{"paper_id":"3982a58a-e904-4f0b-8681-adfd3947980f","body_text":"Tumour-associated and non-tumour-associated bacteria co-abundance groups in colorectal cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tumour-associated and non-tumour-associated bacteria co-abundance groups in colorectal cancer yuxuan liang, Jing Yu, Qingrong Zhang, Wenyan Hu, Sihua Xu, Yiyuan Xiao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3865704/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2024 Read the published version in BMC Microbiology → Version 1 posted 8 You are reading this latest preprint version Abstract Background & Aims: Gut microbiota is closely related to the occurrence and development of colorectal cancer (CRC). However, the differences of bacterial co-abundance groups (CAGs) between tumor tissue (TT) and adjacent normal tissue (NT), as well as their associations with clinical features, were need to be clarified. Methods Bacterial 16S rRNA sequencing was performed by using TT samples and NT samples of 251 patients with colorectal cancer. Microbial diversity, taxonomic characteristics, microbial composition, and functional pathways were compared between TT and NT. Hierarchical clustering was used to construct CAGs. Results Four CAGs were grouped in the hierarchical cluster analysis. CAG 2, which was mainly comprised of pathogenic bacteria, was significantly enriched in TT samples (2.27% in TT vs. 0.78% in NT, p < 0.0001). While CAG 4, which was mainly comprised of non-pathogenic bacteria, was significantly enriched in NT samples (0.62% in TT vs. 0.79% in NT, p = 0.0004). In addition, CAG 2 was also significantly associated with tumor microsatellite status (13.2% in unstable vs. 2.0% in stable, p = 0.016) and CAG 4 was positively correlated with the level of CA199 (r = 0.17, p = 0.009). Conclusions our research will deepen our understanding of the interactions among multiple bacteria and offer insights into the potential mechanism of NT to TT transition. Colorectal Cancer 16S rRNA sequencing Mucosal tissue Microbiota Classification biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The global incidence of colorectal cancer (CRC) has increased rapidly. Especially in China, it ranks second among all malignant tumors[ 1 – 3 ]. Traditional risk factors for CRC include family history, inflammatory bowel disease, processed meat intake, diabetes, obesity, smoking, and alcohol consumption[ 4 , 5 ]. Previous studies found that changes in gut microbiota such as Streptococcus bovis , Helicobacter pylori , Bacteroides fragilis , Enterococcus faecalis , Clostridium septicum , Fusobacterium spp . and Escherichia coli , was closely related to the occurrence of gastrointestinal cancer[ 6 ]. However, for several microorganisms such as Fusobacterium species, the association of their abundance with human colon cancer was not consistent in all reports and lacked a clear conclusion[ 7 , 8 ]. Abreu et al’ research indicated that the inconsistency between studies may be due to the heterogeneity of microbial or host response levels[ 9 ]. Therefore, Flemer et al proposed that combinations or co-abundance groups (CAGs) of organisms may be more operative to express the relationship between microbiota and disease, rather than representing a one organism-one disease model[ 10 ]. In Flemer’s study, they found that it was feasible to use a combination of several bacteria (or microbiome characteristics) in the stool microbiota of CRC patients as a marker to detect the disease[ 10 ]. The utility of CAGs was further confirmed in investigating the association between the microbiota community of tongue coating and the prognosis of gastric cancer[ 11 ]. Recently, a study conducted in Australia demonstrated that the construction of oncomicrobial community subtype, similar to the CAGs in Flemer’s paper, using tumor tissue (TT) and adjacent normal tissue (NT) samples can effectively predict the prognosis of CRC[ 12 ]. However, the performance of CAGs in Chinese CRC patients is not clear. In this study, we conducted a cross-sectional study of colon microbiome in 492 mucosal samples from patients undergoing CRC surgery, including 245 TT and 247 NT. We found that the diversity of microbial between TT and NT was significantly different, with each group exhibiting distinct taxonomic profiling and discriminant taxa. In addition, the intratumor microbiota of CRC could be categorized into four CAGs and CRC patients could be further divided into 6 distinct groups based on four CAGs. This study may provide novel insights into the dynamics of bacterial communities during the conversion of NT to TT. Materials and methods Study Participants Samples were obtained from patients undergoing surgical treatment for colorectal cancer. Ultimately, a total of 251 patients diagnosed with colorectal cancer were recruited at the Sixth Affiliated Hospital of Sun Yat-sen University from 2015 to 2021. Detailed information of these samples is provided in Table 1 . Table 1 The demographic characteristics of all the enrolled samples. Characteristics Normal (247) Tumor (245) Gender Female 97(39.27%) 97(39.59%) Male 150(60.73%) 148(60.41%) Site Left hemicolon 84(34.01%) 83(33.88%) Rectum 108(43.72%) 107(43.67%) Right hemicolon 55(22.27%) 55(22.45%) Stage Advanced 118(47.77%) 117(47.76%) Early 129(52.23%) 128(52.24%) Gross Infiltration 2(0.81%) 2(0.82%) Mass 67(27.13%) 66(26.94%) Ulcer 176(71.26%) 175(71.43%) NA 2(0.81%) 2(0.82%) Differentiation High 38(15.38%) 37(15.1%) Low 17(6.88%) 17(6.94%) Median 178(72.06%) 177(72.24%) NA 14(5.67%) 14(5.71%) Ki67 50% or more 144(58.3%) 144(58.78%) Less than 50% 97(39.27%) 95(38.78%) NA 6(2.43%) 6(2.45%) Microsatellite Unstable 17(6.88%) 17(6.94%) Stable 229(92.71%) 227(92.65%) NA 1(0.4%) 1(0.41%) Age (years) 62.94 ± 12.31 62.97 ± 12.28 Height (cm) 162.85 ± 7.78 162.84 ± 7.82 Weight (kg) 60.17 ± 10.41 60.12 ± 10.43 BMI (kg/m²) 22.64 ± 3.27 22.63 ± 3.28 CEA (ng/ml) 87.94 ± 1022.52 88.68 ± 1026.74 CA199 (U/ml) 74.77 ± 451.94 75.25 ± 453.8 CA125 (U/ml) 18.01 ± 32.12 18.12 ± 32.23 CA153 (U/ml) 10.41 ± 5.83 10.43 ± 5.85 AFP (ng/ml) 3.37 ± 8.92 3.39 ± 8.95 Note: Data are shown as means ± SD Tumor specimens were obtained using a sterile scalpel blade within 1 hour following surgical resection. All samples were promptly frozen in liquid nitrogen and stored at -80°C. Inclusion criteria encompassed patients aged 18 years or older without contraindications to colorectal cancer resection. The exclusion criteria include the use of antibiotics or probiotics within one month, radiotherapy, chemotherapy, intestinal obstruction, and concurrent other severe organic diseases. The stage of CRC was classified according to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging system. The protocol of human sample usage and the informed consent were approved by the Ethical Review Board of the Sixth Affiliated Hospital of Sun Yat-sen University (2020ZSLYEC-101). DNA extraction and PCR amplification Total microbial genomic DNA was extracted from TT and NT samples using FastDNA Spin Kit for Soil (MP Biomedicals) according to the manufacturer’s instructions. The quality and concentration of DNA were determined by 1.0% agarose gel electrophoresis and a NanoDrop® ND-2000 spectrophotometer (Thermo Scientific Inc., USA) and kept at -80℃ prior to further use. The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with primer pairs 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R(5'-GGACTACHVGGGTWTCTAAT-3') [ 49 ] by an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA). The PCR reaction mixture including 4 µL 5 × Fast Pfu buffer, 2 µL 2.5 mM dNTPs, 0.8 µL each primer (5 µM), 0.4 µL Fast Pfu polymerase, 10 ng of template DNA, and ddH 2 O to a final volume of 20 µL. PCR amplification cycling conditions were as follows: initial denaturation at 95 ℃ for 3 min, followed by 27 cycles of denaturing at 95 ℃ for 30 s, annealing at 55 ℃ for 30 s, and extension at 72 ℃for 45 s, and single extension at 72 ℃ for 10 min, and end at 4 ℃. All samples were amplified in triplicate. The PCR product was extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer’s instructions and quantified using Quantus™ Fluorometer (Promega, USA). Illumina MiSeq sequencing Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Statistical Analysis The raw data were processed using QIIME2 (Version 2021.8.0) to remove reads with insufficient repetitions (reads with less than 48 entries or fewer than 25 samples containing the reads) or readings shorter than 148 bp. Subsequently, the filtered reads were clustered into operational taxonomic units (OTUs) at a similarity threshold of 97%. To mitigate the potential impact of sequencing depth on subsequent alpha and beta diversity analyses, the sequence count in all samples was standardized to 16,864 sequences. As sequencing depth increased, the observed feature curves for both sample groups reached a plateau, indicating sufficient sequencing coverage. Taxonomic classification of each OTU was performed by comparing the sequences against SILVA database (version 138.1). Alpha diversity was assessed by calculating the ACE, Chao1, Observe, Pielou, Shannon, and Simpson indices. To compare the diversity differences among groups, Beta diversity was examined through principal coordinates analysis (PCoA) based on Bray-Curtis distance. Permutational multivariate analysis of variance using distance matrices (pMANOVA) was employed to assess the significance of beta diversity. Linear discriminant analysis Effect Size[ 50 ] (LEfSe) was used ( http://galaxy.biobakery.org/ ) to identify key microorganisms associated with different groups, with an LDA threshold of 3.5. Bray-Curtis distance-based hierarchical clustering with Ward linkage method was utilized to construct CAGs, where only genera exhibiting a relative abundance exceeding 0.1% in TT and NT were used. For continuous variables, Mann-Whitney U test was employed to compare differences between groups, while Spearman rank correlation analysis was used for assessing correlations[ 51 ]. Prediction of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis. Statistical analyses and figures were conducted using R version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria.). A statistically significant difference was