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Abstract Background More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. Objective To analyze the characteristic microbes in AA. Methods Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and β diversity. α diversity included box diagram, and β diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. Results The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. Conclusion Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.

Abstract Background Pancreatic cancer (PCA) is an extremely aggressive malignant cancer with an increasing incidence and a low five‐year survival rate. The main reason for this high mortality is that most patients are diagnosed with PCA at an advanced stage, missing early treatment options and opportunities. As important nutrients of the human body, trace elements play an important role in maintaining normal physiological functions. Moreover, trace elements are closely related to many diseases, including PCA. Review This review systematically summarizes the latest research progress on selenium, copper, arsenic, and manganese in PCA, elucidates their application in PCA, and provides a new reference for the prevention, diagnosis and treatment of PCA. Conclusion Trace elements such as selenium, copper, arsenic and manganese are playing an important role in the risk, pathogenesis, diagnosis and treatment of PCA. Meanwhile, they have a certain inhibitory effect on PCA, the mechanism mainly includes: promoting ferroptosis, inducing apoptosis, inhibiting metastasis, and inhibiting excessive proliferation.

Abstract Background Gut microbiota dysbiosis involved in the pathogenesis of colorectal cancer (CRC). The characteristics of enterotypes in CRC development have not been determined. Objective To characterize the gut microbiota of healthy, adenoma, and CRC subjects based on enterotype. Methods The 16 S rRNA sequencing data from 315 newly sequenced individuals and three previously published datasets were collected, providing total data for 367 healthy, 320 adenomas, and 415 CRC subjects. Enterotypes were analyzed for all samples, and differences in microbiota composition across subjects with different disease states in each enterotype were determined. The predictive values of a random forest classifier based on enterotype in distinguishing healthy, adenoma, and CRC subjects were evaluated and validated. Results Subjects were classified into one of three enterotypes, namely, Bacteroide- (BA_E), Blautia- (BL_E), and Streptococcus- (S_E) dominated clusters. The taxonomic profiles of these three enterotypes differed among the healthy, adenoma, and CRC cohorts. BA_E group was enriched with Bacteroides and Blautia; BL_E group was enriched by Blautia and Coprococcus; S_E was enriched by Streptococcus and Ruminococcus. Relative abundances of these genera varying among the three human cohorts. In training and validation sets, the S_E cluster showed better performance in distinguishing among CRC patients, adenoma patients, and healthy controls, as well as between CRC and non-CRC individuals, than the other two clusters. Conclusion This study provides the first evidence to indicate that changes in the microbial composition of enterotypes are associated with disease status, thereby highlighting the diagnostic potential of enterotypes in the treatment of adenoma and CRC.

Abstract Background The most common toxic side effect after chemotherapy, one of the main treatments for colorectal cancer (CRC), is myelosuppression. Objective To analyze the correlation between gut microbiota and leukopenia after chemotherapy in CRC patients. Methods Stool samples were collected from 56 healthy individuals and 55 CRC patients. According to the leukocytes levels in peripheral blood, the CRC patients were divided into hypoleukocytes group (n = 13) and normal leukocytes group (n = 42). Shannon index, Simpson index, Ace index, Chao index and Coverage index were used to analyze the diversity of gut microbiota. LDA and Student's t-test(St test) were used for analysis of differences. Six machine learning algorithms, including logistic regression (LR) algorithm, random forest (RF) algorithm, neural network (NN) algorithm, support vector machine (SVM) algorithm, catboost algorithm and gradient boosting tree algorithm, were used to construct the prediction model of gut microbiota with leukopenia after chemotherapy for CRC. Results Compared with healthy group, the microbiota alpha diversity of CRC patients was significantly decreased (p

Cancer is a leading cause of human death worldwide, and the modulation of the metabolic properties of T cells employed in cancer immunotherapy holds great promise for combating cancer. As a crucial factor, energy metabolism influences the activation, proliferation, and function of T cells, and thus metabolic reprogramming of T cells is a unique research perspective in cancer immunology. Special conditions within the tumor microenvironment and high-energy demands lead to alterations in the energy metabolism of T cells. In-depth research on the reprogramming of energy metabolism in T cells can reveal the mechanisms underlying tumor immune tolerance and provide important clues for the development of new tumor immunotherapy strategies as well. Therefore, the study of T cell energy metabolism has important clinical significance and potential applications. In the study, the current achievements in the reprogramming of T cell energy metabolism were reviewed. Then, the influencing factors associated with T cell energy metabolism were introduced. In addition, T cell energy metabolism in cancer immunotherapy was summarized, which highlighted its potential significance in enhancing T cell function and therapeutic outcomes. In summary, energy exhaustion of T cells leads to functional exhaustion, thus resulting in immune evasion by cancer cells. A better understanding of reprogramming of T cell energy metabolism may enable immunotherapy to combat cancer and holds promise for optimizing and enhancing existing therapeutic approaches.

