Establishing high-accuracy biomarkers for colorectal cancer by comparing fecal microbiomes in patients with healthy families.
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ABSTRACT:
SUBMITTER: Yang J
PROVIDER: S-EPMC7524397 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Objective: The intestinal microbiome is associated with various autoimmune diseases. Regional difference is the main influencing factor of intestinal microbial difference. This study aimed to identify the differences in fecal microbiome between autoimmune hepatitis (AIH) patients and healthy controls (HCs) in Central China, and to validate the efficacy of fecal microbiome as a diagnostic tool for AIH. Design: We collected 115 fecal samples from AIH patients (N = 37) and HCs (N = 78) in Central China and performed gene sequencing. Fecal microbiomes were characterized and microbial markers for AIH were identified. Results: Fecal microbial diversity showed a downward trend in AIH compared with HCs. Fecal microbial communities significantly differed between both groups. At the phylum level, Verrucomicrobia abundance was significantly increased, while Lentisphaerae and Synergistetes were significantly decreased in the AIH patients vs. the HCs. Compared to the HCs, 15 genera, including Veillonella, Faecalibacterium, and Akkermansia, were enriched, while 19 genera, such as Pseudobutyrivibrio, Lachnospira, and Ruminococcaceae, were decreased in the AIH patients. Ten genera, including Veillonella, Faecalibacterium, and Akkermansia, predominated in the AIH patients. Five microbial biomarkers were deemed optimal diagnostic tools for AIH. The probability of disease was significantly increased in AIH group vs. HCs, achieving 83.25% value of area under the curve. Conclusion: We present the characteristics of AIH patients in Central China for the first time. Five microbial biomarkers, including Lachnospiraceae, Veillonella, Bacteroides, Roseburia, and Ruminococcaceae, achieved a high potential distinguishing AIH patients from HCs.
| S-EPMC7416601 | biostudies-literature
Project description:AimTo assess the fecal immunochemical test (FIT) accuracy for colorectal cancer (CRC) and advanced neoplasia (AN) detection in CRC screening.MethodsWe performed a multicentric, prospective, double blind study of diagnostic tests on asymptomatic average-risk individuals submitted to screening colonoscopy. Two stool samples were collected and the fecal hemoglobin concentration was determined in the first sample (FIT1) and the highest level of both samples (FITmax) using the OC-sensor™. Areas under the curve (AUC) for CRC and AN were calculated. The best FIT1 and FITmax cut-off values for CRC were determined. At this threshold, number needed to scope (NNS) to detect a CRC and an AN and the cost per lesion detected were calculated.ResultsAbout 779 individuals were included. An AN was found in 97 (12.5%) individuals: a CRC in 5 (0.6%) and an advanced adenoma (≥ 10 mm, villous histology or high grade dysplasia) in 92 (11.9%) subjects. For CRC diagnosis, FIT1 AUC was 0.96 (95%CI: 0.95-0.98) and FITmax AUC was 0.95 (95%CI: 0.93-0.97). For AN, FIT1 and FITmax AUC were similar (0.72, 95%CI: 0.66-0.78 vs 0.73, 95%CI: 0.68-0.79, respectively, P = 0.34). Depending on the number of determinations and the positivity threshold cut-off used sensitivity for AN detection ranged between 28% and 42% and specificity between 91% and 97%. At the best cut-off point for CRC detection (115 ng/mL), the NNS to detect a CRC were 10.2 and 15.8; and the cost per CRC was 1814€ and 2985€ on FIT1 and FITmax strategies respectively. At this threshold the sensitivity, NNS and cost per AN detected were 30%, 1.76, and 306€, in FIT1 strategy, and 36%, 2.26€ and 426€, in FITmax strategy, respectively.ConclusionPerforming two tests does not improve diagnostic accuracy, but increases cost and NNS to detect a lesion.
