Analysing DNA methylation and transcriptomic signatures to predict prostate cancer recurrence risk
Abstract Prostate cancer (PCa) remains a significant global health challenge, with approximately 1.6 million new cases and 366,000 deaths annually. Despite high survival rates for localized prostate cancer, recurrence poses a substantial risk due to inherent biological factors and residual disease....
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| Format: | Article |
| Language: | English |
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Springer
2025-02-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-01833-8 |
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| author | Fahad M. Aldakheel Hadeel Alnajran Shatha A. Alduraywish Ayesha Mateen Mohammed S. Alqahtani Rabbani Syed |
| author_facet | Fahad M. Aldakheel Hadeel Alnajran Shatha A. Alduraywish Ayesha Mateen Mohammed S. Alqahtani Rabbani Syed |
| author_sort | Fahad M. Aldakheel |
| collection | DOAJ |
| description | Abstract Prostate cancer (PCa) remains a significant global health challenge, with approximately 1.6 million new cases and 366,000 deaths annually. Despite high survival rates for localized prostate cancer, recurrence poses a substantial risk due to inherent biological factors and residual disease. Early detection and intervention are essential for enhancing patient outcomes and reducing mortality. However, traditional diagnostics such as PSA tests, digital rectal examinations, and biopsies often lack specificity resulting in overdiagnosis. There is a pressing need for novel biomarkers to enhance precision medicine approaches for PCa. This study employs a machine learning approach to identify DNA methylation and RNA expression biomarkers predictive of PCa recurrence using datasets from The Cancer Genome Atlas (TCGA). We analyzed 49,133 genes, identifying 684 differentially methylated genes (DMGs) and 691 differentially expressed genes (DEGs) between recurrence and non-recurrence groups. Ten genes (TNNI2, SPIN2, COL5A3, RNF169, CCND1, FGFR1, SLC17A2, FAMM71F2, RREB1, AOX1) were found to have significant correlations between methylation and expression, forming the basis for our predictive model. A support vector machine (SVM) model was developed using these ten genes, achieving an area under the curve (AUC) of 0.773, demonstrating robust predictive capability. Multivariate regression analysis confirmed the SVM score as an independent predictor of recurrence (HR = 0.45; 95% CI 0.28–0.69, P < 0.001). The analysis of recurrence-free survival suggested that patients with low-risk scores experienced significantly better outcomes compared to those with high-risk scores. Functional enrichment analyses of DMGs revealed significant involvement in biological processes such as transcription regulation, signal transduction, and immune response, highlighting the potential mechanistic pathways of these biomarkers. Validation using real-time PCR confirmed differential expression and methylation patterns of the identified genes in prostate cancer (PC3) and non-cancerous cell lines (PNT2). In conclusion, our study hihglights the DNA methylation biomarkers linked to PCa recurrence and introduces a promising SVM model for early prediction, potentially improving treatment outcomes. Further research is needed to explore the biological roles of these genes in PCa aiming to refine therapeutic approaches. |
| format | Article |
| id | doaj-art-09e8a5d0b30a442f8b1d3220045dbb39 |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-09e8a5d0b30a442f8b1d3220045dbb392025-02-02T12:30:35ZengSpringerDiscover Oncology2730-60112025-02-0116111210.1007/s12672-025-01833-8Analysing DNA methylation and transcriptomic signatures to predict prostate cancer recurrence riskFahad M. Aldakheel0Hadeel Alnajran1Shatha A. Alduraywish2Ayesha Mateen3Mohammed S. Alqahtani4Rabbani Syed5Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud UniversityDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud UniversityDepartment of Family and Community Medicine, College of Medicine, King Saud UniversityDepartment of Pharmaceutics, College of Pharmacy, King Saud UniversityDepartment of Pharmaceutics, College of Pharmacy, King Saud UniversityDepartment of Pharmaceutics, College of Pharmacy, King Saud UniversityAbstract Prostate cancer (PCa) remains a significant global health challenge, with approximately 1.6 million new cases and 366,000 deaths annually. Despite high survival rates for localized prostate cancer, recurrence poses a substantial risk due to inherent biological factors and residual disease. Early detection and intervention are essential for enhancing patient outcomes and reducing mortality. However, traditional diagnostics such as PSA tests, digital rectal examinations, and biopsies often lack specificity resulting in overdiagnosis. There is a pressing need for novel biomarkers to enhance precision medicine approaches for PCa. This study employs a machine learning approach to identify DNA methylation and RNA expression biomarkers predictive of PCa recurrence using datasets from The Cancer Genome Atlas (TCGA). We analyzed 49,133 genes, identifying 684 differentially methylated genes (DMGs) and 691 differentially expressed genes (DEGs) between recurrence and non-recurrence groups. Ten genes (TNNI2, SPIN2, COL5A3, RNF169, CCND1, FGFR1, SLC17A2, FAMM71F2, RREB1, AOX1) were found to have significant correlations between methylation and expression, forming the basis for our predictive model. A support vector machine (SVM) model was developed using these ten genes, achieving an area under the curve (AUC) of 0.773, demonstrating robust predictive capability. Multivariate regression analysis confirmed the SVM score as an independent predictor of recurrence (HR = 0.45; 95% CI 0.28–0.69, P < 0.001). The analysis of recurrence-free survival suggested that patients with low-risk scores experienced significantly better outcomes compared to those with high-risk scores. Functional enrichment analyses of DMGs revealed significant involvement in biological processes such as transcription regulation, signal transduction, and immune response, highlighting the potential mechanistic pathways of these biomarkers. Validation using real-time PCR confirmed differential expression and methylation patterns of the identified genes in prostate cancer (PC3) and non-cancerous cell lines (PNT2). In conclusion, our study hihglights the DNA methylation biomarkers linked to PCa recurrence and introduces a promising SVM model for early prediction, potentially improving treatment outcomes. Further research is needed to explore the biological roles of these genes in PCa aiming to refine therapeutic approaches.https://doi.org/10.1007/s12672-025-01833-8MethylationExpressionRecurrenceProstate cancerCell linesMachine learning |
| spellingShingle | Fahad M. Aldakheel Hadeel Alnajran Shatha A. Alduraywish Ayesha Mateen Mohammed S. Alqahtani Rabbani Syed Analysing DNA methylation and transcriptomic signatures to predict prostate cancer recurrence risk Discover Oncology Methylation Expression Recurrence Prostate cancer Cell lines Machine learning |
| title | Analysing DNA methylation and transcriptomic signatures to predict prostate cancer recurrence risk |
| title_full | Analysing DNA methylation and transcriptomic signatures to predict prostate cancer recurrence risk |
| title_fullStr | Analysing DNA methylation and transcriptomic signatures to predict prostate cancer recurrence risk |
| title_full_unstemmed | Analysing DNA methylation and transcriptomic signatures to predict prostate cancer recurrence risk |
| title_short | Analysing DNA methylation and transcriptomic signatures to predict prostate cancer recurrence risk |
| title_sort | analysing dna methylation and transcriptomic signatures to predict prostate cancer recurrence risk |
| topic | Methylation Expression Recurrence Prostate cancer Cell lines Machine learning |
| url | https://doi.org/10.1007/s12672-025-01833-8 |
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