Stage and location related intratumoral microbiota are associated with patient prognosis and immune infiltration in colorectal cancer

doi:10.21203/rs.3.rs-2834643/v1

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Stage and location related intratumoral microbiota are associated with patient prognosis and immune infiltration in colorectal cancer

https://doi.org/10.21203/rs.3.rs-2834643/v1

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Purpose: The colorectal intratumoral microbiome and its association with immune infiltration remain poorly characterized. Our study aims to investigate the relationship between intratumoral microbiota with immune infiltration, patient prognosis, and potential signal pathways.

Methods: We collected biopsy samples of tumor and paracancerous tissue from 92 patients with colorectal cancer (CRC), and acquired microbiota profiling using 16S rRNA sequencing. Meanwhile, the immune markers including CD8, FOXP3, CD163, PD-1 and PD-L1 were stained by immunohistochemistry (IHC) to identify the immune infiltration in tumors. Furthermore, we used The Cancer Genome Atlas databases to conduct analysis on intratumoral flora and patient survival, tumor gene expression profile and potential downstream pathways.

Results: We discovered that the β-diversity of bacterial composition differed considerably by CRC stage (early vs.advanced stage, P = 0.049) and location (left vs. right colon, P= 0.04). Stage-related flora cluster (Porphyromonas, Lachnoclostridium, Bacteroides, Aggregatibacter, and Hungatella) were associated with poor prognosis in CRC patients (HR=1.79, P=0.015). By IHC staining, we found that expression of PD-1 and FOXP3 was significantly reduced at low abundance of stage-related bacterial cluster (P<0.05). Besides, tumor-location related flora cluster (Bacteroides and Blautia) were associated with good prognosis in CRC patients (HR=0.52, P=0.011). Expression of CD163 was decreased at high abundance of location-related bacterial cluster (P<0.05). Furthermore, we identified probable pathways connected to three distinct genera (Blautia, Hungatella, and Bacteroides).

Conclusion: Our study elucidates the relationships among intratumoral microbiota, immune infiltration, patient prognosis, and potential signal pathways, thereby providing new data for future intratumoral microbiota research.

Colorectal cancer

intratumoral microbiota

immune infiltration

prognosis

Colorectal cancer (CRC) is one of the most prevalent malignancies and a significant threat to world health. CRC ranked third in incidence and second in mortality among all malignancies worldwide(Xi and Xu 2021). CRC is quite heterogeneous, and the precise selection of treatment is based on the gene profile of the patient. As with many other cancers, CRC has an inherited microbiome component associated with incidence, disease progression, and treatment response. The gut microbiome has been shown to alter both the onset and development of CRC(Tilg et al. 2018; Louis, Hold, and Flint 2014; Lucas, Barnich, and Nguyen 2017; Raskov, Burcharth, and Pommergaard 2017; Wong and Yu 2019). Several reports have demonstrated that the gut microbiota has exerts a large influence on the effectiveness of cancer therapy and the toxicity of therapeutic medications(Roy and Trinchieri 2017; Montalban-Arques and Scharl 2019; Rebersek 2021; Temraz et al. 2019; Taghinezhad, Mohseni, and Fu 2021).

Though intratumoral microbiomes were discovered more than a century ago, only lately have they become a focus of study(Liu et al. 2021). Nejman et al. discovered that the majority of tumors and their surrounding normal tissues contained various forms of bacteria, mostly in cancer and immune cells(Nejman et al. 2020). It has been reported that the tumor microbiome plays an important role in regulating the response to cancer treatment. Yu et al. reported that, following chemotherapy, abundant Fusobacterium nucleatum were found in CRC tissues from patients who experienced recurrence. The authors showed that F. nucleatum promoted chemoresistance by downregulating microRNAs to activate autophagy(Yu et al. 2017). Likewise, Guo et al. reported that F. nucleatum was enriched in the tumor tissue of patients with CRC, induced tumor cells to produce exocrine substances, and further promoted cancer metastasis(Guo et al. 2020). Shi et al. found that colonic Bifidobacterium gathered at the tumor site and promoted local anti-CD47 treatment through the stimulator of interferon genes (STING) pathway(Shi et al. 2020).

Many other studies have demonstrated a stronger direct relationship between tumor microflora, gene expression, and immunological status in multiple cancers (Riquelme et al. 2019; Chen et al. 2021; Pushalkar et al. 2018; Cogdill et al. 2018; Galeano Niño et al. 2022; Huang et al. 2022; Parida et al. 2023; Neuzillet et al. 2021). In CRC, however, the composition and heterogeneity of the microflora within the tumor (including the relationship between stage, tumor location, immune cell infiltration, etc.) have not been clearly delineated. In attempt to address this deficiency of information, we collected tumor samples from 92 patients with CRC, performed 16S ribosomal RNA sequencing on the bacterial population within the tumor, and used immunohistochemistry to identify tumor-infiltrated immune cells. We also performed multifactorial analysis of the tumor’s bacterial population, immune cells, and gene expression profile using the Cancer Genome Atlas (TCGA) and the Cancer Microbiome Atlas (TCMA) cohorts.

Patients and tumor samples collection

In this study, samples from 92 patients with CRC who were treated in Shandong Cancer Hospital from Apr, 2021 to Dec, 2021 were analyzed. Each patient’s colorectal tumor tissues and nearby normal tissues were obtained. All samples were collected from colonoscopy biopsies. They were promptly frozen in liquid nitrogen for one hour and kept at -80 ℃ before processing. All tissue samples were meticulously histologically inspected to validate the diagnosis of either normal or malignant tissue. Concurrently, clinical and pathological characteristics of the patients, consisting of age, gender, primary tumor location, pathological stage, degree of differentiation, vascular tumor thrombus, nerve invasion, carcinoembryonic antigen (CEA) and CA199 levels were collected for this study(Table 1).

DNA extraction and 16S rRNA gene sequencing

DNA was extracted from CRC tumors and paracancer biopsy samples with a QIAamp Fast DNA Tissue Kit (Qiagen, Germany). The V4 variable region of the 16S rRNA gene was amplified by PCR. Primer sequences were as follows: 515F 5ʹ-GTGCCAGCMGCCGCGGTAA-3ʹ, 806R 5ʹ-GGACTACHVGGGTWTCTAAT-3ʹ) modified with specific barcodes. The PCR product was purified by AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA) and quantified by Qubit (Invitrogen, USA). The purified PCR products were evaluated using Illumina (Kapa Biosciences, Woburn, MA, USA) library quantification kits.

