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Due to their scarcity and absence of specific markers, diagnosing NENs remains a complex challenge. Therefore, new biomarkers are required, ideally, in easy-to-obtain blood samples.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>A panel of blood soluble immune checkpoints (sPD-L1, sPD-L2, sPD-1, sCD25, sTIM3, sLAG3, Galectin-9, sCD27, sB7.2 and sSIGLEC5) and cytokines (IL4, IL6, IP10 and MCP1) was quantified in a cohort of 139 NENs, including 29 pituitary NENs, 46 pheochromocytomas and paragangliomas, and 67 gastroenteropancreatic and pulmonary (GEPP) NENs, as well as in 64 healthy volunteers (HVs). The potential of these circulating immunological parameters to distinguish NENs from HVs, differentiate among various NENs subtypes, and predict their prognosis was evaluated using mathematical regression models. These immunological factors-based models generated scores that were evaluated by Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) analyses. Correlations between these scores and clinical data were performed. From these analyses, a minimal signature emerged, comprising the five shared immunological factors across the models: sCD25, sPD-L2, sTIM3, sLAG3, and Galectin-9. This refined signature was evaluated, validated, and checked for specificity against non-neuroendocrine tumors, demonstrating its potential as a clinically relevant tool for identifying distinct NENs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Most of the immunological factors analyzed showed specific expression patterns among different NENs. Scores based on signatures of these factors identified NENs with high efficiency, showing AUCs ranging between 0.948 and 0.993 depending on the comparison, and accuracies between 92.52% and 95.74%. These scores illustrated biological features of NENs including the similarity between pheochromocytomas and paragangliomas, the divergence between gastrointestinal and pulmonary NENs, and correlated with clinical features. Furthermore, the models demonstrated strong performance in distinguishing metastatic and <jats:italic>exitus<\/jats:italic> GEPP NENs, achieving sensitivities and specificities ranging from 80.95% to 88.89%. Additionally, an easy-to-implement minimal signature successfully identified all analyzed NENs with AUC values exceeding 0.900, and accuracies between 84.11% and 93.12%, which was internally validated by a discovery and validation randomization strategy. These findings highlight the effectiveness of the models and minimal signature in accurately diagnosing and differentiating NENs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The analysis of soluble immunological factors in blood presents a promising liquid biopsy approach for identifying NENs, delivering critical insights for both prognosis and diagnosis. This study serves as a proof-of-concept for an innovative clinical tool that holds the potential to transform the management of these rare malignancies, providing a non-invasive and effective method for early detection and disease monitoring.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s13046-025-03337-3","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T01:58:21Z","timestamp":1741139901000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A blood-based liquid biopsy analyzing soluble immune checkpoints and cytokines identifies distinct neuroendocrine tumors"],"prefix":"10.1186","volume":"44","author":[{"given":"Pablo","family":"Mata-Mart\u00ednez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luc\u00eda","family":"Celada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco J.","family":"Cueto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gonzalo","family":"S\u00e1enz de Santa Mar\u00eda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaime","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ver\u00f3nica","family":"Terr\u00f3n-Arcos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuria","family":"Vald\u00e9s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanesa Garc\u00eda","family":"Moreira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda Isabel Enguita","family":"del Toro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo","family":"L\u00f3pez-Collazo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda-Dolores","family":"Chiara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1771-7254","authenticated-orcid":false,"given":"Carlos","family":"del Fresno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"3337_CR1","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1016\/S1470-2045(17)30445-X","volume":"18","author":"G Gatta","year":"2017","unstructured":"Gatta G, Capocaccia R, Botta L, Mallone S, De Angelis R, Ardanaz E, et al. 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