A novel model to predict positive prostate biopsy based on serum androgen level

Potential role of gut microbiota in prostate cancer: immunity, metabolites, pathways of action?

The gut microbiota helps to reveal the relationship between diseases, but the role of gut microbiota in prostate cancer (PCa) is still unclear. Recent studies have found that the composition and abundance of specific gut microbiota are significantly different between PCa and non-PCa, and the gut microbiota may have common and unique characteristics between different diseases. Intestinal microorganisms are affected by various factors and interact with the host in a variety of ways. In the complex interaction model, the regulation of intestinal microbial metabolites and the host immune system is particularly important, and they play a key role in maintaining the ecological balance of intestinal microorganisms and metabolites. However, specific changes in the composition of intestinal microflora may promote intestinal mucosal immune imbalance, leading to the formation of tumors. Therefore, this review analyzes the immune regulation of intestinal flora and the production of metabolites, as well as their effects and mechanisms on tumors, and briefly summarizes that specific intestinal flora can play an indirect role in PCa through their metabolites, genes, immunity, and pharmacology, and directly participate in the occurrence, development, and treatment of tumors through bacterial and toxin translocation. We also discussed markers of high risk PCa for intestinal microbiota screening and the possibility of probiotic ingestion and fecal microbiota transplantation, in order to provide better treatment options for clinic patients. Finally, after summarizing a number of studies, we found that changes in immunity, metabolites.

Diagnostic and prognostic significance of tartrate-resistant acid phosphatase type 5b in newly diagnosed prostate cancer with bone metastasis: A real-world multi-institutional study

OBJECTIVES

Approximately, 90% of men with advanced prostate cancer will develop bone metastasis. However, there have been few reports about noninvasive biomarker to detect and predict clinical outcome of bone metastasis (BM) in prostate cancer patients.

METHODS

We examined 1127 patients who underwent prostate biopsy from August 2012 to June 2017. We also investigated bone turnover markers such as bone-specific alkaline phosphatase, type I collagen cross-linked N-terminal telopeptide, C-terminal pyridinoline cross-linked telopeptide of type I collagen, and tartrate-resistant acid phosphatase type 5b (TRACP 5b).

RESULTS

A total of 282 patients were diagnosed as prostate cancer with complete clinical data, and 34 patients with bone metastasis. Multivariate analysis revealed C-terminal pyridinoline cross-linked telopeptide of type I collagen, tartrate-resistant acid phosphatase type 5b, and prostate-specific antigen (PSA) were independent biomarkers in detection of BM (p < 0.05, respectively). Furthermore, we developed predictive model formula based on tartrate-resistant acid phosphatase type 5b and PSA, for which the area under the curve was 0.95. In patients with bone metastasis, multivariate cox proportional hazards analysis revealed that this model was significantly associated with poor clinical outcome of cancer-specific survival (p < 0.05). In validation cohort with 137 patients, we also confirmed the utility of this model for diagnosis of BM (the area under the curve = 0.95).

CONCLUSIONS

Our developed formula of tartrate-resistant acid phosphatase type 5b in accordance with PSA may serve as the useful tool in diagnosis and prediction of clinical outcome for prostate cancer with bone metastasis.

Evaluating the association of rs6983267 polymorphism of the CCAT2 gene with thyroid cancer susceptibility in the Iranian Azeri population

Thyroid cancer is the most common malignancy of the endocrine system. LncRNAs play critical role in various cellular processes and are associated with several diseases. CCAT2 is a lncRNA molecule overexpressed in thyroid cancer. Single nucleotide polymorphisms in CCAT2 gene can cause changes in the structure and function of CCAT2 transcripts and susceptibility to several diseases. This study aimed to evaluate the association of rs6983267 in CCAT2 gene with thyroid cancer susceptibility in the Azeri population of Iran. In this "case-control" study, genomic DNA was extracted from peripheral blood of 102 individuals affected by thyroid cancer and 103 healthy individuals as controls. Genotyping was performed using TETRA-ARMS polymerase chain reaction. Statistical analysis showed no significant association between genotypes and/or alleles with the occurrence of thyroid cancer in the studied population, patients' gender, and tumor type. Nevertheless, we found that the allelic and genotypic distribution of this SNP was associated with the size of thyroid tumors in patients. It is assumed that investigating more individuals from both case and control group may further determine the genotypic and allelic frequencies of this SNP locus in Iranian-Azeri population.