Raw data processing and analysis

Raw sequencing data were manipulated by using Quantitative Insights Into Microbial Ecology 2 (QIIME2, version2020.2)(Bolyen et al. 2019). Briefly, single-end reads were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. The demultiplexed reads were then denoised by using the q2-dada2 plugin(Callahan et al. 2016) in QIIME2 for quality control, chimera detection and removal(Callahan et al. 2016), and generation of amplicon sequence variants (ASVs) and representative sequences. Taxonomy of the ASVs was assigned by using the SILVA database (version 138)(Quast et al. 2013) classifier with 99% similarity. Samples with fewer than 10 ASVs and representative sequences with a frequency of < 10 were removed.

Immunohistochemistry

Each patient sample was sliced into 5 µm thick paraffin sections. Immunohistochemistry (IHC) was used to evaluate the expression of CD8, FOXP3, CD163, PD-1 and PD-L1 in all 92 samples, in accordance with established methods. Tissue sections that were formalin-fixed and paraffin-embedded (FFPE) were deparaffinized and dehydrated in xylene and graded ethanol solutions. After deparaffinization, antigen recovery was performed in microwave using EDTA (pH 8.0) and citrate buffer (pH 6.0) for 15 min, after which the slides were allowed to cool to room temperature. The slides were then cleaned with Tris-buffered saline + Tween 20 (TBST). The slides were then incubated for 30 minutes at room temperature with goat serum. Notably, FOXP3 was a nuclear antibody that required incubation with 0.1% Triton X-100 at room temperature for 10 minutes prior to blocking with goat serum in order to enable antibody binding by penetrating the nuclear membrane. Primary antibodies were incubated overnight at 4°C. The following day, slides were withdrawn from 4°C and allowed to rewarm at room temperature for 30 minutes. Slides were then incubated with secondary antibody according to the manufacturer's instructions (Proteintech, China). Specimens were then stained with 3,3′-Diaminobenzidine (DAB) and hematoxylin. Finally, the slides were sealed with neutral balsam after dehydration.

Initially, the slides were scanned at low magnification (10x magnification). After locating a target of interest, magnification was increased to 400x. Three high-magnification fields (400x magnification) were evaluated. We employed the Akoya Phenoptics Vectra Polaris (Akoya, USA) and its own analysis software to quantify human specimens (Inform2.6.0). Following the manufacturer’s recommendations for experimental techniques, we evaluated the proportion of immune cells expressing CD8, CD163 and FOXP3 to the total number, and the density of immune cells expressing PD-1 and PD-L1 in all cells.

Bioinformatics analysis

Microbial raw sequencing data of all samples were received in FASTQ format. Representative OUT sequences were given a taxonomic assignment based on the SILVA 138 microbial database using BLAST. Linear discriminant analysis Effect Size (LEfSe) was carried out between groups to determine the differentially abundant taxonomic features by using the non-parametric Kruskal-Wallis rank sum test. Functional prediction analyses were performed by using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) based on the 16S rRNA operational taxonomic unite (out) membership.

β-diversity analysis was performed by R and QIIME2 software using the Bray-Curtis distance metric to investigate the changes in microbial community structure among samples, and principal coordinate analysis was used to show the differences in species composition among groups. According to the results of the ASV division and taxonomic status identification, the specific-species composition of each sample at each taxonomic level was obtained. The 10 most abundant micro-organisms were included.

Random forest is a robust machine learning algorithm that combines the output of multiple decision trees to obtain a single outcome that can handle both classification and regression problems. The random forest classifier and screened model establishment were based on the high dimensional 16S rDNA sequencing data.

Using the "ClusterProfiler" package of the R programming language, GO enrichment analysis was conducted. Pathways that were considered significantly enriched were those with P values ≤ 0.05. The "ClusterProfiler" package was utilized to perform gene collection enrichment analysis (GSEA) in order to identify biological differences between risk groups. Once again, a P value ≤ 0.05 was considered statistically significant. We used the GSVA package in R to calculate pathway activity in the TCGA sample, and then used the ‘ggcor’ package for correlation analysis.

We used the R survminer package to determine the optimal cut-off values and compute survival differences between groups. R's rstatix package was used to calculate the variance in microflora expression between groups. The ggplot2 package R was used to calculate the positive rate differences between the groups of bacteria and produce a boxplot. The R ggcorrplot package was used to calculate correlation between different bacterial species and to generate a heat map.

Statistical analyses

All statistical analyses were performed and graphs created with R software (version 4.0.3). The Shapiro–Wilk test was used to determine the normality of continuous variables. Normally distributed data were described by means ± standard deviations. For inter-group comparisons at each timepoint, the Wilcoxon rank sum test was used to compare continuous variables, while Chi squared tests and Fisher's exact tests were used to analyze categorical variables. PERMANOVA was used to test for differences in beta diversity (measured by Bray-Curtis distance) between groups. Inter-rater agreement (kappa) was used for intra-group comparisons of categorical variables. P values of ≤ 0.05 were considered to indicate statistically significant differences, as per convention.

Correlation between heterogeneity of the intratumoral microbiome and clinical characteristics in colorectal cancer

We first evaluated the relationship between the microbiota composition in 92 tumor tissues based on 16S rRNA sequencing data and clinical characteristics (such as pathological stage, location, age, sex, etc.) of patients with CRC (Table 1). We noticed no statistically significant differences in α-diversity amongst individuals with all these clinical characteristics (Fig. S1). We then analyzed the β-diversity of the intratumoral microbiome in patients belonging to each clinical feature group and discovered statistically significant variations in the microbiota's β-diversity based on tumor stage (early vs. advanced stage, P = 0.049, Fig. 1A). When we considered tumor location (left colon, right colon, or rectum), the microbiota of rectal cancer and left colon cancer were similar (Fig. S2), whereas the β-diversity of left colon cancer was considerably different from that of right colon cancer (P = 0.04, Fig. 1A). There were no statistically significant differences in β-diversity among other clinical characteristics (Fig. S2), suggesting that the intratumoral microbiome differed between either tumor stage and between locations. We listed the top 10 most prevalent genera according to tumor stage and location, with Escherichia-Shigella and Fusobacterium being the most prevalent bacteria in tumors (Fig. 1B).