considered when the P value < 0.05. Results Baseline Characteristics of Participants A total of 248 patients diagnosed with colorectal cancer were included in this study. Among these patients, 244 had tumor tissues (TT) and paired normal tissues (NT) samples, while one patient solely provided TT samples and three patients solely provided NT samples. Finally, a total of 245 TT samples and 247 NT samples were retained for analysis. The demographic characteristics of all the enrolled samples are presented in Table 1 . Comparison of the Microbial Diversity between TT and NT In terms of alpha diversity, we employed ACE, Chao1, Observe, Pielou, Shannon, and Simpson indices to assess the species richness, evenness, and diversity of TT and NT. Based on the estimated results of the Chao1 index analysis (Fig. 1 A; p = 0.048), we found that the alpha diversity of microbiota in NT was significantly higher than that of TT. Beta diversity was also significantly different between TT and NT (Fig. 1 B; Permanova: Bray-Curtis p = 0.004). Taxonomic Profiling and Discriminant Taxa between TT and NT As illustrated in Fig. 2 A and 2 B, Proteobacteria , Actinobacteriota , Firmicutes , Bacteroidota and Fusobacteriota were five predominant phyla (Fig. 2 A) while Delftia , Actinobacteria unclassified , Pseudomonas , Bacteroidota , Escherichia-shigella , and Hizobiaceae unclassified were six dominant genera (Fig. 2 B). LEfSe analyses were employed to identify significant microbial biomarkers across all taxa, with an LDA score threshold of > 3.5. As depicted in Fig. 2 C and 2 D, at the genus level, Escherichia-Shigella , Fusobacterium , Streptococcus , Peptostreptococcus , Parvimonas, Klebsiella , and Gemella were enriched in TT. While Acinetobacter , Achromobacter , Delftia , and Pseudomonas were enriched in NT. These findings suggest distinct microbiota compositions between TT and NT. Distribution of CAGs in TT and NT We performed hierarchical cluster analysis using a total of 47 genera with relative abundances greater than 0.1%, resulting in the grouping of four co-abundance groups (CAGs). The correlation of these 47 genera with four CAGs is shown in Fig. 3 A and the detailed co-enriched genera of each CAG are shown in Table 2 . By conducting sample-level cluster analysis using the abundance profiles of four CAGs, we identified six distinct sample groups (Fig. 3 B). Notably, due to the high abundance of CAG 1 in sample group 1 and sample group 2, it was challenging to accurately discriminate TT from NT in these two sample groups. However, in the remaining four sample groups (sample group 3, sample group 4, sample group 5, and sample group 6), clear discrimination between TT and NT was observed (Fig. 3 C). Sample group 4 and sample group 6 were primarily composed of TT, while sample group 3 and sample group 5 were predominantly composed of NT (Fig. 3 D). Table 2 The detailed co-enriched genera of each CAG. CAG 1 CAG 2 CAG 3 CAG 4 Achromobacter Anaerococcus Akkermansia Acinetobacter Bacteroides Campylobacter Alistipes Aquabacterium Chloroplast Eikenella Bifidobacterium Bacillus Delftia Erysipelatoclostridium Blautia Chryseobacterium Enterococcus Fusobacterium Collinsella Lactobacillus Escherichia-Shigella Gemella Dialister Ralstonia Klebsiella Granulicatella Faecalibacterium Caulobacteraceae_unclassified Proteus Leptotrichia Holdemanella Clostridiaceae_unclassified Pseudomonas Parvimonas Parabacteroides Peptostreptococcaceae_unclassified Actinobacteria_unclassified Peptostreptococcus Prevotella Sphingomonadaceae_unclassified Enterobacteriaceae_unclassified Porphyromonas Subdoligranulum - Lachnospiraceae_unclassified Streptococcus - - Rhizobiaceae_unclassified - - - Unknown - - - The relative abundance of four CAGs in TT and NT were illustrated in Fig. 4 A. We found that CAG 2 exhibited significantly higher abundance in TT (median: 2.27% in TT vs. 0.78% in NT, p < 0.0001) while CAG 4 exhibited significantly higher abundance in NT (median: 0.62% in TT vs. 0.79% in NT, p = 0.0004). However, no significant differences were observed for CAG 1 and CAG 3. Interestingly, after removing sample group 1 which exhibited a predominant enrichment in CAG 1, CAG 3 displayed an increased abundance in NT (median: 1.9% in TT vs. 6.2% in NT, p = 0.0006, Fig. 4 B and 4 C) and exhibited an inverse correlation with CAG 2 (Fig. 4 D). As shown in Fig. 4 E, CAG 2 was highly abundant in sample group 4 and sample group 6 which mainly consisted of TT, while CAG 3 and CAG 4 were highly abundant in sample group 3 and sample group 5 which mainly consisted of NT. Association of CAGs with clinical features We further investigated the association of identified CAGs with clinical features in TT. Notably, we observed a significant association between CAG 2 and tumor microsatellite status. Specifically, a higher abundance of CAG 2 was found in samples with unstable microsatellites (median: 13.2% in unstable vs. 2.0% in stable, p = 0.016, Fig. 5 A). Furthermore, we evaluated the association of CAGs with tumor markers and specifically observed a positive correlation between CAG 4 and CA199 (r = 0.17, p = 0.009) (Fig. 5 B). Functional analysis of microbiota in each group Finally, PICRUSt was employed to predict the KEGG pathways implicated in TT and NT. The KEGG pathways with an average relative abundance above 1% in all samples are shown in Fig. 6 A. Notably, the membrane transport pathway constituted a substantial proportion, accounting for 13.8%. Of the 20 pathways with an average relative abundance above 1%, 18 of them were significantly upregulated in NT (Fig. 6 B). Discussion Tons of studies indicated that CRC-related microbiota can provide valuable insights into cancer occurrence, progression, and treatment response[ 13 ]. Disparities between TT and NT were primarily arise from individual taxonomic variations on the taxonomic profiling. In this study, by constructing community structures known as co-abundant groups (CAGs), we grouped all samples and examined the association of clinical characteristics with CAGs. Compared with NT, a higher diversity of organisms in TT was observed as indicated by the Chao1 index of alpha diversity. PCoA analysis based on Bray-Curtis distance also demonstrated a significant differentiation between TT and NT. Similar findings were found in Loke’s studies (Loke et al., 2018)[ 14 ]. However, non-significant differences in microbial diversity (α- and β-diversity) between TT and NT were also reported (Liu et al., 2021; Li M. et al., 2020)[ 15 , 16 ]. These discrepancies can be partially attributed to variations in geographical location and tumor heterogeneity. Regarding taxonomic profiling and discriminant taxa, we identified distinct taxa that can differentiate TT from NT. Specifically, Escherichia-Shigella , Fusobacterium , Streptococcus , Peptostreptococcus , Parvimonas, Klebsiella , and Gemella were significantly enriched in TT at the genus level. The enrichment of Fusobacterium and Streptococcus in TT has been consistently reported across numerous studies, highlighting their important role in tumor initiation and progression[ 17 , 18 ]. Notably, Parvimonas exhibited a significant positive correlation with the host gene PARVB, which is highly expressed in CRC tissues[ 19 ]. Furthermore, Escherichia-Shigella , Peptostreptococcus , and Klebsiella were found to be enriched specifically in CRC patients compared to healthy volunteers in an investigation focusing on intestinal flora composition[ 20 ]. Gemella which predominantly resides within the oral cavity and upper gastrointestinal tract, was reported to be associated with oral squamous cell carcinoma[ 21 ]. In summary, the above findings suggest subtle differences in microbial diversity between TT and NT. Furthermore, both TT and NT exhibit unique taxonomic profiles, each characterized by a dominant genus. Recognizing that a single taxonomic group might not fully capture microbial differences between TT and NT, we applied hierarchical clustering based on Bray-Curtis distance to construct four co-abundance groups (CAG 1–4). These constructed CAGs were then used for unsupervised clustering of TT and NT samples, resulting in the classification of all samples into six major categories (sample group 1–6). CAG 2 was notably enriched in TT tissues, while CAG 4 was enriched in NT. Sample group 3 and sample group 5 contained predominantly NT, whereas sample group 4 and sample group 6 contained predominantly TT. The CAGs level analyses revealed that sample group 4 and sample group 6 exhibited a higher abundance of CAG 2, sample group 3 had an increased abundance of CAG 3, and sample group 5 was enriched with CAG 4. Further examination revealed that the abundance of CAG 1 in sample group 1 was exceptionally high, nearly 100%, resulting in a low abundance of the remaining CAGs. To address this question, we excluded sample group 1 and conducted the same analysis with the remaining samples. In the remaining samples, we found that CAG 3 was significantly increased in NT. Additionally, CAG 3 and CAG 4 exhibited a positive correlation, though not statistically significant, and both were negatively correlated with CAG 2. Previous studies have demonstrated the feasibility of classifying experimental subjects using bacterial abundance or CAGs. For instance, in a study on colorectal cancer and adjacent normal tissues, K-means clustering was employed to divide the samples into three distinct subgroups[ 12 ]. Another similar study clustered the Operational Taxonomic Unit (OTU) hierarchy into six CAGs, subsequently categorizing the samples into multiple distinct subgroups, a process replicated in two additional cohorts[ 10 ]. In our study, CAG 1 comprises a substantial number of nonpathogenic or opportunistic pathogens that were widely found in nature or the human body, including Bacteroides , Delftia , Enterococcus , Klebsiella , Proteus , and Pseudomonas [ 22 – 27 ]. CAG 2 includes Fusobacterium , Streptococcus , Peptostreptococcus , and Parvimonas , which were reported to promote the occurrence and progression of CRC in various studies. Interestingly, these four genera were assigned to the same CAG which was considered a pathogenic bacterial cluster in the study by Flemer et al.