| S-EPMC3921527 | biostudies-literature
Project description:Colorectal adenocarcinoma (CRC) ranks one of the five most lethal malignant tumors both in China and worldwide. Early diagnosis and treatment of CRC could substantially increase the survival rate. Emerging evidence has revealed the importance of gut microbiome on CRC, thus fecal microbial community could be termed as a potential screen for non-invasive diagnosis. Importantly, few numbers of bacteria genus as non-invasive biomarkers with high sensitivity and specificity causing less cost would be benefitted more in clinical compared with the whole microbial community analysis. Here we analyzed the gut microbiome between CRC patients and healthy people using 16s rRNA sequencing showing the divergence of microbial composition between case and control. Furthermore, ExtraTrees classifier was performed for the classification of CRC gut microbiome and heathy control, and 13 bacteria were screened as biomarkers for CRC. In addition, 13 biomarkers including 12 bacteria genera and FOBT showed an outstanding sensitivity and specificity for discrimination of CRC patients from healthy controls. This method could be used as a non-invasive method for CRC early diagnosis.
| S-EPMC8383742 | biostudies-literature
Project description:The bovine genital tract harbors a dynamic microbiome. Genital tract microbial communities in healthy animals have been characterized using next-generation sequencing methods showing that microbe compositions differ between the vagina and uterus, more so during the postpartum period. Pre-calving fecal and vaginal, and endometrial swabs at the different postpartum intervals were collected from dairy cows. Microbiomes in these samples were determined based on bacterial 16S amplicon sequencing and compared between healthy (H; n = 10) control animals and cows that developed metritis (M; n = 10) within 21 days postpartum (DPP). Compared to healthy animals the pre-calving fecal and vaginal microbiomes of metritis animals were more abundant in sequences from the phylum Fusobacteria and the bacterial genera such as Escherichia-Shigella and Histophilus. In addition, compared to healthy animals, metritis cows harboured low microbial species diversity in the endometrium, as well as decreasing Proteobacteria and increasing Fusobacteria, Firmicutes, Actinobacteria, and Bacteroidetes abundances. The greatest taxonomic compositional deviations in endometrial microbial communities between the metritis and health cows were detected between 7 and 10 DPP. There was high taxonomic similarity detected between postpartum endometrial microbiomes and the prepartum vaginal and fecal microbiomes suggesting that colonization through bacteria ascending from the rectum and vagina to the uterine cavity might play a major role in establishing the endometrial microbiome postpartum. A deeper understanding of the establishment and dynamics of postpartum endometrial microbial communities in cows will thus provide crucial basic knowledge to guide the development of genital microbiome manipulation strategies for preventing uterine disease and improving fertility in dairy cows.
| S-EPMC10093325 | biostudies-literature
Project description:BackgroundDiet affects the human gastrointestinal microbiota. Blood and urine samples have been used to determine nutritional biomarkers. However, there is a dearth of knowledge on the utility of fecal biomarkers, including microbes, as biomarkers of food intake.ObjectivesThis study aimed to identify a compact set of fecal microbial biomarkers of food intake with high predictive accuracy.MethodsData were aggregated from 5 controlled feeding studies in metabolically healthy adults (n = 285; 21-75 y; BMI 19-59 kg/m2; 340 data observations) that studied the impact of specific foods (almonds, avocados, broccoli, walnuts, and whole-grain barley and whole-grain oats) on the human gastrointestinal microbiota. Fecal DNA was sequenced using 16S ribosomal RNA gene sequencing. Marginal screening was performed on all species-level taxa to examine the differences between the 6 foods and their respective controls. The top 20 species were selected and pooled together to predict study food consumption using a random forest model and out-of-bag estimation. The number of taxa was further decreased based on variable importance scores to determine the most compact, yet accurate feature set.ResultsUsing the change in relative abundance of the 22 taxa remaining after feature selection, the overall model classification accuracy of all 6 foods was 70%. Collapsing barley and oats into 1 grains category increased the model accuracy to 77% with 23 unique taxa. Overall model accuracy was 85% using 15 unique taxa when classifying almonds (76% accurate), avocados (88% accurate), walnuts (72% accurate), and whole grains (96% accurate). Additional statistical validation was conducted to confirm that the model was predictive of specific food intake and not the studies themselves.ConclusionsFood consumption by healthy adults can be predicted using fecal bacteria as biomarkers. The fecal microbiota may provide useful fidelity measures to ascertain nutrition study compliance.