We then compared the microbiota of the paracancerous tissue to that found within the tumor itself to see whether there were any significant differences between the two. We discovered a significant difference in α- and β-diversity (P < 0.05, Fig. S3) between tumor and paracancerous tissues based on 16S rRNA sequence. Likewise, the microbiota in paracancerous tissues showed strikingly different levels of variety depending on the stage and location of the tumor (Fig. 1C). Intriguingly, Bacteroides and Escherichia-Shigella were the most common in paracancerous tissue (Fig. 1D). These findings suggest that tumor microbiome varies significantly across tumor stage (early vs. advanced) and location (left or right colon).

Table 1: Patient characteristics

Overall survival of colorectal cancer patients was associated with tumor stage and location-related microbiota

To further investigate how intratumoral microflora related to staging in CRC, we first identified the top 50 microflora that contributed to tumor staging in our sequencing data. We used two different test algorithms of random forest approach, Escherichia-Shigella, Parvimonas, and Dialister were identified as the three microorganisms that contributed the most to staging (measured by mean decrease accuracy), by using other test algorithm (measured by mean decrease Gini), we found that Escherichia-Shigella, Dialister, and Peptostreptococcus were the top three (Fig. 2A). Meanwhile, we used LEfSe to identify the specific bacteria linked to CRC stage (early or advanced). Several opportunistic pathogens, including f_Enterobacteriaceae, o_enterobacterale, g_Escherichia-Shigella, and s_Escherichia_coli_ATCC-25922, were all significantly overrepresented (all LDA scores (log10) > 4) in early staging cancer, whereas f_Veillonellaceae, g_Dialister, and s_Dialister were the most abundant microbiota in the advanced group (LDA scores (log10) > 4.0 (Fig. 2B). The Wilcoxon test indicated that g_Escherichia-Shigella was highly expressed in early CRC (Fig. 2C).

To determine if stage-related microflora were associated with the overall survival of CRC patients, we used the TCGA microflora database (TCMA) and assessed overall survival. The results indicate that, among all differentially expressed microflora, the intratumoral quantity of Porphyromonas, Lachnoclostridium, Aggregatibacter, and Hungatella was associated with poor prognosis, while high levels of Bacteroides were associated with good prognosis (Fig. 2D). These five bacteria were subsequently studied as a cluster, one whose expression, we discovered, was negatively correlated with overall survival time in CRC patients (Fig. 2E).

Next, we conducted an identical investigation of intratumoral microbiota at the left and right colon locations in our data. The random forest analysis found that Peptostreptococcus, Aeromonas, and Selenomonas were the three most important bacteria associated with tumor location (left or right colon, measured by mean decrease accuracy). Meanwhile, by using another algorithm of random forest (measured by mean decrease Gini), we found Peptostreptococcus, Selenomonas, and Bacteroides were three genera that most contributed to tumor location (Fig. 3A). LEfSe analysis revealed that s_Fusobacterium, g_Selenomonas, s_Selenomonas, and o_Peptostreptococcales-Tissierellales were the four most highly expressed bacterial species (LDA scores (log10) > 4) in the right colon cancer. In left colon cancer, g_Megamonas, s_Megamonas, o_Desulfovibrionales, c_Desulfovibrionia, and p_Desulfobacteriota were the most prevalent genera (LDA scores (log10) > 3.0) (Fig. 3B). Significantly higher amounts of Peptostreptococcus and Selenomonas were found in right colon cancer compared to left colon cancer (Fig. 3C). By comparing the expression data of the microflora aforementioned with patient survival information from TCMA, we determined that Blautia and, once again, Bacteroides were closely associated with good prognosis (Fig. 3D). Furthermore, this intratumoral bacterial profile (Bacteroides and Blautia) was likewise associated with better outcome in individuals with colon cancer (Fig. 3E). These findings suggest that there are distinct microflora linked with the stage and location of CRC, and that these microflora are strongly connected to the overall survival of patients.

Alterations in colorectal cancer pathways are associated with tumor stage and local microflora

To further evaluate the influence of stage- and location-associated intratumoral microflora on CRC-related pathways, we first performed a correlation study on the abundance of microflora related to the characteristic bacteria mentioned (Fig. 4A). We found that stage-related microflora clusters (Porphyromonas, Lachnoclostridium, Bacteroides, Aggregatibacter, and Hungatella) were associated with sulfur metabolism, styrene degradation, apoptosis, thiamine metabolism and cellular antigens, whereas location-related clusters (Bacteroides and Blautia) were associated with glycerolipid metabolism, nitrotoluene degradation and oxidative phosphorylation (Fig. 4B).

We further used TCMA database to analyze the relationship between intratumoral flora clusters and tumor genes. The tumor samples were divided into two groups according to the relative abundance of characteristically intratumoral flora clusters, then we looked for differentially expressed genes, which processed by GO analysis. GO analysis indicated that stage- and site-specific clusters were associated with the humoral immune response, immunoglobulin complexes, and mucosal immune response pathways (Fig. 4C), suggesting a connection between the microbiota and the immune response. GSVA analysis revealed that the stage-dependent cluster was positively correlated with the bile acid metabolism pathway, whereas the location-dependent cluster was negatively correlated there with, indicating that these two flora clusters may have opposite role on bile acid metabolism (Fig. 4D).

Specific microbiota are associated with the infiltration of immune cells in colorectal cancer

The intratumoral microflora may be related to the immune response of CRC patients(Gao et al. 2022). To further confirm the association between the infiltrated intratumoral microflora and the infiltrated tumor immune cells, IHC staining was performed on the tumor tissues of 92 patients, and the expressions of CD8, FOXP3, CD163, PD-1, and PD-L1 were measured in the tumor. Figure 5A depicts representative images of the IHC staining. Among the microflora contained in the stage- and location-related clusters, we discovered a negative correlation between Bacteroides and PD-1 expression, Blautia and tumor-associated macrophage infiltration, and Hungatella and CD8 + T cell infiltration (Fig. 5B). Figures 5C and D depict the relationship between distinctive microorganisms and their connection with immunohistochemical indices.

Patient samples were subsequently separated into two groups based on the level of stage- and site-specific microorganisms. Expression of PD-1 and FOXP3 were significantly reduced (P < 0.05) at low abundance of stage-related bacterial clusters, whereas expression of CD163, a cancer-related macrophage markers, was decreased at high expression of location-related bacterial clusters (Fig. 5E). These findings suggest a connection between intratumoral immune infiltration and the stage- and site-specific microflora that we identified.

