Aims: Gastro-entero-pancreatic neuroendocrine tumours (GEP-NETs) are biologically heterogeneous neoplasms with variable clinical behaviour. We reviewed established and emerging biomarkers that may improve diagnosis, prognostication, therapeutic selection and disease monitoring, with emphasis on their clinical utility, current limitations and future role in precision oncology.
Methods: A structured narrative review was performed using PubMed, Scopus and major international guidance documents. Evidence from landmark studies and contemporary peer-reviewed literature was synthesised, focusing on circulating biomarkers, tissue biomarkers, liquid biopsy platforms, molecular profiling, multi-omic approaches and artificial intelligence (AI)-enabled biomarker integration.
Results: Chromogranin A remains the most widely used circulating biomarker but demonstrates limited specificity owing to multiple biological and analytical confounders. Tissue biomarkers, including synaptophysin, chromogranin and Ki-67, remain fundamental for diagnosis and grading, while molecular alterations involving MEN1, DAXX, ATRX, MGMT and the mTOR pathway increasingly refine tumour classification and therapeutic decision-making. Among liquid biopsy approaches, NETest is the most advanced multigene transcript assay, whereas circulating tumour DNA, circulating tumour cells, microRNAs, long non-coding RNAs and exosome-derived biomarkers remain promising but require further analytical and clinical validation. Emerging evidence suggests that integrated multi-omic platforms combining genomic, transcriptomic, epigenetic, proteomic and imaging data may better capture tumour heterogeneity than single-analyte biomarkers. AI-based analytical tools may further improve biomarker discovery, prognostic modelling and precision treatment selection.
Conclusions: The biomarker landscape in GEP-NETs is evolving from conventional secretory markers towards integrated biology-driven molecular models. Although several emerging biomarkers show considerable promise, widespread clinical implementation requires robust analytical standardisation, prospective multicentre validation and demonstration that biomarker-guided strategies improve patient outcomes. Integrated multi-omic and AI-supported approaches are likely to define the next generation of precision oncology in GEP-NETs.