 overexpression and invasion and migration in HCT116 and HT29 cells requires further research. In HCT116 cells, GADD45B expression levels were significantly decreased, while in HT29 cells, GADD45B expression levels were slightly increased. GADD45B is typically associated with DNA damage response and cell cycle regulation, with its downregulation possibly inhibiting invasion and migration of HT29 cells, and its upregulation possibly promoting invasion and migration of HCT116 cells. However, after C. albicans intervention, GADD45B expression levels were decreased, possibly inhibiting invasion and migration of HT29 cells, but had little effect on GADD45B expression levels in HCT116 cells. The overexpression of GADD45B represents an autonomous risk factor associated with diminished survival outcomes in patients diagnosed with EOC, leading to a reduction in both PFS duration and OS. Elevated GADD45B expression is associated with venous infiltration, lymphatic infiltration, and peritoneal cancer. GADD45B downregulation decreases Endometrial stromal sarcoma cell line 2 (ES2) and SKOV3 cell motility. Further KEGG enrichment analysis and Gene Set Enrichment Analysis (GSEA) indicate that Epithelial-mesenchymal transition (EMT) might be a GADD45B downstream pathway. Moreover, diminished GADD45B expression leads to an upregulation of E-cadherin expression and a downregulation of N-cadherin and vimentin expression [ 21 , 38 ] . EHD4 and LIME1 : In HCT116 and HT29 cells, expression levels of EHD4 and LIME1 were significantly decreased, with significant upregulation of EHD4 and LIME1 expression levels in HCT116 cells after intervention, and lower expression levels of EHD4 and LIME1 in HT29 cells after intervention. The notable elevation in the expression levels of EHD4 and LIME1 may exert an inhibitory effect on the invasion and migration of HCT116 cells, whereas the reduced expression levels of EHD4 and LIME1 may impede the invasion and migration of HT29 cells. Although the alterations in these genes' expression may have an impact on CRC cells' capacity for invasion and migration, deeper research and experimental validation are needed to determine the precise processes. Following intervention, the experimental cohort exhibited notably elevated eATP levels in contrast to the control group. Elevated concentrations of eATP can exert direct or indirect effects on cancer cells. Henceforth, purinergic receptors have been documented to be conspicuously expressed in neoplastic cells. CRC cells predominantly manifest purinergic receptors A2B, P2X4, P2Y1, P2Y2 , and P2Y11 . Of these, in contrast to HCEC-1CT normal colon cells, the genes coding for P2Y1 and P2Y2 receptors exhibit notable upregulation across all CRC cell lines. eATP is always more effective against CRC than adenosine in terms of inducing cell death [ 39 ] . These results indicate that C. albicans metabolites mixture may induce cell death and inhibit proliferation of CRC cells by releasing more ATP into the extracellular space. As per research findings, elevated concentrations (1–10 mM) of ATP and its analogs like AMP-PNP and ATPγs, exhibit a PKC blocking effect in Caco-2 cells akin to GF109203X, thereby impeding proliferation during the S phase of the cell cycle [ 40 ] . Studies have revealed that STC2 modulates PKC activity, regulates Claudin-1 expression, and triggers the expression of EMT-related factors such as ZEB1, ZO-1, Slug, Twist, and MMP9. Suppression of STC2 leads to diminished motility in cells, but inhibiting PKC activity with a PKC inhibitor (Go 6983) can restore normal motility in STC2-silenced cells, while 231 HM cells exhibit restricted migration and invasion [ 41 ] . ATP downregulates the P2Y11 purinergic receptor and CXCR4 to prevent bone metastases and breast cancer migration [ 42 ] . By inducing CRC cell death and inhibiting proliferation in the S phase of the cell cycle, it is revealed that high eATP levels may inhibit the invasion and migration of CRC cells, a process that may be correlated with C. albicans metabolites mixture. Moreover, in recent years, ATP-induced cell death, identified as a discernible form of cell demise triggered by heightened eATP levels, has surfaced as intricately linked with the advancement of diverse cancer types [ 43 ] . We hypothesize that C. albicans metabolites mixture may mediate the invasion, migration, and development of CRC through ATP-induced cell death mechanisms, which require further investigation. FDFT1 expression levels were significantly higher in CRC cells HT29 and HCT116 compared to normal colorectal epithelial cells, whereas TIMP1 expression was significantly higher in HT29 cells but decreased in HCT116 cells. Additionally, In HCT116 cells, there was a notable reduction observed in the expression levels of GADD45B , alongside diminished levels of EHD4 and LIME1 expression. Following intervention with white fungus metabolites mixture, FDFT1 expression in HCT116 cells did not change significantly; however, TIMP1, GADD45B, LIME1 , and EHD4 expression levels were increased. In HT29 cells, expression levels of FDFT1, TIMP1, GADD45B, LIME1 , and EHD4 were significantly decreased after intervention. In NCM460 cells, expression levels of FDFT1 , TIMP1 , and GADD45B were significantly increased, while LIME1 and EHD4 expression levels were significantly decreased after intervention. C. albicans, C. parapsilosis, C. glabrata reference strains Cp2, and C. krusei fragments cleaved TIMP-1 (28 kDa) into 24 kDa, which was associated with a decrease in the inhibitory activity of MMP-9 collagenase. According to the study's results, fungus can contribute to tissue inflammation by altering host MMP-9 and its inhibitors [ 44 ] . Eyes inoculated with C. albicans developed corneal infections on the first day, with an average clinical score of 8.2 ± 0.8. Compared to the control group, MMP-8, -9, -10, -12, -13, -19 , and TIMP-1 were upregulated from 5-fold to 375-fold on day 1 in the microarray, and from 3-fold to 78-fold via real-time RT-PCR. The upregulation of MMPs and TIMP-1 in the corneal epithelium and stroma of infected eyes was related with the influx of acute inflammatory cells. Mechanical injury had no effect on the expression of MMP-8 and MMP-13 , which rose more than 100-fold within a week following fungal keratitis [ 45 ] . FDFT1 is a crucial tumor suppressor in CRC. Mechanistically, FDFT1 exerts its tumor-suppressive function by negatively regulating the AKT/mTOR/HIF1α signaling pathway [ 22 ] . Additionally, mTOR inhibitors can synergistically inhibit the proliferation of CRC with fasting. Moreover, FDFT1 has been demonstrated to have anti-tumor effects in CRC, potentially promoting iron efflux by regulating ISCU expression [ 23 ] . 3β-hydroxy-12-oleanene-27-acid significantly inhibits the growth of xenograft tumors in nude mice and shows a significant decrease in FDFT1 expression in tumor tissues, along with changes in biomarkers such as autophagy, cell cycle, cell apoptosis, and iron efflux [ 46 ] . The serum concentration of TIMP-1 in CRC patients was significantly higher than that in the healthy control group. Elevated serum levels of TIMP-1 were associated with female gender, tumor location in the colon, tumor low differentiation, and increased whole blood platelet count [ 20 ] . The systemic plasma levels of TIMP-1 and MMP-9 in CRC patients were significantly elevated compared to the control group. The systemic and portal plasma levels of TIMP-1 in metastatic disease patients were significantly higher than those in localized disease patients [ 47 ] . Chemotherapy had longer PFS compared to those who did not receive adjuvant chemotherapy. High expression levels of GADD45B are independent prognostic factors for reduced OS and PFS in stage II CRC patients. Patients with high expression of GADD45B in stage II CRC may benefit from adjuvant chemotherapy [ 48 ] . The mRNA and protein levels of GADD45B were significantly higher in CRC tissues than in Adjacent Non-Cancerous Tissues. Upregulation of GADD45B expression is also associated with recurrence and death in CRC patients. Kaplan-Meier survival curves show that CRC patients overexpressing GADD45B have significantly poorer disease-free survival (DFS). Cox multivariate analysis indicates that GADD45B overexpression and TNM staging are important factors affecting patient survival. On the other hand, as a tumor suppressor gene, GADD45B amplified from normal colorectal tissue can induce apoptosis in CRC cell lines and may be associated with the p53-mediated apoptotic pathway [ 18 ] . EHD2 is closely associated with clinical pathological parameters. Upregulation of EHD2 is associated with longer overall survival. Univariate and multivariate analysis results indicate that EHD2 can serve as an independent prognostic indicator [ 16 ] . Additional investigation into EHD4 is warranted to expand our understanding and elucidate its underlying mechanisms, functions, and potential implications. FDFT1 plays a pivotal role in modulating the cholesterol composition of cell membranes and orchestrating the functionality of immune cells. Its interacting protein potentially serves as a mediator in orchestrating immune evasion mechanisms [ 36 ] . Furthermore, an aberrant expression of FDFT1 within tumor-infiltrating lymphocytes (TILs) has been documented [ 23 ] .TIMP-1, a secretory protein renowned for its ability to impede matrix metalloproteinases (MMPs), is intricately involved in inflammatory cascades and correlates with heightened levels of immune cell infiltration [ 49 ] . In the context of anorectal cancer (AOXGD), the IL6-JAK2/STAT3-TIMP-1 signaling axis undergoes activation, concomitant with the engagement of adaptive Th1 and Th17 responses in the pathogenesis of AOXGD. IL-6 fosters the upregulation of TIMP-1 production by M1 macrophages via the stimulation of JAK2 and STAT3 phosphorylation, concurrently promoting the elevation of classical Th1 and Th17 cell markers such as IL-17 and IFN-γ [ 50 ] . Suppression of Gadd45a and Gadd45b expression correlates with the chemotactic response of macrophages to LPS [ 51 ] . Notably, Gadd45b−/− mice exhibit compromised tumor immune surveillance, attributed to diminished expression of IFN-γ, granzyme B, and CCR5 within Gadd45b−/−CD8 + T cells infiltrating tumors. Within Gadd45b−/−CD8 + T cells, stimulation via TCR or IL-12 and IL-18 leads to attenuated activation of p38 MAP kinase, rather than ERK or JNK, resulting in reduced IFN-γ secretion. Moreover, reduced mRNA levels of T-BET and Eomes within Gadd45b−/−CD8 + T cells underscore the pivotal role of Gadd45b in sculpting Th1 lineage commitment [ 52 ] . In EHD1/3/4 knockout mice, CD4 + T cells undergo antigen-driven proliferation but show reduced IL-2 secretion. Interestingly, these mice demonstrate less severe experimental autoimmune encephalomyelitis. Further investigation reveals impaired recycling of the TCR-CD3 complex, leading to increased lysosomal targeting and decreased surface levels of CD4 + T cells in EHD1/3/4 knockout mice [ 53 ] .Taken together, these findings suggest that metabolites mixture derived from C. albicans may modulate the initiation and progression of colorectal cancer by impacting immune cell infiltration dynamics. Conclusion and future prospects In this study, we successfully developed a prognostic model for C. albicans and CRC and validated it using CRC data. This paves the way for further in vitro and in vivo research to identify these genes as potential therapeutic targets for CRC. In vitro experiments, this research used the CCK8 assay to assess cell viability and determine the cytotoxicity of C. albicans metabolites mixture on cells. We observed a decrease in cell viability with increasing concentration and prolonged exposure time. Additionally, C. albicans metabolites mixture inhibited the invasion and migration of CRC cells. Furthermore, after intervention with C. albicans metabolites mixture, we observed a significant increase in eATP levels in CRC cells compared to the control group. In further analysis, we found that the expression levels of FDFT1 were significantly elevated in CRC cells HT29 and HCT116 compared to normal colonic epithelial cells. Additionally, TIMP1 expression was significantly increased in HT29 cells but significantly decreased in HCT116 cells. Moreover, the expression of GADD45B was significantly decreased in HCT116 cells, while the expression levels of EHD4 and LIME1 were also significantly reduced. Following intervention with C. albicans metabolites mixture, we observed no significant change in FDFT1 expression in HCT116 cells, but there was an increase in the expression levels of TIMP1, GADD45B, LIME1 , and EHD4 . Conversely, in HT29 cells, the expression levels of FDFT1, TIMP1, GADD45B, LIME1 , and E HD4 were significantly reduced after intervention. Finally, in NCM460 cells, the expression levels of FDFT1, TIMP1 , and GADD45B were significantly increased after intervention, while the expression levels of LIME1 and EHD4 were significantly decreased. These results suggest a protective role of C. albicans metabolites mixture against CRC. This study primarily focused on cellular validation. Although cancer is studied at the cellular level, validation from other CRC cells was not conducted, and there was a lack of animal in vivo experiments. Following intervention with C. albicans metabolites mixture, this study found an increase in eATP levels. Related research has shown that elevated eATP levels may trigger ATP-induced cell death mechanisms. In previous studies with breast cancer cells, we found that ATP-induced cell death mechanisms (resulting from elevated eATP levels) inhibited the invasion and migration of breast cancer cells, thereby suppressing tumor initiation and progression. Therefore, future investigations will further explore whether C. albicans metabolites mixture activate ATP-induced cell death mechanisms in CRC cells, potentially exerting a protective effect. Declarations Data availability The datasets used and analysed during the current study available from the frist author on reasonable request. Author Contributions HL, HL, MS,WW completed the first draft of the paper. DS,ZG,ZJ had revised the article. Funding Supported by National Natural Science Foundation of China, No. 81960877; University Innovation Fund of Gansu Province, No. 2021A-076; Gansu Province Science and Technology Plan (Innovation Base and Talent Plan), No. 21JR7RA561; Natural Science Foundation of Gansu Province, No. 21JR1RA267 and No. 22JR5RA582; Education Technology Innovation Project of Gansu Province, No. 2022A-067;Innovation Fund of Higher Education of Gansu Province, No. 2023A-088; Gansu Province Science and Technology Plan International Cooperation Field Project, No. 23YFWA0005; and Open Project of Key Laboratory of Dunhuang Medicine and Transformation of Ministry of Education, No. DHYX21-07, No. DHYX22-05, and No. DHYX21-01. Conflict of Interest The authors have no conflicts of interest to declare. References WENG J, LI S, ZHU Z, et al. Exploring immunotherapy in colorectal cancer[J]. J Hematol Oncol, 2022,15(1): 95. MORGAN E, ARNOLD M, GINI A, et al. 