Downstream pathway analysis indicates potential signal pathways in the particular microbiota contained in stage- and location-related clusters

Thus far, we had discovered that the levels of Bacteroides, Blautia, and Hungatella were not only directly connected to patient prognosis, but also independently related to the infiltrating immune cells in the tumor. Therefore, we employed the TCGA database to study potential signal pathways dependent on the expression of these bacteria. Volcanic map, GO analysis, and KEGG pathway analysis were respectively conducted by Bacteroides, Blautia, and Hungatella (Fig. 6A-C). Patient samples were divided into two groups based on the abundance of these three bacteria in TCMA. Within these two groups, we looked for differentially expressed genes, which we then processed by GO (Fig. 6A-C). We discovered that Bacteroides-associated, differentially expressed genes were primarily involved with humoral immunity. KEGG analysis revealed that high abundance of Bacteroides in tumor was positively correlated with glycosaminoglycan degradation, galactose metabolism, amino sugar and nucleotide sugar metabolism and TGF-β signal pathway, and negatively correlated with non-homologous end joining, maturity onset diabetes of the young and thyroid cancer. (Fig. 6A). The majority of the differentially expressed genes in tumor with high Blautia expression were involved in the humoral immune response and immunoglobulin. KEGG analysis revealed a positive correlation between the relative abundance of Blautia and RNA polymerase pathways, and a negative correlation with glycan biosynthesis and sphingolipid metabolism pathway (Fig. 6B). Genes characteristic of tumor with high Hungatella expression were functionally enriched in sphingolipid metabolism, glycan biosynthesis, and p53 signal pathway (Fig. 6C). GSEA analysis revealed that Bacteroides genes were correlated with negative regulation of macromolecule metabolic process, Blautia genes were correlated with cellular aromatic compound metabolic process, and Hungatella genes were correlated with negative regulation of myeloid cell differentiation (Fig. 6D). These findings indicate that Bacteroides, Blautia, and Hungatella were associated with specific signal pathways in CRC.

In recent years, there has been a steady shift toward recognizing the role that the intestinal microflora plays in the process of carcinogenesis and progression. The gut microbiome is thought to be intimately associated with both local and systemic tumor immunity, and, thus, to influence the outcome of cancer therapy. Since the discovery that colorectal cancer harbor their own unique microflora, the topic has increasingly become a hotspot of interest. Despite this, however, how the intratumoral microbiome influences the tumor itself and the infiltrating immune cells within remains murkily understood.

In this study, we obtained tumor samples from 92 patients diagnosed with CRC. We then sequenced the microbiome of these tumors by analyzing the 16S rRNA gene, stained immune cells in the tumor via IHC, and looked for meaningful connections between the two. Concurrently, using the TCGA and TCMA datasets, we conducted multiomics research on patient survival, transcriptome expression, and pathway analysis. We found that both overall patient survival and the number of invading immune cells in the tumor were correlated with the presence of two bacterial clusters, one of which was correlated with tumor stage (early vs. advanced stage), and the other with location (left vs. right colon). The three bacterial genera common to both clusters – Hungetalla, Blautia, and Bacteriodes – were then individually analyzed. Our findings indicate: 1) that the incidence of intratumoral microflora in CRC is connected to patient prognosis and local tumor immunity, and 2) that specific intratumoral flora were associated with tumor related pathways.

We first compared the β-diversity of the intratumoral microbiome in CRC patients with different clinical feature groups (such as age, sex, tumor stage, place, etc.), and discovered that the β-diversity of bacterial composition varied significantly across tumor stage and location. There is little question that tumor stage is a crucial element influencing the survival of cancer patients. We discovered that the composition of intratumoral microflora differs between early and advanced patients. Escherichia-Shigella, Parvimonas, and Dialister were the three most influential bacteria between early and advanced stage in colorectal cancer.

The association of intratumoral microbiota with tumor stage in CRC has been previously studied. Mima et al. reported that higher Fusobacterium nucleatum was associated with advanced disease stage in human CRC tissue(Mima et al. 2016). Leung et al. reported that patients with CRC at the deepest involvement (T4) had higher abundance of Campylobacter and Fusobacterium in the tumor(Leung et al. 2019). Yoon et al. reported that the proportion of Pseudomonas was very high in the samples of patients with stage II-IV CRC compared in the samples of patients with stage I CRC(Yoon et al. 2017). Enrichment of Parvimonas, Gemella, Leptotrichia, Eikenella corrodens, Eubacterium ventriosum, and Bacteroides fragilis has also reported in early-stage CRC(Nakatsu et al. 2015; Osman et al. 2021; Hale et al. 2018). Low levels of Bacteroides, Blautia, Collinsella aerofaciens, and Alistipes putredinis in early-stage CRC were also reported(Nakatsu et al. 2015), as well as increased abundance of Prevotella intermedia, Harryflintia acetispora, and Dialister pneumosintes in advanced CRC(Osman et al. 2021).

Many studies have also reported on the composition of intratumoral microflora as related to the tumor location in CRC. Fusobacterium nucleatum has been reported as significantly higher in both distal and proximal tumors(Mima et al. 2016; Leung et al. 2019). Five microbial genera (Bacteroides, Fusobacterium, Faecalibacterium, Parabacteroides, and Ruminococcus) were significantly different between distal and proximal CRC segments(Sheng et al. 2020). Alistipes, Akkermansia, Shewanella, Escherichia-Shigella, and Leptotrichia were significantly higher in distal CRC, whereas Faecalibacterium, Blautia, Clostridium, Prevotella, Pyramido-bacterium, Selenomonas, and Peptostreptoccus were significantly more abundant in proximal CRC(Flemer et al. 2017; Gao et al. 2015).

We found that Peptostretococcus varied the most by tumor location. Peptostreptococcus is a bacterium that has been found to be overrepresented in CRCs. Peptostreptococcus anaerobius has been found to affect tumor immunity and induce colorectal carcinogenesis(Long et al. 2019). Although we observed no association between the level of Peptostreptococcus and CRC patients’ overall survival in our research of the TCGA database, its intratumoral impact still needed to be further investigated.

