Bioptic intraprostatic chronic inflammation predicts adverse pathology at radical prostatectomy in patients with low-grade prostate cancer

Identification of miR-1-3p, miR-143-3p and miR-145-5p association with bone metastasis of Gleason 3+4 prostate cancer and involvement of LASP1 regulation

Gleason Score (GS) 3 + 4 prostate cancer (PCa) is heterogeneous in clinical course and molecular features. Risk stratification of indolent and aggressive PCa with GS 3 + 4 is critical, especially those with bone metastasis (BM) potential. Microarray-based microRNA(miRNA) profiling with eight PCa cases with or without BM was used to screen the candidate miRNAs associated with BM. Transwell and MTS assays were used to characterize the function of miRNAs and target gene LASP1. RT-qPCR and immunohistochemistry assays were utilized to illustrate the clinical significance of miRNAs and target gene in a cohort of 309 Chinese PCa cases. In the current study, we identified that miR-1-3p, miR-143-3p and miR-145-5p are associated with BM of GS 3 + 4 PCa. Through functional experiments, we show that miR-1-3p/143-3p/145-5p promotes proliferation and migration of PCa in vitro. LASP1 was predicted as the common target of these three miRNAs which was further confirmed by a luciferase assay. Overexpression of LASP1 was correlated with higher GS, higher pathological stage, and the presence of metastasis by immunohistochemistry. siRNA knockdown of LASP1 significantly suppressed proliferation and migration, whereas overexpression of LASP1 promoted it. Bioinformatics analysis revealed the involvement of Wnt signaling pathway in LASP1 mediated function. LASP1 may activate Wnt signaling by interacting with β-catenin. In all, we suggest that miR-1-3p/143-3p/145-5p are associated with BM of Gleason 3 + 4 PCa. LASP1 is the common target of these miRNAs and may active Wnt signaling by interacting with β-catenin.

Plasma IL-6 and TNF-α levels correlate significantly with grading changes in localized prostate cancer

PURPOSE

To study the effect of inflammatory markers in blood such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) on the Gleason score (GS) changes in patients with prostate cancer (PCa) after radical prostatectomy (RP), we conducted this study.

PATIENTS AND METHODS

From November 2012 to September 2021, a total of 237 patients underwent RP at our institution. Blood samples from all patients were collected within 1 week before surgery. Preoperative clinical characteristics include age, serum IL-6 and TNF-α, neutrophil-to-lymphocyte ratio, C-reactive protein, the platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, systemic immune-inflammation index, the prostate imaging reporting and data system (PI-RADS) score, prostate-specific antigen, and biopsy GS were assessed. Univariate and multivariate logistic regression analyzes were used to determine the risk factors of GS changes after RP. The efficiency of this prediction model was identified with the area under the curve of the receiver operating characteristic curve.

RESULTS

Seventy-three patients (30.8%) had GS upgraded in the overall cohort, and 55 patients (23.2%) had GS downgraded. In comparing PCa patients with and without GS upgraded, multivariate logistic regression analysis showed that serum TNF-α (odds ratio [OR]: 2.518, p = 0.019) and IL-6 (OR: 0.478, p = 0.023) were independent factors predicting the occurrence of GS upgrade. We also compared the characteristics of patients with GS upgraded and GS downgraded; multivariate logistic regression analysis also demonstrated significant differences in serum IL-6 and TNF-α between these two groups (all p < 0.05). In addition, we found that low prostate volume and biopsy GS ≥ 7 were significantly associated with higher PI-RADS sores in multivariate analysis.

CONCLUSION

The high expression of serum TNF-α level is positively correlated with GS upgraded in PCa patients. High expression of serum IL-6 level is negatively correlated with GS upgraded in PCa patients and positively related with GS downgraded.

Utility of Clinical-Radiomic Model to Identify Clinically Significant Prostate Cancer in Biparametric MRI PI-RADS V2.1 Category 3 Lesions

Purpose

To determine the predictive performance of the integrated model based on clinical factors and radiomic features for the accurate identification of clinically significant prostate cancer (csPCa) among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions.

