A155 STOOL-BASED PROTEIN SIGNATURES FOR NON-INVASIVE ACCURATE DIAGNOSIS AND SUBTYPING OF INFLAMMATORY BOWEL DISEASE THROUGH HIGH-THROUGHPUT PROTEOMICS AND MACHINE LEARNING APPROACHES
Notice bibliographique
Résumé
Abstract Background Accurate diagnosis of inflammatory bowel disease (IBD) is essential to distinguish it from other conditions with similar symptoms and to identify whether it’s ulcerative colitis (UC) or Crohn’s disease (CD), ensuring appropriate treatment and management. While colonoscopy and biopsy are the current gold standards, they are invasive, costly, and poorly accepted by asymptomatic patients. Fecal biomarkers like calprotectin are commonly used but lack the specificity and lack of ability to differentiate between CD and UC, highlighting the need for more precise, non-invasive diagnostic methods. Aims This study aims to develop a stool-based protein biomarker panel capable of accurately distinguishing IBD from IBD-mimicking conditions. It also seeks to classify the subtypes, CD and UC, by using high-throughput Data-Independent Acquisition mass spectrometry (DIA-MS) to identify precise biomarker signatures from complex stool samples, combined with advanced machine learning techniques for developing predictive model. Methods Stool samples were collected from 46 active-CD patients, 23 active-UC patients, and 53 patients with conditions presenting similar symptoms. Using DIA-MS, we analyzed the stool proteome, identifying and quantifying proteins. Data processing procedures were carefully optimized to establish a robust analytical pipeline. The samples were then split into training and testing groups. Feature selection algorithms were applied to the training group to identify proteins that significantly differed between the groups. Six machine learning algorithms (kNN, Naive Bayes, eXBoost, Random Forest, SVM, and glmnet) were subsequently evaluated to determine the best-performing classifier. Results Signature 1, consisting of 7 proteins for diagnosing true symptomatic IBD cases, and Signature 2, comprising 8 proteins for distinguishing between CD and UC, were developed using the best-performing classifier on the training dataset. Signature 1 achieved an AUC of 0.98, while Signature 2 reached an AUC of 0.96. For validation, the final predictive model was applied to a set of unseen samples. Impressively, the model demonstrated an AUC of 0.96 for both classifications, confirming its robustness and ability to generalize effectively to new samples. Conclusions In conclusion, this study illustrates the effectiveness of utilizing stool proteome obtained through DIA-MS in accurately diagnosing and subtyping active IBD. Further future validation on a larger cohort using targeted MRM mass spectrometry would be served to establish the clinical utility of this approach. Funding Agencies CCCFaculty of Medicine and Health Science of University of Sherbrooke
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».