The association between Bacteroides and the prognosis of CRC patients is controversial. Du et al. reported that the presence of Bacteroides in right-sided CRC had better overall survival(Du et al. 2021; Choi et al. 2021). Choi et al. also reported that Bacteroides was associated with better prognosis of CRC patients(Choi et al. 2021). On the other hand, there are many subspecies of Bacteroides, one of which is enterotoxigenic Bacteroides fragilis (ETBF), which has been reported to be associated with poor prognosis in patients with CRC(Boleij et al. 2015). Viljoen et al. also reported that ETBF levels were significantly higher in late stage (III/IV) CRCs(Viljoen et al. 2015).

In agreement with our findings, Wang et al. identified that high abundance of Blautia was correlated with better survival probability in patients with CRC based on the public databases of TCMA and TCGA(Wang et al. 2021). We discovered that microorganisms related to tumor stage or tumor location are independent prognostic variables. Based on these considerations, we performed stage- and location-specific integrated analyses, discovering clusters that predict tumor patient outcomes.

Further research into the population and genes associated with clusters showed that clusters related to both developmental stage and tumor location influenced the tumor immune microenvironment. Galeano Nino et al. reported that the intratumoral microbiome has immunosuppressive properties, with downregulation of T cell markers such as CD3, CD8 and CD4 in the CRC cancer tissue suggesting that T cells are excluded in bacteria-colonized regions(Galeano Nino et al. 2022). Dong et al. found that the enrichment of Fusobacterium nucleatum in CRC tumor tissues had a positive correlation with MDSCs and negative correlation with T cells(Dong et al. 2020).

However, numerous studies have also found that tumor-resident bacterial species can galvanize the immune response. ETBF was observed to activate TH17 adaptive immunity in CRC(Cheng, Kantilal, and Davamani 2020). Erythrobacter ramosus and B. fragilis facilitated the induction of follicular T helper cells via activation of dendritic cells(Roberti et al. 2020). Shi et al. found that Bifidobacterium accumulating within the tumor microenvironment facilitated local anti-CD47 immunotherapy on tumor tissues by stimulating STING signal and increasing cross-priming of dendritic cells(Shi et al. 2020). In our study, additional IHC staining of each tumor tissue verified that distinct clusters of intracellular bacteria were associated with the immune microenvironment (such as CD8 + T cells, M2 macrophages, and PD-1/PD-L1 expression) of the tumor. High expression of stage related intratumoral bacteria was positively linked with the expression of PD-1 and FOXP3, indicating that high expression of this clusters may contribute to an anti-inflammatory immune microenvironment. On the other hand, CD163 expression was low in tumors with a high concentration of location-related clusters, indicating a negative correlation between the expression level of cluster-related bacteria and the invasion of M2 tumor-associated macrophages. Finally, we conducted a comprehensive analysis of our three characteristic bacteria and determined that they were involved in different intratumoral signal pathways for specific tumor microflora. The role of intratumoral flora on these pathways in CRC requires further investigation.

Our study has several limitations. First, we did not sequence the transcriptome of our data and compare it to the transcriptome of TCMA to further validate the influence of the microflora on the tumor cell transcriptome. Second, although intestinal and tumor microflora have been correlated previously in the literature, fecal microflora were not gathered from patients for integration analysis in our study. In order to get a deeper knowledge of tumor microbiota, we propose to do an integrated investigation of tumor microbiota, paracancer microbiota, intestinal microbiota, transcriptome, and metabolome in our future study. Third, flow cytometry and single-cell sequencing to analyze immunological features of intra-tumor infiltration have not been validated in this study. Fourth, our research did not further verify the intratumoral flora associated signal pathway by laboratory techniques.

We found specific intratumoral bacterial clusters that were related to tumor stage and location. These clusters were strongly associated with tumor immune infiltration and patient prognosis. Our findings may provide new viewpoints for future research between intratumoral microbiota, metabolic pathways, and the tumor microenvironment.

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Funding Statement

This research was funded by National Natural Science Foundation of China (82272753), Young TaiShan Scholars and Academic Promotion Program of Shandong First Medical University (2019RC003), Natural Science Foundation of Shandong Province (ZR2021LZL002) and Bethune Cancer Radiotherapy Translational Medicine Research Fund (flzh202103).

Author contributions

Conceptualization: Jinbo Yue; Formal analysis and investigation: Xiang Zhang, Fuhao Wang; Writing - original draft preparation: Mengyu Zhao, Xiang Zhang; Writing - review and editing: Xiaoyu Hu, Zhuang Xue; Funding acquisition: Jinbo Yue; Resources: Ming Chen; Supervision: Jinbo Yue.

Ethics approval

Approval was obtained from the Shandong Cancer Hospital Ethics Committee. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Data Availability

All data generated or analysed during this study are included in this published article and its supplementary information files. Our raw data which is based on 16S rRNA sequencing and immunohistochemistry staining are available from the corresponding author on reasonable request. Part of the datasets analysed during the current study are available in the TCGA repository, https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga.