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J Cell Physiol, 2012,227(11): 3613-3620. JU S, ZHU Y, LIU L, et al. Gadd45b and Gadd45g are important for anti-tumor immune responses[J]. Eur J Immunol, 2009,39(11): 3010-3018. ISEKA F M, GOETZ B T, MUSHTAQ I, et al. Role of the EHD Family of Endocytic Recycling Regulators for TCR Recycling and T Cell Function[J]. J Immunol, 2018,200(2): 483-499. Additional Declarations No competing interests reported. Supplementary Files Data.7z Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4555778","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":315591720,"identity":"b03e16dd-710b-4651-9372-0c851886de8c","order_by":0,"name":"HaoLing Zhang","email":"","orcid":"","institution":"Department of Biomedical Sciences, Advanced Medical and Dental Institute, Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"HaoLing","middleName":"","lastName":"Zhang","suffix":""},{"id":315591721,"identity":"d605bb74-ab53-4eee-93b3-3759fde4e04f","order_by":1,"name":"Haolong Zhang","email":"","orcid":"","institution":"Department of Biomedical Sciences, Advanced Medical and Dental Institute, Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Haolong","middleName":"","lastName":"Zhang","suffix":""},{"id":315591722,"identity":"a9aa3dbc-b748-4056-a03c-173648e666a9","order_by":2,"name":"Weifang Chen","email":"","orcid":"","institution":"Department of Hematology and Oncology, Putuo District People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weifang","middleName":"","lastName":"Chen","suffix":""},{"id":315591723,"identity":"837413b4-f859-4161-88d0-65211c50050e","order_by":3,"name":"Yong Wang","email":"","orcid":"","institution":"Pathology Center, Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Wang","suffix":""},{"id":315591724,"identity":"5a443542-01d9-4ee5-9a9b-cd44b202ac00","order_by":4,"name":"Siti Nurfatimah Mohd Sapudin","email":"","orcid":"","institution":"Department of Biomedical Sciences, Advanced Medical and Dental Institute, Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Siti","middleName":"Nurfatimah Mohd","lastName":"Sapudin","suffix":""},{"id":315591726,"identity":"f01a3c7c-5709-4683-abd9-a1ae79be381e","order_by":5,"name":"Doblin Sandai","email":"","orcid":"","institution":"Department of Biomedical Sciences, Advanced Medical and Dental Institute, Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Doblin","middleName":"","lastName":"Sandai","suffix":""},{"id":315591728,"identity":"6b55bb8b-4a6e-4d38-a4ab-377ff5c8c710","order_by":6,"name":"Mohammad Syamsul Reza Harun","email":"","orcid":"","institution":"Department of Biomedical Sciences, Advanced Medical and Dental Institute, Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Syamsul Reza","lastName":"Harun","suffix":""},{"id":315591732,"identity":"d3bf37db-5557-4297-832d-d05f35b3be9f","order_by":7,"name":"Zhongwen Zhang","email":"","orcid":"","institution":"School of Public Health, Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhongwen","middleName":"","lastName":"Zhang","suffix":""},{"id":315591733,"identity":"a0e495fd-ab3b-4b42-89d8-853765c8d830","order_by":8,"name":"Wei Wang","email":"","orcid":"","institution":"College of Acupuncture-Moxibustion and Tuina, Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":315591735,"identity":"13202773-1e41-4aa9-b006-5ea4ca61a1f5","order_by":9,"name":"ZhiJing Song","email":"","orcid":"","institution":"Clinical College of Chinese Medicine, Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"ZhiJing","middleName":"","lastName":"Song","suffix":""},{"id":315591737,"identity":"e459826b-0184-4bd5-91ee-5612f507b641","order_by":10,"name":"Zhongxian Fang","email":"data:image/png;base64,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","orcid":"","institution":"Department of Hematology and Oncology, Putuo District People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhongxian","middleName":"","lastName":"Fang","suffix":""}],"badges":[],"createdAt":"2024-06-10 04:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4555778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4555778/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59180216,"identity":"0d13ceb7-f34f-453b-beb2-17c398e25736","added_by":"auto","created_at":"2024-06-27 10:28:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":710599,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic model.\u003cstrong\u003e \u003c/strong\u003e(A) The volcano plot of differential gene expression illustrates down-regulated genes in blue and up-regulated genes in red. Among them, 135 genes exhibited up-regulation, while 78 genes showed down-regulation. (B) The forest plot depicts seven significant mRNAs with a \u003cem\u003eP\u0026lt;0.05\u003c/em\u003e, along with their corresponding hazard ratios obtained from univariate Cox proportional hazards regression analysis. Here, HR denotes the hazard ratio. (C) The tuning parameter (λ) selection process for mRNAs associated with overall survival is demonstrated through the Least Absolute Shrinkage and Selection Operator (LASSO) model. (D) The LASSO coefficient spectra exhibit the four selected mRNAs, with the vertical line indicating the coefficient values chosen by the LASSO algorithm.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/f194d8155841a3236d28e06d.png"},{"id":59180221,"identity":"42889dc7-1fb5-4c98-b9e4-c1ae244e7281","added_by":"auto","created_at":"2024-06-27 10:28:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1374327,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the Cancer Genome Atlas CRC dataset, presenting the risk score distribution and expression heatmap for the entire dataset. (A) Depicts the total risk score, (B) shows the risk score for the training set, (C) displays the risk score for the test set, and (D) demonstrates external validation of risk assessments using GEO datasets. This section provides an overview of the risk score, survival period, and survival status within the dataset. The upper section features a scatter plot showing risk scores ordered from low to high, with high-risk groups depicted in red and low-risk groups in orange. The vertical dotted line represents the median risk score cut-off point. The middle section showcases the scatter plot distribution of survival time and corresponding survival status across different sample risk scores. Lastly, the bottom section exhibits a heatmap illustrating the expression levels of model genes within the dataset.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/d5327bdb20966527523c0b2d.png"},{"id":59180215,"identity":"48205fb2-a163-42ea-b5e4-a12211b94102","added_by":"auto","created_at":"2024-06-27 10:28:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":826093,"visible":true,"origin":"","legend":"\u003cp\u003eShowcases Kaplan-Meier survival analysis. (A) Presents Kaplan-Meier survival analysis for the total dataset; (B) illustrates Kaplan-Meier survival analysis for the training set; (C) depicts Kaplan-Meier survival analysis for the test set, and (D) exhibits Kaplan-Meier survival analysis for the GEO datasets during external validation. The hazard ratio (HR) of the high-risk group compared to the low-risk group is indicated. HR\u0026gt;1 signifies a risk model, whereas HR\u003cem\u003e\u0026lt;\u003c/em\u003e1 suggests a protective model. The 95% confidence interval (CI) represents the uncertainty associated with HR. Median time denotes the duration corresponding to a 50% survival rate for both the high-risk and low-risk groups, expressed in years. The overall survival of the high-risk group was notably lower than that of the low-risk group. The predictive efficacy of this risk score remained consistent across multiple validation sets, encompassing the test group, the training cohort, and the external validation dataset from GEO. These results confirm that the overall survival rate of the high-risk group significantly differed from that of the low-risk group (\u003cem\u003eP\u0026lt;0.05\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/9785fb5ac82b8d1fa2bef2ea.png"},{"id":59179524,"identity":"0e43cc11-f882-4d35-8e81-d588c7e4cf6e","added_by":"auto","created_at":"2024-06-27 10:20:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1031866,"visible":true,"origin":"","legend":"\u003cp\u003eExhibits time-varying ROC curves. Panel (A) illustrates the total dataset, while Panels (B), (C), and (D) depict the training set, test set, and GEO datasets, respectively. The 5-year survival rates were as follows: 0.76 for the total dataset, 0.76 for the training set, 0.70 for the test set, and 0.64 for the GEO datasets.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/16a6acd9494c05d8e30d1dae.png"},{"id":59180217,"identity":"ce00f909-8d35-4217-97d8-0aad5a1c9ee6","added_by":"auto","created_at":"2024-06-27 10:28:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2065978,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003eVolcanic map of differential genes: B:BP enrichment results; C:MF enrichment results;D:CC enrichment results;F:KEGG enrichment results.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/b982ee83aef4386bebec1146.png"},{"id":59179526,"identity":"aa5f0b4c-69fe-4745-8781-d189b85f9375","added_by":"auto","created_at":"2024-06-27 10:20:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":250801,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/1c6042fbdd57230aadeb88ca.png"},{"id":59180218,"identity":"53d0df4c-aad5-41e0-a5e6-577f107d84d2","added_by":"auto","created_at":"2024-06-27 10:28:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":537038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture interfere with the viability of cells. (A): HT29 cell viability; (B) HCT116 cell viability; (C) NCM460 cell viability: As concentration levels increased and exposure time prolonged, cell viability exhibited a decline. Specifically, for HT29 cells, with an OD value of 0.4 and an intervention time of 24 hours, the survival rate was approximately 53%, suggesting the impact of \u003cem\u003eC. albicans\u003c/em\u003emetabolites mixture on the exposed cells. Similarly, HCT116 cells demonstrated a survival rate of approximately 54% under identical conditions, while NCM460 cells exhibited a survival rate of about 57% with an OD value of 0.3 and intervention time of 24 hours, both indicative of the influence exerted by \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture on the exposed cells.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/a6e951cf95002375f78c1541.png"},{"id":59179532,"identity":"90431604-d33b-4d39-a966-abb2bf0a1256","added_by":"auto","created_at":"2024-06-27 10:20:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6478055,"visible":true,"origin":"","legend":"\u003cp\u003eThe Transwell method is used to detect cell invasion.\u003cstrong\u003e \u003c/strong\u003e(A) HCT116 cell control group; (B) HT29 cell control group; (C) NCM460 cell control group; (D) HCT116 cell experimental group; (E) HT29 cell experimental group; (F) NCM460 cell experimental group; (G) Histogram of invasion of HCT116 cells in two groups; (H) Histogram of invasion of HT29 cells in two groups; (I) Histogram of invasion of NCM460 cells in two groups. In comparison to the control group, the experimental group showed a significant decrease in the number of invasive cells of HCT116, HT29, and NCM460 cells.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/2fa2db73202390c3b7b7883a.png"},{"id":59180219,"identity":"62f80d9f-ed37-40c5-971a-6177b664cf11","added_by":"auto","created_at":"2024-06-27 10:28:21","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":6138158,"visible":true,"origin":"","legend":"\u003cp\u003eThe Transwell method is used to detect cell migration.