Establishing a Prognostic Model Correlates to Inflammatory Response Pathways for Prostate Cancer via Multiomic Analysis of Lactylation-Related Genes
Prostate cancer (PCa) continues to pose substantial clinical challenges, with molecular heterogeneity significantly impacting therapeutic decision-making and disease trajectories. Emerging evidence implicates protein lactylation—a novel epigenetic regulatory mechanism—in oncogenic processes, though...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/ijog/6681711 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Prostate cancer (PCa) continues to pose substantial clinical challenges, with molecular heterogeneity significantly impacting therapeutic decision-making and disease trajectories. Emerging evidence implicates protein lactylation—a novel epigenetic regulatory mechanism—in oncogenic processes, though its prognostic relevance in PCa remains underexplored. Through integrative bioinformatics interrogation of lactylation-associated molecular signatures, we established prognostic correlations using multivariable feature selection methodologies. Initial screening via differential expression analysis (limma package) coupled with Cox proportional hazards modeling revealed 11 survival-favorable regulators and 16 hazard-associated elements significantly linked to biochemical recurrence. To enhance predictive precision, ensemble machine learning frameworks were implemented, culminating in a 10-gene lactylation signature demonstrating robust discriminative capacity (concordance index=0.738) across both primary (TCGA-PRAD) and external validation cohorts (DKFZ). Multivariable regression confirmed the lactylation score’s prognostic independence, exhibiting prominent associations with clinicopathological parameters including tumor staging and metastatic potential. The developed clinical-molecular nomogram achieved superior predictive accuracy (C−index>0.7) through the synergistic integration of biological and clinical covariates. Tumor microenvironment deconvolution uncovered distinct immunological landscapes, with high-risk stratification correlating with enriched stromal infiltration and immunosuppressive phenotypes. Pathway enrichment analyses implicated chromatin remodeling processes and cytokine-mediated inflammatory cascades as potential mechanistic drivers of prognostic divergence. Therapeutic vulnerability profiling demonstrated differential response patterns: low-risk patients exhibited enhanced immune checkpoint inhibitor responsiveness, whereas high-risk subgroups showed selective chemosensitivity to docetaxel and mitoxantrone. Functional validation in PC-3 models revealed AK5 silencing induced proapoptotic effects, suppressed metastatic potential of migration and invasion, and modulated immune checkpoint regulation through CD276 coexpression. These multimodal findings position lactylation dynamics, particularly AK5-mediated pathways, as promising therapeutic targets and stratification biomarkers in PCa management. |
|---|---|
| ISSN: | 2314-4378 |