Introduction: Fecal 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.
Methods: Using 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.
Results: We 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).
Discussion: In 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.
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http://dx.doi.org/10.14309/ctg.0000000000000389 | DOI Listing |
Sci Rep
December 2024
Department of Pathology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
Micropapillary adenocarcinoma (MPC) is an aggressive histological subtype of lung adenocarcinoma (LUAD). MPC is composed of small clusters of cancer cells exhibiting inverted polarity. However, the mechanism underlying its formation is poorly understood.
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Beijing Frontier Research Center for Biological Structure, State Key Laboratory of Membrane Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
Exceptionally diverse type V CRISPR-Cas systems provide numerous RNA-guided nucleases as powerful tools for DNA manipulation. Two known Cas12e nucleases, DpbCas12e and PlmCas12e, are both effective in genome editing. However, many differences exist in their in vitro dsDNA cleavage activities, reflecting the diversity in Cas12e's enzymatic properties.
View Article and Find Full Text PDFNat Commun
December 2024
Department of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, SE1 9RT, UK.
The role of myeloid cells in the pathogenesis of SARS-CoV-2 is well established, in particular as drivers of cytokine production and systemic inflammation characteristic of severe COVID-19. However, the potential for myeloid cells to act as bona fide targets of productive SARS-CoV-2 infection, and the specifics of entry, remain unclear. Using a panel of anti-SARS-CoV-2 monoclonal antibodies (mAbs) we performed a detailed assessment of antibody-mediated infection of monocytes/macrophages.
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January 2025
Department of Entomology and Nematology, University of California, Davis, Davis, California, USA.
Plant-microbe associations are ubiquitous, but parsing contributions of dispersal, host filtering, competition and temperature on microbial community composition is challenging. Floral nectar-inhabiting microbes, which can influence flowering plant health and pollination, offer a tractable system to disentangle community assembly processes. We inoculated a synthetic community of yeasts and bacteria into nectars of 31 plant species while excluding pollinators.
View Article and Find Full Text PDFBioinform Adv
December 2024
Computer Science Department, Indiana University, Bloomington, IN 47408, United States.
Motivation: Microbial signatures in the human microbiome are closely associated with various human diseases, driving the development of machine learning models for microbiome-based disease prediction. Despite progress, challenges remain in enhancing prediction accuracy, generalizability, and interpretability. Confounding factors, such as host's gender, age, and body mass index, significantly influence the human microbiome, complicating microbiome-based predictions.
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