\u003cstrong\u003e \u003c/strong\u003e(A) HCT116 cell control group; (B) HT29 cell control group; (C) NCM460 cell control group; (D) HCT116 cell experimental group; (E) HT29 cell experimental group; (F) NCM460 cell experimental group; (G) Histogram of migration of HCT116 cells in two groups; (H) Histogram of migration of HT29 cells in two groups; (I) Histogram of migration of NCM460 cells in two groups. Compared to the control group, the experimental group had significantly less migratory cells of HCT116, HT29, and NCM460 cells.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/48b68d0b54d3b135f769c238.png"},{"id":59179529,"identity":"9b4c35c0-7f24-46cc-8a81-2a6ad802f34a","added_by":"auto","created_at":"2024-06-27 10:20:21","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":558170,"visible":true,"origin":"","legend":"\u003cp\u003eeATP levels of cells. (A) HCT116 cells; (B) HT29 cells; (C) NCM460 cells.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/fe1dd207dd19054039cc2d67.png"},{"id":59179533,"identity":"f41b960e-29b9-4363-b65c-bd5e29f76420","added_by":"auto","created_at":"2024-06-27 10:20:21","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":972018,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of \u003cem\u003eFDFT1, TIMP1, GADD45B, LIME1\u003c/em\u003e and \u003cem\u003eEHD4\u003c/em\u003e in different cells.\u003cstrong\u003e \u003c/strong\u003e(A) \u003cem\u003eFDFT1\u003c/em\u003e; (B) \u003cem\u003eTIMP1\u003c/em\u003e; (C) \u003cem\u003eGADD45B\u003c/em\u003e; (D)\u003cem\u003e LIME1\u003c/em\u003e; (E) \u003cem\u003eEHD4\u003c/em\u003e;\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/a75b783b06e8a82eedd615ce.png"},{"id":59179536,"identity":"618ad8a9-65bf-4fde-a533-6ed2d6dcb7f5","added_by":"auto","created_at":"2024-06-27 10:20:22","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1115580,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of \u003cem\u003eFDFT1, TIMP1, GADD45B, LIME1 \u003c/em\u003eand \u003cem\u003eEHD4\u003c/em\u003e in HCT116 cells.\u003cstrong\u003e \u003c/strong\u003e(A)\u003cem\u003e FDFT1\u003c/em\u003e; (B) \u003cem\u003eTIMP1\u003c/em\u003e; (C)\u003cem\u003e GADD45B\u003c/em\u003e; (D) \u003cem\u003eLIME1\u003c/em\u003e; (E) \u003cem\u003eEHD4\u003c/em\u003e;\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/1bda702472056a4956f1aa4a.png"},{"id":59179537,"identity":"0bcd8f5b-dd66-445a-aa48-b1d60d4ae2f5","added_by":"auto","created_at":"2024-06-27 10:20:22","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":980034,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of \u003cem\u003eFDFT1, TIMP1, GADD45B, LIME1\u003c/em\u003e and \u003cem\u003eEHD4\u003c/em\u003e in HT29 cells. (A) \u003cem\u003eFDFT1\u003c/em\u003e; (B) \u003cem\u003eTIMP1\u003c/em\u003e; (C) \u003cem\u003eGADD45B\u003c/em\u003e; (D) \u003cem\u003eLIME1\u003c/em\u003e; (E) \u003cem\u003eEHD4\u003c/em\u003e;\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/fada7a6e6de0404d141927d6.png"},{"id":59179535,"identity":"ab33e801-f36c-4baf-8767-440d09ee4b37","added_by":"auto","created_at":"2024-06-27 10:20:22","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":968424,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of \u003cem\u003eFDFT1, TIMP1, GADD45B, LIME1\u003c/em\u003e and \u003cem\u003eEHD4\u003c/em\u003e in NCM460 cells.\u003cstrong\u003e \u003c/strong\u003e(A) \u003cem\u003eFDFT1\u003c/em\u003e; (B) \u003cem\u003eTIMP1\u003c/em\u003e; (C) \u003cem\u003eGADD45B\u003c/em\u003e; (D) \u003cem\u003eLIME1\u003c/em\u003e; (E) \u003cem\u003eEHD4\u003c/em\u003e;\u003c/p\u003e","description":"","filename":"Figure14.png","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/34363ecef1c9b12fbd1d96f1.png"},{"id":71192597,"identity":"8f4891d0-2b4c-4cbc-ba36-fdab5ae09985","added_by":"auto","created_at":"2024-12-12 04:17:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24548789,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/3736b470-bbdc-4770-9add-dd57cd998d00.pdf"},{"id":59179548,"identity":"f2b02700-5a35-4467-8bb6-b0fea25c5e5a","added_by":"auto","created_at":"2024-06-27 10:20:35","extension":"7z","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":726606714,"visible":true,"origin":"","legend":"","description":"","filename":"Data.7z","url":"https://assets-eu.researchsquare.com/files/rs-4555778/v1/51733616b7d26970c8c1627c.7z"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and validation of a prognostic model based on metabolic characteristics of Candida albicans in colorectal cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCurrently, colorectal cancer (CRC) is the third most common cancer worldwide and ranks as the second leading cause of cancer-related deaths. As of 2020, the projected global incidence of CRC encompasses 1,993,590 newly diagnosed cases, with 935,173 resultant deaths, constituting 10.0% and 9.4% of the overall incidence and mortality rates, respectively\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The delineation of cancer patterns across different regions and time periods serves as a valuable compass for understanding risk factors, prevalence dynamics, and the formulation of comprehensive cancer control strategies. Our research delved into the CRC burden across 185 countries worldwide, spanning from 2020 into the envisioned landscape of 2040. In 2020 alone, an estimated 1.9\u0026nbsp;million novel instances of CRC emerged, culminating in 930,000 fatalities. Remarkably, the highest incidence rates were observed in Australia/New Zealand and Europe, peaking at 4.06\u0026times;10\u003csup\u003e4\u003c/sup\u003e cases per 10\u003csup\u003e5\u003c/sup\u003e men, while the lowest rates were recorded in Africa and South Asia, registering at 4.4\u0026times;10\u003csup\u003e3\u003c/sup\u003e cases per 10\u003csup\u003e5\u003c/sup\u003e women. Analogous trends were observed in mortality rates, with Eastern Europe demonstrating the highest rate at 2.02\u0026times;10\u003csup\u003e4\u003c/sup\u003e deaths per 10\u003csup\u003e5\u003c/sup\u003e men, while South Asia exhibited the lowest at 2.5\u0026times;10\u003csup\u003e3\u003c/sup\u003e deaths per 10\u003csup\u003e5\u003c/sup\u003e women. Forecasts suggest that the burden of CRC is poised to escalate to 3.2\u0026times;10\u003csup\u003e6\u003c/sup\u003e new cases and 1.6\u0026times;10\u003csup\u003e6\u003c/sup\u003e deaths by 2040, primarily concentrated in nations with high or very high human development indices\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Noteworthy is the status of CRC as the second most prevalent cancer in Malaysia, often diagnosed belatedly\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrognostic models possess the capability to anticipate the likelihood of forthcoming events for an individual patient or a population, enabling the stratification of patients based on these prognostic risks. An exemplary model demonstrates the adeptness to judiciously and dependably categorize patients into distinct prognostic risk strata\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Furthermore, within the medical domain, prognostic models serve as instrumental tools for scrutinizing patient outcomes in correlation with both patient-specific and disease-related characteristics\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdenosine triphosphate (ATP) is a high-energy phosphorylated compound that plays a crucial role in storing and releasing energy within cells, ensuring the energy supply required for various cellular activities by interconverting with ADP. Due to its ease of regeneration within cells, ATP can continuously provide energy. The ATP cycle refers to the continuous utilization of energy through ATP hydrolysis and synthesis, cycling between energy-releasing and energy-absorbing reactions. As ATP is widely utilized as an energy carrier within cells, it is often referred to as the \"currency of the cell.\" As a pivotal biochemical constituent within the tumor microenvironment (TME), ATP has significant effects on tumor progression, with its role in promoting or inhibiting tumors depending on its concentration and the expression of specific extracellular nucleotide enzymes and receptors on immune and cancer cells\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRemarkably, in rats with CRC subjected to chemotherapy drug treatment along with \u003cem\u003eLactobacillus plantarum\u003c/em\u003e and \u003cem\u003eC. albicans\u003c/em\u003e, a notable reduction in cancer cell volume was noted, accompanied by the nucleus displaying heightened darkness, indicative of apoptosis. Notably, serum concentrations of IFN-γ, IL-4, and TGF-β were markedly diminished compared to the control cohort. Noteworthy benefits in CRC management were observed with the administration of \u003cem\u003eLactobacillus plantarum\u003c/em\u003e and \u003cem\u003eC. albicans\u003c/em\u003e, sourced from the gastrointestinal microbiota of both elderly individuals and healthy subjects\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Recent discoveries suggest that \u003cem\u003eC. albicans\u003c/em\u003e could potentially exert a favorable influence on CRC; however, the exact mechanism underlying this phenomenon remains elusive. Nevertheless, as of present, there is a dearth of studies delving into the impact of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture on CRC. Hence, the principal aim of this investigation is to scrutinize the potential prognostic implications of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture in CRC, along with an exploration of the interplay between \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture and the mRNA associated with CRC prognosis-related genes. At the same time, the effects of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture on eATP content in CRC were investigated to evaluate cell energy homeostasis. The outcomes posit that the influence of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture on correlated mRNA could emerge as a novel focal point for both the diagnosis and therapeutic interventions in CRC. This discovery is poised to offer invaluable insights into the realm of precise treatment methodologies and the meticulous prognostic evaluation of CRC.\u003c/p\u003e \u003cp\u003eThere are some differences between studying metabolites and metabolic mixtures, and it is reasonable to choose to study metabolic mixtures rather than individual metabolites. The metabolic mixture may be more in line with the real biological environment, because in the body, cells are often affected by multiple metabolites at the same time. By studying metabolic mixtures, it is possible to gain a more complete understanding of how cells respond to complex environments, rather than just the effects of a single metabolite. In addition, metabolic mixtures may be more clinically relevant because cells tend to be affected by multiple metabolites in the body, rather than a single metabolite. Therefore, the selection of metabolic mixtures can better simulate the environment of real organisms in vivo and improve the biological reliability and clinical relevance of research results.\u003c/p\u003e \u003cp\u003eIn this investigation, a prognostic model for CRC was formulated employing \u003cem\u003eC. albicans\u003c/em\u003e mRNA. Through meticulous \u003cem\u003ein vitro\u003c/em\u003e experimentation, the repercussions of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture on the invasion and migration of CRC cells were elucidated. Does this influence contribute to the protective or detrimental aspects of CRC development? Effects of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture on eATP Content in CRC?\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Source of dataset\u003c/h2\u003e \u003cp\u003eUsing the Gene Expression Omnibus (GEO) dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), this study obtained the gene expression profiles altered by \u003cem\u003eC. albicans\u003c/em\u003e from GSE42606. The retrieval was conducted on August 16, 2023, encompassing 25 samples infected with \u003cem\u003eC. albicans\u003c/em\u003e for 4 hours and 34 samples infected for 24 hours. Simultaneously, mRNA expression profiles and clinical data were sourced from The Cancer Genome Atlas CRC Dataset (TCGA-CRC; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This retrieval, conducted on August 16, 2023, involved 44 samples of both normal and tumor samples derived from a pool of 571. The dataset was randomly divided into a training set (70%) and an internal validation set (30%) in order to build and validate the model successfully. Furthermore, for external validation, GSE41258 (retrieved on August 16, 2023) was acquired from the GEO database, comprising 390 samples of CRC mRNA profiles along with corresponding clinical information. Throughout the analysis, meticulous attention was given to excluding samples with missing clinical data, as well as those with a survival duration of less than 10 days, ensuring the study's reliability and precision.