Materials and Methods

A retrospective study of 103 patients with PI-RADS 3 lesions who underwent pre-operative 3.0-T MRI was performed. Patients were randomly divided into the training set and the testing set at a ratio of 7:3. Radiomic features were extracted from axial T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images of each patient. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) feature selection methods were used to identify the radiomic features and construct a radiomic model for csPCa identification. Moreover, multivariable logistic regression analysis was used to integrate the clinical factors with radiomic feature model to further improve the accuracy of csPCa identification, and the two are presented in the form of normogram. The performance of the integrated model was compared with radiomic model and clinical model on testing set.

Results

A total of four radiomic features were selected and used for radiomic model construction producing a radiomic score (Radscore). Radscore was significantly different between the csPCa and the non-csPCa patients (training set: p < 0.001; testing set: p = 0.035). Multivariable logistic regression analysis showed that age and PSA could be used as independent predictors for csPCa identification. The clinical–radiomic model produced the receiver operating characteristic (ROC) curve (AUC) in the testing set was 0.88 (95%CI, 0.75–1.00), which was similar to clinical model (AUC = 0.85; 95%CI, 0.52–0.90) (p = 0.048) and higher than the radiomic model (AUC = 0.71; 95%CI, 0.68–1.00) (p < 0.001). The decision curve analysis implies that the clinical–radiomic model could be beneficial in identifying csPCa among PI-RADS 3 lesions.

Conclusion

The clinical–radiomic model could effectively identify csPCa among biparametric PI-RADS 3 lesions and thus could help avoid unnecessary biopsy and improve the life quality of patients.

Construction and Validation of a Clinical Predictive Nomogram for Improving the Cancer Detection of Prostate Naive Biopsy Based on Chinese Multicenter Clinical Data

Objectives

Prostate biopsy is a common approach for the diagnosis of prostate cancer (PCa) in patients with suspicious PCa. In order to increase the detection rate of prostate naive biopsy, we constructed two effective nomograms for predicting the diagnosis of PCa and clinically significant PCa (csPCa) prior to biopsy.

Materials and Methods

The data of 1,428 patients who underwent prostate biopsy in three Chinese medical centers from January 2018 to June 2021 were used to conduct this retrospective study. The KD cohort, which consisted of 701 patients, was used for model construction and internal validation; the DF cohort, which consisted of 385 patients, and the ZD cohort, which consisted of 342 patients, were used for external validation. Independent predictors were selected by univariate and multivariate binary logistic regression analysis and adopted for establishing the predictive nomogram. The apparent performance of the model was evaluated via internal validation and geographically external validation. For assessing the clinical utility of our model, decision curve analysis was also performed.

Results

The results of univariate and multivariate logistic regression analysis showed prostate-specific antigen density (PSAD) (P<0.001, OR:2.102, 95%CI:1.687-2.620) and prostate imaging-reporting and data system (PI-RADS) grade (P<0.001, OR:4.528, 95%CI:2.752-7.453) were independent predictors of PCa before biopsy. Therefore, a nomogram composed of PSAD and PI-RADS grade was constructed. Internal validation in the developed cohort showed that the nomogram had good discrimination (AUC=0.804), and the calibration curve indicated that the predicted incidence was consistent with the observed incidence of PCa; the brier score was 0.172. External validation was performed in the DF and ZD cohorts. The AUC values were 0.884 and 0.882, in the DF and ZD cohorts, respectively. Calibration curves elucidated greatly predicted the accuracy of PCa in the two validation cohorts; the brier scores were 0.129 in the DF cohort and 0.131 in the ZD cohort. Decision curve analysis showed that our model can add net benefits for patients. A separated predicted model for csPCa was also established and validated. The apparent performance of our nomogram for PCa was also assessed in three different PSA groups, and the results were as good as we expected.

Conclusions

In this study, we put forward two simple and convenient clinical predictive models comprised of PSAD and PI-RADS grade with excellent reproducibility and generalizability. They provide a novel calculator for the prediction of the diagnosis of an individual patient with suspicious PCa.

