The combination of GCA, C17:0 and C18:2 improve diagnostic accuracy of colorectal cancer liver metastases

The combination of GCA, C17:0 and C18:2 improve diagnostic accuracy of colorectal cancer liver metastases | 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 The combination of GCA, C17:0 and C18:2 improve diagnostic accuracy of colorectal cancer liver metastases Yan Zhao, Xinyu Li, Junqi Shan, Wei Han, Tao Li, Bowen You, Yanlai Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7450452/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 Background: Colorectal cancer (CRC) is a globally prevalent malignancy with high mortality. The diagnostic and therapeutic strategies for particularly metastatic CRC (mCRC) remain limited. Thus, the novel biomarkers and therapeutic avenues for mCRC are needed to be identified. Methods: To identify different metabolite profiles distinguishing between patients with mCRC and CRC, plasma samples from a cohort of 100 patients with CRC (n=50) and mCRC (n=50) were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Results: In training setting, the analysis revealed 13 metabolites that differed significantly between the 27 amino acids, 9 bile acids and 16 fatty acids. The area under the curve (AUC) of the classifier for C18:2 was 0.8089. In addition, the combined area under the curve (AUC) reached 0.86, which was significantly better than those of the traditional markers CEA (0.70) and CA19-9 (0.80). The data of pearson correlation analysis showed a significant correlation between GCA and CA19-9. Furthermore, the individual AUC values for GCA, CEA and CA19-9 in a specific analysis of 25 patients with mCRC were 0.74, 0.74 and 0.70, respectively. However, the AUC of GCA and CEA in combination with CA19-9 significantly increased to 0.87. Conclusions: This study emphasized the excellent performance of the combination of GCA, C17:0 and C18:2in differentiating CRC from mCRC. In addition, the integration of GCA, CEA and CA19-9 significantly improved the diagnostic accuracy of mCRC with liver metastasis. As expected, this research might develop novel diagnostic indicators and innovative therapeutic approaches against mCRC. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Gastroenterology Health sciences/Oncology Colorectal cancer (CRC) Metastatic CRC (mCRC) Targeted metabolomes Markers Serum metabolites Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Colorectal cancer (CRC) ranks as the third most common malignancy in humans and the second leading cause of cancer-associated mortality globally. Early-phase CRC is preventable, with a 5-year survival rate of around 90%. However, less than 12% of metastatic colorectal cancer (mCRC) cases can survive over five years [ 1 , 2 ]. Furthermore, about 25% of CRC patients exhibited distant metastases when diagnosed, and hepatic metastases were considered as the most common site. Currently, chemotherapy coupled with targeted therapy and immunotherapy approaches are the most commonly used strategies against mCRC. However, the outcomes remain no ideal [ 3 – 5 ]. Obtaining tissue biopsy through endoscopy or tumor puncture is the criterion for diagnosis of CRC. However, acquiring sufficient tissue biopsy specimens is challenging, and they may not represent the heterogeneity and longitudinal molecular evolution of tumor. Besides, a biopsy may cause invasive trauma and raise the risk of infection [ 6 , 7 ]. Protein markers (carcinoembryonic antigen(CEA) et al) are inadequate for diagnosis of early stage CRC due to low sensitivity. Blood can circulate throughout the tumor tissue in patients with CRC, which can better represent the heterogeneity and longitudinal molecular characteristics of malignant tumor. In addition, acquiring blood samples is almost non-invasive to patients [ 8 , 9 ]. Liquid biopsy can characterize the molecular profiles of patients with CRC by detecting nucleic acids, proteins, and exosomes in the blood, which can provide a basis for disease diagnosis and treatment monitoring [ 10 , 11 ]. Among these, genomics could assess the susceptibility and familial risk for CRC. However, translating genomic information into phenotype requires complex epigenetics, transcriptomics and proteomics. In contrast to genomics, transcriptomics, and proteomics are more closely associated with the phenotypic state of CRC [ 12 , 13 ]. Especially, cellular metabolism is downstream of other omics which is involved in regulation of nearly all biological processes;therefore, this feature makes it most directly related to the biological phenotype. Recent studies have applied metabolomic techniques to explore highly specific and sensitive biomarkers for CRC[ 14 , 15 ]. Overall, metabolomic analysis is valuable for defining the CRC phenotype and guiding personalized therapy. Our previous research showed that serum metabolites can be used to differentiate CRC from adenomas [ 9 ]. However, few studies on metabolomic biomarkers in mCRC were reported [ 14 , 15 ]. Specifically, amino acids, bile acids, and fatty acids are commonly altered metabolites for screening CRC and its precancerous lesions. However, the use of plasma metabolomics in mCRC were still limited [ 15 – 17 ]. Overall, this study aimed to perform the plasma targeting metabolome for diagnosing mCRC and identifying potential therapeutic targets against mCRC. Materials and methods Patients and sample collection A total of 100 individuals were recruited from Shandong Cancer Hospital between 2020 and 2023, which comprised 50 patients with CRC and 50 age- and sex-matched patients with mCRC. The age range of both cohorts was 32 to 79 years, and their diagnoses were confirmed by two experienced pathologists (Table 1). Venous blood samples were collected from the patients with the signed informed consent. None of the subjects had undergone any prior therapy before their surgeries. Plasma samples from both the CRC and mCRC patients were isolated and stored at -80℃. The procedure was approved by the ethics committee of Shandong Cancer Hospital (No. 2022003135). All methods were performed in accordance with the relevant guidelines and regulations. Inclusion criteria The inclusion criteria was as follows: 1) CRC and mCRC patients who were later confirmed histologically through surgical specimens; 2) patients who aged 18 years or older. Exclusion criteria 1) Those who underwent treatment prior to surgery with a history of or concurrent tumors in other locations; 2) those with incomplete clinical data; 3) those enrolled in studies investigating metabolic indicators of other diseases; 4) those with coexisting blood disorders; 5) those who received antibiotic treatment within one month before the surgery. Table 1. Information of the two cohorts (mean±standard deviation or %) Abbreviations: BMI: body mass index; PLT: platelet; WBC: white blood cell; ALT: alanine aminotransferase; AST: aspartate aminotransferase. Assessment Method: * p < 0.05, CRC vs. mCRC group. Metabolite extraction and data processing For metabolite extraction, 50 μl of plasma was mixed with amino acid (100 μl of sulfosalicylic acid solution containing internal standard), bile acid (200 μl of methanol containing internal standard) or fatty acid (200 µL of methanol containing internal standard) extraction reagent. Then, the temperature is set at 4℃ and centrifuged at 14000 rpm for 15 minutes. For amino acids, 10 µL supernatant, 70 µL borate buffer, and 20 µL AQC reagent were vortexed for 10 seconds. Afterwards, 20 µL of the supernatant was transferred to a single well in a 96-well sample collection plate, and then diluted with 180 µL of water before proceeding to Ultra Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS) analysis. For bile and fatty acids, 100 µL of supernatant