Diagnostic Stratification of Prostate Cancer Through Blood-Based Biochemical and Inflammatory Markers
<b>Background:</b> Prostate cancer (PCa) remains one of the most prevalent malignancies in men, with diagnostic challenges arising from the limited specificity of current biomarkers, like PSA. Improved stratification tools are essential to reduce overdiagnosis and guide personalized pati...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2075-4418/15/11/1385 |
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| author | Donatella Coradduzza Leonardo Sibono Alessandro Tedde Sonia Marra Maria Rosaria De Miglio Angelo Zinellu Serenella Medici Arduino A. Mangoni Massimiliano Grosso Massimo Madonia Ciriaco Carru |
| author_facet | Donatella Coradduzza Leonardo Sibono Alessandro Tedde Sonia Marra Maria Rosaria De Miglio Angelo Zinellu Serenella Medici Arduino A. Mangoni Massimiliano Grosso Massimo Madonia Ciriaco Carru |
| author_sort | Donatella Coradduzza |
| collection | DOAJ |
| description | <b>Background:</b> Prostate cancer (PCa) remains one of the most prevalent malignancies in men, with diagnostic challenges arising from the limited specificity of current biomarkers, like PSA. Improved stratification tools are essential to reduce overdiagnosis and guide personalized patient management. <b>Objective:</b> This study aimed to identify and validate clinical and hematological biomarkers capable of differentiating PCa from benign prostatic hyperplasia (BPH) and precancerous lesions (PL) using univariate and multivariate statistical methods. <b>Methods:</b> In a cohort of 514 patients with suspected PCa, we performed a univariate analysis (Kruskal–Wallis and ANOVA) with preprocessing via adaptive Box–Cox transformation and missing value imputation through probabilistic principal component analysis (PPCA). LASSO regression was used for variable selection and classification. An ROC curve analysis assessed diagnostic performance. <b>Results:</b> Five variables—age, PSA, Index %, hemoglobin (HGB), and the International Index of Erectile Function (IIEF)—were consistently significant across univariate and multivariate analyses. The LASSO regression achieved a classification accuracy of 70% and an AUC of 0.74. Biplot and post-hoc analyses confirmed partial separation between PCa and benign conditions. <b>Conclusions:</b> The integration of multivariate modeling with reconstructed clinical data enabled the identification of blood-based biomarkers with strong diagnostic potential. These routinely available, cost-effective indicators may support early PCa diagnosis and patient stratification, reducing unnecessary invasive procedures. |
| format | Article |
| id | doaj-art-7315454ccdcc484a8cbb1599e3282bc3 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-7315454ccdcc484a8cbb1599e3282bc32025-08-20T03:46:52ZengMDPI AGDiagnostics2075-44182025-05-011511138510.3390/diagnostics15111385Diagnostic Stratification of Prostate Cancer Through Blood-Based Biochemical and Inflammatory MarkersDonatella Coradduzza0Leonardo Sibono1Alessandro Tedde2Sonia Marra3Maria Rosaria De Miglio4Angelo Zinellu5Serenella Medici6Arduino A. Mangoni7Massimiliano Grosso8Massimo Madonia9Ciriaco Carru10Department of Biomedical Sciences, University of Sassari, 07100 Sassari, ItalyDepartment of Mechanical, Chemical, and Materials Engineering, University of Cagliari, 09123 Cagliari, ItalyUnit of Urology, University Hospital of Sassari (A.O.U. SS), 07100 Sassari, ItalyUnit of Urology, University Hospital of Sassari (A.O.U. SS), 07100 Sassari, ItalyDepartment of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, ItalyDepartment of Biomedical Sciences, University of Sassari, 07100 Sassari, ItalyDepartment of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, 07100 Sassari, ItalyDepartment of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide 5042, AustraliaDepartment of Mechanical, Chemical, and Materials Engineering, University of Cagliari, 09123 Cagliari, ItalyUnit of Urology, University Hospital of Sassari (A.O.U. SS), 07100 Sassari, ItalyDepartment of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy<b>Background:</b> Prostate cancer (PCa) remains one of the most prevalent malignancies in men, with diagnostic challenges arising from the limited specificity of current biomarkers, like PSA. Improved stratification tools are essential to reduce overdiagnosis and guide personalized patient management. <b>Objective:</b> This study aimed to identify and validate clinical and hematological biomarkers capable of differentiating PCa from benign prostatic hyperplasia (BPH) and precancerous lesions (PL) using univariate and multivariate statistical methods. <b>Methods:</b> In a cohort of 514 patients with suspected PCa, we performed a univariate analysis (Kruskal–Wallis and ANOVA) with preprocessing via adaptive Box–Cox transformation and missing value imputation through probabilistic principal component analysis (PPCA). LASSO regression was used for variable selection and classification. An ROC curve analysis assessed diagnostic performance. <b>Results:</b> Five variables—age, PSA, Index %, hemoglobin (HGB), and the International Index of Erectile Function (IIEF)—were consistently significant across univariate and multivariate analyses. The LASSO regression achieved a classification accuracy of 70% and an AUC of 0.74. Biplot and post-hoc analyses confirmed partial separation between PCa and benign conditions. <b>Conclusions:</b> The integration of multivariate modeling with reconstructed clinical data enabled the identification of blood-based biomarkers with strong diagnostic potential. These routinely available, cost-effective indicators may support early PCa diagnosis and patient stratification, reducing unnecessary invasive procedures.https://www.mdpi.com/2075-4418/15/11/1385biomarker discoveryprostate cancermultivariate analysisadaptive Box–Cox transformationLASSO regressionprobabilistic principal component analysis (PPCA) |
| spellingShingle | Donatella Coradduzza Leonardo Sibono Alessandro Tedde Sonia Marra Maria Rosaria De Miglio Angelo Zinellu Serenella Medici Arduino A. Mangoni Massimiliano Grosso Massimo Madonia Ciriaco Carru Diagnostic Stratification of Prostate Cancer Through Blood-Based Biochemical and Inflammatory Markers Diagnostics biomarker discovery prostate cancer multivariate analysis adaptive Box–Cox transformation LASSO regression probabilistic principal component analysis (PPCA) |
| title | Diagnostic Stratification of Prostate Cancer Through Blood-Based Biochemical and Inflammatory Markers |
| title_full | Diagnostic Stratification of Prostate Cancer Through Blood-Based Biochemical and Inflammatory Markers |
| title_fullStr | Diagnostic Stratification of Prostate Cancer Through Blood-Based Biochemical and Inflammatory Markers |
| title_full_unstemmed | Diagnostic Stratification of Prostate Cancer Through Blood-Based Biochemical and Inflammatory Markers |
| title_short | Diagnostic Stratification of Prostate Cancer Through Blood-Based Biochemical and Inflammatory Markers |
| title_sort | diagnostic stratification of prostate cancer through blood based biochemical and inflammatory markers |
| topic | biomarker discovery prostate cancer multivariate analysis adaptive Box–Cox transformation LASSO regression probabilistic principal component analysis (PPCA) |
| url | https://www.mdpi.com/2075-4418/15/11/1385 |
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