Systemic Coagulation Markers Especially Fibrinogen Are Closely Associated with the Aggressiveness of Prostate Cancer in Patients Who Underwent Transrectal Ultrasound-Guided Prostate Biopsy

Objective. It has been well elucidated that multiple types of cancers are at high risk of thrombosis. Several studies have indicated the prognostic value of fibrinogen (Fib) and D-dimer (DD) in prostate cancer (PCa). However, it remains unclear regarding the association of the comprehensive coagulation markers with the clinicopathological features of PCa. Methods. A total of 423 pathologically diagnosed patients with PCa were consecutively collected and stratified as low-intermediate-risk or high-risk groups. The association of coagulation parameters including Fib, DD, prothrombin (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and antithrombin III (AT-III) with clinicopathological features was determined by univariate and multivariate logistic regression analyses. Results. The levels of Fib, DD, and PT were significantly higher in the high-risk group ( p < 0.001 , p < 0.001 , and p = 0.043 , resp.), while APTT, TT, and AT-III were similar between two groups ( p > 0.05 , all). Univariate logistic regression analysis demonstrated that Fib, DD, and PT were all positively correlated with high-risk PCa ( OR = 2.041 , p < 0.001 ; OR = 1.003 , p < 0.001 ; OR = 1.247 , p = 0.044 ). Nonetheless, after adjusting for PSA, grade, and stage, Fib (T3 vs. T1, OR = 15.202 , 95% CI: 1.725-133.959, p = 0.014 ) but not DD or PT was the unique independent factor associated with high-risk PCa in the multivariate regression analysis. Conclusions. Our study firstly revealed that Fib but other coagulation markers was independently associated with the severity of PCa, suggesting Fib might be useful in PCa risk stratification beyond PSA, stage, and grade.

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Association of ABO Blood Types and Clinicopathological Features of Prostate Cancer

Disease Markers ◽

10.1155/2017/9237481 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Fang-Ming Wang ◽

Yan Zhang ◽

Gui-Ming Zhang ◽

Ya-Nan Liu ◽

Li-Jiang Sun ◽

...

Keyword(s):

Prostate Cancer ◽

Regression Analysis ◽

High Risk ◽

Gleason Score ◽

Multivariate Regression ◽

Multivariate Regression Analysis ◽

High Risk Group ◽

Blood Type ◽

Blood Types ◽

Significant Difference

Purpose. To investigate the association between ABO blood types and clinicopathological characteristics in patients with prostate cancer (PC). Methods. A total of 237 pathologically diagnosed PC patients were enrolled. All patients were classified as low–middle or high-risk group. The correlation of ABO blood types with high-risk PC was determined by univariate and multivariate regression analysis. Results. Data indicated 144 (85.7%) patients were stratified as high risk in the non-O group, while 50 (72.5%) patients in the O group (p=0.025). However, there was no significant difference regarding PSA, Gleason score, stage, or metastasis between O and non-O group (p>0.05). Univariate logistic regression analyses revealed PSA, Gleason score, and blood type non-O were all correlated with high-risk PC (OR = 1.139, p<0.001; OR = 9.465, p<0.001; OR = 2.280, p=0.018, resp.). In the stepwise multivariate regression analysis, the association between blood type non-O and high-risk PC remained significant (OR = 33.066, 95% CI 2.391–457.323, and p=0.009) after adjusting for confounding factors as well as PSA and Gleason score. Conclusion. The present study firstly demonstrated that non-O blood type was at higher risk of aggressive PC compared with O type, suggesting that PC patients with non-O blood type should receive more attention in clinical practice.

