Searching for New Biomarkers of Neuroendocrine Tumors: A Comparative Analysis of Chromogranin A and Inflammatory Cytokines in Patients with Neuroendocrine Tumors
Bibliographic record
Abstract
Neuroendocrine neoplasms (NENs) present a diagnostic challenge due to their heterogeneous nature and non-specific clinical manifestations. This study aimed to explore novel biomarkers for NENs. Serum chromogranin A (CgA) levels and a panel of 48 inflammatory cytokines were analyzed in a cohort of 84 NEN patients and 40 healthy controls using enzyme-linked immunosorbent assay (ELISA) and multiplex ELISA. Significant alterations in cytokine levels were observed in the NEN patients compared to the controls, including elevated levels of pro-inflammatory cytokines, such as interleukin (IL)-6, IL-8, and tumor necrosis factor alpha (TNF-α), and reduced levels of angiogenic factors like platelet-derived growth factor-BB (PDGF-BB) and tumor necrosis factor beta (TNF-β). Notably, cytokines such as growth-regulated alpha protein (GRO-α) and TNF-β demonstrated strong potential as diagnostic markers, with receiver operating characteristic (ROC) curve analyses showing high sensitivity and specificity. Additionally, a positive correlation was found between CgA levels and several inflammatory cytokines, suggesting their synergistic role in tumor progression. These findings highlight the limited reliability of CgA alone as a diagnostic marker and underscore the importance of a multi-marker approach in diagnosing and monitoring NENs. Further research on a larger cohort is necessary to validate these biomarkers and their potential clinical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".