Versatile Design of Organic Polymeric Nanoparticles for Photodynamic Therapy of Prostate Cancer.

Radical prostatectomy is a primary treatment option for localized prostate cancer (PCa), although high rates of recurrence are commonly observed postsurgery. Photodynamic therapy (PDT) has demonstrated efficacy in treating nonmetastatic localized PCa with a low incidence of adverse events. However, its limited efficacy remains a concern. To address these issues, various organic polymeric nanoparticles (OPNPs) loaded with photosensitizers (PSs) that target prostate cancer have been developed. However, further optimization of the OPNP design is necessary to maximize the effectiveness of PDT and improve its clinical applicability. This Review provides an overview of the design, preparation, methodology, and oncological aspects of OPNP-based PDT for the treatment of PCa.

Prognosis and Therapeutic Efficacy Prediction of Adrenocortical Carcinoma Based on a Necroptosis-Associated Gene Signature.

Background: Adrenocortical carcinoma (ACC) is a rare and poor prognosis malignancy. Necroptosis is a special type of cell apoptosis, which is regulated in caspase-independent pathways and mainly induced through the activation of receptor-interacting protein kinase 1, receptor-interacting protein kinase 3, and mixed lineage kinase domain-like pseudokinase. A precise predictive tool based on necroptosis is needed to improve the level of diagnosis and treatment. Method: Four ACC cohorts were enrolled in this study. The Cancer Genome Atlas ACC (TCGA-ACC) cohort was used as the training cohort; three datasets (GSE19750, GSE33371, and GSE49278) from Gene Expression Omnibus (GEO) platform were combined as the GEO testing cohort after removing of batch effect. Forty-nine necroptosis-associated genes were obtained from a prior study and further filtered by least absolute shrinkage and selection operator Cox regression analysis; corresponding coefficients were used to calculate the necroptosis-associated gene score (NAGs). Patients in the TCGA-ACC cohort were equally divided into two groups with the mean value of NAGs. We investigated the associations between NAGs groups and clinicopathological feature distribution and overall survival (OS) in ACC, the molecular mechanisms, and the value of NAGs in therapy prediction. A nomogram risk model was established to quantify risk stratification for ACC patients. Finally, the results were confirmed in the GEO-combined cohort. Result: Patients in the TCGA-ACC cohort were divided into high and low NAGs groups. The high NAGs group had more fatal cases and advanced stage patients than the low NAGs group (P < 0.001, hazard ratio (HR) = 13.97, 95% confidence interval (95% CI): 4.168-46.844; survival rate: low NAGs, 7.69% vs. high NAGs, 61.53%). NAGs were validated to be negatively correlated with OS (R = -0.48, P < 0.001) and act as an independent factor in ACC with high discriminative efficacy (P < 0.001, HR = 11.76, 95% CI: 2.86-48.42). In addition, a high predictive efficacy nomogram risk model was established combining NAGs with tumor stage. Higher mutation rates were observed in the high NAGs group, and the mutation of TP53 may lead to a high T cell infiltration level among the NAGs groups. Patients belonged to the high NAGs are more sensitive to the chemotherapy of cisplatin, gemcitabine, paclitaxel, and etoposide (all P < 0.05). Ultimately, the same statistical algorithms were conducted in the GEO-combined cohort, and the crucial role of NAGs prediction value was further validated. Conclusion: We constructed a necroptosis-associated gene signature, revealed the prognostic value between ACC and it, systematically explored the molecular alterations among patients with different NAGs, and manifested the value of drug sensitivity prediction in ACC.

[Establishment of a prostate cancer prognostic risk model based on the TCGA database and inflammation-related genes].

OBJECTIVE: To investigate the effect of inflammation-related genes on the prognosis of prostate cancer (PCa). METHODS: We downloaded PCa-related clinical data and mRNA sequencing data from the database Cancer Genome Atlas (TCGA) and inflammation-related pathway gene sets from MsigDB. Using univariate regression and LASSO regression analyses, we screened inflammation-related genes for the construction of a prognostic risk model and evaluated the performance of the model in predicting the prognosis of PCa by Kaplan-Meier and ROC analyses. Based on the nomogram, we calculated the risk scores of the patients, divided them into a high-risk and a low-risk group based on the median values of their risk scores, identified differentially expressed genes for enrichment analysis and verified the expression level of SPHK1 in the PCa tissue microarrays by immunohistochemical staining. RESULTS: Totally 19 inflammation-related genes were identified from 172 candidate genes for the construction of the prognostic risk model, including the risk genes CD14, PIK3R5, GABBR1, RELA, IRF7, SCARF1, MSR1, SPHK1, OSM and STAB1, and the protective genes AQP9, LPAR1, ATP2C1, NDP, CXCL6, P2RY2, DCBLD2, PCDH7, and IFNAR1. Kaplan-Meier analysis showed that the patients with high risk scores had a significantly lower recurrence-free survival and a worse prognosis than those with low risk scores. Differentially expressed genes were involved mainly in the activation of inflammatory response pathways. Immunohistochemical results indicated that the expression of SPHK1 was significantly higher in the tumorous than in the normal tissue and increased with the Gleason score. There was a correlation between the SPHK1 expression and envelope invasion. CONCLUSION: The prognostic risk model of inflammation-related genes constructed based on the TCGA database can effectively predict the prognosis of PCa.

[Infiltration of Th1 and Th17 cells into the L5－S2 spinal cord in CP/CPPS rats and its central sensitization mechanism].

OBJECTIVE: To explore the central sensitization mechanism of pain in chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). METHODS: We randomly divided 40 adult male SPF SD rats, aged 3－4 weeks and weighing 250－350 g, into a normal control and a CP/CPPS model group. After modeling, we analyzed the state of infiltration of CD4+T cells into the L5－S2 spinal cord and detected the expression levels of GFAP and CR3 in the spinal cord tissue using flow cytometry, real-time fluorescent quantitative PCR (RT-qPCR) and immunofluorescence staining. RESULTS: Compared with the normal controls, the CP/CPPS model rats showed dramatically increased expression of CD4+T cells in the mononuclear cells of the L5－S2 spinal cord tissue (P < 0.01), mRNA expressions of interferon-γ (IFN-γ) and tumor necrosis factor α (TNF-α) secreted from the Th1 cells, interleukin (IL)-17 and retinoic acid-associated orphan receptor (ROR) γt secreted from the Th17 cells, cytokines IL-6 and IL-1ß, and chemokines CCL2, CCL20 and CXCL10 (P < 0.01), and expressions of the molecular markers of Th1 and Th17 cells IFN-γ and IL-17 and those of astrocytes and microglias GFAP and CR3. CONCLUSIONS: CD4+T cells, specifically Th1 and Th17 cells, infiltrate L5－S2 spinal cord neurons in CP/CPPS model rats. The inflammatory factors secreted from these cells may damage the neuronal cells, affect nervous conduction, promote central sensitization and activate astrocytes and microglias, leading to the development and progression of pain.