Prostate Specific Antigen, Mean Platelet Volume, and Platelet Distribution Width in Combination to Discriminate Prostate Cancer from Benign Prostate Hyperplasia

Assessment of the utility of platelet indices to diagnose clinical benign prostatic hyperplasia in dogs

Introduction

Platelet indices changes in severely ill people and in dogs with inflammation are compatible findings. This study aimed to compare platelet indices between dogs with clinical benign prostatic hyperplasia (BPH) and healthy controls. Additionally, to determine whether there is a correlation between the relative prostatic size (S _rel) and the platelet indices in BPH dogs.

Methods

Thirty-five adult intact male dogs of different breeds were allocated to the experimental groups: dogs with clinical BPH (groups A; n = 24; median age of 6 years; the median weight of 8.50 kg) and healthy dogs (group B; n = 11; median age 5.50 years; the median weight of 7.00 kg) based on physical examination, clinical signs, and S _rel detected by ultrasonographic findings. The individual prostatic volume (IPV) was divided by the expected prostatic volume (EPV) to determine the relative prostatic size in dogs over 4 years old. Platelet indices were compared between the two groups, and a correlation between S _rel and these indices was calculated.

Results

The median S _rel of dogs in group A was significantly higher (P = 0.001), and the mean plateletcrit (PCT) was significantly lower (P = 0.003) compared with those in group B. S _rel showed a significant negative correlation with PLT and PCT (r = -0.388; P = 0.02 and r = -0.402; P = 0.01). Receiver operating characteristic (ROC) analysis showed PLT and PCT thresholds for estimating S _rel > 1 with 75% and 87.5% sensitivity and 71.82 and 63.64% specificity.

Discussion

The findings of this study support the use of platelet indices like PLT and PCT to detect clinical BPH in dogs. However, more research is needed to confirm their utility in conjunction with other previously described diagnostic factors.

Mean Platelet Volume Enhances the Diagnostic Specificity of PSA for Prostate Cancer

Mean platelet volume (MPV) is an indicator of platelet activation and has been proposed as a diagnostic marker for several kinds of cancers. We investigated the value of MPV as a diagnostic marker for prostate cancer (PCa) and examined whether MPV in combination with prostate-specific antigen (PSA) could increase the sensitivity or specificity of PSA for PCa diagnosis. For this study, 107 pathologically confirmed PCa and 177 non-PCa patients who underwent prostate biopsy were retrospectively studied. Clinical data and pre-biopsy hematological parameters were collected. The above parameters were compared between PCa and non-PCa patients. The correlation between MPV and clinical characteristics was analyzed. Receiver operating characteristic (ROC) analysis was used to explore the diagnostic value of MPV for PCa. Among all parameters analyzed, the difference was only found in MPV, platelet distribution width (PDW), and PSA between PCa and non-PCa patients. MPV was significantly decreased and PDW increased in PCa than that of non-PCa among men. ROC analysis identified MPV ≤ 9.05 fl as a cut-off value for potential PCa with area under the ROC curve (AUC) = 0.783, 95% CI = 0.733–0.833, sensitivity = 0.746, and specificity = 0.708. AUC and the sensitivity of MPV were comparable with total PSA (TPSA) or free PSA (FPSA). However, the specificity of MPV was larger than that of TPSA (0.461) or FPSA (0.561). Furthermore, MPV combined with TPSA or FPSA further enhanced the specificity of TPSA (0.844) or FPSA (0.927), but PDW did not. These findings suggested that MPV could have a predictive value for the diagnosis of PCa. MPV in combination with TPSA or FPSA could enhance the specificity of PSA and may reduce the rate of unnecessary biopsy for patients with high levels of PSA.

Platelets: The Emerging Clinical Diagnostics and Therapy Selection of Cancer Liquid Biopsies

Due to the inherent molecular heterogeneity of metastatic tumours and the dynamic evolution ability of tumour genomes, tumour tissues obtained through biopsy and other methods cannot capture all of the features of tumour genomes. A new diagnostic concept called “liquid biopsy” has received widespread attention in recent years. Liquid biopsy has changed the clinical practice of oncology and is widely used to guide targeted drug utilization, monitor disease progression and track drug resistance. The latest research subject in liquid biopsy is platelets. Platelets originate from multifunctional haematopoietic stem cells in the bone marrow haematopoietic system. They are small cells from the cytoplasm of bone marrow megakaryocytes. Their main physiological functions are to participate in the processes of physiological haemostasis and coagulation. Tumour cells transfer biomolecules (such as RNA) to platelets through direct contact and release of exosomes, which changes the platelet precursor RNA. Under the stimulation of tumour cells and the tumour microenvironment, platelet precursor mRNA is spliced into mature RNA and converted into functional protein to respond to external stimuli, forming tumour-educated platelets (TEPs). The detection of TEPs in the peripheral blood of patients is expected to be used in clinical tumour diagnosis. This emerging liquid biopsy method can replace and supplement the current tumour detection methods. Further research on the role of platelets in tumour diagnosis will help provide a novel theoretical basis for clinical tumour diagnosis.