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Differential gene analysis\u003c/h2\u003e \u003cp\u003eThe limma package was used for the differential analysis of mRNA expression matrices between the 4-hour \u003cem\u003eC. albicans\u003c/em\u003e samples and the 24-hour \u003cem\u003eC. albicans\u003c/em\u003e infection samples\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The criteria for identifying significant mRNAs were set as follows: |log2 (fold change)| \u0026gt; 1 and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Construction and validation of mRNAs related prognostic models\u003c/h2\u003e \u003cp\u003eIn this study, R software version 4.1.0 was utilized for comprehensive data analysis. Initially, the glmnet (version 2.0.18) and survival (version 2.44.1.1) R packages were used to perform univariate Cox Proportional Hazards Model (Cox) regression, multifactor Cox regression analysis, and Less Absolute Shrinkage and Selection Operator (LASSO) regression. The evaluation of the relationship between mRNA expression levels and overall survival was made possible via univariate Cox regression, where P-values less than 0.05 were regarded as statistically significant\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe mRNAs that satisfied the criteria were subjected to LASSO regression analyses to refine their features. Subsequently, using multivariate Cox regression analysis, the prognostic effect and hazard ratio (HR) of the prediction model were assessed, and the 95% confidence intervals (CI) were computed\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing the formula Σ (expmRNAn\u0026thinsp;\u0026times;\u0026thinsp;βmRNA), the prognostic risk score was computed by summing the mRNA expression values and their respective coefficients. This risk score helped stratify samples into high- and low-risk groups. The prognostic significance of the risk scores in training, internal validation, whole cohort, and external validation were assessed using Kaplan-Meier analysis and bilateral log-rank testing with the \"survminer\" software package, with a significance level set at \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, time-dependent receiver operating characteristic (ROC) curves were created using the \"timeROC\" package to assess the accuracy of the prediction model. The AUC provides an estimate of the model's performance. All of the aforementioned indexes combined had p-values less than 0.05, which denotes significant differences. These thorough analyses contributed to the validation and evaluation of the built mRNA prognostic models' ability to predict survival and risk for CRC patients\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eIn this study, microbial informatics analysis tools (\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, accessed on August 20 2023) were utilized to perform gene enrichment analysis, covering both Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied to identify functional items and pathways that showed significant enrichment in the GO and KEGG analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Correlation analysis of immune infiltration in the mRNA prognostic model\u003c/h2\u003e \u003cp\u003eIn this study, data from the Tumor Immune Analysis database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on August 20, 2023) was leveraged to examine the intricate relationship between gene expression patterns and the infiltration of various immune cell types within the prognostic model of colorectal cancer. Additionally, information from the Clinical Letters repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.aclbi.com/static/index.html#\u003c/span\u003e\u003cspan address=\"https://www.aclbi.com/static/index.html#\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on July 30, 2023) was utilized to analyze how gene expression profiles correlate with different classifications of immune cell infiltration, particularly within the context of colorectal cancer prognosis models. This comprehensive analysis facilitated the exploration of the correlation between differentially expressed genes and the tumor immune microenvironment within prognostic models, providing insights into potential interactions between these models and dynamics of immune cell infiltration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Culture and metabolite acquisition of \u003cem\u003eC. albicans\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe experiment utilized the \u003cem\u003eC. albicans\u003c/em\u003e strain BNCC263676 (obtained from Beina Chuanglian Biotechnology Co., Ltd.). \u003cem\u003eC .albicans\u003c/em\u003e strains are typically cultured on Yield Monitoring (YM) agar medium at 30\u0026deg;C. To induce the filamentous phase of \u003cem\u003eC. albicans\u003c/em\u003e, colonies cultured on YM agar medium at 30\u0026deg;C for 24 hours were transferred to YM liquid medium and incubated on a constant temperature shaker at 37\u0026deg;C (250rpm) for 14\u0026ndash;16 hours. Subsequently, \u003cem\u003eC. albicans\u003c/em\u003e from the YM liquid medium was transferred to fresh YM liquid medium and incubated on a constant temperature shaker at 37\u0026deg;C (250rpm) for 3\u0026ndash;8 hours to stabilize the results. Meanwhile, the absorbance values at 600nm wavelength of the \u003cem\u003eC. albicans\u003c/em\u003e liquid culture medium were measured using an enzyme-linked immunosorbent assay (ELISA) reader, and \u003cem\u003eC. albicans\u003c/em\u003e YM liquid culture medium with Optical Density (OD) values of 0.2, 0.3, and 0.4 were collected. Finally, \u003cem\u003eC. albicans\u003c/em\u003e was killed by heating at 95\u0026deg;C in a water bath for 30 minutes to obtain its metabolites mixture.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Cell viability was measured by CCK-8\u003c/h2\u003e \u003cp\u003eNCM460, HT29, and HCT 116 cells were harvested to prepare a cell suspension. Each well of the 96-well plate was seeded with 100\u0026micro;L of cell suspension, ensuring a cell concentration of 1\u0026times;10\u003csup\u003e4\u003c/sup\u003e-10\u003csup\u003e5\u003c/sup\u003e cells per well. Sterile PBS buffer was added to the surrounding wells of each cell suspension well. The 96-well plates containing the cell suspensions were placed in a cell incubator (37℃, 5% CO\u003csub\u003e2\u003c/sub\u003e) for standard incubation. Upon cell adhesion, \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture with OD values of 0.2, 0.3, and 0.4 were applied for intervention, with exposure durations of 12 hours, 24 hours, 48 hours, and 72 hours to assess their impact on cell viability. After removing the surface medium, 10\u0026micro;L of CCK-8 solution was added directly to each well, followed by a 2-hour incubation in the incubator. The absorbance at 450nm was then measured using an enzyme-linked instrument. The ultimate intervention concentration and duration were selected through screening, with OD values serving as the criteria to differentiate between concentrations in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Invasion and migration\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.8.1 Invasion\u003c/h2\u003e \u003cp\u003eThe matrix glue was naturally melted overnight at 4\u0026deg;C, and the microinjection gun and its tip (blue and yellow tip) used in the experiment were pre-frozen at -20\u0026deg;C. The matrix glue was prepared by mixing 2.5\u0026micro;l of Matrigel per well with 97.5\u0026micro;l of medium (without bovine serum and pre-stored at 4\u0026deg;C), gently mixed at the end of the gun and added to the central upper chamber of each 24-well Transwell plate with 100\u0026micro;l of mixture per well. Incubate at 37\u0026deg;C for 30 minutes to 1 hour. After the matrix glue was dried, normal digestion group and \u003cem\u003eC. albicans\u003c/em\u003e metabolite intervention group cells, washed (3 times), and 100\u0026micro;l HTR8 cells were suspended in the upper chamber, while 600\u0026micro;l medium containing 10% FBS was added to the lower chamber, and the culture was continued for 48 hours. After incubation, the liquid in the upper and lower chambers was discarded, washed 3 times with PBS, and then fixed with 4% paraformaldehyde for 30 minutes. After fixing, wash with PBS for 3 times again, add crystal violet and dye for 15 minutes. After staining, the chamber was cleaned with PBS and photographs were taken under a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.8.2 Migration\u003c/h2\u003e \u003cp\u003eDigestive \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture intervention in cells after treatment was compared with normal groups, washed three times, and then suspended in 100\u0026micro;l of medium in the upper chamber. Additionally, 600\u0026micro;l of medium containing 10% FBS was added to the lower chamber for a 48-hour incubation period. Following the completion of the incubation, the fluids from both the upper and lower chambers were discarded, and the chambers were rinsed three times with PBS. Subsequently, 4% paraformaldehyde was applied for fixation and allowed to incubate for 30 minutes. After the fixation period, the chambers were again rinsed three times with PBS before being stained with crystal violet for 15 minutes. Following staining, the chambers were rinsed with PBS and then imaged under a microscope.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.9 PCR was used to detect gene expression in the model\u003c/h2\u003e \u003cp\u003eIn this study, quantitative real-time polymerase chain reaction (qRT-PCR) was employed to assess the expression of model genes. Colorectal cancer cells were seeded in 6-well plates at a density of 5\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells per well. Following treatment with the metabolic mixture, total RNA was extracted from the cells using a RNA extraction kit. Subsequently, complementary DNA (cDNA) was synthesized using a reverse transcription kit. qRT-PCR assays were conducted utilizing the CFX96TM real-time PCR detection system as per the manufacturer's instructions. The expression levels of the target genes were normalized to the expression of GAPDH, serving as an internal control. Detailed information regarding the primer sequences utilized in the experiments is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003e qRT-PCR primer sequence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene (Rabbit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduct length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLogin ID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:GCAAAGTGGATGTTGTCGCC\u003c/p\u003e \u003cp\u003eR:TGATGACCAGCTTCCCGTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNM_001082253.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDFT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: AGATTCGGAAAGGGCAAGCA\u003c/p\u003e \u003cp\u003eR:AACGACAGGTAGATGGGGGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNM_001287742.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:TCAACCAGACCACCTTATACC\u003c/p\u003e \u003cp\u003eR: GCATTCCTCACAGCCAACAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNM_003254.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGADD45B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GCCCTGCAAATCCACTTCAC\u003c/p\u003e \u003cp\u003eR: GTTCGTGACCAGGAGACAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNM_015675.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIME1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GGAAGCGCAAGTCGGACAC\u003c/p\u003e \u003cp\u003eR: CACGTTGGAATAGGTGGCCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNM_001305654.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEHD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CTTCGAGAACAAGCCCATGA\u003c/p\u003e \u003cp\u003eR: TGCCCTCAGTCTCTCCATACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNM_139265.4\u003c/p\u003e \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=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Extracellular ATP content detection\u003c/h2\u003e \u003cp\u003eIn this investigation, eATP levels were assessed. Consequently, the culture medium was aspirated, approximately 0.1 mL of the medium was extracted, and 1mL of extraction solution was introduced. After thorough agitation, centrifugation was carried out at 10,000g for 10 minutes at 4\u0026deg;C. The resulting supernatant was then transferred to another EP tube, to which 500\u0026micro;L of chloroform was added and thoroughly mixed. Subsequent centrifugation at 10,000g for 3 minutes at 4\u0026deg;C was performed, followed by transferring the supernatant onto ice for measurement. The determination process involved the following steps: (1) Preheating the Ultraviolet (UV) spectrophotometer/ELISA for a minimum of 30 minutes, adjusting the wavelength to 340nm, and zeroing with distilled water. (2) Diluting the ATP standard solution of 10 \u0026micro;mol/mL by 16-fold with distilled water, resulting in a 0.625 \u0026micro;mol/mL standard solution. (3) Preparing the working solution according to the ratio of reagent 2 (mL) : reagent 3 (mL) : reagent 4 (mL) : reagent 5 (mL) : reagent 6 (mL)\u0026thinsp;=\u0026thinsp;1:1:0.1:0.4:0.1 before usage. The procedure for adding the samples to the 96-well UV plate is as follows (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003e Loading list\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReagent name(\u0026micro;L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasuring tube\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard tube\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estandard liquid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReagent I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoperating fluid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\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\u003eFollowing thorough mixing, the absorbance value A1 was promptly measured at 340nm for 10 seconds. Subsequently, the colorimetric plate, along with the reaction solution, was immersed in a water bath set to 37\u0026deg;C (for mammals) or 25\u0026deg;C (for other species) for 3 minutes. At 3 minutes and 10 seconds, the absorbance value A2 was promptly determined. The 96-well plate was then incubated in a 37\u0026deg;C (for mammals) or 25\u0026deg;C (for other species) incubator. If the enzyme marker incorporates temperature control functionality, the temperature was adjusted accordingly. The ΔA measurement was calculated as A2 measurement tube - A1 measurement tube, and ΔA standard as A2 standard tube - A1 standard tube, respectively. The ATP content (\u0026micro;mol/mL) was determined using the formula: ATP content\u0026thinsp;=\u0026thinsp;ΔA determination / (ΔA standard / C standard)\u0026times;(Vextraction\u0026thinsp;+\u0026thinsp;Vserum (pulp)) / V serum (pulp)\u0026thinsp;=\u0026thinsp;6.875\u0026thinsp;\u0026times;\u0026thinsp;ΔA determination / ΔA standard.