Enhancing prostate cancer diagnosis: a machine learning-based biomarker approach

The lack of reliable screening biomarkers for prostate cancer (PC) diagnosis makes tissue biopsy the gold-standard strategy. However, its frequent inconclusive results often lead to repeated procedures, increasing patient burden and healthcare costs. In this context, machine learning (ML) presents a novel approach for identifying gene signatures associated with tumor presence, offering a promising avenue for improved PC detection. Therefore, the present study explored the diagnostic potential of an ML-based gene signature and its applicability in tissue and plasma samples. This study evaluated the clinical applicability of an ML-based algorithm developed using TCGA data (n = 608) and tested in an independent dataset (n = 349). The eleven candidate genes that contributed most to the predictive model were initially profiled by targeted RNA-Seq in prostate tissue validation cohort (n = 141) and further validated in replication tissue (n = 75) and plasma (n = 50) cohorts by qPCR and dPCR, respectively. Gene expression analysis in prostate tissue led to the identification of a six-gene signature (DLX1, TDRD1, AMACR, HPN, HOXC6, and OR51E2) with high diagnostic performance (AUC = 95.9%). Expression patterns supported the gene signature’s potential to identify false-negative cases and correctly classify inconclusive biopsy results. In plasma, AMACR demonstrated added diagnostic value as a non-invasive biomarker when integrated with clinical parameters (AUC = 93.21%). These findings demonstrate that our ML-based gene signature can accurately distinguish PC from non-tumoral tissue and resolve ambiguous biopsy results. Its integration alongside histopathology has the potential to reduce diagnostic uncertainty, improving PC early detection and guiding clinical decision-making. This study validates a machine learning–derived gene expression signature that improves prostate cancer diagnosis, particularly in cases with ambiguous or false-negative biopsy results. The six-gene panel was experimentally validated using targeted RNA-Seq and PCR-based techniques in independent cohorts of prostate tissue and plasma, showing excellent diagnostic performance. By complementing standard histopathology, this molecular tool may enhance early detection, reduce unnecessary repeat biopsies, and support the integration of precision medicine into routine prostate cancer care.