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High Lipoprotein(a) Level Is Independently Associated with Adverse Clinicopathological Features in Patients with Prostate Cancer

Disease Markers ◽

10.1155/2019/9483935 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7

Author(s):

Fang-Ming Wang ◽

Yan Zhang

Keyword(s):

Prostate Cancer ◽

Logistic Regression ◽

High Risk ◽

Risk Group ◽

Density Lipoprotein ◽

Clinicopathological Features ◽

The Other ◽

Intermediate Risk ◽

Regression Analyses ◽

Lipid Parameters

Background. The effect of lipoprotein(a) (Lp(a)) on prostate cancer (PCa) is unclear. The aim of this study was to investigate the association between serum Lp(a) levels and clinicopathological features in patients with PCa. Methods. A total of 376 consecutive pathologically diagnosed PCa patients were enrolled and were classified as a low-intermediate-risk group or a high-risk group. The association of Lp(a) and the other lipid parameters including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), TC/HDL-C, LDL-C/HDL-C, and remnant cholesterol (RC) with clinicopathological parameters was tested by univariate and multivariate logistic regression analyses. Results. The high-risk PCa patients tended to have higher Lp(a) levels (p=0.022) while there was no significant difference regarding the other lipid parameters (p>0.05) compared to low-intermediate-risk counterparts. Patients with PSA≥100 ng/ml had significantly higher Lp(a) levels than subjects with PSA<100 ng/ml (p=0.002). Univariate logistic regression analyses revealed that high Lp(a) levels were correlated with high-risk PCa (Q4 vs. Q1, HR=2.687, 95% CI: 1.113-6.491, p=0.028), while the other lipid parameters were not correlated with high-risk PCa. In the stepwise multivariate regression analysis, the association between Lp(a) levels and high-risk PCa remained significant (Q4 vs. Q1,HR=2.890, 95% CI: 1.148-7.274, p=0.024) after adjusting for confounding factors including age, body mass index, hypertension, diabetes, coronary artery disease, and lipid-lowering drugs. Conclusions. This is the first study showing the positive association between high Lp(a) and adverse clinicopathological features of PCa. PCa patients with high Lp(a) tends to be more aggressive and should receive more attention in clinical practice.

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Two-year outcomes of moderately hypofractionated 70 Gy in 28 fractions, intensity-modulated radiotherapy and volumetric modulated arc therapy for localised prostate cancer

Journal of Radiotherapy in Practice ◽

10.1017/s1460396920000175 ◽

2020 ◽

pp. 1-3

Author(s):

Cindy Sharon Ortiz Arce ◽

Cristian Salvador Leon Solorio ◽

David Calderon Mendoza ◽

Jose de Jesus Ornelas Lopez ◽

Ana Luisa Nava Sierra ◽

...

Keyword(s):

Prostate Cancer ◽

High Risk ◽

External Beam Radiotherapy ◽

Volumetric Modulated Arc Therapy ◽

Risk Groups ◽

High Risk Group ◽

Biochemical Failure ◽

Hypofractionated Radiotherapy ◽

Standard Regimen ◽

Grade Ii

Abstract Introduction: Standard external beam radiotherapy is a treatment option for patients with localised prostate cancer and is used in patients with low-, intermediate- and high-risk disease with androgen deprivation according to the risk of the disease. In the last few years, hypofractionated radiotherapy has been demonstrated to be as safe as standard radiotherapy if given over a shorter time than standard radiotherapy with larger doses per fraction. External radiotherapy for localised prostate cancer typically delivers 37–42 fractions of 1·8–2·0 Gy per fraction given 5 days per week over 7·5–8·5 weeks. Hypofractionated radiotherapy delivers 20–28 fractions of 2·5–2·6 Gy per fraction given 5 days per week over 4–5·6 weeks. Methods: A retrospective analysis of assessment of 30 patients was undertaken from 2016 to 2018. The aim of this study was to evaluate the 2-year outcomes of 30 patients with prostate cancer treated with hypofractionated radiotherapy 70 Gy in 28 fractions. Results: Biochemical failure with hypofractionated radiotherapy was found in a total of 20% of patients. In the classification by risk groups, there were no biochemical failures in low-risk patients; in the low intermediate course, 3·3% of patients; in the high intermediate group, 3·3% patients; and in the high-risk group, the largest documented biochemical failure was in 13·3% of patients. For acute urinary toxicity, grade I was 56·6%; grade II, 6·6%. For acute rectal toxicity, grade I was 46·6%; grade II, 3·3%. Conclusion: This is one of the first studies of hypofractionated radiotherapy in prostate cancer in Latin America, and the results of this study demonstrated that the outcomes were similar to the standard regimen in all risk groups.