with 100 µL of water was added to a single well in a 96-well. Afterward, the plate was employed for conducting UPLC-MS/MS analysis. LC-MS analyses were performed using a UPLC system (ACQUITY I-Class, Waters) coupled with a quadruple electrospray ionization mass spectrometer (LC-MS) (Xevo TQ-S, Waters). Raw data were processed and integrated using MassLynx software (version 4.1, Waters, USA). Metabolomics data analysis Metabolomics data analysis was performed using OmicStudio tools (https://www.omicstudio.cn) or OECloud tools (https://cloud.oebiotech.com). Partial least squares discriminant analysis (PLS-DA) scores and model-test plots and the stacking interaction graph were visualized using OECloud tools. Differential metabolites were analyzed using the t-test method, with FDR (false discovery rate) values, P value 1 used as screening criteria. Function and pathway enrichment of differential metabolites in CRC was analyzed online using the MetaboAnalyst tool. The process of exploring interacting molecules of differential metabolites involved in the utilization of the Comparative Toxicogenomics Database (CTD)（http://ctdbase.org/）, and the visualization was carried out using Cytoscape (version 3.7.2). The ability of differential metabolites to identify mCRC was analyzed using receiver operating characteristic (ROC) curves in OmicStudio. characteristic) curves in OmicStudio Tools or OECloud Tool. Random forest analysis of the differential metabolites was performed using OECloud tools. The correlation between differential metabolites and CEA and CA19-9 was analyzed by Pearson's method. Development of a panel of metabolities for mCRC diagnosis The panel was designed in 3 steps. (a) A total of 100 patients , CRC (n=50) and mCRC (n=50) were randomly divided into the training (CRC (n=30) and mCRC (n=30)) and validation (CRC (n=20) and mCRC (n=20)) sets with a ratio of 3:2. (b) From all 13 differential metabolites screened from 60 CRC patients, vital variables for the training set were selected by random forest analysis based on the importance score. (c) The validation set was used to further select the metabolic biomarkers. Statistical analysis Data were processed using R (version 4.0.3). Statistical analysis was performed by t-test with FDR rectify. ROC analysis of metabolites was performed using OmicStudio tools or OECloud tools, and correlations were analyzed using Pearson's analysis. Data were expressed as mean±standard deviation. P <0.05 was considered statistically significant. Results Characterization of metabolic alterations between patients with CRC and mCRC To optimize the identification of mCRC and uncover potential targets and strategies, a targeted metabolomic sequencing of plasma was performed. Amino acids, bile acids, and fatty acids were specifically selected for analysis in this study. Compared with principal component analysis (PCA), PLS-DA was added in variables which allowed a deeper analysis of the primary features among the metabolic samples [18, 19]. The sdata of PLS-DA analysis showed that there were differences in the metabolic components of CRC and mCRC although the samples were all optained from patients with CRC, (Figure 1 A1, B1, C1). Next, to assess the ability for discriminate between samples, a model incorporating metabolomic information with a PLSDA approach was performed. The R2 and Q2 values demonstrated the model was reliable and not overfitting, indicating the robustness in classification (Figure 1 A2, B2,C2 and supplemental table1). The profiling of 27 amino acids (Figure 1 A3) was conducted, and it was observed that all three screened amino acids, namely ETA (ethanolamine), Tau (taurine), and Trp (tryptophan), exhibited a consistent decrease in the mCRC ( P <0.05) (Figure 1 A4 and supplemental table2). Nine bile acids were measured (Figure 1 B3), and interestingly, only GCA (glycinecholic acid) exhibited a noteworthy increase in the mCRC ( P <0.05) (Figure 1 B4). Sixteen fatty acids were characterized (Figure 1 C3) and out of these, nine fatty acids (C15:0, C17:1, C17:0, C18:3, C18:2, C20:3, C20:1, C22:5, C24:1) displayed significant decreases in the mCRC ( P <0.05) (Figure 1 C4). Consistently, the most prominent differences were found in the metabolites related to fatty acids, which accounted for over 50% of the detected metabolites. These findings strongly suggested that metabolomic markers were essential in identification of mCRC. Functional analysis and interaction gene prediction To establish a solid groundwork for future clinical targeted therapy, the MetaboAnalyst tool was performed to conduct a comprehensive functional analysis of the distinct metabolites. In addition, the CTD was used to predict the protein interactions associated with the identified differential metabolites (Table 2). The IDs of these metabolites in the HBMD database were searched, and then analyzed on the MetaboAnalyst website. Our findings revealed that the differential metabolites were primarily associated with key metabolic pathways, including fatty acid synthesis, bile acid biosynthesis, taurine metabolism and linoleic acid metabolism (Figure 2A). Subsequently, the linoleic acid pathway and categorized under fatty acid metabolism were more enriched among the identified pathways. (Figure 2B). CTD serves as a valuable tool that helps researchers identify molecules that interact with specific compounds and provides a comprehensive understanding of the intricate interplay between chemical substances, genes, and diseases[20, 21]. By utilizing the CTD, we investigated the genes that interact with these 13 differential metabolic markers. Moreover, w a set of 6 metabolites (Ethanolamine, Taurine, Glycocholic acid, linoleic acid, Docosapentaenoic acid, Glucosylceramide) that interact with specific genes were identified and presented in Figure 2C. Particularly, linoleic acid (C18:2) exhibits interactions with multiple genes, and both C18:2 and Docosapentaenoic acid (C22:5) are capable of binding with the retinoid X receptor alpha (RXRA) protein. Furthermore, C18:2 was found to enhance the activity of RXRA, which was closely associated with All-trans retinoic acid (ATRA) that has been employed in clinical disease treatments (supplemental table3) [22, 23]. Through examination of the GEPIA2 database, the expression of RXRA was significantly downregulated in tumor cells (Figure 2D). Considering downregulation of C18:2 in metastatic colorectal cancer (mCRC), it was speculated that the expression level of RXRA may also be diminished in mCRC, resulting in inactivation of ATRA. It was reported that C18:2 is involved in cell proliferation, lipid metabolism regulation,which plays a crucial role in CD8 cell immunity [24, 25]. However, tthe research available was limited on its specific functions in mCRC. Overall, our findings suggested a potential significance of C18:2 in mCRC, as it may affect tumor development through its influence on RXRA activity and ATRA levels. Nevertheless, further investigations are required to validate the hypothesis and explore the precise functional mechanisms of C18:2 in mCRC. Table 2. Differential metabolite IDs for comprehensive functional analysis across multiple databases Aberrant metabolities could distinguish CRC from mCRC well The blood contains residual traces of tumors, and the metabolites present in the blood can directly reflect the characteristics of the tumor. Therefore, the plasma metabolic index undoubtedly serves as the best non-invasive method for diagnosing and monitoring the treatment of tumors [26, 27]. Based on these reasons, we conducted further diagnostic capability analysis on these metabolites using ROC (receiver operating curve). The reliability of 13 differential metabolites was assessed based on the AUC (area under the curve) values and optimal thresholds. Six indicators demonstrated AUC value was greater than 0.7, namely Tau (0.7289), C15:0 (0.7433), C17:1 (0.7394), C20:3 (0.7533), C18:2 (0.8089). Interestingly, the AUC value of C18:2 exceeded 0.8. Additionally, the AUC values of the remaining seven metabolites were all more than 0.67 (Figure 3 A-C). These findings suggest that plasma metabolic differential indicators can effectively distinguish between CRC and mCRC, highlighting the potential of our study in identifying diagnostic and therapeutic targets. GCA, C17:0, and C18:2 were validated as biomarkers for mCRC To further validate these markers, an additional set of 40 CRC and mCRC patients was included. We initially analyzed the differences in amino acids, bile acids, and fatty acid products between the two groups. Unfortunately, no significant difference was found in the metabolites belonging to the amino acid group. However, in the bile acid group, only GCA exhibited a statistically significant difference, and in the fatty acid group, C17:0 and C18:2 showed statistical significance (Figure 4 A, B). Subsequently, ROC analysis was performed on these identified differential metabolites. As demonstrated in Figure 5A and 5B, the AUC values of GCA (0.6275), C17:0 (0.6525), and C18:2 (0.6675) all exceeded 0.6, indicating the specific reference value in identification of mCRC. These findings were consistent with the results obtained from the training set, suggesting that the identified target metabolic markers are reliable and stable in the identification of mCRC. GCA, C17:0 combined with C18:2 is superior to the clinical markers By using the random forest approach, it is possible to analyze the metabolite data from both the training and validation sets, identify important features (metabolites) that contribute to the classification, and accurately classify new patients as either CRC or mCRC [28, 29].To explore metabolic markers that could complemently existing clinical indicators for the identification of mCRC, we initially assessed the significance of the differential metabolites obtained from 100 CRC and mCRC cases using a random forest approach. It was discovered C18:2 ranked followed by GCA in third and C17:0 in eighth (Figure 6A). These results indicated that although the three markers identified are significant, they do not precisely align with the top three in terms of importance. Moreover, the AUC of the three indicators combined was 0.86, with a mean of 0.84±0.1, showing clear advantages over CEA (0.70) and CA19-9 (0.80) (Figure 6 B-E). These data showed GCA, C17:0, combined with C18:2, was superior to the clinical markers CEA and CA19-9. GCA improves the ability of clinical indicators to identify CRC liver metastasis To further identify the relationship between GCA, C17:0, C18:2, CEA, and CA19-9, we analyzed the correlation of CEA, CA19-9, and three identified indicators and found that the correlation between CA19-9 and GCA was statistically different (Figure 7A). Several literature reports showed that the levcl of CA19-9 was associated with CRC liver metastasis and the therapeutic effect [30-32].Therefore, 25 mCRC patients with liver metastases were selected. The ability of GCA, C17:0, C18:2, CEA, and CA19-9 to differentiate liver metastases from mCRC was evaluated. ROC analysis showed that the AUC using GCA, CEA, or CA19-9 alone was 0.74, 0.74, and 0.70, respectively. Moreover, the AUC of the three metabolites combined was 0.87, indicating a significant increase in the true positive rate and a significant decrease in the false negative rate. However, the AUC of C17:0 and C18:2 was only 0.5-0.6 (Figure 7 B-G). These results suggested that GCA enhanced the identification of clinical mCRC liver metastases. Discussion Early diagnosis and exploration of therapeutic targets for mCRC are essential to improve the prognosis of CRC patients [2, 3]. The analysis of plasma metabolome offers a precise understanding of CRC metastasis by specifically assessing metabolites that reflect the impact of medical interventions and genetics.[33, 34] . Here, the target-metabolomics technique was applied to analyze the plasma samples of patients with CRC and mCRC. Our study demonstrated that metabolomic markers can effectively differentiate between CRC and mCRC, surpassing the diagnostic accuracy of clinical indicators such as CEA and CA19-9. Furthermore, we observed a significant correlation between GCA and CA19-9 levels, highlighting GCA's potential as a reliable discriminator in cases of liver mCRC. These findings offer a promising new avenue for the diagnosis and potential treatment of mCRC. A growing body of research has provided substantial evidence regarding the importance of amino acids, bile acids, and targeted metabolites associated with fatty acids in the initiation and progression of tumors [35-37]. A total of 100 CRC and mCRC were enrolled in our study, 60 CRC and mCRC were randomly assigned as the training set, and the other 40 CRC and mCRC were defined as the validation set. Amino acid imbalance is a common phenomenon in tumors. Amino acid imbalances are frequently observed in tumors, and previous research has demonstrated their potential as therapeutic targets for tumor-specific therapy [38, 39]. There is a limited amount of oncological literature available on ETA. However, it has been found that TAU has a notable inhibitory effect on the proliferation of various tumors[40, 41]. In 2019, a research paper highlighted the presence of Trp disorders in different types of tumors and emphasized their potential as a shared therapeutic target[42]. Furthermore, our study revealed significant differences in ETA, TAU, and Trp levels between the training set, allowing for effective discrimination between CRC and mCRC. However, these markers did not show outstanding performance in the validation set, which could be attributed to the small sample size. Regarding bile acids, previous reports have shown the significant value of GCA in the early diagnosis of liver cancer[43].Our research results suggest that GCA is a promising biomarker for identifying mCRC, especially liver mCRC. Although the role of fatty acids in tumor resistance and immune function is closely related, their role in the diagnosis and treatment of mCRC has been less studied [44, 45] .In our study, we discovered nine abnormal fatty acid metabolites (C15:0, C17:1, C17:0, C18:3, C18:2, C20:3, C20:1, C22:5, and C24:1) in the training set and two abnormal fatty acid metabolites (C17:0 and C18:2) in the validation set. According to the analysis of Comparative Toxicogenomics Database (CTD), C18:2 was found to interact with multiple genes, particularly RXRA, and enhance its activity, playing important roles in tumor proliferation and immunity. Examination using the GEPIA2 database revealed that the expression level of RXRA was significantly lower in CRC than in mCRC. Furthermore, RXRA acts as a receptor for ATRA, regulating its concentration. It is well known that ATRA is widely used in clinical treatment[22, 23]. This suggests that C18:2 likely plays a key role in mCRC through interactions with RXRA and ATRA, although no reports have been found on this phenomenon. Through validation on the confirmation set, we successfully identified three biomarkers, namely GCA, C17:0, and C18:2, which effectively distinguish between CRC and mCRC. In conclusion, based on the analysis and literature review, these differential metabolites hold potential clinical significance in the diagnosis and treatment of CRC and mCRC. However, further research is required to better understand their mechanisms of action and clinical application prospects. We conducted ROC analysis to evaluate the area AUC for GCA, C17:0, and C18:2 