Pro-gastrin releasing peptide as a tumor marker of medullary thyroid carcinoma: a comparative bivariate meta-analysis
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Petra Petranović Ovčariček
Abstract
Objectives
Calcitonin (CT) is the established biomarker for medullary thyroid carcinoma (MTC), but its measurement is hampered by analytical limitations. Procalcitonin (PCT) and pro-gastrin-releasing peptide (ProGRP) have been proposed as alternative or complementary markers, yet the diagnostic value of ProGRP remains uncertain. We conducted a systematic review and meta-analysis to evaluate the diagnostic accuracy of ProGRP for MTC and compare its performance with CT.
Methods
This study followed PRISMA-DTA and SEDATE guidelines. PubMed, Embase, Cochrane Library, and Open Grey were searched through June 2025 without language or date restrictions. Eligible studies assessed serum ProGRP for diagnosis or follow-up of MTC and reported or allowed reconstruction of 2 × 2 contingency data. Two reviewers independently screened, extracted data, and assessed study quality using QUADAS-2. A Bayesian bivariate random-effects meta-analysis estimated pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratios. Comparative analyses were performed for ProGRP studies also reporting CT.
Results
Eight studies (n=4,080) were included; four reported both ProGRP and CT (n=1,064). ProGRP showed pooled sensitivity of 74 % and specificity of 95 % (AUC 0.82), while CT achieved 94 % sensitivity and 91 % specificity (AUC 0.89). Subgroup analyses confirmed consistent ProGRP performance across diagnostic and follow-up settings. No publication bias was observed.
Conclusions
ProGRP demonstrates high specificity but lower sensitivity than CT for MTC diagnosis and surveillance. Although not a replacement for CT, ProGRP may serve as a valuable complementary biomarker, particularly in inconclusive cases. Harmonization of assays and prospective validation are required to define universal thresholds and integrate ProGRP into multimarker diagnostic algorithms.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0867).
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