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Immune-Related lncRNA Signature for Predicting the Immune Landscape of Head and Neck Squamous Cell Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.689224 ◽

2021 ◽

Vol 8 ◽

Author(s):

Ji Yin ◽

Xiaohui Li ◽

Caifeng Lv ◽

Xian He ◽

Xiaoqin Luo ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Regression Analysis ◽

High Risk ◽

Cell Carcinoma ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

High Risk Group ◽

Survival Prediction ◽

Cox Regression Analysis

Background: Long non-coding RNA (lncRNA) plays a significant role in the development, establishment, and progression of head and neck squamous cell carcinoma (HNSCC). This article aims to develop an immune-related lncRNA (irlncRNA) model, regardless of expression levels, for risk assessment and prognosis prediction in HNSCC patients.Methods: We obtained clinical data and corresponding full transcriptome expression of HNSCC patients from TCGA, downloaded GTF files to distinguish lncRNAs from Ensembl, discerned irlncRNAs based on co-expression analysis, distinguished differentially expressed irlncRNAs (DEirlncRNAs), and paired these DEirlncRNAs. Univariate Cox regression analysis, LASSO regression analysis, and stepwise multivariate Cox regression analysis were then performed to screen lncRNA pairs, calculate the risk coefficient, and establish a prognosis model. Finally, the predictive power of this model was validated through the AUC and the ROC curves, and the AIC values of each point on the five-year ROC curve were calculated to select the maximum inflection point, which was applied as a cut-off point to divide patients into low- or high-risk groups. Based on this methodology, we were able to more effectively differentiate between these groups in terms of survival, clinico-pathological characteristics, tumor immune infiltrating status, chemotherapeutics sensitivity, and immunosuppressive molecules.Results: A 13-irlncRNA-pair signature was built, and the ROC analysis demonstrated high sensitivity and specificity of this signature for survival prediction. The Kaplan–Meier analysis indicated that the high-risk group had a significantly shorter survival rate than the low-risk group, and the chi-squared test certified that the signature was highly related to survival status, clinical stage, T stage, and N stage. Additionally, the signature was further proven to be an independent prognostic risk factor via the Cox regression analyses, and immune infiltrating analyses showed that the high-risk group had significant negative relationships with various immune infiltrations. Finally, the chemotherapeutics sensitivity and the expression level of molecular markers were also significantly different between high- and low-risk groups.Conclusion: The signature established by paring irlncRNAs, with regard to specific expression levels, can be utilized for survival prediction and to guide clinical therapy in HNSCC.

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Immune-Related Genes Are Prognostic Markers for Prostate Cancer Recurrence

Frontiers in Genetics ◽

10.3389/fgene.2021.639642 ◽

2021 ◽

Vol 12 ◽

Author(s):

Min Fu ◽

Qiang Wang ◽

Hanbo Wang ◽

Yun Dai ◽

Jin Wang ◽

...

Keyword(s):

Prostate Cancer ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Gene Mutations ◽

Risk Groups ◽

High Risk Group ◽

Prognosis Model ◽

Immune Related Genes ◽

Risk Prognosis

BackgroundProstate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa.MethodsData were retrieved from the tumor Genome Atlas (TCGA) database and GSE46602 database for performing the least absolute shrinkage and selection operator (LASSO) cox regression model analysis. Immune related genes (IRGs) data were retrieved from ImmPort database.ResultsThe weighted gene co-expression network analysis (WGCNA) showed that nine functional modules are correlated with the biochemical recurrence of PCa, including 259 IRGs. Univariate regression analysis and survival analysis identified 35 IRGs correlated with the prognosis of PCa. LASSO Cox regression model analysis was used to construct a risk prognosis model comprising 18 IRGs. Multivariate regression analysis showed that risk score was an independent predictor of the prognosis of PCa. A nomogram comprising a combination of this model and other clinical features showed good prediction accuracy in predicting the prognosis of PCa. Further analysis showed that different risk groups harbored different gene mutations, differential transcriptome expression and different immune infiltration levels. Patients in the high-risk group exhibited more gene mutations compared with those in the low-risk group. Patients in the high-risk groups showed high-frequency mutations in TP53. Immune infiltration analysis showed that M2 macrophages were significantly enriched in the high-risk group implying that it affected prognosis of PCa patients. In addition, immunostimulatory genes were differentially expressed in the high-risk group compared with the low-risk group. BIRC5, as an immune-related gene in the prediction model, was up-regulated in 87.5% of prostate cancer tissues. Knockdown of BIRC5 can inhibit cell proliferation and migration.ConclusionIn summary, a risk prognosis model based on IGRs was developed. A nomogram comprising a combination of this model and other clinical features showed good accuracy in predicting the prognosis of PCa. This model provides a basis for personalized treatment of PCa and can help clinicians in making effective treatment decisions.