in combination. The AUC for mCRC detection was found to be 0.86, which is significantly higher compared to CEA (0.70) and CA19-9 (0.80). These results indicate that the metabolic markers we identified are crucial for the clinical diagnosis of mCRC. Specifically, there was a positive correlation between GCA and CA19-9. Although GCA exhibited greater specificity than CA19-9 in diagnosing mCRC, it did not show notable advantages over CEA. It is interesting to note that other studies have suggested that CEA is less effective than CA19-9 in diagnosing mCRC [46]. However, when GCA was combined with CA19-9 and CEA in our analysis of mCRC, it resulted in improved true positive rates and reduced false negative rates. These findings further support the significance of our study. Given the high sensitivity and real-time monitoring capabilities of metabolic indicators in serum, we speculate that GCA, C17:0, and C18:2 have strong potential in the detection, treatment monitoring, and early diagnosis of mCRC. Indeed, there is still room for improvement in our study. For example, due to the difficulty in collecting mCRC samples, we were unable to conduct a detailed analysis of the sites of CRC metastasis. In our future research, we plan to address this limitation by collecting more comprehensive samples to enable a thorough classification and analysis of CRC metastatic sites. Imaging phases such as enhanced CT/PET-CT also need to be compared. Furthermore, we intend to explore the connections between the metabolome and the upstream proteome and transcriptome in order to gain a deeper understanding of the molecular mechanisms underlying mCRC. By studying these relationships, we can analyze the interplay between the metabolome and microbial metabolism, which may have implications for identifying novel therapeutic targets and developing personalized treatments for mCRC. In summary, plasma metabolic markers offer a non-invasive and precise approach for detecting and treating cancer by providing individual patients' molecular tumor profiles (Figure 8). The metabolome represents a promising avenue for non-invasive identification of CRC. However, there is currently a limited number of studies that have screened for markers of mCRC and explored the function of metabolites to identify new targets for personalized therapy. Therefore, our future research aims to identify metabolic targets specific to mCRC and systematically investigate their roles and underlying mechanisms. Through this approach, we hope to uncover novel and precise treatment strategies for patients with mCRC. Declarations Declarations acknowledgments Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research was funded by Shandong Provincial Natural Science Foundation Joint Fund Project (ZR2024LZL016), the Cancer Research Fund Project (BH005373), the Health Technology Development Program (XHD-001), China Cancer Foundation Project (CFC-XGB-202401)and the Collaborative Academic Innovation Project of Shandong Cancer Hospital(FC008). Ethics approval and consent to participate Patient samples were collected and analyzed with the approval of the ethics committee (2022003135) of Shandong Cancer Hospital, Shandong First Medical University, and Shandong Academy of Medical Sciences (Shandong, China). Authors contributions Conception and design: Yanlai Sun, Yan Zhao，Xinyu Li and Junqi Shan; Clinical data acquisition: Yan Zhao ，Xinyu Li , Junqi Shan, Bowen You, Wei Han and Tao Li; Bioinformatics analysis and data visualization: Xinyu Li and Junqi Shan; Drafting and revising it critically of the manuscript: Yanlai Sun. All authors have read and approved the final manuscript. 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Integrated Analysis of Colorectal Cancer Reveals Cross-Cohort Gut Microbial Signatures and Associated Serum Metabolites. Gastroenterology 163 (4), 1024–1037e9 (2022). Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":124712,"visible":true,"origin":"","legend":"\u003cp\u003eScreen of differential expressed metabolites: A1, B1, C1: PLSDA analysis of amino acid, bile acid, and fatty acid in CRC and mCRC. A2, B2, C2: PLS-DA model analysis of amino acid, bile acid, and fatty acid in CRC and mCRC. A3, B3, C3: Composition analysis of amino acid, bile acid, and fatty acid in CRC and mCRC. A4, B4, C4: Stamp feature analysis reveals distinctive profiles of amino acids, bile acids, and fatty acids in CRC and mCRC.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7450452/v1/22930a9b16f9223e4682ce3b.jpeg"},{"id":92859421,"identity":"fb36e7d1-d74f-4a8c-a3a6-19a5877d6905","added_by":"auto","created_at":"2025-10-06 12:04:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2501795,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional and interact proteins analysis of differential metabolites: A: Pathway enrichment; B: Pathway enrichment score; C: Proteins that interact with differential metabolites.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7450452/v1/7e3f6ed77f950eecd1217395.png"},{"id":92859418,"identity":"4dd14758-9c9a-43e9-9dc6-22fa69008474","added_by":"auto","created_at":"2025-10-06 12:04:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2462199,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of differential metabolites: A1-A3: Amino acids. B: Bile acids. C1-C9: Fatty acids.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7450452/v1/2077ef6f7a5bc5801e047a5d.png"},{"id":92861077,"identity":"45eed4c1-2376-4573-8b83-0d34eae1dada","added_by":"auto","created_at":"2025-10-06 12:20:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":373203,"visible":true,"origin":"","legend":"\u003cp\u003eValidation set differential metabolites: A: GCA. B: C17:0, C18:2.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7450452/v1/a99d35abbc0899340a74c4db.png"},{"id":92860758,"identity":"258cb87f-76ed-47a2-8dbe-5fc9e60d6b42","added_by":"auto","created_at":"2025-10-06 12:12:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":319618,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of differential metabolites: A: GCA. B: C17:0, C18:2.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7450452/v1/7ecc1737e1b941ae43b53669.png"},{"id":92860760,"identity":"02153ba5-5e3e-47a7-9964-f4713db595ad","added_by":"auto","created_at":"2025-10-06 12:12:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":678579,"visible":true,"origin":"","legend":"\u003cp\u003eImportant and ROC curve analysis of differential metabolites in mCRC: A: Index importance and distribution of metabolites. B: ROC analysis of GCA, C17:0, and C18:2 combined. C: Mean ROC analysis of GCA, C17:0, and C18:2 combined. D: ROC analysis of CEA. E: ROC analysis of CA19-9.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7450452/v1/476453361296813773d7b12b.png"},{"id":92861079,"identity":"3f5b3488-9404-46eb-b843-3aceb702cbbd","added_by":"auto","created_at":"2025-10-06 12:20:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1389140,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation and ROC curve analysis of differential metabolites in liver mCRC: A: Correlation analysis of GCA, C17:0, and C18:2. B: ROC analysis of GCA. C:17.0. D: C18:2. E: CEA. F: CA19-9. G: GCA, C17:0, and C18:2 combined.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7450452/v1/f4e08ebacf2437bc8532a104.png"},{"id":92859428,"identity":"8329b128-13f9-46c6-b720-1cfc6d3b54db","added_by":"auto","created_at":"2025-10-06 12:04:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":855429,"visible":true,"origin":"","legend":"\u003cp\u003eSignificance of plasma metabolic indices\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7450452/v1/08d98b0307380a1c747ced18.png"},{"id":98436199,"identity":"748304e1-eb5b-4a43-8d23-c96eda052b70","added_by":"auto","created_at":"2025-12-17 16:55:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9495360,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7450452/v1/c5f02514-efb8-4d97-85ef-e5edd0f61535.