Hospital-based prostate cancer screening in vietnamese men with lower urinary tract symptoms: a classification and regression tree model

Background

Prostate cancer (PCa) is a common disease in men over 65 years of age, and should be detected early, while reducing unnecessary biopsies. This study aims to construct a classification and regression tree (CART) model (i.e., risk stratification algorithm) using multivariable approach to select Vietnamese men with lower urinary tract symptoms (LUTS) for PCa biopsy.

Methods

We conducted a case-control study on 260 men aged ≥ 50 years who visited MEDIC Medical Center, Vietnam in 2017–2018 with self-reported LUTS. The case group included patients with a positive biopsy and the control group included patients with a negative biopsy diagnosis of PCa. Bayesian Model Averaging (BMA) was used for selecting the most parsimonious prediction model. Then the CART with 5-fold cross-validation was constructed for selecting men who can benefit from PCa biopsy in steps by steps and intuitive way.

Results

BMA suggested five potential prediction models, in which the most parsimonious model including PSA, I-PSS, and age. CART advised the following cut-off points in the marked screening sequence: 18 < PSA < 33.5 ng/mL, I-PSS ≥ 19, and age ≥ 71. Patients with PSA ≥ 33.5 ng/mL have a PCa risk was 91.2%; patients with PSA < 18 ng/mL and I-PSS < 19 have a PCa risk was 7.1%. Patient with 18 ≤ PSA < 33.5ng/mL and I-PSS < 19 have a PCa risk is 70% if age ≥ 71; and is 16% if age < 71. In overall, CART reached high predictive value with AUC = 0.915. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CART at the 20% diagnosis probability threshold were 91.5%, 86.2%, 86.9%, 91.2%, and 88.9% respectively; at 80% diagnosis probability threshold were 79.2%, 92.3%, 91.2%, 81.6%, and 85.8% respectively.

Conclusion

CART combining PSA, I-PSS, and age has practical use in hospital-based PCa screening in Vietnamese men with lower urinary tract symptoms.

HSP27/Menin Expression as New Prognostic Serum Biomarkers of Prostate Cancer Aggressiveness Independent of PSA

The screening of PCa is based on two tests, the total PSA test and the rectal examination. However, PSA is not specific for PCa stage confirmation, leading in false positive result and involving PCa over-diagnosis and over-treatment. HSP27 and Menin have been found to be overexpressed in a wide range of human cancers. Recent studies showed how HSP27 interacts with and stabilizes Menin to lead PCa progression and treatment resistance. The purpose of our study was to evaluate the correlation of HSP27 and Menin molecular expression, and their prognosis value in PCa with respect to clinicopathological features. Elisa was employed to measure serum HSP27 and Menin concentrations in 73 PCa patients and 80 healthy individuals. Immunohistochemistry (IHC) was used to determine HSP27 and Menin tissue expression in 57 tumors and 4 Benign Prostatic Hyperplasia (BPH) tissues. Serum HSP27 expression correlated with its tissue expression in all PCa patients, whereas serum Menin expression correlated only with tissue expression in aggressive PCa patients. Moreover, the results showed a positive correlation between HSP27 and Menin either in serum (r = 0.269; p = 0.021) or in tissue (r = 0.561; p < 0.0001). In aggressive PCa, serum expression of HSP27 and Menin was positively correlated (r = 0.664; R = 0.441; p = 0.001). The correlation between HSP27 and Menin expression in tissue was found only in patients with aggressive PCa (r = 0.606; R = 0.367; p = 0.004). Statistical analysis showed that the expression of both biomarkers was positively correlated with the hormone resistance or sensitivity, tumor aggressiveness, metastasis, Gleason Score, death and did not significantly correlate with age and PSA. Survival was illustrated by Kaplan−Meier curves; increased HSP27 and Menin expression correlated with shorter survival of PCa patients (p = 0.001 and p < 0.0001, respectively). Accuracy in predicting aggressiveness was quantified by the Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC). We demonstrated that the combination of HSP27/Menin was statistically greater than PSA; it achieved an AUC of 0.824 (95% CI, 0.730−0.918; p < 0.0001). However, HSP27/Menin/PSA combination decreased the diagnostic value with an AUC of 0.569 (95% CI, 0.428−0.710; p = 0.645). Our work suggests the potential role of HSP27/Menin as diagnostic and prognostic biomarkers.