The Value of Prostate-Specific Antigen-Related Indexes and Imaging Screening in the Diagnosis of Prostate Cancer

Objective

The aim of this study was to explore the value of the prostate-specific antigen (PSA) levels, the ratio of free PSA to total PSA (fPSA/TPSA), the PSA density (PSAD), digital rectal examination (DRE), transrectal prostate ultrasound (TRUS), and multiparameter MRI (MP-MRI) in the differential diagnosis of benign prostatic hyperplasia (BPH) and prostate cancer (PCa).

Methods

From February 2016 to September 2019, data from 620 patients who underwent systematic transrectal ultrasound-guided prostate biopsy (STURS-PB) in our hospital were retrospectively collected, including the PSA levels, the fPSA/TPSA ratio, the PSAD, DRE, TRUS, MP-MRI, prostate volume, and other clinical data.

Results

Among the 620 patients, 249 patients were in the PCa group, and 371 patients in the BPH group. The positive puncture rate was 40.16%. The positive predictive values of DRE, TRUS, mpMRI, and TPSA levels for PCa were 39.91%, 39.38%, 64.14%, and 41.57%, respectively; the sensitivity of these parameters was 37.35%, 51.41%, 74.69%, and 57.43%, respectively; and the specificity of these parameters was 62.26%, 46.90%, 71.97%, and 45.82%, respectively. When the TPSA concentration was in the range of 4-20 ng/mL, the positive puncture rate of STURS-PB was 23.18%, with a high rate of misdiagnosis. When the TPSA concentration was in the range of 4-20 ng/mL, the fPSA/TPSA ratio was 0.15, the PSAD was 0.16, the comprehensive evaluation of PCa was optimal (the sensitivity of these parameters was 88.85% and 84.09%, respectively; the specificity was 80.17% and 67.29%, respectively; the positive predictive value was 57.41% and 51.39%, respectively). When the TPSA concentration >4 ng/mL, the fPSA/TPSA ratio ≤0.15 and the PSAD ≥0.16, the sensitivity, specificity, and correctness index of the PCa and BPH diagnosis were 80.54%, 82.75%, and 67.07%, respectively.

Conclusion

When using DRE, TRUS, and MP-MRI to screen for PCa, MP-MRI has a relatively high sensitivity and specificity. Using these three thresholds (TPSA >4 ng/mL combined with an fPSA/TPSA ratio ≤0.15 and a PSAD ≥0.16) is significantly better than using TPSA levels alone for the differential diagnosis of PCa and BPH.

Biomarkers That Differentiate Benign Prostatic Hyperplasia from Prostate Cancer: A Literature Review

Prediction of prostate cancer in primary care is typically based upon serum total prostate-specific antigen (tPSA) and digital rectal examination results. However, these tests lack sensitivity and specificity, leading to over-diagnosis of disease and unnecessary, invasive biopsies. Therefore, there is a clinical need for diagnostic tests that can differentiate between benign conditions and early-stage malignant disease in the prostate. In this review, we evaluate research papers published from 2009 to 2019 reporting biomarkers that identified or differentiated benign prostatic hyperplasia (BPH) from prostate cancer. Our review identifies hundreds of potential biomarkers in urine, serum, tissue, and semen proposed as useful targets for differentiating between prostate cancer and BPH patients. However, it is still not apparent which of these candidate biomarkers are most useful, and many will not progress beyond the discovery stage unless they are properly validated for clinical practice. We conclude that this validation will come through the use of multivariate panels which can assess the value of biomarker candidates in combination with clinical parameters as part of a risk prediction calculator. Implementation of such a model will help clinicians stratify patients with prostate cancer symptoms in primary care, with tangible benefits for both the patient and the health service.

A predictive model for prostate cancer incorporating PSA molecular forms and age

The diagnostic specificity of prostate specific antigen (PSA) is limited. We aimed to characterize eight anti-PSA monoclonal antibodies (mAbs) to assess the prostate cancer (PCa) diagnostic utility of different PSA molecular forms, total (t) and free (f) PSA and PSA complexed to α₁-antichymotrypsin (complexed PSA). MAbs were obtained by immunization with PSA and characterized by competition studies, ELISAs and immunoblotting. With them, we developed sensitive and specific ELISAs for these PSA molecular forms and measured them in 301 PCa patients and 764 patients with benign prostate hyperplasia, and analyzed their effectiveness to discriminate both groups using ROC curves. The free-to-total (FPR) and the complexed-to-total PSA (CPR) ratios significantly increased the diagnostic yield of tPSA. Moreover, based on model selection, we constructed a multivariable logistic regression model to predictive PCa that includes tPSA, fPSA, and age as predictors, which reached an optimism-corrected area under the ROC curve (AUC) of 0.86. Our model outperforms the predictive ability of tPSA (AUC 0.71), used in clinical practice. In conclusion, The FPR and CPR showed better diagnostic yield than tPSA. In addition, the PCa predictive model including age, fPSA and complexed PSA, outperformed tPSA detection efficacy. Our model may avoid unnecessary biopsies, preventing harmful side effects and reducing health expenses.