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The combination of GCA, C17:0 and C18:2 improve diagnostic accuracy of colorectal cancer liver metastases","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) ranks as the third most common malignancy in humans and the second leading cause of cancer-associated mortality globally. Early-phase CRC is preventable, with a 5-year survival rate of around 90%. However, less than 12% of metastatic colorectal cancer (mCRC) cases can survive over five years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, about 25% of CRC patients exhibited distant metastases when diagnosed, and hepatic metastases were considered as the most common site. Currently, chemotherapy coupled with targeted therapy and immunotherapy approaches are the most commonly used strategies against mCRC. However, the outcomes remain no ideal [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eObtaining tissue biopsy through endoscopy or tumor puncture is the criterion for diagnosis of CRC. However, acquiring sufficient tissue biopsy specimens is challenging, and they may not represent the heterogeneity and longitudinal molecular evolution of tumor. Besides, a biopsy may cause invasive trauma and raise the risk of infection [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Protein markers (carcinoembryonic antigen(CEA) et al) are inadequate for diagnosis of early stage CRC due to low sensitivity. Blood can circulate throughout the tumor tissue in patients with CRC, which can better represent the heterogeneity and longitudinal molecular characteristics of malignant tumor. In addition, acquiring blood samples is almost non-invasive to patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLiquid biopsy can characterize the molecular profiles of patients with CRC by detecting nucleic acids, proteins, and exosomes in the blood, which can provide a basis for disease diagnosis and treatment monitoring [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Among these, genomics could assess the susceptibility and familial risk for CRC. However, translating genomic information into phenotype requires complex epigenetics, transcriptomics and proteomics. In contrast to genomics, transcriptomics, and proteomics are more closely associated with the phenotypic state of CRC [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Especially, cellular metabolism is downstream of other omics which is involved in regulation of nearly all biological processes;therefore, this feature makes it most directly related to the biological phenotype. Recent studies have applied metabolomic techniques to explore highly specific and sensitive biomarkers for CRC[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Overall, metabolomic analysis is valuable for defining the CRC phenotype and guiding personalized therapy.\u003c/p\u003e\u003cp\u003eOur previous research showed that serum metabolites can be used to differentiate CRC from adenomas [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, few studies on metabolomic biomarkers in mCRC were reported [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Specifically, amino acids, bile acids, and fatty acids are commonly altered metabolites for screening CRC and its precancerous lesions. However, the use of plasma metabolomics in mCRC were still limited [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Overall, this study aimed to perform the plasma targeting metabolome for diagnosing mCRC and identifying potential therapeutic targets against mCRC.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003ePatients and sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 100 individuals were recruited from Shandong Cancer Hospital between 2020 and 2023, which comprised 50 patients with CRC and 50 age- and sex-matched patients with mCRC. The age range of both cohorts was 32 to 79 years, and their diagnoses were confirmed by two experienced pathologists (Table 1). Venous blood samples were collected from the patients with the signed informed consent. None of the subjects had undergone any prior therapy before their surgeries. Plasma samples from both the CRC and mCRC patients were isolated and stored at -80℃. The procedure was approved by the ethics committee of Shandong Cancer Hospital (No. 2022003135). All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria was as follows: 1) \u0026nbsp;CRC and mCRC patients who were later confirmed histologically through surgical specimens; 2) patients who aged 18 years or older.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1) Those who underwent treatment prior to surgery with a history of or concurrent tumors in other locations; 2) those with incomplete clinical data; 3) those enrolled in studies investigating metabolic indicators of other diseases; 4) those with coexisting blood disorders; 5) those who received antibiotic treatment within one month before the surgery.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Information of the two cohorts (mean\u0026plusmn;standard deviation or %)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"395\" height=\"576\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI: body mass index; PLT: platelet; WBC: white blood cell; ALT: alanine aminotransferase; AST: aspartate aminotransferase. Assessment Method: * p \u0026lt; 0.05, CRC vs. mCRC group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolite extraction and data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor metabolite extraction, 50 \u003cu\u003e\u0026mu;l\u0026nbsp;\u003c/u\u003eof plasma was mixed with amino acid (100 \u003cu\u003e\u0026mu;l\u003c/u\u003e of sulfosalicylic acid solution containing internal standard), bile acid (200 \u003cu\u003e\u0026mu;l\u003c/u\u003e of methanol containing internal standard) or fatty acid (200 \u003cu\u003e\u0026micro;L\u003c/u\u003e of methanol containing internal standard) extraction reagent. Then, the temperature is set at 4℃ and centrifuged at 14000 rpm for 15 minutes. For amino acids, 10 \u003cu\u003e\u0026micro;L\u003c/u\u003e supernatant, 70 \u003cu\u003e\u0026micro;L\u003c/u\u003e borate buffer, and 20 \u003cu\u003e\u0026micro;L\u003c/u\u003e AQC reagent were vortexed for 10 seconds. Afterwards, 20 \u0026micro;L of the supernatant was transferred to a single well in a 96-well sample collection plate, and then diluted with 180 \u0026micro;L of water before proceeding to Ultra Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS) analysis. For bile and fatty acids, 100 \u003cu\u003e\u0026micro;L\u003c/u\u003e of supernatant with 100 \u003cu\u003e\u0026micro;L\u003c/u\u003e of water was added to a single well in a 96-well. Afterward, the plate was employed for conducting UPLC-MS/MS analysis.