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Establishment of a Model Linking C. albicans Differential mRNA Features to Colorectal cancer Prognosis\u003c/h2\u003e \u003cp\u003eUnder the thresholds of FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and |log2 FC|\u0026gt;1, 213 differentially expressed genes were discovered, of which 135 were up-regulated and 78 down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eAfter doing univariate Cox proportional hazards regression analysis on the 214 mRNAs within the training cohort, it was observed that 7 of them had p-values less than 0.05 (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Following this, the selection process was enhanced using LASSO-Cox regression analysis, eventually choosing 5 mRNAs for inclusion in the training cohort's prognostic model (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and D). Utilizing these chosen mRNA features, a novel risk scoring formula was formulated as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk score\u0026thinsp;=\u0026thinsp;0.245a\u003c/b\u003e \u003csub\u003e \u003cb\u003e1\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e-0.378a\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e\u0026thinsp;\u003cb\u003e+\u0026thinsp;0.318a\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e-0.357a\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cb\u003e+\u0026thinsp;0.3184a\u003c/b\u003e\u003csub\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eTag:a\u003c/b\u003e \u003csub\u003e \u003cb\u003e1\u003c/b\u003e \u003c/sub\u003e:\u003cb\u003eLIME1\u003c/b\u003e; \u003cb\u003ea\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e:\u003cb\u003eEHD4\u003c/b\u003e; \u003cb\u003ea\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e:\u003cb\u003eGADD45B\u003c/b\u003e; \u003cb\u003ea\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e:\u003cb\u003eFDFT1\u003c/b\u003e;\u003cb\u003ea\u003c/b\u003e\u003csub\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sub\u003e:\u003cb\u003eTIMP1\u003c/b\u003e;\u003c/p\u003e \u003cp\u003eUsing the risk score formula from Section 4.2, we computed the overall risk score for each patient (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Subsequently, the training cohort and the testing set cohort was used, the robustness of these traits was assessed (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Patients in the high-risk group had considerably shorter OS than those in the low-risk group, according to Kaplan-Meier survival analysis (KM) and a two-sided log-rank test on the full dataset (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In the testing and training set cohorts, additional validation of the prognostic effect of this risk score was carried out, and it was shown that the OS difference between the high-risk and low-risk groups was comparable to and significant (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) to the OS (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and C). Furthermore, the AUC value derived from time-dependent ROC curve analysis for the overall population stood at 0.76 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Regarding the 5-year OS predictions, the AUC values obtained from time-dependent ROC curve analysis were 0.76 for the training set and 0.70 for the test set cohort (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and C).\u003c/p\u003e \u003cp\u003eThe risk ratings for each patient were calculated using the established technique, and the utility of these features in the GEO dataset's validation cohort was assessed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) was assessed. The GEO dataset showed a substantial difference in overall survival rates between high-risk and low-risk groups, which aligns with our findings in TCAG (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Additionally, it was observed that the AUC values of the ROC curves varied over time, with values of 0.62, 0.67, and 0.66 at 1 year, 3 years, and 5 years respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), indicating the successful establishment of the model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Differential gene expression mRNA and gene set enrichment analysis in high and low risk groups\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, compared with the low-risk group, 17 genes were up-regulated and 12 genes were down-regulated in the high-low-risk group (adjusted p value 0.05, and |log2 FC|\u0026gt;1). BP enrichment results show they are mainly involved in extracellular matrix organization, extracellular structure organization, antimicrobial humoral response and other signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). MF enrichment results show that they are mainly involved in glycosaminoglycan binding, extracellular matrix structural constituen, Glycosaminoglycan binding, extracellular matrix structural constituen, oligosaccharide binding and other signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), CC enrichment results showed that they were mainly involved in collagen\u0026thinsp;\u0026minus;\u0026thinsp;containing extracellular matrix,Golgi lumen,zymogen granule and other signaling pathways (Figure. 5D). KEGG enrichment results showed that they were mainly involved in Ascorbate and aldarate metabolism,Chemical carcinogenesis\u0026thinsp;\u0026minus;\u0026thinsp;DNA adducts and other signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Immune infiltration analysis of prognostic model genes\u003c/h2\u003e \u003cp\u003eTo estimate the immune infiltrating population by CIBERSORT algorithm, LIME1 is negatively correlated with Macrophage, NK cell, T cell CD4+. GADD45B is positively correlated with Endothelial cell, Macrophage and NK cell, and negatively correlated with T cell CD8\u0026thinsp;+\u0026thinsp;and uncharacterized cell. FDFT1 is negatively correlated with Endothelial cell and Macrophage. EHD4 is positively correlated with B cell, Endothelial cell, Macrophage, NK cell and T cell CD4+(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 The CCK8 method was used to assess the cell viability after intervention with different concentrations of\u003c/b\u003e \u003cb\u003eC. albicans\u003c/b\u003e \u003cb\u003emetabolites mixture and at different time points\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this work, the cytotoxicity of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture on cells was evaluated by measuring cell viability using the CCK8 assay. As the concentration increased and the duration of exposure lengthened, cell viability decreased. For HT29 cells, under conditions of a concentration OD value of 0.4 and intervention time of 24 hours, the survival rate was approximately 53%, indicating that exposed cells had been affected by \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture. Therefore, cells under the condition of an OD value of 0.4 and intervention time of 24 hours were used for subsequent experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Similarly, for HCT116 cells, under conditions of a concentration OD value of 0.4 and intervention time of 24 hours, the survival rate was approximately 54%, suggesting that exposed cells had been affected by \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture, and thus cells under the condition of an OD value of 0.4 and intervention time of 24 hours were selected for subsequent experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Likewise, for NCM460 cells, under conditions of a concentration OD value of 0.3 and intervention time of 24 hours, the survival rate was approximately 57%, indicating that exposed cells had been affected by \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture, and thus cells under the condition of an OD value of 0.3 and intervention time of 24 hours were used for subsequent experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Comparison of invasion and migration ability of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture in Colorectal cancer groups\u003c/h2\u003e \u003cp\u003eRelative to the control group, the experimental group exhibited a significant reduction in the number of invasive cells of HCT116 \u003cem\u003e(P\u0026thinsp;=\u0026thinsp;0.001)\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA.D.G), HT29 \u003cem\u003e(P\u0026thinsp;=\u0026thinsp;0.0044)\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB.E.H), and NCM460 cells \u003cem\u003e(P\u0026thinsp;=\u0026thinsp;0.00076)\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC.F.I) after 48 hours of intervention. Similarly, in comparison to the control group, the experimental group demonstrated a significant decrease in the number of migratory cells of HCT116 (\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.001\u003c/em\u003e) (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA.D.G), HT29 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB.E.H), and NCM460 cells \u003cem\u003e(P\u0026thinsp;=\u0026thinsp;0.047)\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC.F.I) after 48 hours of intervention.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The effect of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture on Extracellular ATP levels in Colorectal cancer cells\u003c/h2\u003e \u003cp\u003eCompared to the control group, the eATP levels significantly increased in the experimental group after intervention for HCT116 \u003cem\u003e(P\u0026thinsp;=\u0026thinsp;0.0076)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA), HT29 \u003cem\u003e(P\u0026thinsp;=\u0026thinsp;0.0013)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB), and NCM460 \u003cem\u003e(P\u0026thinsp;=\u0026thinsp;0.0052)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Expression levels of \u003cem\u003eFDFT1, TIMP1, GADD45B, LIME1\u003c/em\u003e and \u003cem\u003eEHD4\u003c/em\u003e in cells of each group\u003c/h2\u003e \u003cp\u003eThe expression levels of \u003cem\u003eFDFT1\u003c/em\u003e in CRC cells HT29 and HCT116 were considerably higher (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) than in normal colorectal epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA); the expression level of \u003cem\u003eTIMP1\u003c/em\u003e in HT29 cells was significantly increased \u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB), while in HCT116 cells, it was significantly decreased \u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB); the expression level of \u003cem\u003eGADD45B\u003c/em\u003e in HCT116 cells was significantly decreased \u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC); and the expression levels of \u003cem\u003eEHD4\u003c/em\u003e and \u003cem\u003eLIME1\u003c/em\u003e were significantly decreased \u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD and E). After the intervention with white fungus metabolites mixture, the expression level of \u003cem\u003eFDFT1\u003c/em\u003e in HCT116 cells did not change significantly \u003cem\u003e(P\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA); however, the expression levels of \u003cem\u003eTIMP1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB), \u003cem\u003eGADD45B (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC), \u003cem\u003eLIME1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eD), and \u003cem\u003eEHD4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eE) were increased. After the intervention, the expression levels of \u003cem\u003eFDFT1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA), \u003cem\u003eTIMP1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eB), \u003cem\u003eGADD45B\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eC), \u003cem\u003eLIME1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eD), and \u003cem\u003eEHD4\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eE) in HT29 cells were significantly decreased \u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/em\u003e. After intervention, the expression levels of \u003cem\u003eFDFT1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eA), \u003cem\u003eTIMP1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eB), and \u003cem\u003eGADD45B (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eC) in NCM460 cells were significantly increased, while the expression levels of \u003cem\u003eLIME1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eD) and \u003cem\u003eEHD4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eE) were significantly decreased.