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Updated: Sep 15, 2025
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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
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A hematological and inflammatory marker-based model for prostate carcinoma diagnosis
Peiyi Guo1,2, Garu A3, Tao Chen4
1Centrum für Muskuloskeletale Chirurgie, Campus Virchow Klinikum, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin Augustenburger Platz 1, 13353, Berlin, Deutschland.
American Journal of Cancer Research
|July 16, 2025
Summary
This study developed a new diagnostic model for prostate cancer (PC) using hematological and inflammatory markers. The model significantly improves early PC detection accuracy compared to traditional methods.
Area of Science:
- Urology
- Oncology
- Hematology
Background:
- Prostate carcinoma (PC) is a leading cancer in men, with early detection crucial for effective management.
- Current prostate-specific antigen (PSA) screening faces challenges in diagnostic accuracy for differentiating malignant from benign conditions.
- Hematological and inflammatory markers offer potential for improved PC diagnosis.
Purpose of the Study:
- To develop and validate a diagnostic model for early prostate carcinoma detection.
- To enhance diagnostic accuracy by integrating novel inflammatory markers with traditional PSA testing.
- To assess the model's performance against conventional diagnostic approaches.
Main Methods:
- Retrospective study of 317 patients undergoing prostate biopsy (126 PC, 191 benign prostatic hyperplasia).
- Analysis of clinical and laboratory data, including PSA, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and C-reactive protein (CRP).
- Logistic regression and LASSO regression for marker screening and model construction; ROC and decision curve analysis for performance evaluation.
Main Results:
- Neutrophils, monocytes, CRP, NLR, NAR, CK-MB, and PSA were identified as independent diagnostic indicators for PC.
- The LASSO regression-based model achieved an Area Under the Curve (AUC) of 0.850.
- This model demonstrated significantly superior diagnostic accuracy compared to the traditional logistic regression model (AUC=0.792).
Conclusions:
- Combining PSA with novel inflammatory markers enhances early diagnostic accuracy for prostate carcinoma.
- The proposed predictive model shows promise for improving early PC detection and clinical decision-making.
- This approach offers a more accurate and efficient tool for distinguishing PC from benign prostatic hyperplasia.