\u003c/p\u003e\n\u003cp\u003eLC-MS analyses were performed using a UPLC system (ACQUITY I-Class, Waters) coupled with a quadruple electrospray ionization mass spectrometer (LC-MS) (Xevo TQ-S, Waters). Raw data were processed and integrated using MassLynx software (version 4.1, Waters, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolomics data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolomics data analysis was performed using OmicStudio tools (https://www.omicstudio.cn)\u003c/p\u003e\n\u003cp\u003eor OECloud tools (https://cloud.oebiotech.com). Partial least squares discriminant analysis (PLS-DA) scores and model-test plots and the stacking interaction graph were visualized using OECloud tools. Differential metabolites were analyzed using the t-test method, with FDR (false discovery rate) values, \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05, and log2 FC\u0026gt;1 used as screening criteria. Function and pathway enrichment of differential metabolites in CRC was analyzed online using the MetaboAnalyst tool. The process of exploring interacting molecules of differential metabolites involved in the utilization of the Comparative Toxicogenomics Database (CTD)（http://ctdbase.org/）, and the visualization was carried out using Cytoscape (version 3.7.2). The ability of differential metabolites to identify mCRC was analyzed using receiver operating characteristic (ROC) curves in OmicStudio. characteristic) curves in OmicStudio Tools or OECloud Tool. Random forest analysis of the differential metabolites was performed using OECloud tools. The correlation between differential metabolites and CEA and CA19-9 was analyzed by Pearson\u0026apos;s method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of a panel of metabolities for mCRC diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe panel was designed in 3 steps. (a) A total of 100 patients , CRC (n=50) and mCRC (n=50) were randomly divided into the training (CRC (n=30) and mCRC (n=30)) and validation (CRC (n=20) and mCRC (n=20)) sets with a ratio of 3:2. (b) From all 13 differential metabolites screened from 60 CRC patients, vital variables for the training set were selected by random forest analysis based on the importance score. (c) The validation set was used to further select the metabolic biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were processed using R (version 4.0.3). Statistical analysis was performed by t-test with FDR rectify. ROC analysis of metabolites was performed using OmicStudio tools or OECloud tools, and correlations were analyzed using Pearson\u0026apos;s analysis. Data were expressed as mean\u0026plusmn;standard deviation. \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacterization of metabolic alterations between patients with CRC and mCRC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo optimize the identification of mCRC and uncover potential targets and strategies, a targeted metabolomic sequencing of plasma was performed. Amino acids, bile acids, and fatty acids were specifically selected for analysis in this study. Compared with principal component analysis (PCA), PLS-DA was added in variables which allowed a deeper analysis of the primary features among the metabolic samples\u0026nbsp;[18, 19]. The sdata of PLS-DA analysis showed that there were differences in the metabolic components of CRC and mCRC although the samples were all optained from patients with CRC, (Figure 1 A1, B1, C1). Next, to assess the ability for discriminate between samples, a model incorporating metabolomic information with a PLSDA approach was performed. The R2 and Q2 values demonstrated the model was reliable and not overfitting, indicating the robustness in classification (Figure 1 A2, B2,C2 and supplemental table1). The profiling of 27 amino acids (Figure 1 A3) was conducted, and it was observed that all three screened amino acids, namely ETA (ethanolamine), Tau (taurine), and Trp (tryptophan), exhibited a consistent decrease in the mCRC (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) (Figure 1 A4 and supplemental table2). Nine bile acids were measured (Figure 1 B3), and interestingly, only GCA (glycinecholic acid) exhibited a noteworthy increase in the mCRC (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) (Figure 1 B4). Sixteen fatty acids were characterized (Figure 1 C3) and out of these, nine fatty acids (C15:0, C17:1, C17:0, C18:3, C18:2, C20:3, C20:1, C22:5, C24:1) displayed significant decreases in the mCRC (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) (Figure 1 C4). Consistently, the most prominent differences were found in the metabolites related to fatty acids, which accounted for over 50% of the detected metabolites. These findings strongly suggested that metabolomic markers were essential in identification of mCRC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional analysis and interaction gene prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo establish a solid groundwork for future clinical targeted therapy, the MetaboAnalyst tool was performed to conduct a comprehensive functional analysis of the distinct metabolites. In addition, the CTD was used to predict the protein interactions associated with the identified differential metabolites (Table 2). The IDs of these metabolites in the HBMD database were searched, and then analyzed on the MetaboAnalyst website. Our findings revealed that the differential metabolites were primarily associated with key metabolic pathways, including fatty acid synthesis, bile acid biosynthesis, taurine metabolism and linoleic acid metabolism (Figure 2A). Subsequently, the linoleic acid pathway and categorized under fatty acid metabolism were more enriched \u0026nbsp;among the identified pathways. (Figure 2B). CTD serves as a valuable tool that helps researchers identify molecules that interact with specific compounds and provides a comprehensive understanding of the intricate interplay between chemical substances, genes, and diseases[20, 21]. By utilizing the CTD, we investigated the genes that interact with these 13 differential metabolic markers. Moreover, w a set of 6 metabolites (Ethanolamine, Taurine, Glycocholic acid, linoleic acid, Docosapentaenoic acid, Glucosylceramide) that interact with specific genes were identified and presented in Figure 2C. Particularly, linoleic acid (C18:2) exhibits interactions with multiple genes, and both C18:2 and Docosapentaenoic acid (C22:5) are capable of binding with the retinoid X receptor alpha (RXRA) protein. Furthermore, C18:2 was found to enhance the activity of RXRA, which was closely associated with All-trans retinoic acid (ATRA) that has been employed in clinical disease treatments (supplemental table3)\u0026nbsp;[22, 23]. Through examination of the GEPIA2 database, the expression of RXRA was significantly downregulated in tumor cells (Figure 2D). Considering downregulation of C18:2 in metastatic colorectal cancer (mCRC), it was speculated that the expression level of RXRA may also be diminished in mCRC, resulting in inactivation of ATRA. It was reported that C18:2 is involved in cell proliferation, lipid metabolism regulation,which plays a crucial role in CD8 cell immunity\u0026nbsp;[24, 25]. However, tthe research available was limited on its specific functions in mCRC. Overall, our findings suggested a potential significance of C18:2 in mCRC, as it may affect tumor development through its influence on RXRA activity and ATRA levels. Nevertheless, further investigations are required to validate the hypothesis and explore the precise functional mechanisms of C18:2 in mCRC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Differential metabolite IDs for comprehensive functional analysis across multiple databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"541\" height=\"272\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAberrant metabolities could distinguish CRC from mCRC well\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe blood contains residual traces of tumors, and the metabolites present in the blood can directly reflect the characteristics of the tumor. Therefore, the plasma metabolic index undoubtedly serves as the best non-invasive method for diagnosing and monitoring the treatment of tumors\u0026nbsp;[26, 27]. Based on these reasons, we conducted further diagnostic capability analysis on these metabolites using ROC (receiver operating curve). The reliability of 13 differential metabolites was assessed based on the AUC (area under the curve) values and optimal thresholds. Six indicators demonstrated AUC value was greater