Abstract In recent years, bacteria have gained considerable attention as a promising drug carrier that is critical in improving the effectiveness and reducing the side effects of anti‐tumor drugs. Drug carriers can be utilised in various forms, including magnetotactic bacteria, bacterial biohybrids, minicells, bacterial ghosts and bacterial spores. Additionally, functionalised and engineered bacteria obtained through gene engineering and surface modification could provide enhanced capabilities for drug delivery. This review summarises the current studies on bacteria‐based drug delivery systems for anti‐tumor therapy and discusses the prospects and challenges of bacteria as drug carriers. Furthermore, our findings aim to provide new directions and guidance for the research on bacteria‐based drug systems.

Abstract Gastrointestinal (GI) cancers are among the most common and lethal cancers worldwide. GI microbes play an important role in the occurrence and development of GI cancers. The common mechanisms by which GI microbes may lead to the occurrence and development of cancer include the instability of the microbial internal environment, secretion of cancer-related metabolites, and destabilization of the GI mucosal barrier. In recent years, many studies have found that the relationship between GI microbes and the development of cancer is closely associated with the GI redox level. Redox instability associated with GI microbes may induce oxidative stress, DNA damage, cumulative gene mutation, protein dysfunction and abnormal lipid metabolism in GI cells. Redox-related metabolites of GI microbes, such as short-chain fatty acids, hydrogen sulfide and nitric oxide, which are involved in cancer, may also influence GI redox levels. This paper reviews the redox reactions of GI cells regulated by microorganisms and their metabolites, as well as redox reactions in the cancer-related GI microbes themselves. This study provides a new perspective for the prevention and treatment of GI cancers.

BackgroundGut microbiome is a complex community of microbes present in the human gut and plays an important role in the occurrence and progression of colorectal cancer (CRC). However, the relationship between virus and CRC has not been fully understood.ObjectiveTo explore the hot spots and research trends in the field of CRC and virus.MethodsBy using the bibliometric analysis tool CiteSpace and based on the articles of the Web of Science Core Collection (WoSCC) database, the country, institution, highly cited literature, keywords and so on were visually analyzed.ResultsA total of 356 research articles on CRC from 2001 to 2023 were thoroughly analyzed. The USA and China have made the largest contribution in the field of virus and CRC. The Helmholtz Association published the most papers. There were relatively few cooperations among institutions from different countries. The results of keyword cluster analysis proved that the literature on the relationship between human cytomegalovirus (CMV) and CRC was the most widely studied aspect in this field. “Gut microbiota,” “inflammatory bowel disease,” “hepatitis b virus,” and “human papillomavirus infection” are the current research hotspots; “oncolytic virus,” “apoptosis,” and “gut microbiome” are the recent research frontiers and should be paid closer attention.ConclusionBy using CiteSpace bibliometric software, the visual analysis reflected the research trends and hot topics of virus and CRC. In addition, the prevalence and mechanism of specific virus on CRC were also reviewed, which provides valuable references for future CRC research.

Abstract Background Colorectal cancer (CRC) is a multifactorial disease with genetic and environmental factors. Regional differences in risk factors are an important reason for the different incidences of CRC in different regions. Objective The goal was to clarify the intestinal microbial composition and structure of CRC patients in different regions and construct CRC risk prediction models based on regional differences. Methods A metagenomic dataset of 601 samples from 6 countries in the GMrepo and NCBI databases was collected. All whole-genome sequencing (WGS) data were annotated for species by MetaPhlAn2. We obtained the relative abundance of species composition at the species level and genus level. The MicrobiotaProcess package was used to visualize species composition and PCA. LEfSe analysis was used to analyze the differences in the datasets in each region. Spearman correlation analysis was performed for CRC differential species. Finally, the CRC risk prediction model was constructed and verified in each regional dataset. Results The composition of the intestinal bacterial community varied in different regions. Differential intestinal bacteria of CRC in different regions are inconsistent. There was a common diversity of bacteria in all six countries, such as Peptostreptococcus stomatis and Fusobacterium nucleatum at the species level. Peptostreptococcus stomatis (species level) and Peptostreptococcus (genus level) are important CRC-related bacteria that are related to other bacteria in different regions. Region has little influence on the accuracy of the CRC risk prediction model. Peptostreptococcus stomatis is an important variable in CRC risk prediction models in all regions. Conclusion Peptostreptococcus stomatis is a common high-risk pathogen of CRC worldwide, and it is an important variable in CRC risk prediction models in all regions. However, regional differences in intestinal bacteria had no significant impact on the accuracy of the CRC risk prediction model.