[ 10 ]. Campylobacter in CAG 2 was reported to be associated with colorectal and esophageal cancer[ 28 ]. In CAG 3 and CAG 4, we identified more bacteria that are considered to be probiotic or nonpathogenic such as Akkermansia , Alistipes , Bifidobacterium , Blautia , Collinsella , Faecalibacterium , Parabacteroides , Prevotella , Bacillus , and Lactobacillus [ 29 – 38 ]. Hence, human diseases can be attributed not only to a single pathogen but also to overall changes in the microbiota[ 39 ]. For instance, a study on breast cancer described the combination of estrogen in the liver, excretion into the gastrointestinal cavity, conjugation by bacterial β-glucuronidase, reabsorption as free estrogens through the enterohepatic circulation, and distribution to different organs like the breast. These metabolites, produced by several bacteria from the Clostridia and Ruminococcaceae families through estrogen metabolism, may collectively have breast cancer-causing potential[ 40 ]. These findings offer insights into flora changes during the transformation from NT to TT in a higher dimension, explore bacterial interaction from the bacterial clusters, and provide clues to the mechanism of the multi-bacterial joint promotion of CRC occurrence and development. Based on the previously constructed CAGs, we further investigate the association of CAGs in TT with clinical characteristics. CAG 2 was found to be associated with the microsatellite status of tumors, exhibiting higher abundance in TT associated with unstable microsatellites. Previous studies conducted in Japan and the United States have demonstrated a significant correlation between the positive expression of F. nucleatum and unstable microsatellite status[ 41 ]. Notably, Fusobacterium was exactly in our CAG 2. Furthermore, CAG 4 exhibited a positive correlation with CA199 levels. CA199 is a typical marker for gastrointestinal tumors and has high sensitivity for pancreatic cancer diagnosis, as well as aiding in rectal cancer, colon cancer, and primary liver cancer detection[ 42 ]. In intrahepatic cholangiocarcinoma cases, Bacillus anthracis and P. azotoformans were observed to be positively associated with CA199 levels[ 43 ]. Notably, bacillus was exactly in our CAG 4. In addition to compositional changes in bacterial taxa, we also observed predicted functional alterations across different groups. We found that the following metabolic pathways including nucleotide metabolism, lipid metabolism, enzyme metabolism, energy metabolism, carbohydrate metabolism, and amino acid metabolism were enriched in the NT group. Similar findings were reported in previous studies[ 44 – 46 ]. Our findings suggest that microbial changes may impact multiple metabolic pathways including amino acid, lipid, and carbohydrate metabolisms which could potentially underlie the transition from NT to TT. Our research boasts a relatively substantial sample size, contributing to the generation of robust and reliable findings. However, several limitations still require attention. First, in our cluster analysis, two sample groups could not be accurately classified, possibly due to the heterogeneity of tumor samples in terms of location and subtype. Previous studies have highlighted differences in microbial composition between CRC originating from different locations or subtypes[ 47 , 48 ]. Second, cross-sectional studies emphasize the need for prospective trials to fully elucidate the role of microbiota in CRC. Lastly, as we employed 16S rRNA gene sequencing for microbiota analysis, we were unable to determine species-level composition and actual genetic functions. Further investigations utilizing shotgun metagenomic sequencing are warranted to unravel the mechanisms underlying CAGs and CRC. In summary, our research will deepen our understanding of the interactions among multiple bacteria and offer insights into the potential mechanism of NT to TT transition. Declarations Ethics approval and consent to participate The protocol of human sample usage and the informed consent were approved by the Ethical Review Board of the Sixth Affiliated Hospital of Sun Yat-sen University (2020ZSLYEC-101). The participant was informed about the study and signed informed consent prior enrolment. Consent for publication Consent for publication of anonymized data was obtained from the participant as part of the informed consent form that was signed prior enrolment. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. In addition, our data has been uploaded to the NCBI database (PRJNA1068013) and will be released in the future. Competing interests The authors have nothing to disclose. Funding This project is financially supported by the National Natural Science Foundation of China (NSFC, 82272312), the Shenzhen-Hong Kong-Macao Science and Technology Project (Category C project) (SGDX20220530111403024), the National Key R&D Program of China (2023YFC2307004), the 100 Top Talent Programs of Sun Yat-sen University (58000-12230029). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author’s contribution Yuxuan Liang: Conceptualization, Data analysis, Writing - original draft, Writing - Review & Editing. Jing Yu: Data curation, Resources, Validation. Qingrong Zhang: Investigation, Visualization, Writing - Review & Editing. Wenyan Hu: Project administration, Writing - Review & Editing. Sihua Xu: Data curation. 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Lopez-Siles M, Duncan SH, Garcia-Gil LJ, Martinez-Medina M. Faecalibacterium prausnitzii: from microbiology to diagnostics and prognostics. ISME J. 2017;11:841–52. Cui Y, Zhang L, Wang X, Yi Y, Shan Y, Liu B, et al. Roles of intestinal Parabacteroides in human health and diseases. FEMS Microbiol Lett. 2022;369:fnac072. Chang C-J, Lin T-L, Tsai Y-L, Wu T-R, Lai W-F, Lu C-C, et al. Next generation probiotics in disease amelioration. J Food Drug Anal. 2019;27:615–22. Mu Y, Cong Y. Bacillus coagulans and its applications in medicine. Benef Microbes. 2019;:1–10. Scillato M, Spitale A, Mongelli G, Privitera GF, Mangano K, Cianci A, et al. Antimicrobial properties of Lactobacillus cell-free supernatants against multidrug-resistant urogenital pathogens. Microbiologyopen. 2021;10:e1173. Rea D, Coppola G, Palma G, Barbieri A, Luciano A, Del Prete P, et al. Microbiota effects on cancer: from risks to therapies. Oncotarget. 2018;9:17915–27. Fernández MF, Reina-Pérez I, Astorga JM, Rodríguez-Carrillo A, Plaza-Díaz J, Fontana L. Breast Cancer and Its Relationship with the Microbiota. Int J Environ Res Public Health. 2018;15:1747. Nosho K, Sukawa Y, Adachi Y, Ito M, Mitsuhashi K, Kurihara H, et al. Association of Fusobacterium nucleatum with immunity and molecular alterations in colorectal cancer. World J Gastroenterol. 2016;22:557–66. Tong W, Gao H, Wei X, Mao D, Zhang L, Chen Q, et al. Correlation of serum CA199 levels with glycemic control and microvascular complications in patients with type 2 diabetes mellitus. Am J Transl Res. 2021;13:3302–8. Chai X, Wang J, Li H, Gao C, Li S, Wei C, et al. Intratumor microbiome features reveal antitumor potentials of intrahepatic cholangiocarcinoma. Gut Microbes. 2023;15:2156255. Han S, Pan Y, Yang X, Da M, Wei Q, Gao Y, et al. Intestinal microorganisms involved in colorectal cancer complicated with dyslipidosis. Cancer Biol Ther. 2019;20:81–9. Ibrahim A, Hugerth LW, Hases L, Saxena A, Seifert M, Thomas Q, et al. Colitis-induced colorectal cancer and intestinal epithelial estrogen receptor beta impact gut microbiota diversity. Int J Cancer. 2019;144:3086–98. Greathouse KL, White JR, Padgett RN, Perrotta BG, Jenkins GD, Chia N, et al. Gut microbiome meta-analysis reveals dysbiosis is independent of body mass index in predicting risk of obesity-associated CRC. BMJ Open Gastroenterol. 2019;6:e000247. Phipps O, Quraishi MN, Dickson EA, Steed H, Kumar A, Acheson AG, et al. Differences in the On- and Off-Tumor Microbiota between Right- and Left-Sided Colorectal Cancer. Microorganisms. 2021;9:1108. Xu K, Jiang B. Analysis of Mucosa-Associated Microbiota in Colorectal Cancer. Med Sci Monit. 2017;23:4422–30. Liu C, Zhao D, Ma W, Guo Y, Wang A, Wang Q, et al. Denitrifying sulfide removal process on high-salinity wastewaters in the presence of Halomonas sp. Appl Microbiol Biotechnol. 2016;100:1421–6. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6:343–51. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2024 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 15 Mar, 2024 Reviews received at journal 10 Mar, 2024 Reviewers agreed at journal 05 Mar, 2024 Reviewers agreed at journal 26 Feb, 2024 Reviewers invited by journal 15 Feb, 2024 Editor assigned by journal 06 Feb, 2024 Submission checks completed at journal 06 Feb, 2024 First submitted to journal 15 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3865704\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":271349036,\"identity\":\"43bf0ab6-aa32-4581-b4c0-402e9c991381\",\"order_by\":0,\"name\":\"yuxuan liang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"yuxuan\",\"middleName\":\"\",\"lastName\":\"liang\",\"suffix\":\"\"},{\"id\":271349037,\"identity\":\"8a86a945-5bd9-4e43-ae4b-a7f96c788b9a\",\"order_by\":1,\"name\":\"Jing Yu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jing\",\"middleName\":\"\",\"lastName\":\"Yu\",\"suffix\":\"\"},{\"id\":271349038,\"identity\":\"97b0d0b1-f10b-4cfe-9082-4e118a00f2c5\",\"order_by\":2,\"name\":\"Qingrong Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qingrong\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":271349039,\"identity\":\"f255626d-fc4b-4b4e-a906-3d5a19c82dd0\",\"order_by\":3,\"name\":\"Wenyan Hu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Wenyan\",\"middleName\":\"\",\"lastName\":\"Hu\",\"suffix\":\"\"},{\"id\":271349040,\"identity\":\"579fec92-c886-4100-a72b-0d612457fd51\",\"order_by\":4,\"name\":\"Sihua Xu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sihua\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"},{\"id\":271349041,\"identity\":\"7aae3767-1e41-40c5-ac3e-a62fbcc797ff\",\"order_by\":5,\"name\":\"Yiyuan Xiao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yiyuan\",\"middleName\":\"\",\"lastName\":\"Xiao\",\"suffix\":\"\"},{\"id\":271349043,\"identity\":\"68482cff-055a-4b59-bd7d-e9e7472cb7e8\",\"order_by\":6,\"name\":\"Hui