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. 2024 Sep;13(17):e70180.
doi: 10.1002/cam4.70180.

Gut microbial subtypes and clinicopathological value for colorectal cancer

Shuwen Han  1   2   3   4 , Jing Zhuang  1   2   3 , Yifei Song  1   2   3 , Xinyue Wu  1   2   3 , Xiaojian Yu  1   2   3 , Ye Tao  1   5 , Jian Chu  1   2   3 , Zhanbo Qu  1   2   3 , Yinhang Wu  1   2   3 , Shugao Han  6 , Xi Yang  1   2   3
Affiliations

Gut microbial subtypes and clinicopathological value for colorectal cancer

Shuwen Han et al. Cancer Med. 2024 Sep.

Abstract

Background: Gut bacteria are related to colorectal cancer (CRC) and its clinicopathologic characteristics.

Objective: To develop gut bacterial subtypes and explore potential microbial targets for CRC.

Methods: Stool samples from 914 volunteers (376 CRCs, 363 advanced adenomas, and 175 normal controls) were included for 16S rRNA sequencing. Unsupervised learning was used to generate gut microbial subtypes. Gut bacterial community composition and clustering effects were plotted. Differences of gut bacterial abundance were analyzed. Then, the association of CRC-associated bacteria with subtypes and the association of gut bacteria with clinical information were assessed. The CatBoost models based on gut differential bacteria were constructed to identify the diseases including CRC and advanced adenoma (AA).

Results: Four gut microbial subtypes (A, B, C, D) were finally obtained via unsupervised learning. The characteristic bacteria of each subtype were Escherichia-Shigella in subtype A, Streptococcus in subtype B, Blautia in subtype C, and Bacteroides in subtype D. Clinical information (e.g., free fatty acids and total cholesterol) and CRC pathological information (e.g., tumor depth) varied among gut microbial subtypes. Bacilli, Lactobacillales, etc., were positively correlated with subtype B. Positive correlation of Blautia, Lachnospiraceae, etc., with subtype C and negative correlation of Coriobacteriia, Coriobacteriales, etc., with subtype D were found. Finally, the predictive ability of CatBoost models for CRC identification was improved based on gut microbial subtypes.

Conclusion: Gut microbial subtypes provide characteristic gut bacteria and are expected to contribute to the diagnosis of CRC.

Keywords: clinicopathological features; colorectal cancer; gut bacteria; gut microbial subtypes; unsupervised clustering.

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Conflict of interest statement

The authors declare that no potential conflicts of interest exist.

Figures

FIGURE 1
FIGURE 1
Gut microbial typing and their composition analysis. (A) Consensus cumulative distribution function (CDF). K represents the number of clusters, and different colors represent CDF curves with different K values. The higher the stability of CDF curve, the more reliable the clustering results corresponding to the K value. (B) Consensus matrix heat map. Cluster refers to the number of cluster subtypes, and subtype refers to the number of gut microbial subtypes. The clustering results for K = 9 were displayed. (C) Cluster composition of colorectal diseases. The proportion of 9 clusters in different colorectal disease populations including CRC, AA, NC was shown. (D) Subtype composition of colorectal diseases. The proportion of 4 gut microbial subtypes in different colorectal disease populations including CRC, AA, NC was shown. (E) Bacterial structure of gut microbial subtypes. The composition of the top 20 gut bacteria in the four gut microbial subtypes was shown. (F) Chord diagram of gut bacteria among different subtypes. Different colors correspond to gut microbial subtypes. The longer the string, the greater the correlation. (G) Characteristic gut bacteria of gut microbial subtypes. The abundance of Escherichia‐Shigella, Streptococcus, Blautia, Bacteroides in four gut microbial subtypes was analyzed.
FIGURE 2
FIGURE 2
Differences in the abundance of the common gut bacteria in specific clinical information groups. (A) Differential bacteria among four subtypes. The top 20 differential bacteria among four gut microbial subtypes were shown. “***” means 0.0001 < p ≤ 0.001, with significant statistical difference. (B): Venn diagram for subtype and total cholesterol. The orange circle represents the differential bacteria among different gut microbial subtypes, the pink circle represents the differential bacteria between different total cholesterol levels, and the overlapping part of the circle represents the common differential bacteria. (C) Venn diagram for subtype and free fatty acid. The orange circle represents the differential bacteria among different gut microbial subtypes, the pink circle represents the differential bacteria among different free fatty acid levels, and the overlapping part of the circle represents the common differential bacteria. (D) Common differential bacteria in different subtypes and total cholesterol content. (E) Common differential bacteria in different subtypes and free fatty acid. “*” 0.01 < p ≤ 0.05, “**” 0.001< p ≤ 0.01, and “***” 0.0001 < p ≤ 0.001.
FIGURE 3
FIGURE 3
Correlation of CRC‐associated bacteria with different colorectal diseases and subtypes. (A) Correlation heat map of positively related bacteria for colorectal diseases. (B) Correlation heat map of positively related bacteria for gut microbial subtypes. (C) Correlation heat map of negatively related bacteria for colorectal diseases. (D) Correlation heat map of negatively related bacteria for gut microbial subtypes. (E) Correlation heat map of related bacteria for colorectal diseases. (F) Correlation heat map of related bacteria for gut microbial subtypes. Colorectal diseases include CRC, AA, NC, and gut microbial subtypes include subtype A, B, C, and D. The sign of the correlation coefficient r is independent of the magnitude of the correlation, with “+” representing a positive correlation and “‐” representing a negative correlation. The darker the color, the greater the correlation. “*” 0.01 < p ≤ 0.05, “**” 0.001 < p ≤ 0.01, “***” 0.0001< p ≤ 0.001.
FIGURE 4
FIGURE 4
Correlation of gut microbial characteristics with diseases and subtypes and CRC prediction effect of CatBoost models. (A) Correlation heat map of gut microbial characteristics for gut microbial subtypes. (B) Chord diagram of gut microbial characteristics among different subtypes. (C) Correlation heat map of gut microbial characteristics for colorectal diseases. (D) Chord diagram of gut microbial characteristics among different diseases. Prediction targets of CatBoost models include CRC, AA, and CRC/AA, with different ranges of colorectal disease populations for the three targets. The accuracy, sensitivity, and specificity of the model are the comparison indexes before and after classification.
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