Abstract Background The mortality of colorectal cancer is high, the malignant degree of poorly differentiated colorectal cancer is high, and the prognosis is poor. Objective To screen the characteristic intestinal microbiota of poorly differentiated intestinal cancer. Methods Fecal samples were collected from 124 patients with moderately differentiated CRC and 123 patients with poorly differentiated CRC, and the bacterial 16S rRNA V1-V4 region of the fecal samples was sequenced. Alpha diversity analysis was performed on fecal samples to assess the diversity and abundance of flora. The RDP classifier Bayesian algorithm was used to analyze the community structure. Linear discriminant analysis and Student's t test were used to screen the differences in flora. The PICRUSt1 method was used to predict the bacterial function, and six machine learning models, including logistic regression, random forest, neural network, support vector machine, CatBoost and gradient boosting decision tree, were used to construct a prediction model for the poor differentiation of colorectal cancer. Results There was no significant difference in fecal flora alpha diversity between moderately and poorly differentiated colorectal cancer (P > 0.05). The bacteria that accounted for a large proportion of patients with poorly differentiated and moderately differentiated colorectal cancer were Blautia, Escherichia-Shigella, Streptococcus, Lactobacillus, and Bacteroides. At the genus level, there were nine bacteria with high abundance in the poorly differentiated group, including Bifidobacterium , norank_f__Oscillospiraceae , Eisenbergiella, etc. There were six bacteria with high abundance in the moderately differentiated group, including Megamonas , Erysipelotrichaceae_UCG-003 , Actinomyces, etc. The RF model had the highest prediction accuracy (100.00% correct). The bacteria that had the greatest variable importance in the model were Pseudoramibacter, Megamonas and Bifidobacterium. Conclusion The degree of pathological differentiation of colorectal cancer was related to gut flora, and poorly differentiated colorectal cancer had some different bacterial flora, and intestinal bacteria can be used as biomarkers for predicting poorly differentiated CRC.

Background: Oxaliplatin (OXA) is a chemotherapy agent commonly used in the treatment of colorectal cancer (CRC). Sodium butyrate (NaB) has an antitumor effect. Methods: In total, 30 patients in stage III who completed 8 cycles of chemotherapy regimens were recruited for this study. The patients were divided into good and bad groups based on the chemotherapy efficacy. Gas chromatography–mass spectrometry (GC/MS) was used to detect microbial metabolites in stool samples from CRC patients. Cell counting kit-8 (CCK-8), Annexin-V APC/7-AAD double staining, Transwell assays, scratch-wound assays, and EdU assays were used to detect cell proliferation, apoptosis, invasion and migration, respectively. Fluoroelectron microscopy was used to observe the cell structures. To verify the inhibitory effect of NaB and OXA at animal level, a subcutaneous transplanted tumor model was established. Finally, 16S sequencing technology was used to detect intestinal bacteria. GC–MS was used to detect metabolites in mouse stools. Results: NaB was a differential metabolite that affected the efficacy of OXA. NAB and oxaliplatin can synergically inhibit cell proliferation, migration and invasion, and induce cell apoptosis. Animal experiments confirmed the inhibitory effect of oxaliplatin and sodium butyrate on tumor in mice. In addition, the intestinal microbe detection and microbial metabolite detection in fecal samples from mice showed significant differences between butyrate-producing bacteria and NaB. Conclusion: NaB and OXA can synergistically inhibit the proliferation, invasion and metastasis of CRC cells and promote the apoptosis of CRC cells. NaB, as an OXA synergist, has the potential to become a new clinical adjuvant in CRC chemotherapy.

Highlights 1. ORF3a was successfully transfected into SW480 cells, and its expression was localized in the cytoplasm of SW480 cells. 2. ORF3a inhibited SW480 cell proliferation, invasion and migration, induced their apoptosis, and blocked them in the G1 phase. 3. ORF3a inhibited colorectal tumor growth and accelerated cancer cell death in vivo. 4. Our study provides new insights into the use of viruses in anti-tumor regimens in colorectal cancer.