Boleij, Annemarie, Elizabeth M. Hechenbleikner, Andrew C. Goodwin, Ruchi Badani, Ellen M. Stein, Mark G. Lazarev, Brandon Ellis, Karen C. Carroll, Emilia Albesiano, Elizabeth C. Wick, Elizabeth A. Platz, Drew M. Pardoll, and Cynthia L. Sears. 2015. 'The Bacteroides fragilis toxin gene is prevalent in the colon mucosa of colorectal cancer patients', Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America, 60: 208-15.
Bolyen, Evan, Jai Ram Rideout, Matthew R. Dillon, Nicholas A. Bokulich, Christian C. Abnet, Gabriel A. Al-Ghalith, Harriet Alexander, Eric J. Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E. Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J. Brislawn, C. Titus Brown, Benjamin J. Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily K. Cope, Ricardo Da Silva, Christian Diener, Pieter C. Dorrestein, Gavin M. Douglas, Daniel M. Durall, Claire Duvallet, Christian F. Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M. Gauglitz, Sean M. Gibbons, Deanna L. Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin A. Huttley, Stefan Janssen, Alan K. Jarmusch, Lingjing Jiang, Benjamin D. Kaehler, Kyo Bin Kang, Christopher R. Keefe, Paul Keim, Scott T. Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan G. I. Langille, Joslynn Lee, Ruth Ley, Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D. Martin, Daniel McDonald, Lauren J. McIver, Alexey V. Melnik, Jessica L. Metcalf, Sydney C. Morgan, Jamie T. Morton, Ahmad Turan Naimey, Jose A. Navas-Molina, Louis Felix Nothias, Stephanie B. Orchanian, Talima Pearson, Samuel L. Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S. Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R. Spear, Austin D. Swafford, Luke R. Thompson, Pedro J. Torres, Pauline Trinh, Anupriya Tripathi, Peter J. Turnbaugh, Sabah Ul-Hasan, Justin J. J. van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C. Weber, Charles H. D. Williamson, Amy D. Willis, Zhenjiang Zech Xu, Jesse R. Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight, and J. Gregory Caporaso. 2019. 'Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2', Nature Biotechnology, 37: 852-57.
Callahan, Benjamin J., Paul J. McMurdie, Michael J. Rosen, Andrew W. Han, Amy Jo A. Johnson, and Susan P. Holmes. 2016. 'DADA2: High-resolution sample inference from Illumina amplicon data', Nature Methods, 13: 581-83.
Chen, Yun, Boyuan Liu, Yuan Wei, and Dong-Ming Kuang. 2021. 'Influence of gut and intratumoral microbiota on the immune microenvironment and anti-cancer therapy', Pharmacological Research, 174: 105966.
Cheng, W. T., H. K. Kantilal, and F. Davamani. 2020. 'The Mechanism of Bacteroides fragilis Toxin Contributes to Colon Cancer Formation', Malays J Med Sci, 27: 9-21.
Choi, Sukjung, Jongsuk Chung, Mi-La Cho, Donghyun Park, and Sun Shim Choi. 2021. 'Analysis of changes in microbiome compositions related to the prognosis of colorectal cancer patients based on tissue-derived 16S rRNA sequences', Journal of Translational Medicine, 19: 485.
Cogdill, Alexandria P., Pierre Olivier Gaudreau, Reetakshi Arora, Vancheswaran Gopalakrishnan, and Jennifer A. Wargo. 2018. 'The Impact of Intratumoral and Gastrointestinal Microbiota on Systemic Cancer Therapy', Trends In Immunology, 39: 900-20.
Dong, Xue, Pei Pan, Di-Wei Zheng, Peng Bao, Xuan Zeng, and Xian-Zheng Zhang. 2020. 'Bioinorganic hybrid bacteriophage for modulation of intestinal microbiota to remodel tumor-immune microenvironment against colorectal cancer', Science Advances, 6: eaba1590.
Du, Kunli, Xinyou Wang, Shisen Li, Jiahui Ren, Ruikai Li, Mian Wang, Fan Feng, Hansong Du, and Jianyong Zheng. 2021. 'Construction of a gut microbiota-gene-pathway network to reveal the molecular mechanisms underlying right- and left-sided colorectal cancer', FEMS Microbiology Letters, 368.
Flemer, Burkhardt, Denise B. Lynch, Jillian M. R. Brown, Ian B. Jeffery, Feargal J. Ryan, Marcus J. Claesson, Micheal O'Riordain, Fergus Shanahan, and Paul W. O'Toole. 2017. 'Tumour-associated and non-tumour-associated microbiota in colorectal cancer', Gut, 66: 633-43.
Galeano Nino, J. L., H. Wu, K. D. LaCourse, A. G. Kempchinsky, A. Baryiames, B. Barber, N. Futran, J. Houlton, C. Sather, E. Sicinska, A. Taylor, S. S. Minot, C. D. Johnston, and S. Bullman. 2022. 'Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer', Nature, 611: 810-17.
Galeano Niño, Jorge Luis, Hanrui Wu, Kaitlyn D. LaCourse, Andrew G. Kempchinsky, Alexander Baryiames, Brittany Barber, Neal Futran, Jeffrey Houlton, Cassie Sather, Ewa Sicinska, Alison Taylor, Samuel S. Minot, Christopher D. Johnston, and Susan Bullman. 2022. 'Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer', Nature, 611: 810-17.
Gao, Feng, Bo Yu, Benchen Rao, Ying Sun, Jia Yu, Daming Wang, Guangying Cui, and Zhigang Ren. 2022. 'The effect of the intratumoral microbiome on tumor occurrence, progression, prognosis and treatment', Frontiers In Immunology, 13: 1051987.
Gao, Zhiguang, Bomin Guo, Renyuan Gao, Qingchao Zhu, and Huanlong Qin. 2015. 'Microbiota disbiosis is associated with colorectal cancer', Frontiers in Microbiology, 6: 20.
Guo, S., J. Chen, F. Chen, Q. Zeng, W. L. Liu, and G. Zhang. 2020. 'Exosomes derived from Fusobacterium nucleatum-infected colorectal cancer cells facilitate tumour metastasis by selectively carrying miR-1246/92b-3p/27a-3p and CXCL16', Gut.
