Perineural invasion (PNI) by prostate cancer has been associated with adverse pathology, including extraprostatic extension. However, the significance of PNI quantification on prostate biopsy (PBx) remains unclear.
To compare radical prostatectomy (RP) findings and long-term outcomes in patients whose PBx had exhibited PNI.
We assessed 497 consecutive patients undergoing sextant (6-site/≥12-core) PBx showing conventional adenocarcinoma followed by RP.
PNI was found in 1 (n = 290)/2 (n = 132)/3 (n = 47)/4 (n = 19)/5 (n = 5)/6 (n = 4) of the sites/regions of PBx. Compared with a single PNI site, multiple PNIs were significantly associated with higher preoperative prostate-specific antigen, higher Grade Group (GG) on PBx or RP, higher pT or pN category, positive surgical margin, and larger estimated tumor volume. When compared in subgroups of patients based on PBx GG, significant differences in RP GG (GG1–3), pT (GG1–2/GG1–3/GG2/GG3), surgical margin status (GG1–3/GG3/GG5), or tumor volume (GG1–2/GG1–3/GG2/GG3) between 1 versus multiple PNIs were observed. Moreover, there were significant differences in prostate-specific antigen (PNI sites: 1–2 versus 3–6/1–3 versus 4–6/1–4 versus 5–6), RP GG (1–3 versus 4–6/1–4 versus 5–6), pT (1–2 versus 3–6/1–3 versus 4–6), pN (1–3 versus 4–6), or tumor volume (1–2 versus 3–6/1–4 versus 5–6). Outcome analysis revealed significantly higher risks of disease progression in the entire cohort or PBx GG1–2/GG1–3/GG2/GG3/GG5 cases showing 2 to 6 PNIs, compared with respective controls with 1-site PNI. In multivariate analysis, multisite PNI was an independent predictor for progression (hazard ratio = 1.556, P = .03).
Multiple sites of PNI on PBx were associated with worse histopathologic features in RP specimens and poorer prognosis. PNI may thus need to be specified, if present, in every sextant site on PBx, especially those showing GG1–3 cancer.
Screening, monitoring, and diagnosis are critical in oncology treatment. However, there are limitations with the current clinical methods, notably the time, cost, and special facilities required for radioisotope-based methods. An alternative approach, which uses magnetic beads, offers faster analyses with safer materials over a wide range of oncological applications. Magnetic beads have been used to detect extracellular vesicles (EVs) in the serum of pancreatic cancer patients with statistically different EV levels in preoperative, postoperative, and negative control samples. By incorporating fluorescence, magnetic beads have been used to quantitatively measure prostate-specific antigen (PSA), a prostate cancer biomarker, which is sensitive enough even at levels found in healthy patients. Immunostaining has also been incorporated with magnetic beads and compared with conventional immunohistochemical methods to detect lesions; the results suggest that immunostained magnetic beads could be used for pathological diagnosis during surgery. Furthermore, magnetic nanoparticles, such as superparamagnetic iron oxide nanoparticles (SPIONs), can detect sentinel lymph nodes in breast cancer in a clinical setting, as well as those in gallbladder cancer in animal models, in a surgery-applicable timeframe. Ultimately, recent research into the applications of magnetic beads in oncology suggests that the screening, monitoring, and diagnosis of cancers could be improved and made more accessible through the adoption of this technology.
AbstractNucleocapsid protein (NC) in the group-specific antigen (gag) of retrovirus is essential in the interactions of most retroviral gag proteins with RNAs. Computational method to predict NCs would benefit subsequent structure analysis and functional study on them. However, no computational method to predict the exact locations of NCs in retroviruses has been proposed yet. The wide range of length variation of NCs also increases the difficulties. In this paper, a computational method to identify NCs in retroviruses is proposed. All available retrovirus sequences with NC annotations were collected from NCBI. Models based on random forest (RF) and weighted support vector machine (WSVM) were built to predict initiation and termination sites of NCs. Factor analysis scales of generalized amino acid information along with position weight matrix were utilized to generate the feature space. Homology based gene prediction methods were also compared and integrated to bring out better predicting performance. Candidate initiation and termination sites predicted were then combined and screened according to their intervals, decision values and alignment scores. All available gag sequences without NC annotations were scanned with the model to detect putative NCs. Geometric means of sensitivity and specificity generated from prediction of initiation and termination sites under fivefold cross-validation are 0.9900 and 0.9548 respectively. 90.91% of all the collected retrovirus sequences with NC annotations could be predicted totally correct by the model combining WSVM, RF and simple alignment. The composite model performs better than the simplex ones. 235 putative NCs in unannotated gags were detected by the model. Our prediction method performs well on NC recognition and could also be expanded to solve other gene prediction problems, especially those whose training samples have large length variations.
El antígeno específico de próstata (PSA, del inglés, Prostate Specific Antigen) es una glicoproteína producida por la próstata, y es el marcador tumoral de mayor uso. Sin embargo, su baja especificidad para diferenciar entre cáncer de próstata y otras alteraciones no malignas, como la hipertrofia benigna de la próstata (HBP) y la prostatitis aguda, limitan su utilidad diagnóstica.
