https://doi.org/10.21203/rs.3.rs-2838129/v1
Видання: 2023
See Also
Hesperetin alleviates pulmonary injury in a blunt chest trauma-induced pulmonary contusion model in ratsAvailable pseudopotentials - VASP WikiВидавець: Research Square Platform LLC
Автори:
- Scott N. Peterson
- Alexey M. Eroshkin
- Piotr Z. Kozbial
- Ermanno Florio
- Farnaz Fouladi
- Noah Strom
- Yacgley Valdes
- Gregory Kuehn
- Giorgio Casaburi
- Thomas Kuehn
Анотація
Abstract Background: Despite the effectiveness of colonoscopy for reducing colorectal cancer (CRC) mortality, poor screening compliance ranks CRC as the second most deadly malignancy. There is a need to develop a preventative, non-invasive diagnostic test, such as a fecal microbiota test, for early detection of both pre-cancerous adenomas and carcinomas to effectively reduce mortality. Results: We conducted a clinical meta-analysis of published deep metagenomic stool sequence datasets including 1,670 subjects from 9 countries, including 703 healthy controls, 161 precancerous colorectal adenoma (CRA), 48 advanced precancerous colorectal adenoma (CRAA) and 758 CRC cases diagnosed by colonoscopy. We analyzed these data through a novel automated machine learning workflow using a two-stage feature importance ranking and ensemble modeling method to identify and select highly predictive taxonomic and functional biomarkers. Machine learning modeling of selected features differentiated the metagenomic profiles of healthy patients from CRA, CRAA and CRC cases with an average area under the curve (AUC) for external holdout testing of 0.84 (sensitivity=0.82; specificity=0.71, accuracy=0.77) for CRC; an AUC of 0.97 (sensitivity=0.78; specificity=0.98, accuracy=0.97) for CRAA; and an AUC of 0.90 (sensitivity=0.74, specificity=0.89, accuracy=0.86) for CRA. These performance outcomes represented a 2%, 3% and 8% increase in AUC, compared to baseline ML performance, respectively. The predictive features identified for each disease class were largely distinct and represented differing proportions of taxonomic and functional features. Conclusions: The predictive taxonomic features identified for each disease class were largely distinct, whereas many functional gene features were shared across disease classes but displayed differing direction of change. Application of our ensemble approach for feature selection increased the predictive power of each disease class and moreover may generate discriminatory models with greater generalizability.
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https://doi.org/10.1002/jcp.28436 - Supplementary Tables Legends.
- Supplementary Table S1. Top 800 features list (by disease class). A full list of taxonomy and KO features derived from FIRE and SIAMCAT analysis).
- Supplementary Tables S2. Taxonomic key for Fig. 8.
Дані публікації
| Кількість цитувань | 0 |
| Кількість джерел у списку літератури: | 44 |
| Видання індексується в Scopus | Ні |
| Видання індексується в Web of Science | Ні |
