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 coll...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | BMC Microbiology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12866-022-02712-w |
| _version_ | 1797977521887641600 |
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| author | Zhang Qi Zuo Zhibo Zhuang Jing Qu Zhanbo Han Shugao Jin Weili Liu Jiang Han Shuwen |
| author_facet | Zhang Qi Zuo Zhibo Zhuang Jing Qu Zhanbo Han Shugao Jin Weili Liu Jiang Han Shuwen |
| author_sort | Zhang Qi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T05:09:24Z |
| format | Article |
| id | doaj.art-258f008d65bc45b39ebd4ba2e154c789 |
| institution | Directory Open Access Journal |
| issn | 1471-2180 |
| language | English |
| last_indexed | 2024-04-11T05:09:24Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Microbiology |
| spelling | doaj.art-258f008d65bc45b39ebd4ba2e154c7892022-12-25T12:06:55ZengBMCBMC Microbiology1471-21802022-12-0122111510.1186/s12866-022-02712-wPrediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteriaZhang Qi0Zuo Zhibo1Zhuang Jing2Qu Zhanbo3Han Shugao4Jin Weili5Liu Jiang6Han Shuwen7Huzhou Central Hospital, Affiliated Central Hospital Huzhou UniversityFirst Hospital of JiaxingHuzhou Central Hospital, Affiliated Central Hospital Huzhou UniversityZhejiang Chinese Medical UniversitySecond Affiliated Hospital of School of Medicine, Zhejiang UniversityNanxun District People’s HospitalHuzhou Central Hospital, Affiliated Central Hospital Huzhou UniversityHuzhou Central Hospital, Affiliated Central Hospital Huzhou UniversityAbstract 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.https://doi.org/10.1186/s12866-022-02712-wColorectal cancer (CRC)Poorly differentiatedGut bacteriaPrediction modelPathology |
| spellingShingle | Zhang Qi Zuo Zhibo Zhuang Jing Qu Zhanbo Han Shugao Jin Weili Liu Jiang Han Shuwen Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria BMC Microbiology Colorectal cancer (CRC) Poorly differentiated Gut bacteria Prediction model Pathology |
| title | Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria |
| title_full | Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria |
| title_fullStr | Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria |
| title_full_unstemmed | Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria |
| title_short | Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria |
| title_sort | prediction model of poorly differentiated colorectal cancer crc based on gut bacteria |
| topic | Colorectal cancer (CRC) Poorly differentiated Gut bacteria Prediction model Pathology |
| url | https://doi.org/10.1186/s12866-022-02712-w |
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