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe prognostic model\u0026apos;s updated risk score algorithm was created as follows: Score for risk\u0026thinsp;=\u0026thinsp;0.245a1-0.378a2\u0026thinsp;+\u0026thinsp;0.318a3-0.357a4\u0026thinsp;+\u0026thinsp;0.3184a5\u003c/p\u003e\n\u003cp\u003eTag:a1:LIME1; a2:EHD4; a3:GADD45B; a4:FDFT1;a5:TIMP1;\u003c/p\u003e\n\u003cp\u003e\u0026quot;\u003cem\u003eEHD4\u003c/em\u003e\u0026quot; represents Eps15 Homology Domain 4, which encodes a gene associated with the endogenous protein \u003cem\u003eEHD4\u003c/em\u003e\u003csup\u003e[\u003cspan\u003e14\u003c/span\u003e, \u003cspan\u003e15\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eEHD4\u003c/em\u003e belongs to the Eps15 Homology Domain (\u003cem\u003eEHD\u003c/em\u003e) family, whose proteins play important biological functions within cells, particularly involving activities related to endocytosis and cellular membrane trafficking. The C-terminal \u003cem\u003eEHD\u003c/em\u003e proteins play crucial roles in regulating membrane transport during endocytosis. To put it simply, the amino acid sequences of the four \u003cem\u003eEHD\u003c/em\u003e proteins (\u003cem\u003eEHD1-EHD4\u003c/em\u003e) that have been identified in mammals found to have a 70\u0026ndash;86% sequence identity in common. Among them, actin filaments allow \u003cem\u003eEHD2\u003c/em\u003e, the least preserved a member of the family \u003cem\u003eEHD\u003c/em\u003e, to interface with the plasma membrane. It has been demonstrated that \u003cem\u003eEHD2\u003c/em\u003e takes part in membrane resealing/fusion in muscle cells, which controls the actions of many cytoskeletal proteins in diverse cellular configurations. There is evidence linking \u003cem\u003eEHD2\u003c/em\u003e to the development of several malignant tumours. Prior research has demonstrated a positive correlation between increased malignancy and \u003cem\u003eEHD2\u003c/em\u003e downregulation. Tumor tissues have downregulated levels of \u003cem\u003eEHD2\u003c/em\u003e, particularly in cases of advanced and poorly differentiated malignancies. For patients with upregulated \u003cem\u003eEHD2\u003c/em\u003e levels, their survival rates are significantly correlated with prolonged overall survival. According to all of these findings, \u003cem\u003eEHD2\u003c/em\u003e might represent a separate prognostic factor for CRC\u003csup\u003e[\u003cspan\u003e16\u003c/span\u003e]\u003c/sup\u003e. The potential mechanisms through which miR-4701-3p and miR-4793-3p trigger apoptosis in CRC cells were investigated. Screening using MirTarBase identified 62 shared targets for these miRNAs, such as \u003cem\u003eSMARCA5, MBD3, VPS53\u003c/em\u003e, and \u003cem\u003eEHD4.\u003c/em\u003e These targets significantly influenced the endocytic cycle pathway and ACR. \u003cem\u003eVPS53\u003c/em\u003e and \u003cem\u003eEHD4\u003c/em\u003e are connected to endocytic cycling, whereas \u003cem\u003eSMARCA5\u003c/em\u003e and \u003cem\u003eMBD3\u003c/em\u003e are linked to ACR\u003csup\u003e[\u003cspan\u003e17\u003c/span\u003e]\u003c/sup\u003e. However, the role of \u003cem\u003eEHD4\u003c/em\u003e in the occurrence and development of rectal cancer and its prognostic factors remain unknown.\u003c/p\u003e\n\u003cp\u003eCell division, apoptosis, and DNA damage repair all depend on \u003cem\u003eGADD45B\u003c/em\u003e, a member of the growth arrest and DNA damage-inducible gene family. In II stage CRC cohorts exhibiting elevated \u003cem\u003eGADD45B\u003c/em\u003e expression, individuals subjected to adjuvant chemotherapy demonstrated significantly prolonged progression-free survival (PFS) compared to their counterparts who did not receive such treatment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Elevated expression levels of GADD45B serve as an autonomous prognostic determinant associated with diminished OS and PFS among patients diagnosed with stage II CRC. Therefore, adjuvant chemotherapy may be beneficial for patients with II stage CRC who express high levels of \u003cem\u003eGADD45B\u003c/em\u003e\u003csup\u003e[\u003cspan\u003e18\u003c/span\u003e]\u003c/sup\u003e. Moreover, heightened expression of \u003cem\u003eGADD45B\u003c/em\u003e correlates significantly with both recurrence and mortality rates in CRC patients \u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e. \u003cem\u003eGADD45B\u003c/em\u003e overexpression in CRC patients is associated with a considerably lower DFS, according to KM survival curves. \u003cem\u003eGADD45B\u003c/em\u003e overexpression and Tumor Node Metastasis (TNM) staging are significant factors impacting patient survival, as confirmed by Cox multivariate analysis\u003csup\u003e[\u003cspan\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA pathological diagnosis study involving 819 CRC patients demonstrated that the overall sensitivity, specificity, and diagnostic odds ratio DOR of \u003cem\u003eTIMP-1\u003c/em\u003e for diagnosing CRC were 0.65, 0.87, and 12.73, respectively. The area under the summary receiver operating characteristic curve was 0.77, indicating the potential diagnostic value of \u003cem\u003eTIMP-1\u003c/em\u003e in CRC patients. In patients with a 20% probability of CRC, the post-test probabilities for \u003cem\u003eTIMP-1\u003c/em\u003e positivity and negativity were 56% and 9%, respectively. According to the study\u0026apos;s findings, \u003cem\u003eTIMP-1\u003c/em\u003e has a moderate to high level of sensitivity and specificity, making it a possible biomarker for CRC diagnosis. Therefore, the detection of \u003cem\u003eTIMP-1\u003c/em\u003e holds promise as a valuable non-invasive screening modality for CRC in clinical settings\u003csup\u003e[\u003cspan\u003e19\u003c/span\u003e]\u003c/sup\u003e. Another study, using 43 CRC patients and 24 healthy volunteers as controls, discovered that the serum content of \u003cem\u003eTIMP-1\u003c/em\u003e in CRC patients was considerably greater than in the healthy control group. Increased serum \u003cem\u003eTIMP-1\u003c/em\u003e levels were associated with female gender, tumor location in the colon, tumor dedifferentiation, and increased whole blood platelet count\u003csup\u003e[\u003cspan\u003e20\u003c/span\u003e]\u003c/sup\u003e. Additionally, the study results emphasized the overexpression of \u003cem\u003eTIMP-1\u003c/em\u003e in colorectal tumor tissues and lymph node metastasis specimens\u003csup\u003e[\u003cspan\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe downregulation of \u003cem\u003eFDFT1\u003c/em\u003e is intricately associated with the malignant progression and adverse prognosis of CRC. Furthermore, \u003cem\u003eFDFT1\u003c/em\u003e has been elucidated as a pivotal tumor suppressor in CRC. Mechanistically, \u003cem\u003eFDFT1\u003c/em\u003e exerts its tumor-suppressive effects by disrupting the AKT/mTOR/HIF1\u0026alpha; signaling pathway\u003csup\u003e[\u003cspan\u003e22\u003c/span\u003e]\u003c/sup\u003e. Furthermore, numerous studies have elucidated the pervasive downregulation of \u003cem\u003eFDFT1\u003c/em\u003e expression in CRC\u003csup\u003e[\u003cspan\u003e23\u003c/span\u003e, \u003cspan\u003e24\u003c/span\u003e]\u003c/sup\u003e. As for \u003cem\u003eLIME1\u003c/em\u003e, its role in CRC has not been reported yet.\u003c/p\u003e\n\u003cp\u003eFurthermore, a unique prognostic model based on four important genes, \u003cem\u003eCXCL8\u003c/em\u003e, \u003cem\u003eIL13RA2\u003c/em\u003e, \u003cem\u003eMELK\u003c/em\u003e, and \u003cem\u003ePOP1\u003c/em\u003e, has been created to accurately predict the survival of CRC patients. The finding might offer a new perspective on treating CRC associated with pyroptosis\u003csup\u003e[\u003cspan\u003e25\u003c/span\u003e]\u003c/sup\u003e. Yet another study devised a prognostic prognostication model comprising five genes (\u003cem\u003eRPX, CXCL13, MMP10, FABP4, CLDN23\u003c/em\u003e), creating another novel prognostic model for CRC\u003csup\u003e[\u003cspan\u003e26\u003c/span\u003e]\u003c/sup\u003e. This study successfully constructed a prognostic model for CRC associated with \u003cem\u003eC. albicans\u003c/em\u003e, which accurately predicts the survival of CRC patients, while also providing a scientific basis for treatment.\u003c/p\u003e\n\u003cp\u003eThe research results indicated that with increasing concentration and prolonged exposure time, cell viability decreased. For HT29 cells, under the condition of a concentration with an OD value of 0.4 and an intervention time of 24 hours, the survival rate was approximately 53%, indicating that the exposed cells were affected by \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture. Similarly, for HCT116 cells, under the conditions of a concentration with an OD\u003csub\u003e600\u003c/sub\u003e value of 0.4 and an intervention time of 24 hours, the survival rate was approximately 54%. For NCM460 cells, under the conditions of a concentration with an OD\u003csub\u003e600\u003c/sub\u003e value of 0.3 and an intervention time of 24 hours, the survival rate was approximately 57%.\u003c/p\u003e\n\u003cp\u003e\u0026beta;-glucan was isolated from the cellular membrane of \u003cem\u003eC. albicans\u003c/em\u003e. Within 48 hours, the viability of MSCs decreased significantly, but it was dose-dependent. The research indicated that treating MSC with \u0026beta;-glucan increased cancer cell apoptosis\u003csup\u003e[\u003cspan\u003e27\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eC. albicans\u003c/em\u003e promoted proliferation of WSU-HN4, WSU-HN6, and CAL27 cells in oral squamous cell carcinoma (OSCC) via the TLR2/MyD88 pathway, as demonstrated by CCK-8 experiments. OSCC cells treated with zymosan exhibited a significantly elevated density of \u003cem\u003eC. albicans\u003c/em\u003e cells per field compared to the control group\u003csup\u003e[\u003cspan\u003e27\u003c/span\u003e]\u003c/sup\u003e. Physical obstruction of Sanguisorba officinalis by \u003cem\u003eC. albicans\u003c/em\u003e \u0026beta;-glucan may weaken the antifungal activity of its derivative, sodium sanguinarine. Exposure to \u0026beta;-glucan induced by sodium sanguinarine significantly enhanced the phagocytic capacity of \u003cem\u003eC. albicans\u003c/em\u003e and inhibited its growth\u003csup\u003e[\u003cspan\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eKEGG enrichment results showed that they were mainly involved in Ascorbate and aldarate metabolism,Chemical carcinogenesis\u0026thinsp;\u0026minus;\u0026thinsp;DNA adducts and other signaling pathways. Chemical carcinogenesis is a common mechanism of cancer that involves in vivo or in vitro exposure to specific chemicals that interact with DNA to form DNA adducts. These DNA adducts may interfere with DNA replication and repair processes, leading to the accumulation of DNA damage and error repair, which may eventually lead to mutations in the cell\u0026apos;s genetic information. These mutations may affect the growth regulation, apoptosis and repair ability of cells, thus promoting the occurrence and development of cancer \u003csup\u003e[\u003cspan\u003e29\u003c/span\u003e]\u003c/sup\u003e. In human tissues, there is a positive association between the highest levels of DNA adducts in blood cells or target organs and the highest risk of cancer; The magnitude of the increased risk was similar when DNA adducts were measured in blood cell DNA or target tissue DNA; In individuals with the highest levels of DNA adducts, the increase in cancer risk is generally less than 10, however, an increase in a second strong cancer risk factor may increase these numbers exponentially \u003csup\u003e[\u003cspan\u003e30\u003c/span\u003e]\u003c/sup\u003e. In addition, studies have shown that DNA adducts in colorectal cancer tissues are significantly higher than those in the control group and up-regulation of ascorbic acid and uronate metabolism and fatty acid degradation may enhance the immunosuppression of Tregs\u003csup\u003e[\u003cspan\u003e31\u003c/span\u003e]\u003c/sup\u003e. Enhanced metabolism of alpha-linolenic acid, linoleic acid and arachidonic acid may inhibit the pro-inflammatory function of CD4\u0026thinsp;+\u0026thinsp;tcm and CD8\u0026thinsp;+\u0026thinsp;TEMs in PSO and PSA, and protect the immunosuppression of Tregs in PSA\u003csup\u003e[\u003cspan\u003e32\u003c/span\u003e]\u003c/sup\u003e. This signaling pathway is consistent with the results of immunoinfiltration in this study.