Hale, V. L., P. Jeraldo, M. Mundy, J. Yao, G. Keeney, N. Scott, E. H. Cheek, J. Davidson, M. Greene, C. Martinez, J. Lehman, C. Pettry, E. Reed, K. Lyke, B. A. White, C. Diener, O. Resendis-Antonio, J. Gransee, T. Dutta, X. M. Petterson, L. Boardman, D. Larson, H. Nelson, and N. Chia. 2018. 'Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer', Methods, 149: 59-68.
Huang, Jian-Hang, Jie Wang, Xiao-Qiang Chai, Zhong-Chen Li, Ying-Hua Jiang, Jun Li, Xing Liu, Jia Fan, Jia-Bin Cai, and Feng Liu. 2022. 'The Intratumoral Bacterial Metataxonomic Signature of Hepatocellular Carcinoma', Microbiology Spectrum, 10: e0098322.
Leung, Polly H. M., Rao Subramanya, Qianqian Mou, Katherine Ting-Wei Lee, Farhadul Islam, Vinod Gopalan, Cu-Tai Lu, and Alfred King-Yin Lam. 2019. 'Characterization of Mucosa-Associated Microbiota in Matched Cancer and Non-neoplastic Mucosa From Patients With Colorectal Cancer', Frontiers in Microbiology, 10: 1317.
Liu, W., X. Zhang, H. Xu, S. Li, H. C. Lau, Q. Chen, B. Zhang, L. Zhao, H. Chen, J. J. Sung, and J. Yu. 2021. 'Microbial Community Heterogeneity Within Colorectal Neoplasia and its Correlation With Colorectal Carcinogenesis', Gastroenterology, 160: 2395-408.
Long, Xiaohang, Chi Chun Wong, Li Tong, Eagle S. H. Chu, Chun Ho Szeto, Minne Y. Y. Go, Olabisi Oluwabukola Coker, Anthony W. H. Chan, Francis K. L. Chan, Joseph J. Y. Sung, and Jun Yu. 2019. 'Peptostreptococcus anaerobius promotes colorectal carcinogenesis and modulates tumour immunity', Nature Microbiology, 4: 2319-30.
Louis, Petra, Georgina L. Hold, and Harry J. Flint. 2014. 'The gut microbiota, bacterial metabolites and colorectal cancer', Nature Reviews Microbiology, 12: 661-72.
Lucas, Cécily, Nicolas Barnich, and Hang Nguyen. 2017. 'Microbiota, Inflammation and Colorectal Cancer', International Journal of Molecular Sciences, 18.
Mima, K., R. Nishihara, Z. R. Qian, Y. Cao, Y. Sukawa, J. A. Nowak, J. Yang, R. Dou, Y. Masugi, M. Song, A. D. Kostic, M. Giannakis, S. Bullman, D. A. Milner, H. Baba, E. L. Giovannucci, L. A. Garraway, G. J. Freeman, G. Dranoff, W. S. Garrett, C. Huttenhower, M. Meyerson, J. A. Meyerhardt, A. T. Chan, C. S. Fuchs, and S. Ogino. 2016. 'Fusobacterium nucleatum in colorectal carcinoma tissue and patient prognosis', Gut, 65: 1973-80.
Montalban-Arques, Ana, and Michael Scharl. 2019. 'Intestinal microbiota and colorectal carcinoma: Implications for pathogenesis, diagnosis, and therapy', EBioMedicine, 48: 648-55.
Nakatsu, Geicho, Xiangchun Li, Haokui Zhou, Jianqiu Sheng, Sunny Hei Wong, William Ka Kai Wu, Siew Chien Ng, Ho Tsoi, Yujuan Dong, Ning Zhang, Yuqi He, Qian Kang, Lei Cao, Kunning Wang, Jingwan Zhang, Qiaoyi Liang, Jun Yu, and Joseph J. Y. Sung. 2015. 'Gut mucosal microbiome across stages of colorectal carcinogenesis', Nature Communications, 6: 8727.
Nejman, D., I. Livyatan, G. Fuks, N. Gavert, Y. Zwang, L. T. Geller, A. Rotter-Maskowitz, R. Weiser, G. Mallel, E. Gigi, A. Meltser, G. M. Douglas, I. Kamer, V. Gopalakrishnan, T. Dadosh, S. Levin-Zaidman, S. Avnet, T. Atlan, Z. A. Cooper, R. Arora, A. P. Cogdill, M. A. W. Khan, G. Ologun, Y. Bussi, A. Weinberger, M. Lotan-Pompan, O. Golani, G. Perry, M. Rokah, K. Bahar-Shany, E. A. Rozeman, C. U. Blank, A. Ronai, R. Shaoul, A. Amit, T. Dorfman, R. Kremer, Z. R. Cohen, S. Harnof, T. Siegal, E. Yehuda-Shnaidman, E. N. Gal-Yam, H. Shapira, N. Baldini, M. G. I. Langille, A. Ben-Nun, B. Kaufman, A. Nissan, T. Golan, M. Dadiani, K. Levanon, J. Bar, S. Yust-Katz, I. Barshack, D. S. Peeper, D. J. Raz, E. Segal, J. A. Wargo, J. Sandbank, N. Shental, and R. Straussman. 2020. 'The human tumor microbiome is composed of tumor type-specific intracellular bacteria', Science, 368: 973-80.
Neuzillet, Cindy, Manon Marchais, Sophie Vacher, Marc Hilmi, Anne Schnitzler, Didier Meseure, Renaud Leclere, Charlotte Lecerf, Coraline Dubot, Emmanuelle Jeannot, Jerzy Klijanienko, Odette Mariani, Valentin Calugaru, Caroline Hoffmann, Maria Lesnik, Nathalie Badois, Edith Borcoman, Eliane Piaggio, Maud Kamal, Christophe Le Tourneau, and Ivan Bieche. 2021. 'Prognostic value of intratumoral Fusobacterium nucleatum and association with immune-related gene expression in oral squamous cell carcinoma patients', Scientific Reports, 11: 7870.
Osman, Muhammad Afiq, Hui-Min Neoh, Nurul-Syakima Ab Mutalib, Siok-Fong Chin, Luqman Mazlan, Raja Affendi Raja Ali, Andee Dzulkarnaen Zakaria, Chai Soon Ngiu, Mia Yang Ang, and Rahman Jamal. 2021. 'Parvimonas micra, Peptostreptococcus stomatis, Fusobacterium nucleatum and Akkermansia muciniphila as a four-bacteria biomarker panel of colorectal cancer', Scientific Reports, 11: 2925.
Parida, Sheetal, Sumit Siddharth, Yuqing Xia, and Dipali Sharma. 2023. 'Concomitant analyses of intratumoral microbiota and genomic features reveal distinct racial differences in breast cancer', NPJ Breast Cancer, 9: 4.
Pushalkar, S., M. Hundeyin, D. Daley, C. P. Zambirinis, E. Kurz, A. Mishra, N. Mohan, B. Aykut, M. Usyk, L. E. Torres, G. Werba, K. Zhang, Y. Guo, Q. Li, N. Akkad, S. Lall, B. Wadowski, J. Gutierrez, J. A. Kochen Rossi, J. W. Herzog, B. Diskin, A. Torres-Hernandez, J. Leinwand, W. Wang, P. S. Taunk, S. Savadkar, M. Janal, A. Saxena, X. Li, D. Cohen, R. B. Sartor, D. Saxena, and G. Miller. 2018. 'The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression', Cancer Discov, 8: 403-16.