Background: Prostate cancer (PC) is the second most prevalent cancer and the sixth cancer leading to death in men worldwide. Objectives: The purpose of this study was to examine the effect of eight weeks of combined training on specific markers of prostate cancer in older adults. Methods: Twenty older adults (62 ± 7 years) with prostate cancer were divided randomly into the control (n = 10) and training (n = 10) groups. The training group performed exercise training in three sessions a week for eight weeks. Resistance training included two sets of 10 repetitions at 60 - 75% of one-repetition maximum, and endurance training contained treadmill running for 20 - 35 min at 60 - 75% of maximum heart rate. Bruce test, one-repetition maximum, and ELISA technique were used respectively to measure the aerobic performance, strength performance, and serum levels of prostate specific antigen (PSA), sex hormone binding globulin (SHBG), phosphatase and tensin homolog (PTEN), and testosterone (TS). A two-way analysis of variance with repeated measures was used to specify the differences. Results: Weight, fat percentage, Body Mass Index (BMI), waist-hip ratio (WHR), glucose, insulin, and PSA were significantly lower in the training group than the control group (P < 0.05). Furthermore, strength performance, aerobic performance, SHBG, TS, and PTEN were significantly higher in the training group than in the control group (P < 0.05). Conclusions: Combined training can have an influential role in physical condition improvement through decreasing the PSA serum level and increasing SHBG, TS, and PETEN serum levels, which helps patients with prostate cancer to be cured.
Aim: To study the efficacy and impact of the local pre-biopsy multiparametric magnetic resonance imaging (mpMRI) pathway for prostate cancer diagnosis. Methods: In this tertiary centre, 570 patients had prostate mpMRI across a 6-month period in 2019. A total of 511 patients met inclusion criteria for retrospective analysis. MRI reporting used the Prostate Imaging-Reporting and Data System (PI-RADS) v2.1. These were assessed alongside histological outcomes and diagnostic times. PI-RADS ⩾ 3 were recommended for biopsy consideration. Gleason scoring ⩾ 3 + 4 and 3 + 3 were used to define clinically and non-clinically significant prostate cancer (csPCa and nsPCa), respectively. Results: Overall prostate cancer prevalence was 40% (204/511, csPCa in 31.1%) with an overall biopsy avoidance of 32.1% (164/511). Around 69.7% (356/511) scored PI-RADS ⩾ 3 and 30.3% (155/511) scored PI-RADS 1–2. About 22.6% (35/155) of PI-RADS 1–2 patients proceeded to biopsy, demonstrating a negative predictive value of 91.43% for csPCa. For PI-RADS ⩾ 3 patients, 63.4% (197/312) of those biopsied had cancer (Gleason ⩾ 3 + 3), with 50% (156/312) demonstrating csPCa. Around 76.7% (102/133) of PI-RADS 5, 35.3% (48/136) of PI-RADS 4, 14.0% (6/43) of PI-RADS 3 and 8.6% (3/35) of PI-RADS 1–2 scores demonstrated csPCa. Overall median prostate-specific antigen (PSA) density was 0.15 ng/mL2 (IQR: 0.10–0.27/mL2). PSA density were significantly different across PI-RADS cohorts ( H = 118.8, p < 0.0001) and across all three biopsy outcomes ( H = 99.72, p < 0.0001). Only 34.3% (119/347) of biopsied patients met the NHS 28-day standard. MRI acquisition and reporting met the 14-day local standard in 96.1% (491/511). The biopsy was the most delayed component with a median of 20 days (IQR: 8–43). Conclusion: Pre-biopsy mpMRI with PI-RADS scoring safely avoided biopsy in almost one-third (32.1%) of patients. The use of PSA-density in risk stratifying PI-RADS 3 lesions has informed local practice in the period 2020–2021, with implementation of a PSA-density threshold of 0.12 ng/mL2. Biopsy scheduling issues and anaesthetic requirements need to be overcome to improve diagnostic waiting times. Level of evidence: 2
AbstractIt is being debated whether prostate-specific antigen (PSA)-based screening effectively reduces prostate cancer mortality. Some of the uncertainty could be related to deficiencies in the age-based PSA cut-off thresholds used in screening. Current study considered 2779 men with prostate cancer and 1606 men without a cancer diagnosis, recruited for various studies in New Zealand, US, and Taiwan. Association of PSA with demographic, lifestyle, clinical characteristics (for cases), and the aldo–keto reductase 1C3 (AKR1C3) rs12529 genetic polymorphisms were analysed using multiple linear regression and univariate modelling. Pooled multivariable analysis of cases showed that PSA was significantly associated with demographic, lifestyle, and clinical data with an interaction between ethnicity and age further modifying the association. Pooled multivariable analysis of controls data also showed that demographic and lifestyle are significantly associated with PSA level. Independent case and control analyses indicated that factors associated with PSA were specific for each cohort. Univariate analyses showed a significant age and PSA correlation among all cases and controls except for the US-European cases while genetic stratification in cases showed variability of correlation. Data suggests that unique PSA cut-off thresholds factorized with demographics, lifestyle and genetics may be more appropriate for prostate cancer screening.