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. 2025 Apr 29;8(5):e70796.
doi: 10.1002/hsr2.70796. eCollection 2025 May.

Evaluating the Diagnostic Potential of Biomarker Panels in Breast Cancer and Prostate Adenocarcinoma

Kldiashvili Ekaterina  1 , Iordanishvili Saba  1   2 , Adamia Sophia  2 , Abiatari Ivane  2 , Zarnadze Maia  1
Affiliations

Evaluating the Diagnostic Potential of Biomarker Panels in Breast Cancer and Prostate Adenocarcinoma

Kldiashvili Ekaterina et al. Health Sci Rep. .

Abstract

Background: Noninvasive diagnostic methods are essential for early cancer detection and improved patient outcomes. Circulating biomarkers, measurable indicators of pathological processes, offer a promising avenue, yet optimal panels for reliable cancer diagnosis remain undefined. This study evaluates the diagnostic performance of selected plasma biomarkers in distinguishing breast cancer and prostate adenocarcinoma patients from healthy individuals, using statistical analysis and machine learning.

Materials and methods: We analyzed blood samples from 162 participants (73 cancer patients: 51 with breast cancer and 22 with prostate adenocarcinoma; 89 healthy controls). Levels of 12 cancer-associated biomarkers-including Ki67, DNMT1, BRCA1, and MPO-were quantified using enzyme-linked immunosorbent assays (ELISA). Statistical analyses, including the Mann-Whitney U test and machine learning models (random forest), were employed to assess the predictive accuracy of these biomarkers in distinguishing between cancerous and healthy states.

Results: Biomarkers such as Ki67, DNMT1, and MPO were significantly elevated in cancer groups. Random forest models using selected combinations (e.g., BRCA1-CTA-TP53) achieved perfect classification accuracy (AUC = 1.00). However, high inter-marker correlations suggested potential redundancy, underscoring the need for biomarker panel optimization.

Conclusion: Our findings support the potential of biomarker panels for accurate, noninvasive cancer diagnostics. Further validation in larger, more diverse cohorts is warranted to establish clinical utility and generalizability.

Keywords: biomarkers; blood samples; cancer; machine learning; panel.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Mean biomarker levels across groups. This figure illustrates the mean levels of 12 biomarkers in four groups: breast cancer, healthy females, prostate adenocarcinoma, and healthy males. Error bars indicate the standard error of the mean.
Figure 2
Figure 2
Correlation heatmap of biomarkers. Note: Negative correlations are not displayed as they were not present.
Figure 3
Figure 3
Variable importance plot for all biomarkers models. Note: Higher Mean Decrease Gini values correspond to higher level of importance.
See this image and copyright information in PMC

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