Highlights A total of 309 CD8 T cell-related genes and 120 activated memory CD4 T cell-related genes were screened in liver cancer tumor samples. Forty-four chemical small-molecule–target interaction pairs associated with activated memory CD4 T cells and 276 pairs associated with CD8 T cells were screened. Upregulated expression of T cell-related genes including EOMES, CST7, and CD5L indicated the favorable prognosis of liver cancer.

Background This study aimed to estimate population-level and state-level lead-attributable mortality burdens stratified by socioeconomic status (SES) class in the USA. Methods Based on the National Health and Nutrition Examination Survey (NHANES), we constructed individual-level SES scores from income, employment, education and insurance data. We assessed the association between the blood lead levels (BLL) and all-cause mortality by Cox regression in the NHANES cohort (n  = 31 311, 4467 deaths). With estimated hazard ratios (HR) and prevalences of medium (2–5 μg/dL) and high (≥ 5 μg/dL) BLL, we computed SES-stratified population-attributable fractions (PAFs) of all-cause mortality from lead exposure across 1999–2019. We additionally conducted a systematic review to estimate the lead-attributable mortality burden at state-level. Results The HR for every 2-fold increase in the BLL decreased from 1.23 (1.10–1.38) for the lowest SES class to 1.05 (0.90–1.23) for the highest SES class. Across all SES quintiles, medium BLL exhibited a greater mortality burden. Individuals with lower SES had higher lead-attributable burdens, and such disparities haver persisted over the past two decades. In 2017–19, annually 67 000 (32 000–112 000) deaths in the USA were attributable to lead exposure, with 18 000 (2000–41 000) of these deaths occurring in the lowest SES class. Substantial disparities in the state-level mortality burden attributable to lead exposure were also highlighted. Conclusions These findings suggested that disparities in lead-attributable mortality burden persisted within US adults, due to heterogeneities in the effect sizes of lead exposure as well as in the BLL among different SES classes. [ABSTRACT FROM AUTHOR]

Phages are the most abundant and diverse biological entities on Earth and exert specific effects on bacterial hosts. The coexistence of phages and bacteria in the intestinal tract is dynamic and interdependent. Phages are involved in maintaining the stability and composition of the bacterial community, and an imbalance in phages and bacteria in the intestinal tract can cause diseases. This review elucidates interactions between phages and bacteria in the human intestinal tract and their roles in the pathogenesis and treatment of diseases. Understanding the relationship among phages, bacteria and host diseases is conducive to promoting the application of phages in the treatment of human diseases.List of abbreviations EMBL-EBI The European Bioinformatics Institute; E. coli Escherichia coli; E. faecalis Enterobacter faecalis; B. fragilis Bacteroides fragilis; B. vulgatus Bacteroides vulgatus; SaPIs Staphylococcus aureus pathogenicity islands; ARGs Antibiotic resistance genes; STEC Shiga toxigenic E. coli; Stx Shiga toxin; BLAST Basic Local Alignment Search Tool; TSST-1 Toxic shock toxin 1; RBPs Receptor-binding proteins; LPS lipopolysaccharide; OMVs Outer membrane vesicles; PT Phosphorothioate; BREX Bacteriophage exclusion; OCR Overcome classical restriction; Pgl Phage growth limitation; DISARM Defense island system associated with restrictionmodification; R-M system Restriction-modification system; BREX system Bacteriophage exclusion system; CRISPR Clustered regularly interspaced short palindromic repeats; Cas CRISPR-associated; PAMs Prospacer adjacent motifs; crRNA CRISPR RNA; SIE; OMPs; Superinfection exclusion; Outer membrane proteins; Abi Abortive infection; TA Toxin-antitoxin; TLR Toll-like receptor; APCs Antigen-presenting cells; DSS Dextran sulfate sodium; IELs Intraepithelial lymphocytes; FMT Fecal microbiota transfer; IFN-γ Interferon-gamma; IBD Inflammatory bowel disease; AgNPs Silver nanoparticles; MDSC Myeloid-derived suppressor cell; CRC Colorectal cancer; VLPs Virus-like particles; TMP Tape measure protein; PSMB4 Proteasome subunit beta type-4; ALD Alcohol-related liver disease; GVHD Graft-versus-host disease; ROS Reactive oxygen species; RA Rheumatoid arthritis; CCP Cyclic citrullinated protein; AMGs Accessory metabolic genes; T1DM Type 1 diabetes mellitus; T2DM Type 2 diabetes mellitus; SCFAs Short-chain fatty acids; GLP-1 Glucagon-like peptide-1; A. baumannii Acinetobacter baumannii; CpG Deoxycytidylinate-phosphodeoxyguanosine; PEG Polyethylene glycol; MetS Metabolic syndrome; OprM Outer membrane porin M.

