Abstract A056: Integrative Machine Learning Approaches for Predicting Prostate Cancer Risk Using Multi-Omics Data
Notice bibliographique
Résumé
Abstract Introduction: Prostate cancer is one of the most common cancers affecting men globally. According to the American Cancer Society, it is estimated that in 2025, there will be approximately 313,780 new cases of prostate cancer and about 35,770 deaths from the disease in the United States. This study aims to improve prostate cancer risk predictions by integrating multi-omics data (mRNA, miRNA, and methylation) using advanced machine learning techniques. Methods: We analyzed multi-omics data from 493 patients in the Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset. Patients were stratified into low (Gleason score <=7) and high-risk (Gleason score >=8) groups based on their Gleason scores. Data normalization was performed using z-scores, and missing values were imputed using the missForest method. Differential expression analysis (DEGs) was conducted for mRNA, miRNA, and methylation data. To enhance predictive accuracy, machine learning models, including Lasso, Random Forest, SVM, XGBoost, and Gradient Boosting, were applied to various data combinations, employing 5-fold cross-validation. Model performance was evaluated using ROC curves and AUC values generated by the 'pROC' package, with DeLong's test for AUC comparisons between models. Two-side P value < 0.05 were considered statistical significance. All the analyses were performed using R. Results: The analysis identified significant differential expression: 186 upregulated and 468 downregulated genes in mRNA; 21 upregulated in miRNA (downregulated data not specified); 651 upregulated and 955 downregulated methylation sites. The dataset was randomly divided into training and testing sets in a 6:4 ratio. Gradient Boosting model showing exceptional effectiveness, especially those integrating mRNA with methylation, and miRNA with methylation with 100% and 89% AUC in the training and testing sets. Further analysis identified 70 target mRNAs, which were used to explore potential biological pathways implicated in prostate cancer. Pathway analysis using Ingenuity Pathway Analysis (IPA) highlighted the Calcium signaling and ABRA signaling pathways as potentially crucial in miRNA-mRNA interactions, suggesting their significant roles in modulating prostate cancer risk. These pathways are known to be critical for various cellular processes that could influence cancer progression. Conclusions: The integration of multi-omics data via machine learning significantly improves the prediction of prostate cancer risk, highlighting the potential of such models in clinical applications. Pathways analysis may provide new targets for therapeutic intervention. Our findings need to be validated with larger, independent external cohorts. Acknowledgement: This research is supported by The Hawaii Advanced Training in Artificial Intelligence for Precision Nutrition Science Research (AIPrN) (T32DK137523) Citation Format: Zhanwei Wang, Lenora WM. Loo, Herbert Yu, Youping Deng. Integrative Machine Learning Approaches for Predicting Prostate Cancer Risk Using Multi-Omics Data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A056.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».