Ding\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"First Affiliated Hospital of Jinan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hui\",\"middleName\":\"\",\"lastName\":\"Ding\",\"suffix\":\"\"},{\"id\":271349044,\"identity\":\"6ee0ae49-80b9-42c9-af01-52f0f08682f0\",\"order_by\":7,\"name\":\"Jiaming Zhou\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiaming\",\"middleName\":\"\",\"lastName\":\"Zhou\",\"suffix\":\"\"},{\"id\":271349045,\"identity\":\"1ac83911-c57f-4722-8933-6669bbf3233a\",\"order_by\":8,\"name\":\"Haitao Chen\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYDACCQYGZgaGf3L8QPaBBzBRHoJaEg4YSzYAtSSQoiXR4ACQQ5QW/tnNxx4X/riTYHzt8EOgLXWJ82ckMD5428Ygb47LkjvH0o1nJDzLM7udZgDUcjhxw40EZsO5bQyGOxuwazGQyDGT5klgLja7nQDSciBxg0QCmzRvG0MC2KlYteR/A2lJ3Dw7/QPMYey/8WvJYQNqAbpHOgdkC3Niw40ENmZ8WiRupAEdlpZmLHE7p+BAgsFh4w1nHjZLzjknYbgBhxb+GcnPpHlsbOT4Z6dv/vChok52fnvywQ9vymzkcdmC7k4GxwYGxgYGcHwRC+yJVzoKRsEoGAUjBQAAMhBeUhdQor4AAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Haitao\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-01-15 07:30:31\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3865704/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3865704/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12866-024-03402-5\",\"type\":\"published\",\"date\":\"2024-07-03T00:32:47+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":50926860,\"identity\":\"2a1e660c-93c7-4415-804b-278130ebc886\",\"added_by\":\"auto\",\"created_at\":\"2024-02-09 17:12:50\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":577767,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe microbial Alpha diversity and Beta diversity analysis in TT (Tumor Tissue) and NT (Normal Tissue). (A) Violin plots of Alpha diversity based on Chao1. (B) Beta diversity was calculated using Bray-Curtis by PCoA. The test method is Permanova.\\u003c/p\\u003e\\n\\u003cp\\u003e*\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt; 0.05.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3865704/v1/87e04f55573e9d9ef0f84b9a.png\"},{\"id\":50924582,\"identity\":\"09b9f860-25c8-48df-8f4b-d3f2f45b3fb9\",\"added_by\":\"auto\",\"created_at\":\"2024-02-09 17:04:50\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1467861,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCharacteristics of the microbiota in TT (Tumor Tissue) and NT (Normal Tissue). Each group Barplots of the relative abundance of the main bacterial taxa at (A) phylum, (B) genus level for the TT and NT. Cladogram (C) and Linear discriminant analysis effect size (LEfSe) analysis (D) showed the most abundant taxa from the phylum to the genus level among the TT and NT, LDA score threshold \\u0026gt;3.5.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3865704/v1/33b943b2745cf6b2bb9760fc.png\"},{\"id\":50924581,\"identity\":\"dd8d305d-aa1a-4fbe-9e4a-659714cd7d7f\",\"added_by\":\"auto\",\"created_at\":\"2024-02-09 17:04:50\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":428929,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCluster analysis. (A) Hierarchical Ward-linkage clustering based on the Spearman rank correlation coefficients of the genera with relative abundances greater than 0.1% in TT (Tumor Tissue) and NT (Normal Tissue). CAGs were defined based on the clusters in the tree. (B) Hierarchical Ward-linkage clustering based on the relative abundances of bacterial groups in TT and NT. Sample groups were defined based on the CAGs. (C) Part of B. (D) Barplots of distribution of TT and NT in sample groups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3865704/v1/cb140af37018dff004566355.png\"},{\"id\":50924584,\"identity\":\"2863e03c-85da-43ab-9d8a-2a0692736571\",\"added_by\":\"auto\",\"created_at\":\"2024-02-09 17:04:50\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":439973,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCharacteristics of the CAGs and sample groups. (A) Boxplots of relative abundances of the four CAGs. (B) Hierarchical Ward-linkage clustering based on the relative abundances of CAGs in TT (Tumor Tissue) and NT (Normal Tissue). (C) Boxplots of relative abundances of the four CAGs. (D) Correlation coefficient matrix of CAGs based on Spearman rank correlation. (E) Barplots of distribution of CAGs in each sample group.\\u003c/p\\u003e\\n\\u003cp\\u003eSG: Sample Group. ****\\u003cem\\u003ep \\u003c/em\\u003e\\u0026lt; 0.0001, ***\\u003cem\\u003ep \\u003c/em\\u003e\\u0026lt; 0.001, **\\u003cem\\u003ep \\u003c/em\\u003e\\u0026lt; 0.01, *\\u003cem\\u003ep \\u003c/em\\u003e\\u0026lt; 0.05.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3865704/v1/21c989362d02d7a57cf2623a.png\"},{\"id\":50924586,\"identity\":\"342e1f72-9d99-43ee-8a9e-865612078361\",\"added_by\":\"auto\",\"created_at\":\"2024-02-09 17:04:50\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":200446,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAssociation of CAGs with clinical features. (A) Boxplots of correlation between CAG 2 and tumor stage, microsatellite status, Ki-67. (B) Spearman rank correlation coefficient matrix heatmap between CAGs and CEA, CA199, CA125, CA153, AFP.\\u003c/p\\u003e\\n\\u003cp\\u003e****p \\u0026lt; 0.0001, ***p \\u0026lt; 0.001, **p \\u0026lt; 0.01, *p \\u0026lt; 0.05.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3865704/v1/4e6d66264a5564dae2a1bec1.png\"},{\"id\":50924585,\"identity\":\"1decb66b-ea51-40ec-b173-f442962f6948\",\"added_by\":\"auto\",\"created_at\":\"2024-02-09 17:04:50\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":581207,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe function prediction of the two groups. (A) Barplots of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways with an average relative abundance above 1% in TT and NT. (B) Boxplots of Differential Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed using PICRUSt for the TT and NT. The y-axis represents the counts of annotations to the pathways, using a base 10 logarithmic scale.\\u003c/p\\u003e\\n\\u003cp\\u003eTT: Tumor Tissue, NT: ****p \\u0026lt; 0.0001, ***p \\u0026lt; 0.001, **p \\u0026lt; 0.01, *p \\u0026lt; 0.05.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3865704/v1/e8753f064b13ab7e99f75e78.png\"},{\"id\":59617322,\"identity\":\"2a47ac93-32e4-492b-a424-6aa920a47129\",\"added_by\":\"auto\",\"created_at\":\"2024-07-04 00:32:56\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3831016,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3865704/v1/6e27678d-f07e-41f3-81cf-8df3e708c40b.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Tumour-associated and non-tumour-associated bacteria co-abundance groups in colorectal cancer\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe global incidence of colorectal cancer (CRC) has increased rapidly. Especially in China, it ranks second among all malignant tumors[\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Traditional risk factors for CRC include family history, inflammatory bowel disease, processed meat intake, diabetes, obesity, smoking, and alcohol consumption[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003ePrevious studies found that changes in gut microbiota such as \\u003cem\\u003eStreptococcus bovis\\u003c/em\\u003e, \\u003cem\\u003eHelicobacter pylori\\u003c/em\\u003e, \\u003cem\\u003eBacteroides fragilis\\u003c/em\\u003e, \\u003cem\\u003eEnterococcus faecalis\\u003c/em\\u003e, \\u003cem\\u003eClostridium septicum\\u003c/em\\u003e, \\u003cem\\u003eFusobacterium spp\\u003c/em\\u003e. and \\u003cem\\u003eEscherichia coli\\u003c/em\\u003e, was closely related to the occurrence of gastrointestinal cancer[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. However, for several microorganisms such as \\u003cem\\u003eFusobacterium\\u003c/em\\u003e species, the association of their abundance with human colon cancer was not consistent in all reports and lacked a clear conclusion[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Abreu et al\\u0026rsquo; research indicated that the inconsistency between studies may be due to the heterogeneity of microbial or host response levels[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Therefore, Flemer et al proposed that combinations or co-abundance groups (CAGs) of organisms may be more operative to express the relationship between microbiota and disease, rather than representing a one organism-one disease model[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. In Flemer\\u0026rsquo;s study, they found that it was feasible to use a combination of several bacteria (or microbiome characteristics) in the stool microbiota of CRC patients as a marker to detect the disease[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. The utility of CAGs was further confirmed in investigating the association between the microbiota community of tongue coating and the prognosis of gastric cancer[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Recently, a study conducted in Australia demonstrated that the construction of oncomicrobial community subtype, similar to the CAGs in Flemer\\u0026rsquo;s paper, using tumor tissue (TT) and adjacent normal tissue (NT) samples can effectively predict the prognosis of CRC[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. However, the performance of CAGs in Chinese CRC patients is not clear.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we conducted a cross-sectional study of colon microbiome in 492 mucosal samples from patients undergoing CRC surgery, including 245 TT and 247 NT. We found that the diversity of microbial between TT and NT was significantly different, with each group exhibiting distinct taxonomic profiling and discriminant taxa. In addition, the intratumor microbiota of CRC could be categorized into four CAGs and CRC patients could be further divided into 6 distinct groups based on four CAGs. This study may provide novel insights into the dynamics of bacterial communities during the conversion of NT to TT.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Participants\\u003c/h2\\u003e \\u003cp\\u003eSamples were obtained from patients undergoing surgical treatment for colorectal cancer. Ultimately, a total of 251 patients diagnosed with colorectal cancer were recruited at the Sixth Affiliated Hospital of Sun Yat-sen University from 2015 to 2021. Detailed information of these samples is provided in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe demographic characteristics of all the enrolled samples.