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

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.