| S-EPMC7849973 | biostudies-literature
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.
| S-EPMC9840004 | biostudies-literature
Project description:IntroductionFecal immunochemical tests (FITs) detect colorectal adenoma inefficiently. The gut microbiota participates in colorectal cancer development. We aimed to explore fecal microbial signatures for advanced adenomas and evaluate their diagnostic value and complementary capacity to FIT.MethodsUsing 16S rRNA sequencing, we studied gut microbiota in feces from 1,546 subjects in a screening setting, including 268 patients with advanced adenomas, 490 patients with nonadvanced adenomas, and 788 healthy subjects. Feature selections were performed using linear discriminant analysis effect size, multivariate association with linear models, and least absolute shrinkage and selection operator. The diagnostic performance of microbial signatures and their auxiliary role to FITs and the added value of the Asia-Pacific Colorectal Screening score were evaluated. We applied 0.632+ bootstrapping to adjust the potential overfitting.ResultsWe identified 13 microbial signatures to show the joint diagnostic value for advanced adenoma, with genus Tyzzerella 4 demonstrating the highest adjusted area under the curve (AUC) of 0.545 (95% confidence interval [CI], 0.520-0.610). The 13-bacteria increased the adjusted AUC to 0.607 (95% CI, 0.548-0.660). Compared with individual FIT (adjusted AUC = 0.527; 95% CI, 0.519-0.571), 13-bacteria and FITs collectively reached an adjusted AUC of 0.641 (95% CI, 0.579-0.691). At cutoff values yielding specificities of 90% and 80%, the adjusted sensitivities were 28.4% (95% CI, 19.3-36.8) and 41.1% (95% CI, 29.9-49.4), respectively. The Asia-Pacific Colorectal Screening score further boosted the adjusted AUC to 0.706 (95% CI, 0.648-0.750).DiscussionIn this study using fecal samples from a screening setting, the identified microbial signatures could complement FITs for detecting advanced adenomas. Gut microbiota can act as a promising tool to optimize the current colorectal cancer screening modalities.
| S-EPMC8373536 | biostudies-literature
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.
| S-EPMC4189821 | biostudies-literature
Project description:Background/aimsFecal calprotectin (FC) is reported to have a broad diagnostic accuracy for colorectal cancer (CRC). Therefore, we explored the diagnostic value of FC for CRC using meta-analytical techniques to substantiate the assertion.Materials and methodsAn electronic search of the MEDLINE and Embase databases was conducted to identify studies that assessed the diagnostic accuracy of FC for CRC. The sensitivities and specificities of the eligible studies were summarized using a bivariable random-effects model.ResultsIn total, 20 studies were included in the final analysis. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of FC for CRC detection were 0.83 (95% confidence interval CI, 0.77-0.88), 0.61 (95% CI, 0.54-0.68), 2.15 (95% CI, 1.82-2.55), and 0.28 (95% CI, 0.21-0.37), respectively. The overall diagnostic odds ratio of FC for CRC was 7.76 (95% CI, 5.41-11.12) with an area under the curve of 0.81 (95% CI, 0.77-0.84), whereas the diagnostic value of FC for colorectal adenoma was relatively inferior (area under the curve, 0.55; 95% CI, 0.51-0.59; diagnostic odds ratio, 1.27; 95% CI, 0.91-1.78).ConclusionThe results imply that the FC test, as currently implemented, cannot be recommended for CRC detection.
| S-EPMC6284629 | biostudies-literature