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNormal\\u003c/p\\u003e \\u003cp\\u003e(247)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTumor\\u003c/p\\u003e \\u003cp\\u003e(245)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGender\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e97(39.27%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e97(39.59%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e150(60.73%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e148(60.41%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSite\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLeft hemicolon\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e84(34.01%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e83(33.88%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRectum\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e108(43.72%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e107(43.67%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRight hemicolon\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e55(22.27%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e55(22.45%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eStage\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAdvanced\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e118(47.77%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e117(47.76%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEarly\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e129(52.23%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e128(52.24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGross\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eInfiltration\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2(0.81%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2(0.82%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMass\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e67(27.13%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e66(26.94%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUlcer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e176(71.26%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e175(71.43%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2(0.81%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2(0.82%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDifferentiation\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38(15.38%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37(15.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17(6.88%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17(6.94%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e178(72.06%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e177(72.24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14(5.67%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14(5.71%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKi67\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50% or more\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e144(58.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e144(58.78%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLess than 50%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e97(39.27%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e95(38.78%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6(2.43%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6(2.45%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMicrosatellite\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnstable\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17(6.88%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17(6.94%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStable\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e229(92.71%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e227(92.65%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1(0.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1(0.41%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e62.94\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e62.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHeight\\u003c/b\\u003e (cm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e162.85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e162.84\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.82\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eWeight\\u003c/b\\u003e (kg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e60.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e60.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBMI\\u003c/b\\u003e (kg/m\\u0026sup2;)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCEA\\u003c/b\\u003e (ng/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e87.94\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1022.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e88.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1026.74\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCA199\\u003c/b\\u003e (U/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e74.77\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;451.94\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;453.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCA125\\u003c/b\\u003e (U/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCA153\\u003c/b\\u003e (U/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10.41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.43\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAFP\\u003c/b\\u003e (ng/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.92\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.39\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eNote: Data are shown as means\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eTumor specimens were obtained using a sterile scalpel blade within 1 hour following surgical resection. All samples were promptly frozen in liquid nitrogen and stored at -80\\u0026deg;C. Inclusion criteria encompassed patients aged 18 years or older without contraindications to colorectal cancer resection. The exclusion criteria include the use of antibiotics or probiotics within one month, radiotherapy, chemotherapy, intestinal obstruction, and concurrent other severe organic diseases. The stage of CRC was classified according to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging system. The protocol of human sample usage and the informed consent were approved by the Ethical Review Board of the Sixth Affiliated Hospital of Sun Yat-sen University (2020ZSLYEC-101).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDNA extraction and PCR amplification\\u003c/h2\\u003e \\u003cp\\u003eTotal microbial genomic DNA was extracted from TT and NT samples using FastDNA Spin Kit for Soil (MP Biomedicals) according to the manufacturer\\u0026rsquo;s instructions. The quality and concentration of DNA were determined by 1.0% agarose gel electrophoresis and a NanoDrop\\u0026reg; ND-2000 spectrophotometer (Thermo Scientific Inc., USA) and kept at -80℃ prior to further use. The hypervariable region \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eV3-V4\\u003c/span\\u003e of the bacterial 16S rRNA gene was amplified with primer pairs \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R(5'-GGACTACHVGGGTWTCTAAT-3')\\u003c/span\\u003e[\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e] by an ABI GeneAmp\\u0026reg; 9700 PCR thermocycler (ABI, CA, USA). The PCR reaction mixture including 4 \\u0026micro;L 5 \\u0026times; Fast Pfu buffer, 2 \\u0026micro;L 2.5 mM dNTPs, 0.8 \\u0026micro;L each primer (5 \\u0026micro;M), 0.4 \\u0026micro;L Fast Pfu polymerase, 10 ng of template DNA, and ddH\\u003csub\\u003e2\\u003c/sub\\u003eO to a final volume of 20 \\u0026micro;L. PCR amplification cycling conditions were as follows: initial denaturation at 95 ℃ for 3 min, followed by 27 cycles of denaturing at 95 ℃ for 30 s, annealing at 55 ℃ for 30 s, and extension at 72 ℃for 45 s, and single extension at 72 ℃ for 10 min, and end at 4 ℃. All samples were amplified in triplicate. The PCR product was extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer\\u0026rsquo;s instructions and quantified using Quantus\\u0026trade; Fluorometer (Promega, USA).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eIllumina MiSeq sequencing\\u003c/h2\\u003e \\u003cp\\u003ePurified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe raw data were processed using QIIME2 (Version 2021.8.0) to remove reads with insufficient repetitions (reads with less than 48 entries or fewer than 25 samples containing the reads) or readings shorter than 148 bp. Subsequently, the filtered reads were clustered into operational taxonomic units (OTUs) at a similarity threshold of 97%. To mitigate the potential impact of sequencing depth on subsequent alpha and beta diversity analyses, the sequence count in all samples was standardized to 16,864 sequences. As sequencing depth increased, the observed feature curves for both sample groups reached a plateau, indicating sufficient sequencing coverage. Taxonomic classification of each OTU was performed by comparing the sequences against SILVA database (version 138.1). Alpha diversity was assessed by calculating the ACE, Chao1, Observe, Pielou, Shannon, and Simpson indices. To compare the diversity differences among groups, Beta diversity was examined through principal coordinates analysis (PCoA) based on Bray-Curtis distance. Permutational multivariate analysis of variance using distance matrices (pMANOVA) was employed to assess the significance of beta diversity. Linear discriminant analysis Effect Size[\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e] (LEfSe) was used (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://galaxy.biobakery.org/\\u003c/span\\u003e\\u003cspan address=\\\"http://galaxy.biobakery.