Predicting Neuroendocrine Tumor (Carcinoid) Neoplasia using Gene Expression Profiling and supervised Machine Learning

摘要

BackgroundA more accurate taxonomy of small intestinal (SI) neuroendocrine tumors (NETs) is necessary to accurately predict tumor behavior, prognosis and define therapeutic strategy. We identified a panel of such markers implicated in tumorogenicity, metastasis, and hormone production and hypothesized that transcript levels of MAGE-D2, MTA1, NAP1L1, Ki-67, Survivin, FZD7, Kiss1, NRP2, and CgA could be used to define primary SI NETs and predict the development of metastases.