Evidence map›Paper›PMID 41647707›Full record

ArticleESMO real world data and digital oncology2025

Advanced risk signature analysis of inflammation markers in predicting prostate cancer using the Swedish Apolipoprotein-related MOrtality RISk (AMORIS) cohort.

G George, M Rowley, A C C Coolen, A Santa Olalla, N Hammar, M Feychting, S N Karagiannis, D Enting, M Van Hemelrijck

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Article in ESMO real world data and digital oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

G GeorgeTransforming Cancer Outcomes Through Research, King's College London, London, UK.
M RowleyTransforming Cancer Outcomes Through Research, King's College London, London, UK.
A C C CoolenSaddle Point Science Europe, Nijmegen, The Netherlands.
A Santa OlallaTransforming Cancer Outcomes Through Research, King's College London, London, UK.
N HammarInstitute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
M FeychtingInstitute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
S N KaragiannisCancer Antibody Discovery & Immunotherapy Group, St. John's Institute of Dermatology, School of Basic & Medical Biosciences & King's Health Partners Centre for Translational Medicine, King's College London, London, UK.
D EntingMedical Oncology, Guy's and St Thomas' NHS Foundation Trust, London, UK.
M Van HemelrijckTransforming Cancer Outcomes Through Research, King's College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Elevated post-diagnosis levels of C-reactive protein (CRP) and haptoglobin, and low albumin levels, have been associated with poor prostate cancer (PCa) prognosis. Advanced techniques are needed for biomarker-based cancer risk prediction. We evaluated PCa risk using Cox models and risk signature analysis in the Swedish Apolipoprotein-related MOrtality RISk (AMORIS) cohort. Materials and methods: AMORIS includes biomarker data on >800 000 individuals from 1985 to 1996 in primary care and occupational setting, linked to national health and population registers through 2020. PCa risk was analysed using Cox proportional hazard models for albumin, CRP, haptoglobin and white blood cells at the third time point, adjusting for age, socioeconomic status, education level, Charlson comorbidity index (CCI) and cancer history, and risk signature analysis with training and validation sets (preventing overfitting) including repeated biomarker measurements, covariate interactions and baseline factors. Sensitivity analysis categorised age and CCI. Results: Cox model showed elevated CRP and CCI ≥2 significantly increased PCa risk, with age consistently predictive. Risk signatures confirmed age as the dominant risk factor (hazard ratio 1.15 per year, 95% confidence interval 1.13-1.18) and highlighted interaction effects: younger men with cancer history had higher PCa risk, while elevated CRP with high CCI amplified risk, demonstrating strong predictive accuracy (receiver operating characteristic area under the curve: 0.82; Harrell's C: 0.72). Categorising age and CCI further refined risk stratification. Conclusion: Although elevated CRP was associated with higher PCa risk, no clear associations were detected for other markers. Advanced risk analysis found age to be the sole predictor, indicating conventional methods may overestimate biomarker roles in PCa prediction.

Indexed as

AMORISinflammation markersprostate cancer risksignature analysis

Identifiers

PMID41647707
PMCPMC12836697

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.