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) to identify key microorganisms associated with different groups, with an LDA threshold of 3.5. Bray-Curtis distance-based hierarchical clustering with Ward linkage method was utilized to construct CAGs, where only genera exhibiting a relative abundance exceeding 0.1% in TT and NT were used. For continuous variables, Mann-Whitney U test was employed to compare differences between groups, while Spearman rank correlation analysis was used for assessing correlations[\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]. Prediction of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis. Statistical analyses and figures were conducted using R version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria.). A statistically significant difference was considered when the P value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBaseline Characteristics of Participants\\u003c/h2\\u003e \\u003cp\\u003eA total of 248 patients diagnosed with colorectal cancer were included in this study. Among these patients, 244 had tumor tissues (TT) and paired normal tissues (NT) samples, while one patient solely provided TT samples and three patients solely provided NT samples. Finally, a total of 245 TT samples and 247 NT samples were retained for analysis. The demographic characteristics of all the enrolled samples are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eComparison of the Microbial Diversity between TT and NT\\u003c/h2\\u003e \\u003cp\\u003eIn terms of alpha diversity, we employed ACE, Chao1, Observe, Pielou, Shannon, and Simpson indices to assess the species richness, evenness, and diversity of TT and NT. Based on the estimated results of the Chao1 index analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.048), we found that the alpha diversity of microbiota in NT was significantly higher than that of TT. Beta diversity was also significantly different between TT and NT (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB; Permanova: Bray-Curtis \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTaxonomic Profiling and Discriminant Taxa between TT and NT\\u003c/h2\\u003e \\u003cp\\u003eAs illustrated in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA and \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB, \\u003cem\\u003eProteobacteria\\u003c/em\\u003e, \\u003cem\\u003eActinobacteriota\\u003c/em\\u003e, \\u003cem\\u003eFirmicutes\\u003c/em\\u003e, \\u003cem\\u003eBacteroidota\\u003c/em\\u003e and \\u003cem\\u003eFusobacteriota\\u003c/em\\u003e were five predominant phyla (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA) while \\u003cem\\u003eDelftia\\u003c/em\\u003e, \\u003cem\\u003eActinobacteria unclassified\\u003c/em\\u003e, \\u003cem\\u003ePseudomonas\\u003c/em\\u003e, \\u003cem\\u003eBacteroidota\\u003c/em\\u003e, \\u003cem\\u003eEscherichia-shigella\\u003c/em\\u003e, \\u003cem\\u003eand Hizobiaceae unclassified\\u003c/em\\u003e were six dominant genera (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eLEfSe analyses were employed to identify significant microbial biomarkers across all taxa, with an LDA score threshold of \\u0026gt;\\u0026thinsp;3.5. As depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC and \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD, at the genus level, \\u003cem\\u003eEscherichia-Shigella\\u003c/em\\u003e, \\u003cem\\u003eFusobacterium\\u003c/em\\u003e, \\u003cem\\u003eStreptococcus\\u003c/em\\u003e, \\u003cem\\u003ePeptostreptococcus\\u003c/em\\u003e, \\u003cem\\u003eParvimonas, Klebsiella\\u003c/em\\u003e, and \\u003cem\\u003eGemella\\u003c/em\\u003e were enriched in TT. While \\u003cem\\u003eAcinetobacter\\u003c/em\\u003e, \\u003cem\\u003eAchromobacter\\u003c/em\\u003e, \\u003cem\\u003eDelftia\\u003c/em\\u003e, and \\u003cem\\u003ePseudomonas\\u003c/em\\u003e were enriched in NT. These findings suggest distinct microbiota compositions between TT and NT.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDistribution of CAGs in TT and NT\\u003c/h2\\u003e \\u003cp\\u003eWe performed hierarchical cluster analysis using a total of 47 genera with relative abundances greater than 0.1%, resulting in the grouping of four co-abundance groups (CAGs). The correlation of these 47 genera with four CAGs is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA and the detailed co-enriched genera of each CAG are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. By conducting sample-level cluster analysis using the abundance profiles of four CAGs, we identified six distinct sample groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB). Notably, due to the high abundance of CAG 1 in sample group 1 and sample group 2, it was challenging to accurately discriminate TT from NT in these two sample groups. However, in the remaining four sample groups (sample group 3, sample group 4, sample group 5, and sample group 6), clear discrimination between TT and NT was observed (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). Sample group 4 and sample group 6 were primarily composed of TT, while sample group 3 and sample group 5 were predominantly composed of NT (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe detailed co-enriched genera of each CAG.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCAG 1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCAG 2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCAG 3\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCAG 4\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAchromobacter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAnaerococcus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAkkermansia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eAcinetobacter\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBacteroides\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCampylobacter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAlistipes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eAquabacterium\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChloroplast\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEikenella\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBifidobacterium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eBacillus\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDelftia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eErysipelatoclostridium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBlautia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eChryseobacterium\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEnterococcus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFusobacterium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCollinsella\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLactobacillus\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEscherichia-Shigella\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGemella\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDialister\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eRalstonia\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKlebsiella\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGranulicatella\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFaecalibacterium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCaulobacteraceae_unclassified\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eProteus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLeptotrichia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHoldemanella\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eClostridiaceae_unclassified\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePseudomonas\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eParvimonas\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eParabacteroides\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePeptostreptococcaceae_unclassified\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eActinobacteria_unclassified\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePeptostreptococcus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePrevotella\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSphingomonadaceae_unclassified\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEnterobacteriaceae_unclassified\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePorphyromonas\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSubdoligranulum\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLachnospiraceae_unclassified\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eStreptococcus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRhizobiaceae_unclassified\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe relative abundance of four CAGs in TT and NT were illustrated in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA. We found that CAG 2 exhibited significantly higher abundance in TT (median: 2.27% in TT vs. 0.78% in NT, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) while CAG 4 exhibited significantly higher abundance in NT (median: 0.62% in TT vs. 0.79% in NT, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0004). However, no significant differences were observed for CAG 1 and CAG 3. Interestingly, after removing sample group 1 which exhibited a predominant enrichment in CAG 1, CAG 3 displayed an increased abundance in NT (median: 1.9% in TT vs. 6.2% in NT, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0006, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB and \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC) and exhibited an inverse correlation with CAG 2 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD). As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE, CAG 2 was highly abundant in sample group 4 and sample group 6 which mainly consisted of TT, while CAG 3 and CAG 4 were highly abundant in sample group 3 and sample group 5 which mainly consisted of NT.