Dataset Information

Detection of Core2 ?-1,6-N-Acetylglucosaminyltransferase in Post-Digital Rectal Examination Urine Is a Reliable Indicator for Extracapsular Extension of Prostate Cancer.

ABSTRACT: To identify appropriate candidates for aggressive treatment such as radical prostatectomy or radiation therapy of localized prostate cancer (PCa), novel predictive biomarkers of PCa aggressiveness are essential. Core2 ?-1,6-N-acetylglucosaminyltransferase-1 (GCNT1) is a key enzyme that forms core 2-branched O-glycans. Its expression is associated with the progression of several cancers. We established a mouse IgG monoclonal antibody (mAb) against GCNT1 and examined the relationship of GCNT1 expression to the clinicopathological status of PCa. Paraffin-embedded PCa specimens were analyzed by immunohistochemistry for GCNT1 expression using a newly established mouse anti-GCNT1 mAb by ourselves. GCNT1-positive tumor showed significantly higher Gleason score and larger tumor volume. The number of GCNT1-positive cases was significantly lower in cases of organ-confined disease than in cases of extracapsular extension. GCNT1-negative tumors were associated with significantly better prostate-specific antigen (PSA)-free survival compared with GCNT1-positive tumors. Multivariate analysis revealed that detection of GCNT1 expression was an independent risk factor for PSA recurrence. We established new methods for GCNT1 detection from PCa specimens. Immunoblotting was used to examine post-digital rectal examination (DRE) urine from PCa patients. Over 90% of GCNT1-positive PCa patients with high concentrations of PSA showed extracapsular extension. In conclusion, GCNT1 expression closely associates with the aggressive potential of PCa. Further research aims to develop GCNT1 detection in post-DRE urine as a marker for PCa aggressiveness.

SUBMITTER: Kojima Y

PROVIDER: S-EPMC4577128 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Detection of Core2 β-1,6-N-Acetylglucosaminyltransferase in Post-Digital Rectal Examination Urine Is a Reliable Indicator for Extracapsular Extension of Prostate Cancer.

Kojima Yuta Y Yoneyama Tohru T Hatakeyama Shingo S Mikami Jotaro J Sato Tendo T Mori Kazuyuki K Hashimoto Yasuhiro Y Koie Takuya T Ohyama Chikara C Fukuda Minoru M Tobisawa Yuki Y

PloS one 20150921 9

To identify appropriate candidates for aggressive treatment such as radical prostatectomy or radiation therapy of localized prostate cancer (PCa), novel predictive biomarkers of PCa aggressiveness are essential. Core2 β-1,6-N-acetylglucosaminyltransferase-1 (GCNT1) is a key enzyme that forms core 2-branched O-glycans. Its expression is associated with the progression of several cancers. We established a mouse IgG monoclonal antibody (mAb) against GCNT1 and examined the relationship of GCNT1 expr ...[more]

PMID: 26390303

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Project description:The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.Critical relevant statementThe use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.Protocol of systematic review registrationPROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 .Key pointsRadiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.

Project description:BackgroundPrediction of extracapsular extension (ECE) is essential to achieve a balance between oncologic resection and neural tissue preservation. Microultrasound (MUS) is an attractive alternative to multiparametric magnetic resonance imaging (mpMRI) in the staging scenario.ObjectiveTo create a side-specific nomogram integrating clinicopathologic parameters and MUS findings to predict ipsilateral ECE and guide nerve sparing.Design setting and participantsProspective data were collected from consecutive patients who underwent robotic-assisted radical prostatectomy from June 2021 to May 2022 and had preoperative MUS and mpMRI. A total of 391 patients and 612 lobes were included in the analysis.Outcome measurements and statistical analysisECE on surgical pathology was the primary outcome. Multivariate regression analyses were carried out to identify predictors for ECE. The resultant multivariable model's performance was visualized using the receiver-operating characteristic curve. A nomogram was developed based on the coefficients of the logit function for the MUS-based model. A decision curve analysis (DCA) was performed to assess clinical utility.Results and limitationsThe areas under the receiver-operating characteristic curve (AUCs) of the MUS-based model were 81.4% and 80.9% (95% confidence interval [CI] 75.6, 84.6) after internal validation. The AUC of the mpMRI-model was also 80.9% (95% CI 77.2, 85.7). The DCA demonstrated the net clinical benefit of the MUS-based nomogram and its superiority compared with MUS and MRI alone for detecting ECE. Limitations of our study included its sample size and moderate inter-reader agreement.ConclusionsWe developed a side-specific nomogram to predict ECE based on clinicopathologic variables and MUS findings. Its performance was comparable with that of a mpMRI-based model. External validation and prospective trials are required to corroborate our results.Patient summaryThe integration of clinical parameters and microultrasound can predict extracapsular extension with similar results to models based on magnetic resonance imaging findings. This can be useful for tailoring the preservation of nerves during surgery.

Project description:INTRODUCTION:Extracapsular extension (ECE) in regional lymph nodes and positive surgical margins (PSM) are considered high-risk adverse pathologic features in patients with oropharyngeal squamous cell carcinoma (OPSCC) that each constitute an indication for postoperative adjuvant chemoradiation. We identify pre-operative clinical factors that can predict post-operative ECE and/or PSM and create a nomogram to help clinical decision making. METHODS:Adult patients with non-metastatic OPSCC with initial surgical treatment and confirmed HPV status diagnosed between 2010 and 2014 were selected from the National Cancer Database. Clinical staging was modified to American Joint Committee on Cancer 8th edition parameters. Logistic regression was used for multivariate analysis to identify predictors of pathologic ECE and/or PSM. RESULTS:5065 patients were included. 47.5% of the 3336 HPV-positive (HPV+) patients had ECE/PSM. 40.4% of the 1729 HPV-negative (HPV-) patients with had ECE/PSM. A model was built that included age, clinical ECE, tumor grade, and clinical T and N staging for HPV+ patients. Increasing N-classification was highly predictive of pathologic ECE and/or PSM (N1 OR?=?3.6, N2 OR?=?7.0, N3 OR?=?11.2, p?<?0.01). Clinical ECE (OR?=?4.1, p?<?0.01), tumor grade (ORs 2.2-4.4 with p?<?0.05), and increasing clinical T-classification (ORs 1.2-1.8, p?<?0.05) were also associated with ECE and/or PSM. A similar model was built for HPV- with similar predictive capability. Two internally validated nomograms were designed that demonstrated good discrimination (HPV+ AUC?=?0.66, 95% CI: 0.64-0.68, and HPV- AUC?=?0.70, 95% CI: 0.67-0.72) and good calibration (goodness-of-fit statistic of HPV+ 6.32, p?=?0.61 and HPV- 11.66, p?=?0.17). CONCLUSIONS:These are the first nomograms designed to help predict ECE or PSM for both HPV+ and HPV- OPSCC. The nomograms can facilitate shared decision-making between clinicians and patients as they consider upfront treatment selection for OPSCC.

Dataset Information

Detection of Core2 ?-1,6-N-Acetylglucosaminyltransferase in Post-Digital Rectal Examination Urine Is a Reliable Indicator for Extracapsular Extension of Prostate Cancer.

Publications

Detection of Core2 β-1,6-N-Acetylglucosaminyltransferase in Post-Digital Rectal Examination Urine Is a Reliable Indicator for Extracapsular Extension of Prostate Cancer.

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