\u003c/p\u003e\n\u003cp\u003eCell migration, sometimes referred to as cell motility, cell crawling, or cell movement, and the term used to describe how cells migrate in response to migratory signals or specific chemical gradients. Cell migration involves the extension of pseudopodia at the forefront of cellular protrusion, the establishment of new adhesions, and the contraction of the cell body at the trailing edge in a spatiotemporal manner. Cell invasion is a type of cell migration and is inseparable from it. It refers to the process in which cells break through the basement membrane in situ, then infiltrate into blood vessels or lymphatic vessels, namely the invasion of cells (such as malignant tumor cells), which penetrate through the extracellular matrix or basement membrane extracellular matrix from one area to another. After a 48-hour intervention, the experimental group\u0026apos;s HCT116, HT29, and NCM460 invasive cell count was considerably lower than that of the control group, which inhibited CRC cells from migrating and invading.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC. albicans\u003c/em\u003e, a common human fungal infection, colonizes the cutaneous and mucosal surfaces of the majority of healthy individuals\u003csup\u003e[\u003cspan\u003e33\u003c/span\u003e]\u003c/sup\u003e. This study presents novel findings on the influence of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture on the invasion and migration of CRC cells, contributing to the expanding body of scientific knowledge elucidating the dual roles of \u003cem\u003eC. albicans\u003c/em\u003e in promoting and ameliorating CRC. Previous research has shown that 786-O cells\u0026apos; ability to proliferate, migrate, and invade can be inhibited by upregulating the expression of \u003cem\u003eFDFT1\u003c/em\u003e, with \u003cem\u003eFDFT1\u003c/em\u003e inhibition leading to reduced cell movement\u003csup\u003e[\u003cspan\u003e34\u003c/span\u003e, \u003cspan\u003e35\u003c/span\u003e]\u003c/sup\u003e. In HT29 and HCT116 CRC cells, \u003cem\u003eFDFT1\u003c/em\u003e expression levels were significantly increased, which may be associated with enhanced invasive and migratory abilities. However, after metabolite intervention, \u003cem\u003eFDFT1\u003c/em\u003e expression levels were significantly decreased in HT29 and HCT116 cells, while significantly increased in NCM460 cells, which may be correlated with decreased invasive and migratory abilities, as confirmed by related studies\u003csup\u003e[\u003cspan\u003e36\u003c/span\u003e]\u003c/sup\u003e. In HT29 cells, \u003cem\u003eTIMP1\u003c/em\u003e expression levels were significantly increased, while significantly decreased in HCT116 cells compared to NCM460 cells. \u003cem\u003eTIMP1\u003c/em\u003e is typically associated with tissue metalloproteinase inhibition, with its upregulation possibly inhibiting cell invasion and migration, and its downregulation possibly promoting cell invasion and migration\u003csup\u003e[\u003cspan\u003e24\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, studies have elucidated that heightened \u003cem\u003eTIMP1\u003c/em\u003e expression fosters the in vivo proliferation of human HCT116 and HT29 colon cancer cells\u003csup\u003e[\u003cspan\u003e37\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eTIMP1\u003c/em\u003e deficiency can suppress the proliferation, migration, and invasion of colon cancer cells. The relationship between \u003cem\u003eTIMP1\u003c/em\u003e overexpression and invasion and migration in HCT116 and HT29 cells requires further research. In HCT116 cells, \u003cem\u003eGADD45B\u003c/em\u003e expression levels were significantly decreased, while in HT29 cells, \u003cem\u003eGADD45B\u003c/em\u003e expression levels were slightly increased. \u003cem\u003eGADD45B\u003c/em\u003e is typically associated with DNA damage response and cell cycle regulation, with its downregulation possibly inhibiting invasion and migration of HT29 cells, and its upregulation possibly promoting invasion and migration of HCT116 cells. However, after \u003cem\u003eC. albicans\u003c/em\u003e intervention, \u003cem\u003eGADD45B\u003c/em\u003e expression levels were decreased, possibly inhibiting invasion and migration of HT29 cells, but had little effect on \u003cem\u003eGADD45B\u003c/em\u003e expression levels in HCT116 cells. The overexpression of \u003cem\u003eGADD45B\u003c/em\u003e represents an autonomous risk factor associated with diminished survival outcomes in patients diagnosed with EOC, leading to a reduction in both PFS duration and OS. Elevated \u003cem\u003eGADD45B\u003c/em\u003e expression is associated with venous infiltration, lymphatic infiltration, and peritoneal cancer. \u003cem\u003eGADD45B\u003c/em\u003e downregulation decreases Endometrial stromal sarcoma cell line 2 (ES2) and SKOV3 cell motility. Further KEGG enrichment analysis and Gene Set Enrichment Analysis (GSEA) indicate that Epithelial-mesenchymal transition (EMT) might be a \u003cem\u003eGADD45B\u003c/em\u003e downstream pathway. Moreover, diminished GADD45B expression leads to an upregulation of E-cadherin expression and a downregulation of N-cadherin and vimentin expression\u003csup\u003e[\u003cspan\u003e21\u003c/span\u003e, \u003cspan\u003e38\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eEHD4\u003c/em\u003e and \u003cem\u003eLIME1\u003c/em\u003e: In HCT116 and HT29 cells, expression levels of \u003cem\u003eEHD4\u003c/em\u003e and \u003cem\u003eLIME1\u003c/em\u003e were significantly decreased, with significant upregulation of \u003cem\u003eEHD4\u003c/em\u003e and \u003cem\u003eLIME1\u003c/em\u003e expression levels in HCT116 cells after intervention, and lower expression levels of \u003cem\u003eEHD4\u003c/em\u003e and \u003cem\u003eLIME1\u003c/em\u003e in HT29 cells after intervention. The notable elevation in the expression levels of EHD4 and LIME1 may exert an inhibitory effect on the invasion and migration of HCT116 cells, whereas the reduced expression levels of EHD4 and LIME1 may impede the invasion and migration of HT29 cells. Although the alterations in these genes\u0026apos; expression may have an impact on CRC cells\u0026apos; capacity for invasion and migration, deeper research and experimental validation are needed to determine the precise processes.\u003c/p\u003e\n\u003cp\u003eFollowing intervention, the experimental cohort exhibited notably elevated eATP levels in contrast to the control group. Elevated concentrations of eATP can exert direct or indirect effects on cancer cells. Henceforth, purinergic receptors have been documented to be conspicuously expressed in neoplastic cells. CRC cells predominantly manifest purinergic receptors \u003cem\u003eA2B, P2X4, P2Y1, P2Y2\u003c/em\u003e, and \u003cem\u003eP2Y11\u003c/em\u003e. Of these, in contrast to HCEC-1CT normal colon cells, the genes coding for \u003cem\u003eP2Y1\u003c/em\u003e and \u003cem\u003eP2Y2\u003c/em\u003e receptors exhibit notable upregulation across all CRC cell lines. eATP is always more effective against CRC than adenosine in terms of inducing cell death\u003csup\u003e[\u003cspan\u003e39\u003c/span\u003e]\u003c/sup\u003e. These results indicate that \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture may induce cell death and inhibit proliferation of CRC cells by releasing more ATP into the extracellular space. As per research findings, elevated concentrations (1\u0026ndash;10 mM) of ATP and its analogs like AMP-PNP and ATP\u0026gamma;s, exhibit a PKC blocking effect in Caco-2 cells akin to GF109203X, thereby impeding proliferation during the S phase of the cell cycle\u003csup\u003e[\u003cspan\u003e40\u003c/span\u003e]\u003c/sup\u003e. Studies have revealed that STC2 modulates PKC activity, regulates Claudin-1 expression, and triggers the expression of EMT-related factors such as ZEB1, ZO-1, Slug, Twist, and MMP9. Suppression of STC2 leads to diminished motility in cells, but inhibiting PKC activity with a PKC inhibitor (Go 6983) can restore normal motility in STC2-silenced cells, while 231 HM cells exhibit restricted migration and invasion\u003csup\u003e[\u003cspan\u003e41\u003c/span\u003e]\u003c/sup\u003e. ATP downregulates the \u003cem\u003eP2Y11\u003c/em\u003e purinergic receptor and \u003cem\u003eCXCR4\u003c/em\u003e to prevent bone metastases and breast cancer migration\u003csup\u003e[\u003cspan\u003e42\u003c/span\u003e]\u003c/sup\u003e. By inducing CRC cell death and inhibiting proliferation in the S phase of the cell cycle, it is revealed that high eATP levels may inhibit the invasion and migration of CRC cells, a process that may be correlated with \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture. Moreover, in recent years, ATP-induced cell death, identified as a discernible form of cell demise triggered by heightened eATP levels, has surfaced as intricately linked with the advancement of diverse cancer types\u003csup\u003e[\u003cspan\u003e43\u003c/span\u003e]\u003c/sup\u003e. We hypothesize that \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture may mediate the invasion, migration, and development of CRC through ATP-induced cell death mechanisms, which require further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFDFT1\u003c/em\u003e expression levels were significantly higher in CRC cells HT29 and HCT116 compared to normal colorectal epithelial cells, whereas \u003cem\u003eTIMP1\u003c/em\u003e expression was significantly higher in HT29 cells but decreased in HCT116 cells. Additionally, In HCT116 cells, there was a notable reduction observed in the expression levels of \u003cem\u003eGADD45B\u003c/em\u003e, alongside diminished levels of \u003cem\u003eEHD4\u003c/em\u003e and \u003cem\u003eLIME1\u003c/em\u003e expression. Following intervention with white fungus metabolites mixture, \u003cem\u003eFDFT1\u003c/em\u003e expression in HCT116 cells did not change significantly; however, \u003cem\u003eTIMP1, GADD45B, LIME1\u003c/em\u003e, and \u003cem\u003eEHD4\u003c/em\u003e expression levels were increased. In HT29 cells, expression levels of \u003cem\u003eFDFT1, TIMP1, GADD45B, LIME1\u003c/em\u003e, and \u003cem\u003eEHD4\u003c/em\u003e were significantly decreased after intervention. In NCM460 cells, expression levels of \u003cem\u003eFDFT1\u003c/em\u003e, \u003cem\u003eTIMP1\u003c/em\u003e, and \u003cem\u003eGADD45B\u003c/em\u003e were significantly increased, while \u003cem\u003eLIME1\u003c/em\u003e and \u003cem\u003eEHD4\u003c/em\u003e expression levels were significantly decreased after intervention.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cem\u003eC. albicans, C. parapsilosis, C. glabrata\u003c/em\u003e reference strains Cp2, and \u003cem\u003eC. krusei\u003c/em\u003e fragments cleaved \u003cem\u003eTIMP-1\u003c/em\u003e (28 kDa) into 24 kDa, which was associated with a decrease in the inhibitory activity of \u003cem\u003eMMP-9\u003c/em\u003e collagenase. According to the study\u0026apos;s results, fungus can contribute to tissue inflammation by altering host MMP-9 and its inhibitors\u003csup\u003e[\u003cspan\u003e44\u003c/span\u003e]\u003c/sup\u003e. Eyes inoculated with \u003cem\u003eC. albicans\u003c/em\u003e developed corneal infections on the first day, with an average clinical score of 8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8. Compared to the control group, \u003cem\u003eMMP-8, -9, -10, -12, -13, -19\u003c/em\u003e, and \u003cem\u003eTIMP-1\u003c/em\u003e were upregulated from 5-fold to 375-fold on day 1 in the microarray, and from 3-fold to 78-fold via real-time RT-PCR. The upregulation of \u003cem\u003eMMPs\u003c/em\u003e and \u003cem\u003eTIMP-1\u003c/em\u003e in the corneal epithelium and stroma of infected eyes was related with the influx of acute inflammatory cells. Mechanical injury had no effect on the expression of \u003cem\u003eMMP-8\u003c/em\u003e and \u003cem\u003eMMP-13\u003c/em\u003e, which rose more than 100-fold within a week following fungal keratitis\u003csup\u003e[\u003cspan\u003e45\u003c/span\u003e]\u003c/sup\u003e.