Integrative multi-omics analysis enables a comprehensive characterization of prostate cancer and unveils metastasis-associated candidate biomarkers
Prostate cancer is a complex disease that necessitates precise evaluation and treatment decisions dependent on cancer stage and aggressiveness. Nonetheless, existing methods have limitations in capturing the complete range of prostate cancer behavior and progression. Although methods such as the his...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402501919X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Prostate cancer is a complex disease that necessitates precise evaluation and treatment decisions dependent on cancer stage and aggressiveness. Nonetheless, existing methods have limitations in capturing the complete range of prostate cancer behavior and progression. Although methods such as the histological assessment of the Gleason score provide a valuable approximation of cancer behavior, understanding the fundamental mechanisms of each neoplasm and effectively translating this knowledge into clinical practice present challenges that can impact treatment approaches.Here, we perform a comprehensive analysis of large-scale multi-omics datasets from The Cancer Genome Atlas and other studies, aiming to unravel the molecular and clinical features underlying prostate cancer progression. Using an integrative clustering approach, we determine distinct molecular subtypes associated with potential prognostic biomarkers. Through computational validation in independent cohorts, we reinforce their potential for molecular subtyping, demonstrating the clinical significance of the hypothesized markers. To evaluate the clinical impact of these biomarkers, we perform immunohistochemistry assays on patient samples, confirming their prognostic potential. Among the investigated biomarkers, CCNB1, FOXM1, and RAD51 emerged as the most promising candidates for prognostic evaluation.The results validate the utility of these biomarkers, bridging the gap between bioinformatics analyses and experimental validation. This study expands our understanding of prostate cancer progression through comprehensive multi-omics analyses. The identification and validation of molecular subtypes for the identification of potential prognostic biomarker offers valuable insights for improved treatment decisions and personalized care in prostate cancer. These findings provide a foundation for future investigations and pave the way for the development of targeted therapeutic approaches in prostate cancer management. |
|---|---|
| ISSN: | 2405-8440 |