The incidence of cancer is high worldwide, and biological factors such as viruses and bacteria play an important role in the occurrence of cancer. Helicobacter pylori, human papillomavirus, hepatitis B viruses and other organisms have been identified as carcinogens. Cancer is a disease driven by the accumulation of genome changes. Viruses can directly cause cancer by changing the genetic composition of the human body, such as cervical cancer caused by human papillomavirus DNA integration and liver cancer caused by hepatitis B virus DNA integration. Recently, bacterial DNA has been found around cancers such as pancreatic cancer, breast cancer and colorectal cancer, and the idea that bacterial genes can also be integrated into the human genome has become a hot topic. In the present paper, we reviewed the latest phenomenon and specific integration mechanism of bacterial DNA into the human genome. Based on these findings, we also suggest three sources of bacterial DNA in cancers: bacterial DNA around human tissues, free bacterial DNA in bacteremia or sepsis, and endogenous bacterial DNA in the human genome. Clarifying the theory that bacterial DNA integrates into the human genome can provide a new perspective for cancer prevention and treatment.

Multiple primary malignancies in a single patient are relatively rare; however, the frequency of this has increased significantly in recent decades. Here, we retrospectively reported an unusual case of a 70-year-old man who was admitted to the hospital with mantle cell lymphoma (MCL) and colon cancer and aimed to explore measures to reduce missed diagnosis. Based on the data, the investigation of the related literatures, colonoscopy examination, and abdominal computed tomography (CT) scan were conducted for the detection of colon cancer. Following this, a precise diagnosis of MCL was confirmed by immunohistochemistry and bone marrow biopsy, which were performed to analyze the clinical characteristics and essentials for the diagnosis and differential of the disease. The results of colonoscopy showed that the patient had colon cancer, while the abdominal CT scan demonstrated colon cancer accompanied by multiple lymphadenopathy throughout the entire body. Besides, the results of immunohistochemistry confirmed that the patient suffered from MCL. The bone marrow biopsy revealed the active hematopoietic tissue hyperplasia but no tumor issue involvement in the bone marrow. In conclusion, our study combined the analysis and summary of the diagnosis and treatment of the colon cancer with MCL to provide clinical guidance for the rare multiple primary malignancy.

Abstract Background Sodium butyrate (NaB) is produced through the fermentation of dietary fiber that is not absorbed and digested by the small intestine. Purpose Here, we aimed to investigate the effects of NaB on the proliferation, invasion, and metastasis of CRC cells and their potential underlying molecular mechanism(s). Methods The cell counting kit-8 (CCK-8) assay and EdU assay were used to detect cell proliferation ability, flow cytometry was used to investigate the induction of apoptosis and cell cycle progression, and the scratch-wound healing and transwell assays were used to evaluate cell migration and invasion, respectively. The human CRC genome information for tissues and CRC cells treated with NaB obtained from the NCBI GEO database was reannotated and used for differential RNA analysis. Functional and pathway enrichment analyses were performed for differentially expressed lncRNAs and mRNAs. A protein-protein interaction (PPI) network for the hub genes was constructed using the Cytoscape software. Targeted miRNAs were predicted based on the lnCeDB database, and a ceRNA network was constructed using the Cytoscape software. The Kaplan-Meier method was used to analyze patient prognosis using the clinical information and exon-seq data for CRC obtained from the Broad Institute’s GDAC Firehose platform. Results NaB decreased the proliferation ability of CRC cells in a dose- and time-dependent manner. The number of apoptotic CRC cells increased with the increase in NaB concentrations, and NaB induced a G1 phase block in CRC cells. Moreover, NaB suppressed the migratory and invasive capabilities of CRC cells. There were 666 differentially expressed mRNAs and 30 differentially expressed lncRNAs involved in the CRC inhibition by NaB. The PPI network and ceRNA network were constructed based on the differentially expressed mRNAs and lncRNAs. Three differentially expressed mRNAs, including HMGA2, LOXL2, and ST7, were significantly correlated with the prognosis of CRC. Conclusion NaB induces the apoptosis and inhibition of CRC cell proliferation, invasion, and metastasis by modulating complex molecular networks. RNA prediction and molecular network construction need to be the focus of further research in this direction.

Abstract Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of GSE131418 and GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug‐targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways (eg FGA, F2, and F9). In a drug‐target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis.