Backgrounds. Pancreatic neuroendocrine neoplasm (pNEN) is a highly heterogeneous entity, presenting widely varied biological behavior as well as long-term prognosis. Reliable biomarkers are urgently needed to make risk stratifications for pNEN patients, which could be beneficial to the development of individualized therapeutic strategy in the clinical practice. Here, we aimed to evaluate the predictive and prognostic roles of serum alkaline phosphatase-to-albumin ratio (APAR) in well-differentiated pNEN patients. Methods. We retrospectively analyzed the pathologically confirmed grade 1/2 pNEN patients, who were originally treated in our hospital from February 2008 to April 2018. Univariate and multivariate analyses were performed to assess the value of APAR in detecting synchronous metastases and predicting relapses following curative resections. Results. A total of 170 eligible cases were included into analysis. Logistic univariate analysis indicated APAR (P=0.002) was significantly associated with synchronous distant metastasis among well-differentiated pNEN patients, which was further demonstrated to be an independent risk factor by multivariate analysis (odds ratio 8.127, 95% confidence interval (CI) 2.105–31.372, P=0.002). For the prognostic value, APAR (P=0.007) was statistically associated with recurrence-free survival (RFS) in nonmetastatic resected pNEN patients, but it was not an independent predictor. Further subgroup analysis showed that APAR was independently related to RFS in patients with no nerve (hazard ratio (HR) 7.685, 95% CI 1.433–41.209, P=0.017) or vascular invasion (HR 4.789, 95% CI 1.241–18.473, P=0.023), respectively. Conclusion. APAR may work as a convenient pretreatment marker to detect synchronous distant metastasis for well-differentiated pNEN patients and predict recurrences for curatively resected cases without nerve or vascular invasion. However, these findings should be further verified in prospectively well-designed studies.
Pancreatic Neuroendocrine Neoplasm Associated with a Familial MAX Deletion