Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers

Li, Min; Liu, Jinxin; Zhu, Jiaying; Wang, Huarui; Sun, Chuqing; Gao, Na; Zhao, Xing‐Ming; Chen, Wei-Hua

doi:10.1080/19490976.2023.2205386

Cited by 10 publications

(8 citation statements)

References 129 publications

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“…The predictive performance of GM as an independent diagnostic tool has been demonstrated in more than 20 diseases. 32 For LC, the diagnostic ability of the GM model constructed by Wang et al (AUC = 0.85) was slightly higher than that of our model (AUC = 0.81). 19 Through methodological comparison, we found that their study used logistic regression analysis.…”

Section: Discussioncontrasting

confidence: 80%

Identification of microbial markers associated with lung cancer based on multi‐cohort 16 s rRNA analyses: A systematic review and meta‐analysis

Han,

Wang,

Han

et al. 2023

Cancer Medicine

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BackgroundThe relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large‐scale meta‐analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine‐learning classifier.MethodsIn total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony‐related functions.ResultsThe α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top‐ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota‐associated functions between cancer‐affected and healthy individuals that were primarily associated with metabolic disturbances.ConclusionsGM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC‐specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.

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Section: Discussioncontrasting

confidence: 80%

Identification of microbial markers associated with lung cancer based on multi‐cohort 16 s rRNA analyses: A systematic review and meta‐analysis

Han,

Wang,

Han

et al. 2023

Cancer Medicine

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“…No feature selection was performed throughout the analysis, which was in line with the recommendations provided by SIAMCAT 28 and recent studies. 29 …”

Section: Methodsmentioning

confidence: 99%

“…No feature selection was performed throughout the analysis, which was in line with the recommendations provided by SIAMCAT 28 and recent studies. 29 The RandomForest models are available on Figshare (https://figshare.com/articles/dataset/RandomForest_classifiers/ 23912331).…”

Section: Machine Learning Classifiers For Discriminating Preintervent...mentioning

confidence: 99%

Compared to histamine-2 receptor antagonist, proton pump inhibitor induces stronger oral-to-gut microbial transmission and gut microbiome alterations: a randomised controlled trial

Zhu,

Sun,

et al. 2023

Gut

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ObjectiveWe aim to compare the effects of proton pump inhibitors (PPIs) and histamine-2 receptor antagonists (H2RAs) on the gut microbiota through longitudinal analysis.DesignHealthy volunteers were randomly assigned to receive either PPI (n=23) or H2RA (n=26) daily for seven consecutive days. We collected oral (saliva) and faecal samples before and after the intervention for metagenomic next-generation sequencing. We analysed intervention-induced alterations in the oral and gut microbiome including microbial abundance and growth rates, oral-to-gut transmissions, and compared differences between the PPI and H2RA groups.ResultsBoth interventions disrupted the gut microbiota, with PPIs demonstrating more pronounced effects. PPI usage led to a significantly higher extent of oral-to-gut transmission and promoted the growth of specific oral microbes in the gut. This led to a significant increase in both the number and total abundance of oral species present in the gut, including the identification of known disease-associated species likeFusobacterium nucleatumandStreptococcus anginosus. Overall, gut microbiome-based machine learning classifiers could accurately distinguish PPI from non-PPI users, achieving an area under the receiver operating characteristic curve (AUROC) of 0.924, in contrast to an AUROC of 0.509 for H2RA versus non-H2RA users.ConclusionOur study provides evidence that PPIs have a greater impact on the gut microbiome and oral-to-gut transmission than H2RAs, shedding light on the mechanism underlying the higher risk of certain diseases associated with prolonged PPI use.Trial registration numberChiCTR2300072310.

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“…Gut microbiota has been increasingly used as biomarkers for non-invasive disease diagnosis [20], and validated in many diseases, such as colorectal cancer [21], inflammatory bowel disease [22], non-small cell lung carcinomas [23] and liver cirrhosis [24]. In recent years, the relationship between gut microbiota and lung diseases has received great attention, irritable bowel syndrome (IBS) patients with dysbiosis of the gut microbiota are prone to developed impaired lung function [17], specific alterations in the composition and metabolism of the gut microbiota influence the occurrence and development of lung cancer [4,10,25].…”

Section: Discussionmentioning

confidence: 99%

Analyzing lung cancer risks in patients with impaired pulmonary function through characterization of gut microbiome and metabolites

Luan,

Zhang,

Suo

et al. 2024

BMC Pulm Med

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Background Lung cancer (LC) is one of the most devastating diseases worldwide, there is growing studies confirm the role of impaired lung function in LC susceptibility. Moreover, gut microbiota dysbiosis is associated with LC severity. Whether alterations in gut microbiota and metabolites are associated with long-term lung dysfunction in LC patients remain unclear. Our study aimed to analyze the risk factors in LC patients with impaired pulmonary function based on the characteristics of the gut microbiome and metabolites. Methods Fecal samples from 55 LC patients and 28 benign pulmonary nodules patients were collected. Pulmonary ventilation function was graded according to the American Thoracic Society/ European Respiratory Society (ATS/ERS) method. LC patients were divided into 3 groups, including 20 patients with normal lung ventilation, 23 patients with mild pulmonary ventilation dysfunction and 12 patients with moderate or above pulmonary ventilation dysfunction. The fecal samples were analyzed using 16 S rRNA gene amplicon sequencing and metabolomics. Results The gut microbiome composition between LC patients and benign pulmonary nodules patients presented clearly differences based on Partial Least Squares Discriminant Analysis (PLS-DA). Pulmonary ventilation function was positively correlated with LC tumor stage, the richness and diversity of the gut microbiota in LC patients with moderate or above pulmonary ventilation dysfunction increased significantly, characterized by increased abundance of Subdoligranulum and Romboutsia. The metabolomics analysis revealed 69 differential metabolites, which were mainly enriched in beta-Alanine metabolism, styrene degradation and pyrimidine metabolism pathway. The area under the curve (AUC) combining the gut microbiome and metabolites was 90% (95% CI: 79-100%), indicating that the two species and four metabolites might regarded as biomarkers to assess the prediction of LC patients with impaired pulmonary function. Conclusions Our results showed that microbiome and metabolomics analyses provide important candidate to be used as clinically diagnostic biomarkers and therapeutic targets related to lung cancer with impaired pulmonary function.

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Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers

Cited by 10 publications

References 129 publications

Identification of microbial markers associated with lung cancer based on multi‐cohort 16 s rRNA analyses: A systematic review and meta‐analysis

Identification of microbial markers associated with lung cancer based on multi‐cohort 16 s rRNA analyses: A systematic review and meta‐analysis

Compared to histamine-2 receptor antagonist, proton pump inhibitor induces stronger oral-to-gut microbial transmission and gut microbiome alterations: a randomised controlled trial

Analyzing lung cancer risks in patients with impaired pulmonary function through characterization of gut microbiome and metabolites

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