Inflammation-related signature for prognostic prediction, tumor immune, genomic heterogeneity, and drug choices in prostate cancer: Integrated analysis of bulk and single-cell RNA-sequencing.

Background: Prostate cancer (PCa) ranks as the second most prevalent malignancy among males on a global scale. Accumulating evidence suggests that inflammation has an intricate relationship with tumorigenesis, tumor progression and tumor immune microenvironment. However, the overall impact of inflammation-related genes on the clinical prognosis and tumor immunity in PCa remains unclear. Methods: Machine learning methods were utilized to construct and validate a signature using The Cancer Genome Atlas (TCGA) for training, while the Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70769 cohorts for independent validation. The efficacy of the signature in predicting outcomes and its clinical utility were assessed through a series of investigations encompassing in vitro experiments, survival analysis, and nomogram development. The association between the signature and precision medicine was explored via tumor immunity, genomic heterogeneity, therapeutic response, and molecular docking analyses, using bulk and single-cell RNA-sequencing data. Results: We identified 7 inflammation-related genes with prognostic significance and developed an inflammation-related prognostic signature (IRPS) with 6 genes. Furthermore, we demonstrated that both the IRPS and a nomogram integrating risk score and pathologic T stage exhibited excellent predictive ability for the survival outcomes in PCa patients. Moreover, the IRPS was found to be significantly associated with the tumor immune, genomic heterogeneity, therapeutic response, and drug selection. Conclusion: IRPS can serve as a reliable predictor for PCa patients. The signature may provide clinicians with valuable information on the efficacy of therapy and help personalize treatment for PCa patients.

Integrated machine learning identifies epithelial cell marker genes for improving outcomes and immunotherapy in prostate cancer.

BACKGROUND: Prostate cancer (PCa), a globally prevalent malignancy, displays intricate heterogeneity within its epithelial cells, closely linked with disease progression and immune modulation. However, the clinical significance of genes and biomarkers associated with these cells remains inadequately explored. To address this gap, this study aimed to comprehensively investigate the roles and clinical value of epithelial cell-related genes in PCa. METHODS: Leveraging single-cell sequencing data from GSE176031, we conducted an extensive analysis to identify epithelial cell marker genes (ECMGs). Employing consensus clustering analysis, we evaluated the correlations between ECMGs, prognosis, and immune responses in PCa. Subsequently, we developed and validated an optimal prognostic signature, termed the epithelial cell marker gene prognostic signature (ECMGPS), through synergistic analysis from 101 models employing 10 machine learning algorithms across five independent cohorts. Additionally, we collected clinical features and previously published signatures from the literature for comparative analysis. Furthermore, we explored the clinical utility of ECMGPS in immunotherapy and drug selection using multi-omics analysis and the IMvigor cohort. Finally, we investigated the biological functions of the hub gene, transmembrane p24 trafficking protein 3 (TMED3), in PCa using public databases and experiments. RESULTS: We identified a comprehensive set of 543 ECMGs and established a strong correlation between ECMGs and both the prognostic evaluation and immune classification in PCa. Notably, ECMGPS exhibited robust predictive capability, surpassing traditional clinical features and 80 published signatures in terms of both independence and accuracy across five cohorts. Significantly, ECMGPS demonstrated significant promise in identifying potential PCa patients who might benefit from immunotherapy and personalized medicine, thereby moving us nearer to tailored therapeutic approaches for individuals. Moreover, the role of TMED3 in promoting malignant proliferation of PCa cells was validated. CONCLUSIONS: Our findings highlight ECMGPS as a powerful tool for improving PCa patient outcomes and supply a robust conceptual framework for in-depth examination of PCa complexities. Simultaneously, our study has the potential to develop a novel alternative for PCa diagnosis and prognostication.

Multi-omics analysis reveals a macrophage-related marker gene signature for prognostic prediction, immune landscape, genomic heterogeneity, and drug choices in prostate cancer.

Introduction: Macrophages are components of the innate immune system and can play an anti-tumor or pro-tumor role in the tumor microenvironment owing to their high heterogeneity and plasticity. Meanwhile, prostate cancer (PCa) is an immune-sensitive tumor, making it essential to investigate the value of macrophage-associated networks in its prognosis and treatment. Methods: Macrophage-related marker genes (MRMGs) were identified through the comprehensive analysis of single-cell sequencing data from GSE141445 and the impact of macrophages on PCa was evaluated using consensus clustering of MRMGs in the TCGA database. Subsequently, a macrophage-related marker gene prognostic signature (MRMGPS) was constructed by LASSO-Cox regression analysis and grouped based on the median risk score. The predictive ability of MRMGPS was verified by experiments, survival analysis, and nomogram in the TCGA cohort and GEO-Merged cohort. Additionally, immune landscape, genomic heterogeneity, tumor stemness, drug sensitivity, and molecular docking were conducted to explore the relationship between MRMGPS and the tumor immune microenvironment, therapeutic response, and drug selection. Results: We identified 307 MRMGs and verified that macrophages had a strong influence on the development and progression of PCa. Furthermore, we showed that the MRMGPS constructed with 9 genes and the predictive nomogram had excellent predictive ability in both the TCGA and GEO-Merged cohorts. More importantly, we also found the close relationship between MRMGPS and the tumor immune microenvironment, therapeutic response, and drug selection by multi-omics analysis. Discussion: Our study reveals the application value of MRMGPS in predicting the prognosis of PCa patients. It also provides a novel perspective and theoretical basis for immune research and drug choices for PCa.