than 0.7, namely Tau (0.7289), C15:0 (0.7433), C17:1 (0.7394), C20:3 (0.7533), C18:2 (0.8089). Interestingly, the AUC value of C18:2 exceeded 0.8. Additionally, the AUC values of the remaining seven metabolites were all more than 0.67 (Figure 3 A-C). These findings suggest that plasma metabolic differential indicators can effectively distinguish between CRC and mCRC, highlighting the potential of our study in identifying diagnostic and therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGCA, C17:0, and C18:2 were validated as biomarkers for mCRC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further validate these markers, an additional set of 40 CRC and mCRC patients was included. We initially analyzed the differences in amino acids, bile acids, and fatty acid products between the two groups. Unfortunately, no significant difference was found in the metabolites belonging to the amino acid group. However, in the bile acid group, only GCA exhibited a statistically significant difference, and in the fatty acid group, C17:0 and C18:2 showed statistical significance (Figure 4 A, B). Subsequently, ROC analysis was performed on these identified differential metabolites. As demonstrated in Figure 5A and 5B, the AUC values of GCA (0.6275), C17:0 (0.6525), and C18:2 (0.6675) all exceeded 0.6, indicating the specific reference value in identification of mCRC. These findings were consistent with the results obtained from the training set, suggesting that the identified target metabolic markers are reliable and stable in the identification of mCRC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGCA, C17:0 combined with C18:2 is superior to the clinical markers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy using the random forest approach, it is possible to analyze the metabolite data from both the training and validation sets, identify important features (metabolites) that contribute to the classification, and accurately classify new patients as either CRC or mCRC [28, 29].To explore metabolic markers that could complemently existing clinical indicators for the identification of mCRC, we initially assessed the significance of the differential metabolites obtained from 100 CRC and mCRC cases using a random forest approach. It was discovered C18:2 ranked followed by GCA in third and C17:0 in eighth (Figure 6A). These results indicated that although the three markers identified are significant, they do not precisely align with the top three in terms of importance. Moreover, the AUC of the three indicators combined was 0.86, with a mean of 0.84±0.1, showing clear advantages over CEA (0.70) and CA19-9 (0.80) (Figure 6 B-E). These data showed GCA, C17:0, combined with C18:2, was superior to the clinical markers CEA and CA19-9.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGCA improves the ability of clinical indicators to identify CRC liver metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further identify the relationship between GCA, C17:0, C18:2, CEA, and CA19-9, we analyzed the correlation of CEA, CA19-9, and three identified indicators and found that the correlation between CA19-9 and GCA was statistically different (Figure 7A). Several literature reports showed that the levcl of CA19-9 was associated with CRC liver metastasis and the therapeutic effect [30-32].Therefore, 25 mCRC patients with liver metastases were selected. The ability of GCA, C17:0, C18:2, CEA, and CA19-9 to differentiate liver metastases from mCRC was evaluated. ROC analysis showed that the AUC using GCA, CEA, or CA19-9 alone was 0.74, 0.74, and 0.70, respectively. Moreover, the AUC of the three metabolites combined was 0.87, indicating a significant increase in the true positive rate and a significant decrease in the false negative rate. However, the AUC of C17:0 and C18:2 was only 0.5-0.6 (Figure 7 B-G). These results suggested that GCA enhanced the identification of clinical mCRC liver metastases.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEarly diagnosis and exploration of therapeutic targets for mCRC are essential to improve the prognosis of CRC patients\u0026nbsp;[2, 3]. The analysis of plasma metabolome offers a precise understanding of CRC metastasis by specifically assessing metabolites that reflect the impact of medical interventions and genetics.[33, 34] . Here, the target-metabolomics technique was applied to analyze the plasma samples of patients with CRC and mCRC. Our study demonstrated that metabolomic markers can effectively differentiate between CRC and mCRC, surpassing the diagnostic accuracy of clinical indicators such as CEA and CA19-9. Furthermore, we observed a significant correlation between GCA and CA19-9 levels, highlighting GCA's potential as a reliable discriminator in cases of liver mCRC. These findings offer a promising new avenue for the diagnosis and potential treatment of mCRC.\u003c/p\u003e\n\u003cp\u003eA growing body of research has provided substantial evidence regarding the importance of amino acids, bile acids, and targeted metabolites associated with fatty acids in the initiation and progression of tumors [35-37]. A total of 100 CRC and mCRC were enrolled in our study, 60 CRC and mCRC were randomly assigned as the training set, and the other 40 CRC and mCRC were defined as the validation set. Amino acid imbalance is a common phenomenon in tumors. Amino acid imbalances are frequently observed in tumors, and previous research has demonstrated their potential as therapeutic targets for tumor-specific therapy [38, 39]. There is a limited amount of oncological literature available on ETA. However, it has been found that TAU has a notable inhibitory effect on the proliferation of various tumors[40, 41]. In 2019, a research paper highlighted the presence of Trp disorders in different types of tumors and emphasized their potential as a shared therapeutic target[42]. Furthermore, our study revealed significant differences in ETA, TAU, and Trp levels between the training set, allowing for effective discrimination between CRC and mCRC. However, these markers did not show outstanding performance in the validation set, which could be attributed to the small sample size. Regarding bile acids, previous reports have shown the significant value of GCA in the early diagnosis of liver cancer[43].Our research results suggest that GCA is a promising biomarker for identifying mCRC, especially liver mCRC. Although the role of fatty acids in tumor resistance and immune function is closely related, their role in the diagnosis and treatment of mCRC has been less studied [44, 45] .In our study, we discovered nine abnormal fatty acid metabolites (C15:0, C17:1, C17:0, C18:3, C18:2, C20:3, C20:1, C22:5, and C24:1) in the training set and two abnormal fatty acid metabolites (C17:0 and C18:2) in the validation set. According to the analysis of Comparative Toxicogenomics Database (CTD), C18:2 was found to interact with multiple genes, particularly RXRA, and enhance its activity, playing important roles in tumor proliferation and immunity. Examination using the GEPIA2 database revealed that the expression level of RXRA was significantly lower in CRC than in mCRC. Furthermore, RXRA acts as a receptor for ATRA, regulating its concentration. It is well known that ATRA is widely used in clinical treatment[22, 23]. This suggests that C18:2 likely plays a key role in mCRC through interactions with RXRA and ATRA, although no reports have been found on this phenomenon. Through validation on the confirmation set, we successfully identified three biomarkers, namely GCA, C17:0, and C18:2, which effectively distinguish between CRC and mCRC. In conclusion, based on the analysis and literature review, these differential metabolites hold potential clinical significance in the diagnosis and treatment of CRC and mCRC. However, further research is required to better understand their mechanisms of action and clinical application prospects.