Dataset Information

Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria.

ABSTRACT:

Background

The mortality of colorectal cancer is high, the malignant degree of poorly differentiated colorectal cancer is high, and the prognosis is poor.

Objective

To screen the characteristic intestinal microbiota of poorly differentiated intestinal cancer.

Methods

Fecal samples were collected from 124 patients with moderately differentiated CRC and 123 patients with poorly differentiated CRC, and the bacterial 16S rRNA V1-V4 region of the fecal samples was sequenced. Alpha diversity analysis was performed on fecal samples to assess the diversity and abundance of flora. The RDP classifier Bayesian algorithm was used to analyze the community structure. Linear discriminant analysis and Student's t test were used to screen the differences in flora. The PICRUSt1 method was used to predict the bacterial function, and six machine learning models, including logistic regression, random forest, neural network, support vector machine, CatBoost and gradient boosting decision tree, were used to construct a prediction model for the poor differentiation of colorectal cancer.

Results

There was no significant difference in fecal flora alpha diversity between moderately and poorly differentiated colorectal cancer (P > 0.05). The bacteria that accounted for a large proportion of patients with poorly differentiated and moderately differentiated colorectal cancer were Blautia, Escherichia-Shigella, Streptococcus, Lactobacillus, and Bacteroides. At the genus level, there were nine bacteria with high abundance in the poorly differentiated group, including Bifidobacterium, norank_f__Oscillospiraceae, Eisenbergiella, etc. There were six bacteria with high abundance in the moderately differentiated group, including Megamonas, Erysipelotrichaceae_UCG-003, Actinomyces, etc. The RF model had the highest prediction accuracy (100.00% correct). The bacteria that had the greatest variable importance in the model were Pseudoramibacter, Megamonas and Bifidobacterium.

Conclusion

The degree of pathological differentiation of colorectal cancer was related to gut flora, and poorly differentiated colorectal cancer had some different bacterial flora, and intestinal bacteria can be used as biomarkers for predicting poorly differentiated CRC.

SUBMITTER: Qi Z

PROVIDER: S-EPMC9764708 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria.

Qi Zhang Z Zhibo Zuo Z Jing Zhuang Z Zhanbo Qu Q Shugao Han H Weili Jin J Jiang Liu L Shuwen Han H

BMC microbiology 20221220 1

<h4>Background</h4>The mortality of colorectal cancer is high, the malignant degree of poorly differentiated colorectal cancer is high, and the prognosis is poor.<h4>Objective</h4>To screen the characteristic intestinal microbiota of poorly differentiated intestinal cancer.<h4>Methods</h4>Fecal samples were collected from 124 patients with moderately differentiated CRC and 123 patients with poorly differentiated CRC, and the bacterial 16S rRNA V1-V4 region of the fecal samples was sequenced. Alp ...[more]

PMID: 36539710

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Project description:Gut microbiota dysbiosis with increased pathogenic bacteria and decreased beneficial bacteria is associated with colorectal cancer (CRC) development. This study examined the effect of a newly developed probiotic formula in modulating CRC-related bacteria. We developed a probiotic formula containing three bifidobacteria (B. adolescentis, B. longum, and B. bifidum) based on the identification of bacterial species that showed significant correlations with CRC-related bacteria including Fusobacterium nucleatum (Fn), Lachnoclostridium sp. m3, Clostridium hathewayi (Ch), and Bacteroides clarus (Bc). We co-cultured Fn with each bifidobacterium or the combined formula and examined the growth of Fn by qPCR. The three individual bifidobacteria significantly inhibited the growth of Fn compared to the control treatment (24~65% inhibition; all p < 0.001). The combination of the three bifidobacteria showed a greater inhibitory effect on Fn growth (70% inhibition) than the individual bifidobacteria (all p < 0.05). We further examined the effect of the probiotic formula in a pilot study of 72 subjects (40 on probiotics; 32 with no intervention) for 4 weeks and followed them up for 12 weeks. The relative fecal abundances of the bifidobacteria in the formula and the CRC-related markers (Fn, m3, Ch, and Bc) were quantitated by qPCR before and after the intervention, and the combined CRC risk score (4Bac; Fn, m3, Ch, and Bc) was evaluated. Subjects with probiotics intervention showed significantly increased abundances of the bifidobacteria from week 2 to week 5 compared to baseline (p < 0.05), and the abundances dropped to baseline levels after the cessation of the intervention. There were significant decreases in the levels of CRC-related markers (Fn and m3) and the CRC risk score (4Bac) from week 2 to week 12 compared to baseline levels (p < 0.05) in the intervention group but not in the control group. A novel probiotic formula containing B. adolescentis, B. longum, and B. bifidum was effective in inhibiting the growth of F. nucleatum in vitro and improving the gut microbial environment against CRC development.

Project description:UnlabelledThe composition of the human intestinal microbiota is linked to health status. The aim was to analyze the microbiota of normal and colon cancer patients in order to establish cancer-related dysbiosis.Patients and methodsStool bacterial DNA was extracted prior to colonoscopy from 179 patients: 60 with colorectal cancer, and 119 with normal colonoscopy. Bacterial genes obtained by pyrosequencing of 12 stool samples (6 Normal and 6 Cancer) were subjected to a validated Principal Component Analysis (PCA) test. The dominant and subdominant bacterial population (C. leptum, C. coccoides, Bacteroides/Prevotella, Lactobacillus/Leuconostoc/Pediococcus groups, Bifidobacterium genus, and E. coli, and Faecalibacterium prausnitzii species) were quantified in all individuals using qPCR and specific IL17 producer cells in the intestinal mucosa were characterized using immunohistochemistry.FindingsPyrosequencing (Minimal sequence 200 nucleotide reads) revealed 80% of all sequences could be assigned to a total of 819 taxa based on default parameter of Classifier software. The phylogenetic core in Cancer individuals was different from that in Normal individuals according to the PCA analysis, with trends towards differences in the dominant and subdominant families of bacteria. Consequently, All-bacteria [log(10) (bacteria/g of stool)] in Normal, and Cancer individuals were similar [11.88±0.35, and 11.80±0.56, respectively, (P = 0.16)], according to qPCR values whereas among all dominant and subdominant species only those of Bacteroides/Prevotella were higher (All bacteria-specific bacterium; P = 0.009) in Cancer (-1.04±0.55) than in Normal (-1.40±0.83) individuals. IL17 immunoreactive cells were significantly expressed more in the normal mucosa of cancer patients than in those with normal colonoscopy.ConclusionThis is the first large series to demonstrate a composition change in the microbiota of colon cancer patients with possible impact on mucosal immune response. These data open new filed for mass screening and pathophysiology investigations.

Dataset Information

Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria.

Background

Objective

Methods

Results

Conclusion

Publications

Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria.

Similar Datasets

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