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Prediction model of colorectal cancer (CRC) lymph node metastasis based on intestinal bacteria

  • RESEARCH ARTICLE
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Clinical and Translational Oncology Aims and scope Submit manuscript

Abstract

Background

Lymph node metastasis is the main metastatic mode of CRC. Lymph node metastasis affects patient prognosis.

Objective

To screen differential intestinal bacteria for CRC lymph node metastasis and construct a prediction model.

Methods

First, fecal samples of 119 CRC patients with lymph node metastasis and 110 CRC patients without lymph node metastasis were included for the detection of intestinal bacterial 16S rRNA. Then, bioinformatics analysis of the sequencing data was performed. Community structure and composition analysis, difference analysis, and intragroup and intergroup correlation analysis were conducted between the two groups. Finally, six machine learning models were used to construct a prediction model for CRC lymph node metastasis.

Results

The community richness and the community diversity at the genus level of the two groups were basically consistent. A total of 12 differential bacteria (Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, Lachnospiraceae_FCS020_group, Lachnospiraceae_UCG-004, etc.) were screened at the genus level. Differential bacteria, such as Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, and Lachnospiraceae_FCS020_group, were more associated with no lymph node metastasis in CRC. In the discovery set, the RF model had the highest prediction accuracy (AUC = 1.00, 98.89% correct, specificity = 55.21%, sensitivity = 55.95%). In the test set, SVM model had the highest prediction accuracy (AUC = 0.73, 72.92% correct, specificity = 69.23%, sensitivity = 88.89%). Lachnospiraceae_FCS020_group was the most important variable in the RF model. Lachnospiraceae_UCG − 004 was the most important variable in the SVM model.

Conclusion

CRC lymph node metastasis is closely related to intestinal bacteria. The prediction model based on intestinal bacteria can provide a new evaluation method for CRC lymph node metastasis.

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Availability of data and materials

The datasets generated for this study can be accessed from the NCBI Sequence Read Archive (SRA) database under the accession number PRJNA911321 and PRJNA911611.

Abbreviations

PCC:

Pearson correlation coefficient

LDA:

Linear discriminant analysis

LR:

Logistic regression

RF:

Random forest

NN:

Neural network

GBDT:

Gradient boosted decision tree

SVM:

Support vector machine

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Acknowledgements

The authors gratefully acknowledge the database available to us for this study. We thank the patients and volunteers for their contributions to sample collection. All methods were performed in accordance with the relevant guidelines and regulations.

Funding

This work was supported by the Key Research and Development Project of Zhejiang Province (2022C03026) and Zhejiang Medical and Health Technology Project (2022KY1220).

Author information

Author notes

  1. Wu Yinhang and Zhuang Jing have contributed equally to this work.

Authors and Affiliations

  1. The Second School of Clinical Medicine, Zhejiang Chinese Medical University, No. 548 Binwen Road, Binjiang District, Hangzhou, 310053, Zhejiang Province, China

    Wu Yinhang

  2. Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No. 1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China

    Wu Yinhang, Zhuang Jing, Zhou Jie, Jin Yin, Wu Xinyue, Song Yifei, Fan Zhiqing, Wu Wei & Han Shuwen

  3. Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, No. 1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China

    Wu Yinhang, Zhuang Jing, Jin Yin, Wu Wei & Han Shuwen

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Contributions

All authors participated in the conception and design of the study. Conceived and drafted the manuscript: WW and HS. Wrote the paper: WY and ZJ. Recruited the sample and analyzed the data: WY, ZJ and JY. Drew figures: WX, SY and FZ. All authors read and approved the paper.

Corresponding authors

Correspondence to Wu Wei or Han Shuwen.

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Competing interests

The authors declare that no potential conflicts of interest exist.

Ethics approval and consent to participate

All subjects signed informed consent according to the guidelines approved by the Ethics Committee of Huzhou Central Hospital.

Informed consent

Informed consent was obtained from all the participants. All methods were carried out in accordance with relevant guidelines and regulations.

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Not applicable.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Figure 1. The flowchart for recruiting participants. (TIF 884 KB)

Supplementary Figure 2. Violin diagram of 12 different bacteria. (TIF 952 KB)

12094_2022_3061_MOESM3_ESM.tif

Supplementary Figure 3. Function prediction based on intestinal bacteria. A: Histogram of COG functional classification statistics. The abscissa represents the relative abundance, and the ordinate represents groups. B: COG functional classification statistical box diagram. The abscissa represents the COG secondary function number, and the ordinate represents the functional abundance. (TIF 936 KB)

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Yinhang, W., Jing, Z., Jie, Z. et al. Prediction model of colorectal cancer (CRC) lymph node metastasis based on intestinal bacteria. Clin Transl Oncol 25, 1661–1672 (2023). https://doi.org/10.1007/s12094-022-03061-w

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  • DOI: https://doi.org/10.1007/s12094-022-03061-w

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