Melatonin attenuates prostatic inflammation and pelvic pain via Sirt1-dependent inhibition of the NLRP3 inflammasome in an EAP mouse model

BACKGROUND

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is a common male genitourinary system disease. As a neuroendocrine hormone, melatonin possesses a variety of biological functions, among which its anti-inflammatory effects have recently drawn substantial attention. The purpose of the current research was to study the effect of melatonin on CP/CPPS and the underlying mechanisms using a mouse model of experimental autoimmune prostatitis (EAP).

METHODS

The EAP mouse model was successfully established by subcutaneously injecting a mixture of prostate antigen and complete Freund's adjuvant. On Day 42, hematoxylin-eosin staining was used to evaluate the histological appearance of prostate tissues. Chronic pelvic pain development was assessed by suprapubic allodynia. The levels of inflammation-related cytokines, such as interferon-γ, interleukin (IL)-17, and IL-1β, were detected by enzyme-linked immunosorbent assay. Then, we explored the anti-inflammatory effects of melatonin on CP/CPPS by Western blotting and immunohistochemical staining, by measuring the expression of silent information regulator 1 (Sirt1) and NLRP3 inflammasome-related proteins in EAP mice.

RESULTS

The EAP model mice exhibited severe diffuse leukocyte infiltration and significantly increased pelvic pain compared to the control mice. In the melatonin treatment group, the histological appearance of the prostate tissues, pelvic pain development, and the levels of proinflammatory cytokines were significantly alleviated compared to the EAP + dimethyl sulfoxide group. Furthermore, we found that the protective effects of melatonin were achieved through activation of the Sirt1 pathway and downregulation of the NLRP3 inflammasome.

CONCLUSIONS

The results indicated that melatonin could attenuate prostate inflammation and pelvic pain by inhibiting the NLRP3 inflammasomes signaling pathway through the activation of Sirt1 in mice with EAP, and these efforts should provide a promising therapeutic strategy for CP/CPPS.

Significance of the prostate central gland and total gland volume ratio in the diagnosis of prostate cancer patients in the prostate specific antigen grey zone

Objective

To explore the significance of the prostate central gland to total gland volume ratio (PVc/PV) in the diagnosis of prostate cancer (PCa) in patients with prostate specific antigen (PSA) levels in the grey zone (4–10 ng/ml).

Methods

This retrospective study enrolled patients that had undergone prostate biopsy. The volume of the prostate and the central prostate gland were measured. The differences in PSA, the ratio of free to total PSA (f/tPSA), PSA density (PSAD) and PVc/PV between the PCa and non-PCa groups were compared. Receiver operating characteristic curve analysis for PCa and clinically significant PCa (csPCa) diagnosis were calculated according to PSA (reference), f/tPSA, PSAD and PVc/PV.

Results

This study enrolled 136 patients. There was no significant difference in PSA and f/tPSA between the PCa and non-PCa groups, while there were significant differences in PSAD and PVc/PV. The area under the curve values of PVc/PV for PCa or csPCa diagnosis were 0.876 and 0.933, respectively; and for PSAD, they were 0.705 and 0.790, respectively. These were significantly different compared with the PSA curve, whereas f/tPSA showed no significant difference from the PSA curve.

Conclusion

PVc/PV could be a predictor of PCa when PSA is between 4–10 ng/ml.

Prostatic Inflammation in Prostate Cancer: Protective Effect or Risk Factor?

The relationship between prostatic chronic inflammation (PCI) and prostate cancer (PCa) is unclear and controversial. Some authors reported that a history of chronic prostatitis may be correlated with PCa induction, while others associate chronic inflammation with less aggressive disease or consider inflammation as a possible protective factor against PCa. Four different types of prostatitis are known: bacterial acute prostatic inflammation, bacterial chronic prostatic inflammation, abacterial prostatitis/chronic pelvic pain syndrome, and asymptomatic prostatic chronic inflammation. Prostatic inflammation is underestimated during daily clinical practice, and its presence and degree often go unmentioned in the pathology report of prostate biopsies. The goal of this report is to further our understanding of how PCI influences the biology of PCa. We investigated the main pathogenetic mechanisms responsible for prostatic inflammation, including the cellular response and inflammatory mediators to describe how inflammation modifies the prostatic environment and can lead to benign or malignant prostatic diseases. We found that prostatic inflammation might have a pivotal role in the pathogenesis of prostatic diseases. Details about PCI in all prostate biopsy reports should be mandatory. This will help us better understand the prostatic microenvironment pathways involved in PCa biology, and it will allow the development of specific risk stratification and a patient-tailored therapeutic approach to prostatic diseases.

