Microbiome-Based Colon Cancer Patient Stratification and Survival Analysis

IF 2.9 2区医学 Q2 ONCOLOGY Cancer Medicine Pub Date : 2024-11-21 DOI:10.1002/cam4.70434

Joshua Smyth, Julien Godet, Anisa Choudhary, Anubrata Das, Georgios V. Gkoutos, Animesh Acharjee

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Abstract

Background

Colorectal cancer (CRC) is any cancer that starts in the colon or the rectum and presents a significant health concern. It is the third most diagnosed and the second deadliest cancer, with an estimated 153,020 new cases and 52,550 deaths in 2023. The severity of colon cancer may be attributed to its ability to avoid the host immune system and growth suppressors, its asymptomatic nature in the early stages, its association with a continually ageing population and unfavourable diet and obesity. The composition of the gut microbiome plays an important role in the development of CRC and presents as an important target in early detection and in predicting treatment outcomes in CRC. This study aims to identify microbiome-specific derived clusters in CRC patients and conduct subsequent survival analysis using the specific microbiome features within clusters.

Methods

Consensus clustering and feature selection, involving a Kruskal–Wallis test, a random forest and least absolute shrinkage and selection operator (LASSO) were applied resulting in the identification of differently expressed microbiomes between clusters. Lastly, survival analysis was performed on the selected features using Kaplan-Meier curves and Cox regression. K-means clustering, as selected using consensus clustering interpretation, presented three distinct clusters with clear differences in alpha and beta diversity and baseline clinical variables.

Results

A total 1311 of the 1406 microbes were selected using the Kruskal Wallis and passed to the random forest and LASSO, which narrowed the dataset to 140 features. Following the survival analysis, eight microbiome species, namely N4likevirus, Ambidensovirus, Synechococcus, Thermithiobacillus, Hydrocarboniphaga, Rhodovibrio, Gloeobacter and Candidatus Nitrosotenuis, were selected as significant in clustering and survival.

Conclusion

This study reveals the heterogeneity of the CRC microbiome and its effect on disease prognosis and necessitates further exploration of the biological mechanisms of these selected microbiomes as well further investigation of whether the approach depicted here is applicable to other cancer types.

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基于微生物组的结肠癌患者分层和生存分析

背景：结肠直肠癌（CRC）是指任何始于结肠或直肠的癌症，是一个重大的健康问题。据估计，2023 年将有 153,020 例新发病例和 52,550 例死亡病例。结肠癌的严重性可能归因于它能够避开宿主免疫系统和生长抑制剂、早期无症状、与人口持续老龄化以及不利的饮食和肥胖有关。肠道微生物组的组成在 CRC 的发展过程中起着重要作用，是早期检测和预测 CRC 治疗效果的重要目标。本研究旨在确定 CRC 患者的微生物组特异性衍生聚类，并利用聚类中的特定微生物组特征进行后续生存分析：方法：采用 Kruskal-Wallis 检验、随机森林和最小绝对收缩和选择算子（LASSO）进行共识聚类和特征选择，从而识别出不同聚类之间表达不同的微生物组。最后，利用 Kaplan-Meier 曲线和 Cox 回归对所选特征进行了生存分析。采用共识聚类解释法选出的 K-means 聚类呈现出三个不同的聚类，在阿尔法和贝塔多样性以及基线临床变量方面存在明显差异：使用 Kruskal Wallis 方法从 1406 个微生物中筛选出 1311 个，并将其传递给随机森林和 LASSO，从而将数据集缩小到 140 个特征。经过存活率分析，8 个微生物组物种，即 N4likevirus、Ambidensovirus、Synechococcus、Thermithiobacillus、Hydrocarboniphaga、Rhodovibrio、Gloeobacter 和 Candidatus Nitrosotenuis 被选为聚类和存活率显著的微生物组：这项研究揭示了 CRC 微生物组的异质性及其对疾病预后的影响，因此有必要进一步探索这些选定微生物组的生物学机制，并进一步研究此处描述的方法是否适用于其他癌症类型。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.