Firmicutes in Gut Microbiota Correlate with Blood Testosterone Levels in Elderly Men

PURPOSE

In males, testosterone levels have been implicated in various diseases. Recently, the influence of gut microbial-derived compounds on host metabolism has become evident, and it has been suggested that some gut bacteria may be involved in testosterone metabolism. In the present study, we examined the relationship between testosterone levels and gut microbiota in elderly Japanese men.

MATERIALS AND METHODS

We collected samples from Japanese male subjects suspected of having prostate cancer and underwent prostate biopsies and excluded patients with positive biopsies to avoid the effect of prostate cancer on the gut microbiota. In total, 54 Japanese males with negative biopsy results were included in our study. The gut microbiota was analyzed by 16S rRNA gene sequencing of bacterial DNA extracted from rectal swabs. Gut microbiota compositions were compared between the two groups according to the level of serum testosterone (above or below 3.5 ng /mL).

RESULTS

The median age of the cohort was 71 years, and the quartile range was 67 to 73 years. We observed no significant difference in alpha or beta diversity, but some bacteria belonging to the phylum Firmicutes (Clostridiales, Turicibacter, and Gemella) were increased in the high testosterone group. Serum testosterone levels positively correlated with the relative amount of Firmicutes (rS=0.3323, p=0.0141), and the amount of Firmicutes affected serum testosterone levels independent of host factors (age, body mass index, triglyceride, and total cholesterol; β=0.770, p=0.0396).

CONCLUSIONS

Some intestinal bacteria belonging to the phylum Firmicutes were associated with testosterone levels in elderly males. Therefore, the gut microbiota could affect testosterone metabolism in elderly males.

A multicenter retrospective study on evaluation of predicative factors for positive biopsy of prostate cancer in real-world setting

OBJECTIVE

This study aimed to evaluate the predictors for positive biopsy in prostate cancer (PCa) patients and develop a risk-stratification score model for positive biopsy rate in patients with prostate specific antigen (PSA) in the gray zone.

METHODS

In this retrospective, multicenter, real-world study, Chinese patients receiving prostate biopsy for the first time were included. The study evaluated the positive biopsy rate, predictors for positive biopsy and a risk prediction model for PSA 4-10 ng/mL PCa was developed. The univariate and multivariate logistic regression analyses were used to identify the risk factors.

RESULTS

A total of 2426 patients were included in the study. The biopsy positive rate was 47.57%, 25.77%, and 60.57% among overall patients, total PSA (t-PSA) 4-10 ng/mL patients, and PSA > 10 ng/mL patients respectively. Elderly age 60-74, ≥75, multi parametric magnetic resonance imaging (MP-MRI), pre-operative PSA > 10 and PSA density (PSAD) significantly increased the positive rate in overall population, and elderly age, MP-MRI, positive digital rectal examination and f-PSA were significant predictors for positive biopsy in PSA 4-10 ng/mL population. A risk prediction model for positive biopsy rate in patients with PSA in the gray zone was developed. Area under curve (AUC) was associated with low accuracy for all the variables used such as tPSA (0.53), PSAD (0.57), frequency of puncture (0.53) and MP-MRI (0.64) in prediction of biopsy positive rate.

