Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection

Jalali, Amirhossein; Kitching, Michael; Martin, Kenneth W.; Richardson, Ciarán; Fitzgerald, S.; Watson, R. W. G.; Perry, Antoinette S.

doi:10.1038/s41598-021-81965-3

Cited by 8 publications

(8 citation statements)

References 37 publications

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“…In Fig. 6 , we investigated the additive effect of multiple metrics on discerning higher levels of oxidative stress using a logistic regression model [ [81] , [82] , [83] ]. First, Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Spearman's correlation coefficients were calculated to examine the relationship between the 5 OxSt metrics and the 5 clinical assessments. A logistic regression model [ [81] , [82] , [83] ] (‘nnet’ package) [ 84 ], was used to assess the combination effect of multiple OxSt metrics on i) the discerning ability of SCZ group from HC group and ii) discriminating ability of post-stressed status relative to baseline. We used Akaike Information Criterion (AIC) to estimate the quality of each model.…”

Section: Methodsmentioning

confidence: 99%

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Association of acute psychosocial stress with oxidative stress: Evidence from serum analysis

et al. 2021

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Growing evidence implicates an association between psychosocial stress and oxidative stress (OxSt) although there are not yet reliable biomarkers to study this association. We used a Trier Social Stress Test (TSST) and compared the response of a healthy control group (HC; N=10) against the response of a schizophrenia group (SCZ; N=10) that is expected to have higher levels of OxSt. Because our previous study showed inconsistent changes in conventional molecular markers for stress responses in the neuroendocrine and immune systems, we analyzed the same serum samples using a separate reducing capacity assay that provides a more global measurement of OxSt. This assay uses the moderately strong oxidizing agent iridium (Ir) to probe a sample's reducing capacity. Specifically, we characterized OxSt by this Ir-reducing capacity assay (Ir-RCA) using two measurement modalities (optical and electrochemical) and we tuned this assay by imposing an input voltage sequence that generates multiple output metrics for data-driven analysis. We defined five OxSt metrics (one optical and four electrochemical metrics) and showed: (i) internal consistency among each metric in the measurements of all 40 samples (baseline and post TSST for N=20); (ii) all five metrics were consistent with expectations of higher levels of OxSt for the SCZ group (three individual metrics showed statistically significant differences); and (iii) all five metrics showed higher levels of OxSt Post-TSST (one metric showed statistically significant difference). Using multivariant analysis, we showed that combinations of OxSt metrics could discern statistically significant increases in OxSt for both the SCZ and HC groups 90 min after the imposed acute psychosocial stress.

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“…In Fig. 6 , we investigated the additive effect of multiple metrics on discerning higher levels of oxidative stress using a logistic regression model [ [81] , [82] , [83] ]. First, Fig.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Association of acute psychosocial stress with oxidative stress: Evidence from serum analysis

et al. 2021

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“…Our study demonstrated that the combination of PEDF with PSA, DRE and bxGS improved the prediction of post-operative BCR in both the individual cohort and combinedcohort studies. Previous studies by our group and others have demonstrated that the integration of serum biomarkers improves prediction models [33][34][35]. PEDF has multiple biological actions and is expressed by prostate epithelial and stromal cells [36].…”

Section: Discussionmentioning

confidence: 98%

Integrating Serum Biomarkers into Prediction Models for Biochemical Recurrence Following Radical Prostatectomy

Moghaddam

Jalali

O’Neill

et al. 2021

Cancers

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This study undertook to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy using serum biomarkers and clinical features. Three radical prostatectomy cohorts were used to build and validate a model of clinical variables and serum biomarkers to predict BCR. The Cox proportional hazard model with stepwise selection technique was used to develop the model. Model evaluation was quantified by the AUC, calibration, and decision curve analysis. Cross-validation techniques were used to prevent overfitting in the Irish training cohort, and the Austrian and Norwegian independent cohorts were used as validation cohorts. The integration of serum biomarkers with the clinical variables (AUC = 0.695) improved significantly the predictive ability of BCR compared to the clinical variables (AUC = 0.604) or biomarkers alone (AUC = 0.573). This model was well calibrated and demonstrated a significant improvement in the predictive ability in the Austrian and Norwegian validation cohorts (AUC of 0.724 and 0.606), compared to the clinical model (AUC of 0.665 and 0.511). This study shows that the pre-operative biomarker PEDF can improve the accuracy of the clinical factors to predict BCR. This model can be employed prior to treatment and could improve clinical decision making, impacting on patients’ outcomes and quality of life.

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“…Evidence suggests that the use of multiple markers to differentiate non-clinically significant from clinically significant disease is an important strategy for reducing unnecessary referrals for further investigation ( 40 ). Integrating inflammatory serum biomarkers into a risk calculator may provide additional information for detecting and managing PCa risk ( 40 ). The predictive value of inflammatory markers for PCa diagnosis has been evaluated in primary care ( 41 ).…”

Section: Discussionmentioning

confidence: 99%

A Novel Combination of Serum Markers in a Multivariate Model to Help Triage Patients Into “Low-” and “High-Risk” Categories for Prostate Cancer

et al. 2022

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BackgroundAlmost 50,000 men in the United Kingdom (UK) are diagnosed each year with prostate cancer (PCa). Secondary referrals for investigations rely on serum prostate-specific antigen (PSA) levels and digital rectal examination. However, both tests lack sensitivity and specificity, resulting in unnecessary referrals to secondary care for costly and invasive biopsies.Materials and MethodsSerum samples and clinical information were collected from N = 125 age-matched patients (n = 61 non-PCa and n = 64 PCa) and analyzed using Biochip Array Technology on high-sensitivity cytokine array I (IL-2, IL-4, IL-6, IL-8, IL-10, IL-1α, IL-1β, TNFα, MCP-1, INFγ, EGF, and VEGF), cerebral array II (CRP, D-dimer, neuron-specific enolase, and sTNFR1), and tumor PSA oncology array (fPSA, tPSA, and CEA).ResultsThe data showed that 11/19 (68.8%) markers were significantly different between the non-PCa and the PCa patients. A combination of EGF, log10 IL-8, log10 MCP-1, and log10 tPSA significantly improved the predictive potential of tPSA alone to identify patients with PCa (DeLong, p < 0.001). This marker combination had an increased area under the receiver operator characteristic (0.860 vs. 0.700), sensitivity (78.7 vs. 68.9%), specificity (76.5 vs. 67.2%), PPV (76.2 vs. 66.7%), and NPV (79.0 vs. 69.4%) compared with tPSA.ConclusionsThe novel combination of serum markers identified in this study could be employed to help triage patients into “low-” and “high-risk” categories, allowing general practitioners to improve the management of patients in primary care settings and potentially reducing the number of referrals for unnecessary, invasive, and costly treatments.

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Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection

Cited by 8 publications

References 37 publications

Association of acute psychosocial stress with oxidative stress: Evidence from serum analysis

Association of acute psychosocial stress with oxidative stress: Evidence from serum analysis

Integrating Serum Biomarkers into Prediction Models for Biochemical Recurrence Following Radical Prostatectomy

A Novel Combination of Serum Markers in a Multivariate Model to Help Triage Patients Into “Low-” and “High-Risk” Categories for Prostate Cancer

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