Prostate cancer, particularly metastatic castration-resistant prostate cancer (mCRPC), presents therapeutic challenges rooted in adaptive lineage plasticity and neuroendocrine transdifferentiation. Conventional genome-based models fail to account for the divergent clinical trajectories observed among tumors that share identical driver mutations. This limitation requires reconceptualizing cancer as a dynamic system in which tumor cells can execute context-dependent molecular programs governed by epigenetic and transcriptional network remodeling. This review critically evaluates three convergent technological pillars reshaping prostate cancer research and clinical care. First, conditional reprogramming (CR) enables the rapid generation of patient-derived models that preserve genomic fidelity, intratumoral heterogeneity, and reversible phenotypic plasticity without genetic manipulation. Second, single-cell and spatial multi-omics approaches have clarified the cellular trajectories underlying luminal-to-neuroendocrine transdifferentiation, identifying a therapeutically actionable intermediate state. They have revealed the hierarchical transcription factor network (FOXA2–NKX2-1–p300/CBP) which orchestrates chromatin remodeling during this lethal transition. Third, physics-informed machine learning and digital twin architectures aim to move beyond correlative risk prediction toward mechanistically sound forecasting of tumor evolution, treatment response, and resistance emergence. We address unresolved challenges in prospective clinical validation, spatial heterogeneity capture, regulatory pathways for functional diagnostics, and the imperative for causal, as opposed to associative, inference from perturbational datasets. The integration of these three domains through closed-loop experimental–computational feedback cycles represents a paradigm shift from reactive to anticipatory precision oncology.

Cyanobacteria harbor sophisticated molecular defense systems that have evolved over billions of years to protect against viral invasion and foreign genetic elements. These ancient photosynthetic organisms possess a diverse array of restriction-modification (R-M) systems and CRISPR-Cas arrays that present challenges for genetic engineering, but also offer unique opportunities for cancer-targeted biotechnological applications. These systems exist in prokaryotes mainly as defense mechanisms but they are currently used in molecular applications as gene editing tools. Moreover, latest developments in nucleases such as zinc finger nucleases (ZFNs), TALENs (transcription-activator-like effector nucleases) are discussed. A comprehensive genomic analysis of 126 cyanobacterial species found 89% encode multiple R-M systems, averaging 3.2 systems per genome, creating formidable barriers to transformation but also providing molecular machinery that could be harnessed for precise recognition and targeting of cancer cells. This review critically examines the dual nature of these defense systems, their ecological functions, and the emerging strategies to translate their molecular precision into advanced anticancer therapeutics. Hence, the review main objectives are to explore the recent understanding of these mechanisms and to exploit the knowledge gained in opening new avenues for cancer-focused targeted interventions, while acknowledging the significant challenges to translate these systems from laboratory curiosities to practical applications.

Using AI to analyze metabolite profiles provides critical insights into health, aging, and disease. Metabolomic signatures reveal how lifestyle and therapy impact organ function and cancer progression. This review highlights emerging toolkits for high-throughput data analysis, emphasizing their integration with other omics. Advanced AI approaches facilitate metabolic pathway mapping and accelerate biomarker discovery. By combining AI with multi-omics, researchers can optimize interventions and enhance precision medicine. This article serves as a resource demonstrating AI’s potential in diagnostics and drug discovery.