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociation of CAGs with clinical features\\u003c/h2\\u003e \\u003cp\\u003eWe further investigated the association of identified CAGs with clinical features in TT. Notably, we observed a significant association between CAG 2 and tumor microsatellite status. Specifically, a higher abundance of CAG 2 was found in samples with unstable microsatellites (median: 13.2% in unstable vs. 2.0% in stable, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.016, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). Furthermore, we evaluated the association of CAGs with tumor markers and specifically observed a positive correlation between CAG 4 and CA199 (r\\u0026thinsp;=\\u0026thinsp;0.17, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.009) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFunctional analysis of microbiota in each group\\u003c/h2\\u003e \\u003cp\\u003eFinally, PICRUSt was employed to predict the KEGG pathways implicated in TT and NT. The KEGG pathways with an average relative abundance above 1% in all samples are shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA. Notably, the membrane transport pathway constituted a substantial proportion, accounting for 13.8%. Of the 20 pathways with an average relative abundance above 1%, 18 of them were significantly upregulated in NT (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eTons of studies indicated that CRC-related microbiota can provide valuable insights into cancer occurrence, progression, and treatment response[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Disparities between TT and NT were primarily arise from individual taxonomic variations on the taxonomic profiling. In this study, by constructing community structures known as co-abundant groups (CAGs), we grouped all samples and examined the association of clinical characteristics with CAGs.\\u003c/p\\u003e \\u003cp\\u003eCompared with NT, a higher diversity of organisms in TT was observed as indicated by the Chao1 index of alpha diversity. PCoA analysis based on Bray-Curtis distance also demonstrated a significant differentiation between TT and NT. Similar findings were found in Loke\\u0026rsquo;s studies (Loke et al., 2018)[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. However, non-significant differences in microbial diversity (α- and β-diversity) between TT and NT were also reported (Liu et al., 2021; Li M. et al., 2020)[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. These discrepancies can be partially attributed to variations in geographical location and tumor heterogeneity. Regarding taxonomic profiling and discriminant taxa, we identified distinct taxa that can differentiate TT from NT. Specifically, \\u003cem\\u003eEscherichia-Shigella\\u003c/em\\u003e, \\u003cem\\u003eFusobacterium\\u003c/em\\u003e, \\u003cem\\u003eStreptococcus\\u003c/em\\u003e, \\u003cem\\u003ePeptostreptococcus\\u003c/em\\u003e, \\u003cem\\u003eParvimonas, Klebsiella\\u003c/em\\u003e, \\u003cem\\u003eand Gemella\\u003c/em\\u003e were significantly enriched in TT at the genus level. The enrichment of \\u003cem\\u003eFusobacterium\\u003c/em\\u003e and \\u003cem\\u003eStreptococcus\\u003c/em\\u003e in TT has been consistently reported across numerous studies, highlighting their important role in tumor initiation and progression[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Notably, \\u003cem\\u003eParvimonas\\u003c/em\\u003e exhibited a significant positive correlation with the host gene PARVB, which is highly expressed in CRC tissues[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Furthermore, \\u003cem\\u003eEscherichia-Shigella\\u003c/em\\u003e, \\u003cem\\u003ePeptostreptococcus\\u003c/em\\u003e, and \\u003cem\\u003eKlebsiella\\u003c/em\\u003e were found to be enriched specifically in CRC patients compared to healthy volunteers in an investigation focusing on intestinal flora composition[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. \\u003cem\\u003eGemella\\u003c/em\\u003e which predominantly resides within the oral cavity and upper gastrointestinal tract, was reported to be associated with oral squamous cell carcinoma[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. In summary, the above findings suggest subtle differences in microbial diversity between TT and NT. Furthermore, both TT and NT exhibit unique taxonomic profiles, each characterized by a dominant genus.\\u003c/p\\u003e \\u003cp\\u003eRecognizing that a single taxonomic group might not fully capture microbial differences between TT and NT, we applied hierarchical clustering based on Bray-Curtis distance to construct four co-abundance groups (CAG 1\\u0026ndash;4). These constructed CAGs were then used for unsupervised clustering of TT and NT samples, resulting in the classification of all samples into six major categories (sample group 1\\u0026ndash;6). CAG 2 was notably enriched in TT tissues, while CAG 4 was enriched in NT. Sample group 3 and sample group 5 contained predominantly NT, whereas sample group 4 and sample group 6 contained predominantly TT. The CAGs level analyses revealed that sample group 4 and sample group 6 exhibited a higher abundance of CAG 2, sample group 3 had an increased abundance of CAG 3, and sample group 5 was enriched with CAG 4. Further examination revealed that the abundance of CAG 1 in sample group 1 was exceptionally high, nearly 100%, resulting in a low abundance of the remaining CAGs. To address this question, we excluded sample group 1 and conducted the same analysis with the remaining samples. In the remaining samples, we found that CAG 3 was significantly increased in NT. Additionally, CAG 3 and CAG 4 exhibited a positive correlation, though not statistically significant, and both were negatively correlated with CAG 2. Previous studies have demonstrated the feasibility of classifying experimental subjects using bacterial abundance or CAGs. For instance, in a study on colorectal cancer and adjacent normal tissues, K-means clustering was employed to divide the samples into three distinct subgroups[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Another similar study clustered the Operational Taxonomic Unit (OTU) hierarchy into six CAGs, subsequently categorizing the samples into multiple distinct subgroups, a process replicated in two additional cohorts[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. In our study, CAG 1 comprises a substantial number of nonpathogenic or opportunistic pathogens that were widely found in nature or the human body, including \\u003cem\\u003eBacteroides\\u003c/em\\u003e, \\u003cem\\u003eDelftia\\u003c/em\\u003e, \\u003cem\\u003eEnterococcus\\u003c/em\\u003e, \\u003cem\\u003eKlebsiella\\u003c/em\\u003e, \\u003cem\\u003eProteus\\u003c/em\\u003e, \\u003cem\\u003eand Pseudomonas\\u003c/em\\u003e[\\u003cspan additionalcitationids=\\\"CR23 CR24 CR25 CR26\\\" citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. CAG 2 includes \\u003cem\\u003eFusobacterium\\u003c/em\\u003e, \\u003cem\\u003eStreptococcus\\u003c/em\\u003e, \\u003cem\\u003ePeptostreptococcus\\u003c/em\\u003e, and \\u003cem\\u003eParvimonas\\u003c/em\\u003e, which were reported to promote the occurrence and progression of CRC in various studies. Interestingly, these four genera were assigned to the same CAG which was considered a pathogenic bacterial cluster in the study by Flemer et al.[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Campylobacter in CAG 2 was reported to be associated with colorectal and esophageal cancer[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. In CAG 3 and CAG 4, we identified more bacteria that are considered to be probiotic or nonpathogenic such as \\u003cem\\u003eAkkermansia\\u003c/em\\u003e, \\u003cem\\u003eAlistipes\\u003c/em\\u003e, \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e, \\u003cem\\u003eBlautia\\u003c/em\\u003e, \\u003cem\\u003eCollinsella\\u003c/em\\u003e, \\u003cem\\u003eFaecalibacterium\\u003c/em\\u003e, \\u003cem\\u003eParabacteroides\\u003c/em\\u003e, \\u003cem\\u003ePrevotella\\u003c/em\\u003e, \\u003cem\\u003eBacillus\\u003c/em\\u003e, and \\u003cem\\u003eLactobacillus\\u003c/em\\u003e[\\u003cspan additionalcitationids=\\\"CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Hence, human diseases can be attributed not only to a single pathogen but also to overall changes in the microbiota[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. For instance, a study on breast cancer described the combination of estrogen in the liver, excretion into the gastrointestinal cavity, conjugation by bacterial β-glucuronidase, reabsorption as free estrogens through the enterohepatic circulation, and distribution to different organs like the breast. These metabolites, produced by several bacteria from the Clostridia and Ruminococcaceae families through estrogen metabolism, may collectively have breast cancer-causing potential[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. These findings offer insights into flora changes during the transformation from NT to TT in a higher dimension, explore bacterial interaction from the bacterial clusters, and provide clues to the mechanism of the multi-bacterial joint promotion of CRC occurrence and development.\\u003c/p\\u003e \\u003cp\\u003eBased on the previously constructed CAGs, we further investigate the association of CAGs in TT with clinical characteristics. CAG 2 was found to be associated with the microsatellite status of tumors, exhibiting higher abundance in TT associated with unstable microsatellites. Previous studies conducted in Japan and the United States have demonstrated a significant correlation between the positive expression of \\u003cem\\u003eF. nucleatum\\u003c/em\\u003e and unstable microsatellite status[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. Notably, \\u003cem\\u003eFusobacterium\\u003c/em\\u003e was exactly in our CAG 2. Furthermore, CAG 4 exhibited a positive correlation with CA199 levels. CA199 is a typical marker for gastrointestinal tumors and has high sensitivity for pancreatic cancer diagnosis, as well as aiding in rectal cancer, colon cancer, and primary liver cancer detection[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. In intrahepatic cholangiocarcinoma cases, \\u003cem\\u003eBacillus anthracis\\u003c/em\\u003e and \\u003cem\\u003eP. azotoformans\\u003c/em\\u003e were observed to be positively associated with CA199 levels[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Notably, bacillus was exactly in our CAG 4.