Quast, Christian, Elmar Pruesse, Pelin Yilmaz, Jan Gerken, Timmy Schweer, Pablo Yarza, Jörg Peplies, and Frank Oliver Glöckner. 2013. 'The SILVA ribosomal RNA gene database project: improved data processing and web-based tools', Nucleic Acids Research, 41: D590-D96.
Raskov, Hans, Jakob Burcharth, and Hans-Christian Pommergaard. 2017. 'Linking Gut Microbiota to Colorectal Cancer', Journal of Cancer, 8: 3378-95.
Rebersek, Martina. 2021. 'Gut microbiome and its role in colorectal cancer', BMC Cancer, 21.
Riquelme, Erick, Yu Zhang, Liangliang Zhang, Maria Montiel, Michelle Zoltan, Wenli Dong, Pompeyo Quesada, Ismet Sahin, Vidhi Chandra, Anthony San Lucas, Paul Scheet, Hanwen Xu, Samir M. Hanash, Lei Feng, Jared K. Burks, Kim-Anh Do, Christine B. Peterson, Deborah Nejman, Ching-Wei D. Tzeng, Michael P. Kim, Cynthia L. Sears, Nadim Ajami, Joseph Petrosino, Laura D. Wood, Anirban Maitra, Ravid Straussman, Matthew Katz, James Robert White, Robert Jenq, Jennifer Wargo, and Florencia McAllister. 2019. 'Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes', Cell, 178.
Roberti, M. P., S. Yonekura, C. P. M. Duong, M. Picard, G. Ferrere, M. Tidjani Alou, C. Rauber, V. Iebba, C. H. K. Lehmann, L. Amon, D. Dudziak, L. Derosa, B. Routy, C. Flament, C. Richard, R. Daillere, A. Fluckiger, I. Van Seuningen, M. Chamaillard, A. Vincent, S. Kourula, P. Opolon, P. Ly, E. Pizzato, S. Becharef, J. Paillet, C. Klein, F. Marliot, F. Pietrantonio, S. Benoist, J. Y. Scoazec, P. Dartigues, A. Hollebecque, D. Malka, F. Pages, J. Galon, I. Gomperts Boneca, P. Lepage, B. Ryffel, D. Raoult, A. Eggermont, T. Vanden Berghe, F. Ghiringhelli, P. Vandenabeele, G. Kroemer, and L. Zitvogel. 2020. 'Chemotherapy-induced ileal crypt apoptosis and the ileal microbiome shape immunosurveillance and prognosis of proximal colon cancer', Nature Medicine, 26: 919-31.
Roy, Soumen, and Giorgio Trinchieri. 2017. 'Microbiota: a key orchestrator of cancer therapy', Nature Reviews Cancer, 17: 271-85.
Sheng, Q. S., K. X. He, J. J. Li, Z. F. Zhong, F. X. Wang, L. L. Pan, and J. J. Lin. 2020. 'Comparison of Gut Microbiome in Human Colorectal Cancer in Paired Tumor and Adjacent Normal Tissues', Onco Targets Ther, 13: 635-46.
Shi, Y., W. Zheng, K. Yang, K. G. Harris, K. Ni, L. Xue, W. Lin, E. B. Chang, R. R. Weichselbaum, and Y. X. Fu. 2020. 'Intratumoral accumulation of gut microbiota facilitates CD47-based immunotherapy via STING signaling', J Exp Med, 217.
Taghinezhad, S. S., A. H. Mohseni, and X. Fu. 2021. 'Intervention on gut microbiota may change the strategy for management of colorectal cancer', J Gastroenterol Hepatol, 36: 1508-17.
Temraz, S., F. Nassar, R. Nasr, M. Charafeddine, D. Mukherji, and A. Shamseddine. 2019. 'Gut Microbiome: A Promising Biomarker for Immunotherapy in Colorectal Cancer', Int J Mol Sci, 20.
Tilg, Herbert, Timon E. Adolph, Romana R. Gerner, and Alexander R. Moschen. 2018. 'The Intestinal Microbiota in Colorectal Cancer', Cancer Cell, 33: 954-64.
Viljoen, K. S., A. Dakshinamurthy, P. Goldberg, and J. M. Blackburn. 2015. 'Quantitative profiling of colorectal cancer-associated bacteria reveals associations between fusobacterium spp., enterotoxigenic Bacteroides fragilis (ETBF) and clinicopathological features of colorectal cancer', PLoS One, 10: e0119462.
Wang, J., Y. Wang, Z. Li, X. Gao, and D. Huang. 2021. 'Global Analysis of Microbiota Signatures in Four Major Types of Gastrointestinal Cancer', Front Oncol, 11: 685641.
Wong, Sunny H., and Jun Yu. 2019. 'Gut microbiota in colorectal cancer: mechanisms of action and clinical applications', Nature Reviews Gastroenterology & Hepatology, 16: 690-704.
Xi, Yue, and Pengfei Xu. 2021. 'Global colorectal cancer burden in 2020 and projections to 2040', Translational Oncology, 14: 101174.
Yoon, H., N. Kim, J. H. Park, Y. S. Kim, J. Lee, H. W. Kim, Y. J. Choi, C. M. Shin, Y. S. Park, D. H. Lee, and H. C. Jung. 2017. 'Comparisons of Gut Microbiota Among Healthy Control, Patients With Conventional Adenoma, Sessile Serrated Adenoma, and Colorectal Cancer', J Cancer Prev, 22: 108-14.
Yu, T., F. Guo, Y. Yu, T. Sun, D. Ma, J. Han, Y. Qian, I. Kryczek, D. Sun, N. Nagarsheth, Y. Chen, H. Chen, J. Hong, W. Zou, and J. Y. Fang. 2017. 'Fusobacterium nucleatum Promotes Chemoresistance to Colorectal Cancer by Modulating Autophagy', Cell, 170: 548-63 e16.

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SupplementaryMaterials.zip
Supplementary Materials Figure S1. α-diversity of intratumoral microbiota at different clinical characteristics in colorectal cancer. Figure S2. β-diversity of intratumoral microbiota at different clinical characteristics in colorectal cancer. Figure S3. Bacterial α-diversity(A) and β-diversity (B) measured by Bray-Curtis distances in tumor and adjunct normal tissues.

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Stage and location related intratumoral microbiota are associated with patient prognosis and immune infiltration in colorectal cancer

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Correlation between heterogeneity of the intratumoral microbiome and clinical characteristics in colorectal cancer

Overall survival of colorectal cancer patients was associated with tumor stage and location-related microbiota

Alterations in colorectal cancer pathways are associated with tumor stage and local microflora

Specific microbiota are associated with the infiltration of immune cells in colorectal cancer

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