Title Liquid-based diagnostic panels for prostate cancer: The synergistic role of soluble PD-L1, PD-1, and mRNA biomarkers /
Authors Žvirblė, Margarita ; Vaicekauskaitė, Ieva ; Survila, Žilvinas ; Bosas, Paulius ; Dobrovolskienė, Neringa ; Mlynska, Agata ; Sabaliauskaitė, Rasa ; Pašukonienė, Vita
DOI 10.3390/ijms26020704
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Is Part of International journal of molecular sciences: Special Issue: Liquid Biopsies in Oncology II.. Basel : MDPI. 2025, vol. 26, iss. 2, art. no. 704, p. 1-11.. ISSN 1661-6596. eISSN 1422-0067
Keywords [eng] prostate cancer ; sPD-L1 ; sPD-1 ; mRNA transcripts ; circulating molecules ; liquid biopsy
Abstract [eng] This study aimed to evaluate the diagnostic potential of soluble Programmed Death Ligand 1 (sPD-L1) and Programmed Death 1 (sPD-1) molecules in plasma, along with urinary mRNA biomarkers—Prostate-Specific Membrane Antigen (PSMA), Prostate Cancer Antigen 3 (PCA3), and androgen receptor (AR) genes—for identifying clinically significant prostate cancer (PCa), defined as pathological stage 3. In a cohort of 68 PCa patients, sPD-L1 and sPD-1 levels were quantified using ELISA, while mRNA transcripts were measured by RT-qPCR. Results highlight the potential of integrating these liquid-based biomarkers. In particular, the combination of sPD-L1, sPD-1, and AR demonstrated the most significant improvement in diagnostic performance, increasing the area under the curve (AUC) from 0.65 to 0.81 and sensitivity from 60% to 88%, compared to AR alone. PSMA demonstrated an AUC of 0.82 and a specificity of 52.8%, which improved to an AUC of 0.85 and a specificity of 94.4% with the inclusion of sPD-L1 and sPD-1. Similarly, PCA3 achieved an AUC of 0.75 and a specificity of 53.8%, increasing to an AUC of 0.78 and a specificity of 76.9% when combined with these biomarkers. Incorporating sPD-L1 into a three-gene panel further elevated the AUC from 0.74 to 0.94. These findings underscore the value of multimodal liquid-based diagnostic panels in improving the management of clinically significant PCa.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2025
CC license CC license description