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Background <p>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.</p> Methods <p>This study evaluated the clinical applicability of an ML-based algorithm developed using TCGA data (<i>n</i> = 608) and tested in an independent dataset (<i>n</i> = 349). The eleven candidate genes that contributed most to the predictive model were initially profiled by targeted RNA-Seq in prostate tissue validation cohort (<i>n</i> = 141) and further validated in replication tissue (<i>n</i> = 75) and plasma (<i>n</i> = 50) cohorts by qPCR and dPCR, respectively.</p> Results <p>Gene expression analysis in prostate tissue led to the identification of a six-gene signature (<i>DLX1</i>,<i> TDRD1</i>,<i> AMACR</i>,<i> HPN</i>,<i> HOXC6</i>, and <i>OR51E2)</i> 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, <i>AMACR</i> demonstrated added diagnostic value as a non-invasive biomarker when integrated with clinical parameters (AUC = 93.21%).</p> Conclusions <p>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.</p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Patricia Porras-Quesada,
  • Alberto Ramírez-Mena,
  • Verónica Arenas-Rodríguez,
  • Fernando Vázquez-Alonso,
  • Jesús Alcalá-Fdez,
  • Beatriz Álvarez-González,
  • Luis Javier Martínez-González,
  • María Jesús Álvarez-Cubero

摘要

Background

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.

Methods

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.

Results

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%).

Conclusions

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.

Graphical Abstract