Gut Microbiota Dysbiosis Accelerates Prostate Cancer Progression Through Increased LPCAT1 Expression and Enhanced DNA Repair Pathways

Gut microbiota dysbiosis is related to cancer development and progression. Our previous study showed that Ruminococcus was more abundant in CRPC (Castration-resistant prostate cancer) than HSPC (Hormone-sensitive prostate cancer) individuals. Here, we determined the potential mechanism of microbiota dysbiosis in prostate cancer (PCa) progression. Metagenomics was used to verify the gut microbial discrepancies between CRPC and HSPC individuals. Fecal microbiota transplantation (FMT) was performed by transferring the fecal suspension of CRPC or HSPC individuals to TRAMP mice. Afterwards, the mice’s prostate histopathology and gut microbiota composition were determined. Since Ruminococcus was demonstrated to correlate with phospholipid metabolism, we used lipidomics to examine the mice’s fecal lipid profiles. The expression of LPCAT1 the key enzyme for phospholipid remodeling in mice prostate was also examined. Meanwhile, both microbial functions prediction and LPCAT1 GSEA analysis (Gene Set Enrichment Analysis) indicated DNA repair pathways, we further determined the expressions of RAD51 and DNA-PKcs in mice prostate. The results showed that gut Ruminococcus was significantly more abundant in CRPC individuals. FMT using CRPC feces accelerated mice’s PCa progression and increased their gut Ruminococcus abundance. Majority of fecal lipids including lysophosphatidylcholine and phosphatidylcholine were upregulated in CRPC FMT treated mice, accompanied with enhanced expressions of LPCAT1, RAD51, and DNA-PKcs in mice prostate. We reported an abundant colonization of Ruminococcus in the gut of CRPC individuals and mice receiving their fecal suspensions, and revealed the promotive capability of Ruminococcus in PCa progression via upregulating LPCAT1 and DNA repair protein expressions. The bacterium and its downstream pathways may become the targets of therapies for PCa in the future.

PSA Density Help to Identify Patients With Elevated PSA Due to Prostate Cancer Rather Than Intraprostatic Inflammation: A Prospective Single Center Study

The association between PSA density, prostate cancer (PCa) and BPH is well established. The aim of the present study was to establish whether PSA density can be used as a reliable parameter to predict csPCa and to determine its optimal cutoff to exclude increased PSA levels due to intraprostatic inflammation. This is a large prospective single-center, observational study evaluating the role of PSA density in the discrimination between intraprostatic inflammation and clinically significant PCa (csPCa). Patients with PSA ≥ 4 ng/ml and/or positive digito-rectal examination (DRE) and scheduled for prostate biopsy were enrolled. Prostatic inflammation (PI) was assessed and graded using the Irani Scores. Multivariable binary logistic regression analysis was used to assess if PSA density was associated with clinically significant PCa (csPCa) rather than prostatic inflammation. A total of 1988 patients met the inclusion criteria. Any PCa and csPCa rates were 47% and 24% respectively. In the group without csPCa, patients with prostatic inflammation had a higher PSA (6.0 vs 5.0 ng/ml; p=0.0003), higher prostate volume (58 vs 52 cc; p<0.0001), were more likely to have a previous negative biopsy (29% vs 21%; p=0.0005) and a negative DRE (70% vs 65%; p=0.023) but no difference in PSA density (0.1 vs 0.11; p=0.2). Conversely in the group with csPCa, patients with prostatic inflammation had a higher prostate volume (43 vs 40 cc; p=0.007) but no difference in the other clinical parameters. At multivariable analysis adjusting for age, biopsy history, DRE and prostate volume, PSA density emerged as a strong predictor of csPCA but was not associated with prostatic inflammation. The optimal cutoffs of PSA density to diagnose csPCa and rule out the presence of prostatic inflammation in patients with an elevated PSA (>4 ng/ml) were 0.10 ng/ml² in biopsy naïve patients and 0.15 ng/ml² in patients with a previous negative biopsy. PSA density rather than PSA, should be used to evaluate patients at risk of prostate cancer who may need additional testing or prostate biopsy. This readily available parameter can potentially identify men who do not have PCa but have an elevated PSA secondary to benign conditions.

