Dataset Information

Establishing high-accuracy biomarkers for colorectal cancer by comparing fecal microbiomes in patients with healthy families.

ABSTRACT: Colorectal cancer (CRC) causes high morbidity and mortality worldwide, and noninvasive gut microbiome (GM) biomarkers are promising for early CRC diagnosis. However, the GM varies significantly based on ethnicity, diet and living environment, suggesting varied GM biomarker performance in different regions. We performed a metagenomic association analysis on stools from 52 patients and 55 corresponding healthy family members who lived together to identify GM biomarkers for CRC in Chongqing, China. The GM of patients differed significantly from that of healthy controls. A total of 22 microbial genes were included as screening biomarkers with high accuracy in additional 46 cases and 40 randomly selected healthy adults in Chongqing (area under the receive-operation curve (AUC) = 0.905, 95% CI 0.832-0.977). The classifier based on the identified 22 biomarkers also performed well in the cohort from Hong Kong (AUC = 0.811, 95% CI 0.715-0.907) and French (AUC = 0.859, 95% CI 0.773-0.944) populations. Quantitative PCR was applied for measuring three selected biomarkers in the classification of CRC patients in independent Chongqing population containing 30 cases and 30 controls and the best biomarker from Coprobacillus performed well with high AUC (0.930, 95% CI 0.904-0.955). This study revealed increased sensitivity and applicability of our GM biomarkers compared with previous biomarkers significantly promoting the early diagnosis of CRC.

SUBMITTER: Yang J

PROVIDER: S-EPMC7524397 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Establishing high-accuracy biomarkers for colorectal cancer by comparing fecal microbiomes in patients with healthy families.

Yang Jian J Li Dongfang D Yang Zhenyu Z Dai Wenkui W Feng Xin X Liu Yanhong Y Jiang Yiqi Y Li Pingang P Li Yinhu Y Tang Bo B Zhou Qian Q Qiu Chuangzhao C Zhang Chao C Xu Ximing X Feng Su S Wang Daxi D Wang Heping H Wang Wenjian W Zheng Yuejie Y Zhang Lin L Wang Wenjie W Zhou Ke K Li Shuaicheng S Yu Peiwu P

Gut microbes 20200123 4

Colorectal cancer (CRC) causes high morbidity and mortality worldwide, and noninvasive gut microbiome (GM) biomarkers are promising for early CRC diagnosis. However, the GM varies significantly based on ethnicity, diet and living environment, suggesting varied GM biomarker performance in different regions. We performed a metagenomic association analysis on stools from 52 patients and 55 corresponding healthy family members who lived together to identify GM biomarkers for CRC in Chongqing, China. ...[more]

PMID: 31971861

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Project description:BackgroundThe fecal metabolome is affected by diet and includes metabolites generated by human and microbial metabolism. Advances in -omics technologies and analytic approaches have allowed researchers to identify metabolites and better utilize large data sets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake.ObjectivesWe aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake.MethodsData were aggregated from 5 controlled feeding studies in adults that tested the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. Fecal samples underwent GC-MS metabolomic analysis; 344 metabolites were detected in preintervention samples, whereas 307 metabolites were detected postintervention. After removing metabolites that were only detected in either pre- or postintervention and those undetectable in ≥80% of samples in all study groups, changes in 96 metabolites relative concentrations (treatment postintervention minus preintervention) were utilized in random forest models to 1) examine the relation between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score).ResultsUsing the change in relative concentration of 96 fecal metabolites, 6 single-food random forest models for almond, avocado, broccoli, walnuts, whole-grain barley, and whole-grain oats revealed prediction accuracies between 47% and 89%. When comparing foods with one another, almond intake was differentiated from walnut intake with 91% classification accuracy.ConclusionsOur findings reveal promise in utilizing fecal metabolites as objective complements to certain self-reported food intake estimates. Future research on other foods at different doses and dietary patterns is needed to identify biomarkers that can be applied in feeding study compliance and clinical settings.

Project description:BackgroundPerformance characteristics of fecal immunochemical tests (FITs) to screen for colorectal cancer (CRC) have been inconsistent.PurposeTo synthesize data about the diagnostic accuracy of FITs for CRC and identify factors affecting its performance characteristics.Data sourcesOnline databases, including MEDLINE and EMBASE, and bibliographies of included studies from 1996 to 2013.Study selectionAll studies evaluating the diagnostic accuracy of FITs for CRC in asymptomatic, average-risk adults.Data extractionTwo reviewers independently extracted data and critiqued study quality.Data synthesisNineteen eligible studies were included and meta-analyzed. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of FITs for CRC were 0.79 (95% CI, 0.69 to 0.86), 0.94 (CI, 0.92 to 0.95), 13.10 (CI, 10.49 to 16.35), 0.23 (CI, 0.15 to 0.33), respectively, with an overall diagnostic accuracy of 95% (CI, 93% to 97%). There was substantial heterogeneity between studies in both the pooled sensitivity and specificity estimates. Stratifying by cutoff value for a positive test result or removal of discontinued FIT brands resulted in homogeneous sensitivity estimates. Sensitivity for CRC improved with lower assay cutoff values for a positive test result (for example, 0.89 [CI, 0.80 to 0.95] at a cutoff value less than 20 µg/g vs. 0.70 [CI, 0.55 to 0.81] at cutoff values of 20 to 50 µg/g) but with a corresponding decrease in specificity. A single-sample FIT had similar sensitivity and specificity as several samples, independent of FIT brand.LimitationsOnly English-language articles were included. Lack of data prevented complete subgroup analyses by FIT brand.ConclusionFecal immunochemical tests are moderately sensitive, are highly specific, and have high overall diagnostic accuracy for detecting CRC. Diagnostic performance of FITs depends on the cutoff value for a positive test result.Primary funding sourceNational Institute of Diabetes and Digestive and Kidney Diseases and National Cancer Institute.

Dataset Information

Establishing high-accuracy biomarkers for colorectal cancer by comparing fecal microbiomes in patients with healthy families.

Publications

Establishing high-accuracy biomarkers for colorectal cancer by comparing fecal microbiomes in patients with healthy families.

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