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Evaluation of the Ginsburg Scheme: Where Is Significant Prostate Cancer Missed?

Cancers ◽

10.3390/cancers13102502 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2502

Author(s):

August Sigle ◽

Cordula A. Jilg ◽

Timur H. Kuru ◽

Nadine Binder ◽

Jakob Michaelis ◽

...

Keyword(s):

Prostate Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Cancer Detection ◽

Logistic Regression Analysis ◽

Anterior Region ◽

Targeted Biopsy ◽

Detection Rates ◽

Clinically Significant ◽

Systematic Biopsy

Background: Systematic biopsy (SB) according to the Ginsburg scheme (GBS) is widely used to complement MRI-targeted biopsy (MR-TB) for optimizing the diagnosis of clinically significant prostate cancer (sPCa). Knowledge of the GBS’s blind sectors where sPCa is missed is crucial to improve biopsy strategies. Methods: We analyzed cancer detection rates in 1084 patients that underwent MR-TB and SB. Cancerous lesions that were missed or underestimated by GBS were re-localized onto a prostate map encompassing Ginsburg sectors and blind-sectors (anterior, central, basodorsal and basoventral). Logistic regression analysis (LRA) and prostatic configuration analysis were applied to identify predictors for missing sPCa with the GBS. Results: GBS missed sPCa in 39 patients (39/1084, 3.6%). In 27 cases (27/39, 69.2%), sPCa was missed within a blind sector, with 17/39 lesions localized in the anterior region (43.6%). Neither LRA nor prostatic configuration analysis identified predictors for missing sPCa with the GBS. Conclusions: This is the first study to analyze the distribution of sPCa missed by the GBS. GBS misses sPCa in few men only, with the majority localized in the anterior region. Adding blind sectors to GBS defined a new sector map of the prostate suited for reporting histopathological biopsy results.

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Multivariable Logistic Regression Equation to Predict Prostate Cancer

10.21203/rs.3.rs-923836/v1 ◽

2021 ◽

Author(s):

Lu Ma ◽

Dong Cheng ◽

Qinghua Li ◽

Jingbo Zhu ◽

Yu Wang ◽

...

Keyword(s):

Prostate Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Prostate Specific Antigen ◽

Sensitivity And Specificity ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Specific Antigen ◽

Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

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The national impact of the COVID-19 pandemic on U.S. prostate cancer community care.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.5061 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 5061-5061

Author(s):

Matthew R. Cooperberg ◽

Paul Brendel ◽

Daniel J. Lee ◽

Rahul Doraiswami ◽

Hariesh Rajasekar ◽

...

Keyword(s):

