Serum paraoxonase 1 and 3 activities in benign and malignant diseases of the prostate and changes in levels following robotic-assisted laparoscopic radical prostatectomy

8-Hydroxy-2-Deoxyguanosine and 8-Iso-Prostaglandin F2α: Putative Biomarkers to assess Oxidative Stress Damage Following Robot-Assisted Radical Prostatectomy (RARP)

Objective: Prostate cancer (PCa) is the most common type of cancer. Biomarkers help researchers to understand the mechanisms of disease and refine diagnostic panels. We measured urinary 8-hydroxy-2-deoxyguanosine (8-OHdG) and 8-iso-prostaglandin F2α (8-IsoF2α) to assess oxidative stress damage in PCa patients undergoing robot-assisted radical prostatectomy (RARP). Methods: Forty PCa patients were enrolled in the study. Urine was collected before (T0) and 3 months after the RARP procedure (T1). 8-OHdG and 8-IsoF2α were measured through liquid chromatography-tandem mass spectrometry. Sex- and age-matched healthy subjects served as controls (CTRL). Results: At T0, patients exhibited significantly higher levels of 8-OHdG than CTRL (p = 0.026). At T1, 23/40 patients who completed the 3-month follow-up showed levels of 8-OHdG that were significantly lower than at T0 (p = 0.042), and comparable to those of the CTRL subjects (p = 0.683). At T0, 8-Iso-PGF2α levels were significantly higher in PCa patients than in CTRL subjects (p = 0.0002). At T1, 8-Iso-PGF2α levels were significantly lower than at T0 (p < 0.001) and were comparable to those of CTRL patients (p = 0.087). Conclusions: A liquid chromatography-tandem mass spectrometry method reveals enhanced OHdG and 8-Iso-PGF2α in the urine of PCa patients. RARP normalizes such indices of oxidative stress. Large-sized sample studies and long-term follow-ups are now needed to validate these urinary biomarkers for use in the early prevention and successful treatment of PCa.

Serum Paraoxonase-1 Activity and the Risk of Prostate Cancer Recurrence in Patients Treated with Radiotherapy

The antioxidant paraoxonase-1 (PON1) may be involved in the response to radiation-induced oxidative stress and possibly prevent cell apoptosis. The correlation of PON1 with the risk of cancer recurrence after radiotherapy (RT) is not yet explored. We investigated changes in the activity of PON1 in patients with prostate cancer (PCa) undergoing RT, and the relation of PON1 activity to the risk of recurrence after RT. We included 56 men with PCa. Blood samples were obtained before irradiation and after the completion of RT. Patients were followed for an average of 51.2 months. Each case of biochemical recurrence was confirmed with biopsy. The control group was composed of 60 healthy men. There was no significant difference in PON1 activity between the control group and patients pre-radiotherapy. Irradiation was associated with a significant decrease in PON1 activity. Patients with PCa recurrence had significantly higher serum PON1 activity than those recurrence-free, both before and after RT. PON1 activity was a predictor of PCa recurrence, with sensitivity over 80% and specificity over 64%. Our results suggest that PON1 activity may be a predictor of PCa recurrence risk after RT. Studies with a larger number of patients and longer follow-up are needed to confirm this hypothesis.

Paraoxonase Role in Human Neurodegenerative Diseases

The human body has biological redox systems capable of preventing or mitigating the damage caused by increased oxidative stress throughout life. One of them are the paraoxonase (PON) enzymes. The PONs genetic cluster is made up of three members (PON1, PON2, PON3) that share a structural homology, located adjacent to chromosome seven. The most studied enzyme is PON1, which is associated with high density lipoprotein (HDL), having paraoxonase, arylesterase and lactonase activities. Due to these characteristics, the enzyme PON1 has been associated with the development of neurodegenerative diseases. Here we update the knowledge about the association of PON enzymes and their polymorphisms and the development of multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD) and Parkinson's disease (PD).

Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer

BACKGROUND

There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa.

METHODS

We downloaded the data of 487 PCa patients from The Cancer Genome Atlas (TCGA) database. We used immunologically relevant genes as input for consensus clustering and applied survival analysis and principal component analysis to determine the properties of the subtypes. We also explored differences of somatic variations, copy number variations, TMPRSS2-ERG fusion, and androgen receptor (AR) scores among the subtypes. Then, we examined the infiltration of different immune cells into the tumor microenvironment in each subtype. We next performed Gene Set Enrichment Analysis (GSEA) to illustrate the characteristics of the subtypes. Finally, based on the subtypes, we constructed a risk predictive model and verified it in TCGA, Gene Expression Omnibus (GEO), cBioPortal, and International Cancer Genome Consortium (ICGC) databases.

RESULTS

Four PCa subtypes (C1, C2, C3, and C4) were identified on immune status. Patients with the C3 subtype had the worst prognosis, while the other three groups did not differ significantly from each other in terms of their prognosis. Principal component analysis clearly distinguished high-risk (C3) and low-risk (C1 + 2 + 4) patients. Compared with the case in the low-risk subtype, the Speckle-type POZ Protein (SPOP) had a higher mutation frequency and lower transcriptional level in the high-risk subtype. In C3, there was also a higher frequency of copy number alterations (CNA) of Clusterin (CLU) and lower CLU expression. In addition, C3 had a higher frequency of TMPRSS2-ERG fusion and higher AR scores. M2 macrophages also showed significantly higher infiltration in the high-risk subtype, while CD8+ T cells and dendritic cells had significantly higher infiltration in the low-risk subtype. GSEA revealed that MYC, androgen, and KRAS were relatively activated and p53 was relatively suppressed in high-risk subtype, compared with the levels in the low-risk subtype. Finally, we trained a six-gene signature risk predictive model, which performed well in TCGA, GEO, cBioPortal, and ICGC databases.

CONCLUSION

PCa can be divided into four subtypes based on immune-related genes, among which the C3 subtype is associated with a poor prognosis. Based on these subtypes, a risk predictive model was developed, which could indicate patient prognosis.