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFDFT1\u003c/em\u003e is a crucial tumor suppressor in CRC. Mechanistically, \u003cem\u003eFDFT1\u003c/em\u003e exerts its tumor-suppressive function by negatively regulating the AKT/mTOR/HIF1\u0026alpha; signaling pathway\u003csup\u003e[\u003cspan\u003e22\u003c/span\u003e]\u003c/sup\u003e. Additionally, \u003cem\u003emTOR\u003c/em\u003e inhibitors can synergistically inhibit the proliferation of CRC with fasting. Moreover, \u003cem\u003eFDFT1\u003c/em\u003e has been demonstrated to have anti-tumor effects in CRC, potentially promoting iron efflux by regulating ISCU expression\u003csup\u003e[\u003cspan\u003e23\u003c/span\u003e]\u003c/sup\u003e. 3\u0026beta;-hydroxy-12-oleanene-27-acid significantly inhibits the growth of xenograft tumors in nude mice and shows a significant decrease in \u003cem\u003eFDFT1\u003c/em\u003e expression in tumor tissues, along with changes in biomarkers such as autophagy, cell cycle, cell apoptosis, and iron efflux\u003csup\u003e[\u003cspan\u003e46\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe serum concentration of \u003cem\u003eTIMP-1\u003c/em\u003e in CRC patients was significantly higher than that in the healthy control group. Elevated serum levels of \u003cem\u003eTIMP-1\u003c/em\u003e were associated with female gender, tumor location in the colon, tumor low differentiation, and increased whole blood platelet count\u003csup\u003e[\u003cspan\u003e20\u003c/span\u003e]\u003c/sup\u003e. The systemic plasma levels of \u003cem\u003eTIMP-1\u003c/em\u003e and \u003cem\u003eMMP-9\u003c/em\u003e in CRC patients were significantly elevated compared to the control group. The systemic and portal plasma levels of \u003cem\u003eTIMP-1\u003c/em\u003e in metastatic disease patients were significantly higher than those in localized disease patients\u003csup\u003e[\u003cspan\u003e47\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eChemotherapy had longer PFS compared to those who did not receive adjuvant chemotherapy. High expression levels of \u003cem\u003eGADD45B\u003c/em\u003e are independent prognostic factors for reduced OS and PFS in stage II CRC patients. Patients with high expression of \u003cem\u003eGADD45B\u003c/em\u003e in stage II CRC may benefit from adjuvant chemotherapy\u003csup\u003e[\u003cspan\u003e48\u003c/span\u003e]\u003c/sup\u003e. The mRNA and protein levels of \u003cem\u003eGADD45B\u003c/em\u003e were significantly higher in CRC tissues than in Adjacent Non-Cancerous Tissues. Upregulation of \u003cem\u003eGADD45B\u003c/em\u003e expression is also associated with recurrence and death in CRC patients. Kaplan-Meier survival curves show that CRC patients overexpressing \u003cem\u003eGADD45B\u003c/em\u003e have significantly poorer disease-free survival (DFS). Cox multivariate analysis indicates that \u003cem\u003eGADD45B\u003c/em\u003e overexpression and TNM staging are important factors affecting patient survival. On the other hand, as a tumor suppressor gene, \u003cem\u003eGADD45B\u003c/em\u003e amplified from normal colorectal tissue can induce apoptosis in CRC cell lines and may be associated with the p53-mediated apoptotic pathway\u003csup\u003e[\u003cspan\u003e18\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eEHD2\u003c/em\u003e is closely associated with clinical pathological parameters. Upregulation of \u003cem\u003eEHD2\u003c/em\u003e is associated with longer overall survival. Univariate and multivariate analysis results indicate that \u003cem\u003eEHD2\u003c/em\u003e can serve as an independent prognostic indicator\u003csup\u003e[\u003cspan\u003e16\u003c/span\u003e]\u003c/sup\u003e. Additional investigation into \u003cem\u003eEHD4\u003c/em\u003e is warranted to expand our understanding and elucidate its underlying mechanisms, functions, and potential implications.\u003c/p\u003e\n\u003cp\u003eFDFT1 plays a pivotal role in modulating the cholesterol composition of cell membranes and orchestrating the functionality of immune cells. Its interacting protein potentially serves as a mediator in orchestrating immune evasion mechanisms \u003csup\u003e[\u003cspan\u003e36\u003c/span\u003e]\u003c/sup\u003e. Furthermore, an aberrant expression of FDFT1 within tumor-infiltrating lymphocytes (TILs) has been documented\u003csup\u003e[\u003cspan\u003e23\u003c/span\u003e]\u003c/sup\u003e.TIMP-1, a secretory protein renowned for its ability to impede matrix metalloproteinases (MMPs), is intricately involved in inflammatory cascades and correlates with heightened levels of immune cell infiltration\u003csup\u003e[\u003cspan\u003e49\u003c/span\u003e]\u003c/sup\u003e. In the context of anorectal cancer (AOXGD), the IL6-JAK2/STAT3-TIMP-1 signaling axis undergoes activation, concomitant with the engagement of adaptive Th1 and Th17 responses in the pathogenesis of AOXGD. IL-6 fosters the upregulation of TIMP-1 production by M1 macrophages via the stimulation of JAK2 and STAT3 phosphorylation, concurrently promoting the elevation of classical Th1 and Th17 cell markers such as IL-17 and IFN-\u0026gamma;\u003csup\u003e[\u003cspan\u003e50\u003c/span\u003e]\u003c/sup\u003e. Suppression of Gadd45a and Gadd45b expression correlates with the chemotactic response of macrophages to LPS\u003csup\u003e[\u003cspan\u003e51\u003c/span\u003e]\u003c/sup\u003e. Notably, Gadd45b\u0026minus;/\u0026minus; mice exhibit compromised tumor immune surveillance, attributed to diminished expression of IFN-\u0026gamma;, granzyme B, and CCR5 within Gadd45b\u0026minus;/\u0026minus;CD8\u0026thinsp;+\u0026thinsp;T cells infiltrating tumors. Within Gadd45b\u0026minus;/\u0026minus;CD8\u0026thinsp;+\u0026thinsp;T cells, stimulation via TCR or IL-12 and IL-18 leads to attenuated activation of p38 MAP kinase, rather than ERK or JNK, resulting in reduced IFN-\u0026gamma; secretion. Moreover, reduced mRNA levels of T-BET and Eomes within Gadd45b\u0026minus;/\u0026minus;CD8\u0026thinsp;+\u0026thinsp;T cells underscore the pivotal role of Gadd45b in sculpting Th1 lineage commitment\u003csup\u003e[\u003cspan\u003e52\u003c/span\u003e]\u003c/sup\u003e. In EHD1/3/4 knockout mice, CD4\u0026thinsp;+\u0026thinsp;T cells undergo antigen-driven proliferation but show reduced IL-2 secretion. Interestingly, these mice demonstrate less severe experimental autoimmune encephalomyelitis. Further investigation reveals impaired recycling of the TCR-CD3 complex, leading to increased lysosomal targeting and decreased surface levels of CD4\u0026thinsp;+\u0026thinsp;T cells in EHD1/3/4 knockout mice\u003csup\u003e[\u003cspan\u003e53\u003c/span\u003e]\u003c/sup\u003e.Taken together, these findings suggest that metabolites mixture derived from \u003cem\u003eC. albicans\u003c/em\u003e may modulate the initiation and progression of colorectal cancer by impacting immune cell infiltration dynamics.\u003c/p\u003e"},{"header":"Conclusion and future prospects","content":"\u003cp\u003eIn this study, we successfully developed a prognostic model for \u003cem\u003eC. albicans\u003c/em\u003e and CRC and validated it using CRC data. This paves the way for further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e research to identify these genes as potential therapeutic targets for CRC. In vitro experiments, this research used the CCK8 assay to assess cell viability and determine the cytotoxicity of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture on cells. We observed a decrease in cell viability with increasing concentration and prolonged exposure time. Additionally, \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture inhibited the invasion and migration of CRC cells. Furthermore, after intervention with \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture, we observed a significant increase in eATP levels in CRC cells compared to the control group. In further analysis, we found that the expression levels of \u003cem\u003eFDFT1\u003c/em\u003e were significantly elevated in CRC cells HT29 and HCT116 compared to normal colonic epithelial cells. Additionally, \u003cem\u003eTIMP1\u003c/em\u003e expression was significantly increased in HT29 cells but significantly decreased in HCT116 cells. Moreover, the expression of \u003cem\u003eGADD45B\u003c/em\u003e was significantly decreased in HCT116 cells, while the expression levels of \u003cem\u003eEHD4\u003c/em\u003e and \u003cem\u003eLIME1\u003c/em\u003e were also significantly reduced. Following intervention with \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture, we observed no significant change in \u003cem\u003eFDFT1\u003c/em\u003e expression in HCT116 cells, but there was an increase in the expression levels of \u003cem\u003eTIMP1, GADD45B, LIME1\u003c/em\u003e, and \u003cem\u003eEHD4\u003c/em\u003e. Conversely, in HT29 cells, the expression levels of \u003cem\u003eFDFT1, TIMP1, GADD45B, LIME1\u003c/em\u003e, and E\u003cem\u003eHD4\u003c/em\u003e were significantly reduced after intervention. Finally, in NCM460 cells, the expression levels of \u003cem\u003eFDFT1, TIMP1\u003c/em\u003e, and \u003cem\u003eGADD45B\u003c/em\u003e were significantly increased after intervention, while the expression levels of \u003cem\u003eLIME1\u003c/em\u003e and \u003cem\u003eEHD4\u003c/em\u003e were significantly decreased. These results suggest a protective role of \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture against CRC.\u003c/p\u003e \u003cp\u003eThis study primarily focused on cellular validation. Although cancer is studied at the cellular level, validation from other CRC cells was not conducted, and there was a lack of animal \u003cem\u003ein vivo\u003c/em\u003e experiments. Following intervention with \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture, this study found an increase in eATP levels. Related research has shown that elevated eATP levels may trigger ATP-induced cell death mechanisms. In previous studies with breast cancer cells, we found that ATP-induced cell death mechanisms (resulting from elevated eATP levels) inhibited the invasion and migration of breast cancer cells, thereby suppressing tumor initiation and progression. Therefore, future investigations will further explore whether \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture activate ATP-induced cell death mechanisms in CRC cells, potentially exerting a protective effect.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study available from the frist author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHL, HL, MS,WW completed the first draft of the paper. DS,ZG,ZJ had revised the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by National Natural Science Foundation of China, No. 81960877; University Innovation Fund of Gansu Province, No. 2021A-076; Gansu Province Science and Technology Plan (Innovation Base and Talent Plan), No. 21JR7RA561; Natural Science Foundation of Gansu Province, No. 21JR1RA267 and No. 22JR5RA582; Education Technology Innovation Project of Gansu Province, No. 2022A-067;Innovation Fund of Higher Education of Gansu Province, No. 2023A-088; Gansu Province Science and Technology Plan International Cooperation Field Project, No. 23YFWA0005; and Open Project of Key Laboratory of Dunhuang Medicine and Transformation of Ministry of Education, No. DHYX21-07, No. DHYX22-05, and No. DHYX21-01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWENG J, LI S, ZHU Z, et al. 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Eur J Immunol, 2009,39(11): 3010-3018.\u003c/li\u003e\n\u003cli\u003eISEKA F M, GOETZ B T, MUSHTAQ I, et al. Role of the EHD Family of Endocytic Recycling Regulators for TCR Recycling and T Cell Function[J]. J Immunol, 2018,200(2): 483-499.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Candida albicans, Colorectal cancer, metabolic characteristics, eATP, prognostic model","lastPublishedDoi":"10.21203/rs.3.rs-4555778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4555778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere is evidence supporting the notion that \u003cem\u003eCandida albicans\u003c/em\u003e (\u003cem\u003eC. albicans)\u003c/em\u003e indeed contributes to human cancers. Interestingly, the efficacy of \u003cem\u003eC. albicans\u003c/em\u003e in improving Colorectal cancer (CRC) has been confirmed. This study primarily explores the paradox of whether \u003cem\u003eC. albicans\u003c/em\u003e promotes or inhibits the development of CRC, focusing on its metabolites mixture for relevant arguments. This study identified a total of 214 differentially expressed genes. A prognostic model containing 5 specific mRNA markers, namely \u003cem\u003eEHD4, LIME1, GADD45B, TIMP1\u003c/em\u003e, and \u003cem\u003eFDFT1\u003c/em\u003e, was constructed. \u003cem\u003eC. albicans\u003c/em\u003e metabolites mixture reduced CRC cell activity. qRT-PCR results showed that compared to normal colonic epithelial cells, \u003cem\u003eLIME\u003c/em\u003e and \u003cem\u003eEHD4\u003c/em\u003e were downregulated in CRC cells, while \u003cem\u003eFDFT1\u003c/em\u003e expression was significantly upregulated. Notably, the \u003cem\u003eTIMP1\u003c/em\u003e gene was significantly upregulated in HT29 cells, while it was significantly downregulated in HCT116 cells. Furthermore, post-intervention analysis showed a significant decrease in gene expression levels in HT29 cells, while the expression of \u003cem\u003eTIMP1, EHD4\u003c/em\u003e, and \u003cem\u003eGADD45B\u003c/em\u003e increased in HCT116 cells, with \u003cem\u003eLIME\u003c/em\u003e and other CRC cells showing a corresponding decrease in expression. In NCM460 normal colonic epithelial cells, the expression levels of \u003cem\u003eGADD45B, TIMP1\u003c/em\u003e, and \u003cem\u003eFDFT1\u003c/em\u003e genes were significantly upregulated, while the expression levels of \u003cem\u003eLIME\u003c/em\u003e and \u003cem\u003eEHD4\u003c/em\u003e showed a significant downward trend. After metabolite intervention, the invasion and migration capabilities of NCM460 cells, HT29 cells, and HCT116 cells decreased. Additionally, quantitative measurement of eATP levels after intervention showed a significant increase \u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/em\u003e. This study's prognostic model opens up a new paradigm for prognostic assessment in CRC. The metabolites mixture of \u003cem\u003eC. albicans\u003c/em\u003e play a protective role in the onset and progression of CRC, exhibiting dynamic interactions with cellular energetics.\u003c/p\u003e","manuscriptTitle":"Construction and validation of a prognostic model based on metabolic characteristics of Candida albicans in colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-27 10:20:15","doi":"10.21203/rs.3.rs-4555778/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb93f186-fbe7-49f8-a756-77746ff2afcd","owner":[],"postedDate":"June 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-12T04:08:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-27 10:20:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4555778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4555778","identity":"rs-4555778","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}