\u003c/p\u003e\n\u003cp\u003eWe conducted ROC analysis to evaluate the area AUC for GCA, C17:0, and C18:2 in combination. The AUC for mCRC detection was found to be 0.86, which is significantly higher compared to CEA (0.70) and CA19-9 (0.80). These results indicate that the metabolic markers we identified are crucial for the clinical diagnosis of mCRC. Specifically, there was a positive correlation between GCA and CA19-9. Although GCA exhibited greater specificity than CA19-9 in diagnosing mCRC, it did not show notable advantages over CEA. It is interesting to note that other studies have suggested that CEA is less effective than CA19-9 in diagnosing mCRC [46]. However, when GCA was combined with CA19-9 and CEA in our analysis of mCRC, it resulted in improved true positive rates and reduced false negative rates. These findings further support the significance of our study. Given the high sensitivity and real-time monitoring capabilities of metabolic indicators in serum, we speculate that GCA, C17:0, and C18:2 have strong potential in the detection, treatment monitoring, and early diagnosis of mCRC. Indeed, there is still room for improvement in our study. For example, due to the difficulty in collecting mCRC samples, we were unable to conduct a detailed analysis of the sites of CRC metastasis. In our future research, we plan to address this limitation by collecting more comprehensive samples to enable a thorough classification and analysis of CRC metastatic sites. Imaging phases such as enhanced CT/PET-CT also need to be compared. Furthermore, we intend to explore the connections between the metabolome and the upstream proteome and transcriptome in order to gain a deeper understanding of the molecular mechanisms underlying mCRC. By studying these relationships, we can analyze the interplay between the metabolome and microbial metabolism, which may have implications for identifying novel therapeutic targets and developing personalized treatments for mCRC.\u003c/p\u003e\n\u003cp\u003eIn summary, plasma metabolic markers offer a non-invasive and precise approach for detecting and treating cancer by providing individual patients' molecular tumor profiles (Figure 8). The metabolome represents a promising avenue for non-invasive identification of CRC. However, there is currently a limited number of studies that have screened for markers of mCRC and explored the function of metabolites to identify new targets for personalized therapy. Therefore, our future research aims to identify metabolic targets specific to mCRC and systematically investigate their roles and underlying mechanisms. Through this approach, we hope to uncover novel and precise treatment strategies for patients with mCRC.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclarations acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Shandong Provincial Natural Science Foundation Joint Fund Project (ZR2024LZL016), the Cancer Research Fund Project (BH005373), the Health Technology Development Program (XHD-001), China Cancer Foundation Project (CFC-XGB-202401)and the Collaborative Academic Innovation Project of Shandong Cancer Hospital(FC008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient samples were collected and analyzed with the approval of the ethics committee (2022003135) of Shandong Cancer Hospital, Shandong First Medical University, and Shandong Academy of Medical Sciences (Shandong, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: Yanlai Sun, Yan Zhao，Xinyu Li and Junqi Shan; Clinical data acquisition: Yan Zhao\u0026nbsp;，Xinyu Li , Junqi Shan, Bowen You, Wei Han and Tao Li; Bioinformatics analysis and data visualization: Xinyu Li and Junqi Shan; Drafting and revising it critically of the manuscript: Yanlai Sun. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSu, Y. L. et al. Development and Validation of a Novel Serum Prognostic Marker for Patients with Metastatic Colorectal Cancer on Regorafenib Treatment. \u003cem\u003eCancers (Basel)\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e (20), 5080 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan, A. et al. Immunotherapy in colorectal cancer: current achievements and future perspective. \u003cem\u003eInt. J. Biol. Sci.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (14), 3837\u0026ndash;3849 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCiardiello, F. et al. 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Integrated Analysis of Colorectal Cancer Reveals Cross-Cohort Gut Microbial Signatures and Associated Serum Metabolites. \u003cem\u003eGastroenterology\u003c/em\u003e \u003cb\u003e163\u003c/b\u003e (4), 1024\u0026ndash;1037e9 (2022).\u003c/span\u003e\u003c/li\u003e\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":"Colorectal cancer (CRC), Metastatic CRC (mCRC), Targeted metabolomes, Markers, Serum metabolites","lastPublishedDoi":"10.21203/rs.3.rs-7450452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7450452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Colorectal cancer (CRC) is a globally prevalent malignancy with high mortality. The diagnostic and therapeutic strategies for particularly metastatic CRC (mCRC) remain limited. Thus, the novel biomarkers and therapeutic avenues for mCRC are needed to be identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e \u0026nbsp;To identify different metabolite profiles distinguishing between patients with mCRC and CRC, plasma samples from a cohort of 100 patients with CRC (n=50) and mCRC (n=50) were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e \u0026nbsp;In training setting, the analysis revealed 13 metabolites that differed significantly between the 27 amino acids, 9 bile acids and 16 fatty acids. The area under the curve (AUC) of the classifier for C18:2 was 0.8089. In addition, the combined area under the curve (AUC) reached 0.86, which was significantly better than those of the traditional markers CEA (0.70) and CA19-9 (0.80). The data of pearson correlation analysis showed a significant correlation between GCA and CA19-9. Furthermore, the individual AUC values for GCA, CEA and CA19-9 in a specific analysis of 25 patients with mCRC were 0.74, 0.74 and 0.70, respectively. However, the AUC of GCA and CEA in combination with CA19-9 significantly increased to 0.87.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study emphasized the excellent performance of the combination of GCA, C17:0 and C18:2in differentiating CRC from mCRC. In addition, the integration of GCA, CEA and CA19-9 significantly improved the diagnostic accuracy of mCRC with liver metastasis. As expected, this research might develop novel diagnostic indicators and innovative therapeutic approaches against mCRC.\u003c/p\u003e","manuscriptTitle":"The combination of GCA, C17:0 and C18:2 improve diagnostic accuracy of colorectal cancer liver metastases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 12:04:38","doi":"10.21203/rs.3.rs-7450452/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":"96d5cd59-4448-4b69-85b1-8df2d63f24ec","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55794744,"name":"Health sciences/Biomarkers"},{"id":55794745,"name":"Biological sciences/Cancer"},{"id":55794746,"name":"Health sciences/Gastroenterology"},{"id":55794747,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-12-16T05:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 12:04:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7450452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7450452","identity":"rs-7450452","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}