CONCLUSION

Our study evaluated the significant predicative factors for positive biopsy and the PCa risk prediction model developed might help Clinicians to avoid unnecessary biopsy in patients with PSA in gray zone.

Establishment of two new predictive models for prostate cancer to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone (4-10 ng ml-1)

Our goal was to establish two new predictive models of prostate cancer to determine whether to require a prostate biopsy when the prostate-specific antigen level is in the diagnostic gray zone. A retrospective analysis of 197 patients undergoing prostate biopsy with prostate-specific antigens between 4 and 10 ng ml⁻¹ was conducted. Of these, 47 patients were confirmed to have cancer, while the remaining 150 patients were diagnosed with benign prostate disease after examining biopsy pathology. Two multivariate logistic regression models were established including age, prostate volumes, free/total prostate-specific antigen ratio, and prostate-specific antigen density using SPSS 19.0 to obtain the predicted probability and Logit P, and then, two receiver operating characteristic (ROC) curves were drawn to obtain the best cutoff value for prostate biopsy: one for the group of all the prostate cancers and one for the group of clinically significant prostate cancers. The best cutoff value for prostate biopsy was 0.25 from the multivariate logistic regression ROC curve model of all the prostate cancers, which gave a sensitivity of 75.4% and a specificity of 75.8%. The best cutoff value for prostate biopsy was 0.20 from the multivariate logistic regression model of clinically significant prostate cancers, which gave a sensitivity of 76.7% and a specificity of 80.1%. We identified the best cutoff values for prostate biopsy (0.25 for all prostate cancers and 0.20 for clinically significant prostate cancers) to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone.

Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models

Background

Two diagnostic models of prostate cancer (PCa) and clinically significant prostate cancer (CS-PCa) were established using clinical data of among patients whose prostate-specific antigen (PSA) levels are in the gray area (4.0–10.0 ng/ml).

Material/Methods

Data from 181 patients whose PSA levels were in the gray area were retrospectively analyzed, and the following data were collected: age, digital rectal examination, total PSA, PSA density (PSAD), free/total PSA (f/t PSA), transrectal ultrasound, multiparametric magnetic resonance imaging (mpMRI), and pathological reports. Patients were diagnosed with benign prostatic hyperplasia (BPH) and PCa by pathology reports, and PCa patients were separated into non-clinically significant PCa (NCS-PCa) and CS-PCa by Gleason score. Afterward, predictor models constructed by above parameters were researched to diagnose PCa and CS-PCa, respectively.

Results

According to the analysis of included clinical data, there were 109 patients with BPH, 44 patients with NCS-PCa, and 28 patients with CS-PCa. Regression analysis showed PCa was correlated with f/t PSA, PSAD, and mpMRI (P<0.01), and CS-PCa was correlated with PSAD and mpMRI (P<0.01). The area under the receiver operating characteristic curves of 2 models for PCa (sensitivity=73.64%, specificity=64.23%) and for CS-PCa (sensitivity=71.41%, specificity=81.82%) were 0.79 and 0.87, respectively.

Conclusions

The prediction models had satisfactory diagnostic value for PCa and CS-PCa among patients with PSA in the gray area, and use of these models may help reduce overdiagnosis.

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.

Obesity, Inflammation, and Prostate Cancer

The prevalence of obesity is increasing in the world, and obesity-induced disease, insulin-resistance, cardiovascular disease, and malignancies are becoming a problem. Epidemiological studies have shown that obesity is associated with advanced prostate cancer and that obese men with prostate cancer have a poorer prognosis. Obesity induces systemic inflammation via several mechanisms. High-fat diet-induced prostate cancer progresses via adipose-secretory cytokines or chemokines. Inflammatory cells play important roles in tumor progression. A high-fat diet or obesity changes the local profile of immune cells, such as myeloid-derived suppressor cells and macrophages, in prostate cancer. Tumor-associated neutrophils, B cells, and complements may promote prostate cancer in the background of obesity. Interventions to control systemic and/or local inflammation and changes in lifestyle may also be viable therapies for prostate cancer.