Computational analysis of eugenol inhibitory activity in lipoxygenase and cyclooxygenase pathways

Chronic inflammation is triggered by numerous diseases such as osteoarthritis, Crohn's disease and cancer. The control of the pro-inflammatory process can prevent, mitigate and/or inhibit the evolution of these diseases. Therefore, anti-inflammatory drugs have been studied as possible compounds to act in these diseases. This paper proposes a computational analysis of eugenol in relation to aspirin and diclofenac and analyzing the ADMET profile and interactions with COX-2 and 5-LOX enzymes, important enzymes in the signaling pathway of pro-inflammatory processes. Through the analysis of ADMET in silico, it was found that the pharmacokinetic results of eugenol are similar to NSAIDs, such as diclofenac and aspirin. Bioinformatics analysis using coupling tests showed that eugenol can bind to COX-2 and 5-LOX. These results corroborate with different findings in the literature that demonstrate anti-inflammatory activity with less gastric irritation, bleeding and ulcerogenic side effects of eugenol. The results of bioinformatics reinforce studies that try to propose eugenol as an anti-inflammatory compound that can act in the COX-2/5-LOX pathways, replacing some NSAIDs in different diseases.

Prostate cancer management: long-term beliefs, epidemic developments in the early twenty-first century and 3PM dimensional solutions

In the early twenty-first century, societies around the world are facing the paradoxal epidemic development of PCa as a non-communicable disease. PCa is the most frequently diagnosed cancer for men in several countries such as the USA. Permanently improving diagnostics and treatments in the PCa management causes an impressive divergence between, on one hand, permanently increasing numbers of diagnosed PCa cases and, on the other hand, stable or even slightly decreasing mortality rates. Still, aspects listed below are waiting for innovate solutions in the context of predictive approaches, targeted prevention and personalisation of medical care (PPPM / 3PM).A.PCa belongs to the cancer types with the highest incidence worldwide. Corresponding economic burden is enormous. Moreover, the costs of treating PCa are currently increasing more quickly than those of any other cancer. Implementing individualised patient profiles and adapted treatment algorithms would make currently too heterogeneous landscape of PCa treatment costs more transparent providing clear "road map" for the cost saving.B.PCa is a systemic multi-factorial disease. Consequently, predictive diagnostics by liquid biopsy analysis is instrumental for the disease prediction, targeted prevention and curative treatments at early stages.C.The incidence of metastasising PCa is rapidly increasing particularly in younger populations. Exemplified by trends observed in the USA, prognosis is that the annual burden will increase by over 40% in 2025. To this end, one of the evident deficits is the reactive character of medical services currently provided to populations. Innovative screening programmes might be useful to identify persons in suboptimal health conditions before the clinical onset of metastasising PCa. Strong predisposition to systemic hypoxic conditions and ischemic lesions (e.g. characteristic for individuals with Flammer syndrome phenotype) and low-grade inflammation might be indicative for specific phenotyping and genotyping in metastasising PCa screening and disease management. Predictive liquid biopsy tests for CTC enumeration and their molecular characterisation are considered to be useful for secondary prevention of metastatic disease in PCa patients.D.Particular rapidly increasing PCa incidence rates are characteristic for adolescents and young adults aged 15-40 years. Patients with early onset prostate cancer pose unique challenges; multi-factorial risks for these trends are proposed. Consequently, multi-level diagnostics including phenotyping and multi-omics are considered to be the most appropriate tool for the risk assessment, prediction and prognosis. Accumulating evidence suggests that early onset prostate cancer is a distinct phenotype from both aetiological and clinical perspectives deserving particular attention from view point of 3P medical approaches.