\\u003c/p\\u003e \\u003cp\\u003eIn addition to compositional changes in bacterial taxa, we also observed predicted functional alterations across different groups. We found that the following metabolic pathways including nucleotide metabolism, lipid metabolism, enzyme metabolism, energy metabolism, carbohydrate metabolism, and amino acid metabolism were enriched in the NT group. Similar findings were reported in previous studies[\\u003cspan additionalcitationids=\\\"CR45\\\" citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. Our findings suggest that microbial changes may impact multiple metabolic pathways including amino acid, lipid, and carbohydrate metabolisms which could potentially underlie the transition from NT to TT.\\u003c/p\\u003e \\u003cp\\u003e Our research boasts a relatively substantial sample size, contributing to the generation of robust and reliable findings. However, several limitations still require attention. First, in our cluster analysis, two sample groups could not be accurately classified, possibly due to the heterogeneity of tumor samples in terms of location and subtype. Previous studies have highlighted differences in microbial composition between CRC originating from different locations or subtypes[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. Second, cross-sectional studies emphasize the need for prospective trials to fully elucidate the role of microbiota in CRC. Lastly, as we employed 16S rRNA gene sequencing for microbiota analysis, we were unable to determine species-level composition and actual genetic functions. Further investigations utilizing shotgun metagenomic sequencing are warranted to unravel the mechanisms underlying CAGs and CRC.\\u003c/p\\u003e \\u003cp\\u003eIn summary, our research will deepen our understanding of the interactions among multiple bacteria and offer insights into the potential mechanism of NT to TT transition.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe protocol of human sample usage and the informed consent were approved by the Ethical Review Board of the Sixth Affiliated Hospital of Sun Yat-sen University (2020ZSLYEC-101). The participant was informed about the study and signed informed consent prior enrolment.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication of anonymized data was obtained from the participant as part of the informed consent form that was signed prior enrolment.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\\u0026nbsp;In addition, our data has been uploaded to the NCBI database (PRJNA1068013) and will be released in the future.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have nothing to disclose.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis project is financially supported by the National Natural Science Foundation of China (NSFC, 82272312), the Shenzhen-Hong Kong-Macao Science and Technology Project (Category C project) (SGDX20220530111403024), the National Key R\\u0026amp;D Program of China (2023YFC2307004), the 100 Top Talent Programs of Sun Yat-sen University (58000-12230029). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eAuthor\\u0026rsquo;s contribution\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eYuxuan Liang: Conceptualization, Data analysis, Writing - original draft, Writing - Review \\u0026amp; Editing. Jing Yu: Data curation, Resources, Validation. Qingrong Zhang: Investigation, Visualization, Writing - Review \\u0026amp; Editing. Wenyan Hu: Project administration, Writing - Review \\u0026amp; Editing. Sihua Xu: Data curation. Yiyuan Xiao: Data curation. Hui Ding: Project administration, Data curation, Supervision. Jiaming Zhou: Project administration, Data curation, Supervision. Haitao Chen: Project administration, Funding acquisition, Supervision.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eQiu H, Cao S, Xu R. 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Bacillus coagulans and its applications in medicine. Benef Microbes. 2019;:1\\u0026ndash;10.\\u003c/li\\u003e\\n\\u003cli\\u003eScillato M, Spitale A, Mongelli G, Privitera GF, Mangano K, Cianci A, et al. Antimicrobial properties of Lactobacillus cell-free supernatants against multidrug-resistant urogenital pathogens. Microbiologyopen. 2021;10:e1173.\\u003c/li\\u003e\\n\\u003cli\\u003eRea D, Coppola G, Palma G, Barbieri A, Luciano A, Del Prete P, et al. Microbiota effects on cancer: from risks to therapies. Oncotarget. 2018;9:17915\\u0026ndash;27.\\u003c/li\\u003e\\n\\u003cli\\u003eFern\\u0026aacute;ndez MF, Reina-P\\u0026eacute;rez I, Astorga JM, Rodr\\u0026iacute;guez-Carrillo A, Plaza-D\\u0026iacute;az J, Fontana L. Breast Cancer and Its Relationship with the Microbiota. Int J Environ Res Public Health. 2018;15:1747.\\u003c/li\\u003e\\n\\u003cli\\u003eNosho K, Sukawa Y, Adachi Y, Ito M, Mitsuhashi K, Kurihara H, et al. 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Colitis-induced colorectal cancer and intestinal epithelial estrogen receptor beta impact gut microbiota diversity. Int J Cancer. 2019;144:3086\\u0026ndash;98.\\u003c/li\\u003e\\n\\u003cli\\u003eGreathouse KL, White JR, Padgett RN, Perrotta BG, Jenkins GD, Chia N, et al. Gut microbiome meta-analysis reveals dysbiosis is independent of body mass index in predicting risk of obesity-associated CRC. BMJ Open Gastroenterol. 2019;6:e000247.\\u003c/li\\u003e\\n\\u003cli\\u003ePhipps O, Quraishi MN, Dickson EA, Steed H, Kumar A, Acheson AG, et al. Differences in the On- and Off-Tumor Microbiota between Right- and Left-Sided Colorectal Cancer. Microorganisms. 2021;9:1108.\\u003c/li\\u003e\\n\\u003cli\\u003eXu K, Jiang B. Analysis of Mucosa-Associated Microbiota in Colorectal Cancer. Med Sci Monit. 2017;23:4422\\u0026ndash;30.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu C, Zhao D, Ma W, Guo Y, Wang A, Wang Q, et al. Denitrifying sulfide removal process on high-salinity wastewaters in the presence of Halomonas sp. Appl Microbiol Biotechnol. 2016;100:1421\\u0026ndash;6.\\u003c/li\\u003e\\n\\u003cli\\u003eSegata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.\\u003c/li\\u003e\\n\\u003cli\\u003eBarber\\u0026aacute;n A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6:343\\u0026ndash;51.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-microbiology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"mcro\",\"sideBox\":\"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/mcro\",\"title\":\"BMC Microbiology\",\"twitterHandle\":\"#bmcmicrobiology\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Colorectal Cancer, 16S rRNA sequencing, Mucosal tissue, Microbiota Classification, biomarkers\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3865704/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3865704/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground \\u0026amp; Aims:\\u003c/h2\\u003e \\u003cp\\u003eGut microbiota is closely related to the occurrence and development of colorectal cancer (CRC). However, the differences of bacterial co-abundance groups (CAGs) between tumor tissue (TT) and adjacent normal tissue (NT), as well as their associations with clinical features, were need to be clarified.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eBacterial 16S rRNA sequencing was performed by using TT samples and NT samples of 251 patients with colorectal cancer. Microbial diversity, taxonomic characteristics, microbial composition, and functional pathways were compared between TT and NT. Hierarchical clustering was used to construct CAGs.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eFour CAGs were grouped in the hierarchical cluster analysis. CAG 2, which was mainly comprised of pathogenic bacteria, was significantly enriched in TT samples (2.27% in TT vs. 0.78% in NT, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). While CAG 4, which was mainly comprised of non-pathogenic bacteria, was significantly enriched in NT samples (0.62% in TT vs. 0.79% in NT, p\\u0026thinsp;=\\u0026thinsp;0.0004). In addition, CAG 2 was also significantly associated with tumor microsatellite status (13.2% in unstable vs. 2.0% in stable, p\\u0026thinsp;=\\u0026thinsp;0.016) and CAG 4 was positively correlated with the level of CA199 (r\\u0026thinsp;=\\u0026thinsp;0.17, p\\u0026thinsp;=\\u0026thinsp;0.009).\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eour research will deepen our understanding of the interactions among multiple bacteria and offer insights into the potential mechanism of NT to TT transition.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Tumour-associated and non-tumour-associated bacteria co-abundance groups in colorectal cancer\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-02-09 17:04:45\",\"doi\":\"10.21203/rs.3.rs-3865704/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-03-15T09:03:08+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-03-10T11:15:20+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"916fc580-98a1-4646-afde-7149bd8574cd\",\"date\":\"2024-03-05T09:51:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"e3b3791b-3455-48fb-9806-654c56f08487\",\"date\":\"2024-02-26T12:22:23+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-02-16T01:14:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-02-06T12:45:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-02-06T12:44:49+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Microbiology\",\"date\":\"2024-01-15T07:22:40+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-microbiology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"mcro\",\"sideBox\":\"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/mcro\",\"title\":\"BMC Microbiology\",\"twitterHandle\":\"#bmcmicrobiology\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"fc14c024-4cb0-4b3b-8021-fd41917978b5\",\"owner\":[],\"postedDate\":\"February 9th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-07-04T00:32:47+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-3865704\",\"link\":\"https://doi.org/10.1186/s12866-024-03402-5\",\"journal\":{\"identity\":\"bmc-microbiology\",\"isVorOnly\":false,\"title\":\"BMC Microbiology\"},\"publishedOn\":\"2024-07-03 00:32:47\",\"publishedOnDateReadable\":\"July 3rd, 2024\"},\"versionCreatedAt\":\"2024-02-09 17:04:45\",\"video\":\"\",\"vorDoi\":\"10.1186/s12866-024-03402-5\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12866-024-03402-5\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3865704\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3865704\",\"identity\":\"rs-3865704\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}