Abstract A056: Integrative Machine Learning Approaches for Predicting Prostate Cancer Risk Using Multi-Omics Data
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".