Prostate Cancer ◽

High Risk ◽

Language Processing ◽

Care Delivery ◽

Specific Antigen ◽

Risk Groups ◽

Low Risk ◽

T Stage ◽

Risk Stratum ◽

The Impact

5061 Background: We used data from a specialty-wide, community-based urology registry to determine trends in outpatient prostate cancer (PCa) care during the COVID-19 pandemic. Methods: 3,165 (̃ 25%) of US urology providers, representing 48 states and territories, participate in the American Urological Association Quality (AQUA) Registry, which collects data via automated extraction from electronic health record systems. We analyzed trends in PCa care delivery from 156 practices contributing data in 2019 and 2020. Risk stratification was based on prostate-specific antigen (PSA) at diagnosis, biopsy Gleason, and clinical T-stage, and we used a natural language processing algorithm to determine Gleason and T-stage from unstructured clinical notes. The primary outcome was mean weekly visit volume by PCa patients per practice (visits defined as all MD and mid-level visits, telehealth and face-to-face), and we compared each week in 2020 through week 44 (November 1) to the corresponding week in 2019. Results: There were 267,691 PCa patients in AQUA who received care between 2019 and 2020. From mid-March to early November, 2020 (week 10 – week 44) the magnitude of the decline and recovery varied by risk stratum, with the steepest drops for low-risk PCa (Table). For 2020, overall mean visits per day (averaged weekly) were similar to 2019 for the first 9 weeks (̃25). Visits declined to week 14 (18.19; a 31% drop from 2019), recovered to 2019 levels by week 23, and declined steadily to 11.89 (a 58% drop from 2019) as of week 44, the cut off of this analysis. Conclusions: Access to care for men with PCa was sharply curtailed by the COVID-19 pandemic, and while the impact was less for men with high-risk disease compared to those with low-risk disease, visits even for high-risk individuals were down nearly one-third and continued to fall through November. This study provides real-world evidence on the magnitude of decline in PCa care across risk groups. The impact of this decline on cancer outcomes should be followed closely.[Table: see text]

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A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

10.21203/rs.3.rs-892534/v1 ◽

2021 ◽

Author(s):

Shaopei Ye ◽

Wenbin Tang ◽

Ke Huang

Keyword(s):

Regression Analysis ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Wilms Tumor ◽

Risk Group ◽

High Risk Group ◽

Differentially Expressed ◽

Clinical Prognosis ◽

Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

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Validation of a prospective urinalysis-based prediction model for ICU-resources and outcome of Covid-19 disease: A multicenter cohort study

10.21203/rs.3.rs-97097/v1 ◽

2020 ◽

Author(s):

Oliver Gross ◽

Onnen Moerer ◽

Thomas Rauen ◽

Jan Böckhaus ◽

Elion Hoxha ◽

...

Keyword(s):

High Risk ◽

Risk Group ◽

Kidney Injury ◽

High Risk Group ◽

Adverse Outcomes ◽

Organ Support ◽

Multicenter Cohort Study ◽

Multicenter Cohort ◽

At Iii ◽

Secondary Endpoints

Abstract Purpose: Identifying preventive strategies in Covid-19 patients helps to improve ICU-resource-allocation and reduce mortality. We recently demonstrated in a post-mortem cohort that SARS-CoV-2 renal tropism was associated with kidney injury, disease severity and mortality. We also proposed an algorithm to predict the need for ICU-resources and the risk of adverse outcomes in Covid-19 patients harnessing urinalysis and protein/coagulation parameters on admission for signs of kidney injury. Here, we aimed to validate this hypothesis in a multicenter cohort. Methods: Patients hospitalized for Covid-19 at four tertiary centers were screened for an available urinalysis, serum albumin (SA) and antithrombin-III activity (AT-III) obtained prospectively within 48h upon admission. The respective presumed risk for an unfavorable course was categorized as “low”, “intermediate” or “high”, depending on a normal urinalysis, an abnormal urinalysis with SA ≥2 g/dl and AT-III ≥70%, or an abnormal urinalysis with at least one SA or AT-III abnormality. Time to ICU or death within ten days served as primary, in-hospital mortality and required organ support served as secondary endpoints.Results: Among a total of N=223 screened patients, N=145 were eligible for enrollment, falling into the low (N=43), intermediate (N=84), and high risk (N=18) categories. The risk for ICU transfer or death was 100% in the high risk group and significantly elevated in the composite of high and intermediate risk as compared to the low risk group (63.7% vs. 27.9%; HR 2.6; 95%-CI 1.4 to 4.9; P=0.0020). Having an abnormal urinalysis was associated with mortality, need for mechanical ventilation, extra-corporeal membrane oxygenation (ECMO) or renal replacement therapy (RRT). Conclusion: Our data confirm that Covid-19-associated urine abnormalities on admission predict disease aggravation and need for ICU. By engaging a simple urine dipstick on hospital admission our algorithm allows for early preventive measures and appropriate patient stratification. (ClinicalTrials.gov number NCT04347824)

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