Microbiome Profiling from Fecal Immunochemical Test Reveals Microbial Signatures with Potential for Colorectal Cancer Screening
Simple Summary Colorectal cancer (CRC) is a global healthcare challenge that involves both genetic and environmental factors. Several pieces of evidence suggest that alterations of the gut microbiome can influence CRC development. In the present study we analyzed 16S rRNA sequencing data from fecal...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/193146 |
| Acceso en línea: | https://hdl.handle.net/2445/193146 |
| Access Level: | acceso abierto |
| Palabra clave: | Càncer colorectal Microbiota intestinal Cribratge Diagnòstic Gastrointestinal microbiome Medical screening Diagnosis Colorectal cancer |
| Sumario: | Simple Summary Colorectal cancer (CRC) is a global healthcare challenge that involves both genetic and environmental factors. Several pieces of evidence suggest that alterations of the gut microbiome can influence CRC development. In the present study we analyzed 16S rRNA sequencing data from fecal immunochemical test (FIT) samples from a large cohort, observing a predictive potential of the microbiome, revealing changes along the path from healthy tissue to carcinoma. Our work has implications in the understanding of the roles of microbes on the adenoma to carcinoma progression and opens the door to an improvement of the current CRC screening programmes. Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer deaths worldwide. Early diagnosis of CRC, which saves lives and enables better outcomes, is generally implemented through a two-step population screening approach based on the use of Fecal Immunochemical Test (FIT) followed by colonoscopy if the test is positive. However, the FIT step has a high false positive rate, and there is a need for new predictive biomarkers to better prioritize cases for colonoscopy. Here we used 16S rRNA metabarcoding from FIT positive samples to uncover microbial taxa, taxon co-occurrence and metabolic features significantly associated with different colonoscopy outcomes, underscoring a predictive potential and revealing changes along the path from healthy tissue to carcinoma. Finally, we used machine learning to develop a two-phase classifier which reduces the current false positive rate while maximizing the inclusion of CRC and clinically relevant samples. |
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