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When applied to 47 independent validation samples, the testing sensitivity ranged from 75% for DRF to 94% for GBM specificity ranged from 84% for DRF to 94% to XGboost and GLM positive predictive value ranged from 72% for DRF to 86% for GLM negative predictive value ranged from 88% for GLM to 97% to GBM. Training ROC ranged from 0.92 for DRF to 1 for XGboost. Distributed random forests (DRF), generalized linear models (GLM), gradient boosting machines (GBM) and extreme gradient boosting (XGBoost) models were trained utilizing these 9 genes. Nine genes, including AURKA, ARX, CDCA8, CPB2, MYT1L, NDC80, PAPPA2, SFMBT1 and ZPLD1, were identified as sufficient to classify the localized or metastatic outcome. The datasets were subsequently randomized in a 1:1 ratio and informative features with respect to metastatic status were identified utilizing a Boruta algorithm, with a priori exclusion of highly-correlative genes and those that displayed near zero variance. Log transformed, batch corrected TPM values for these 49 genes were combined with an additional 10 clinically-relevant genes, including ARX and PDX1, that are known to contribute to PNET signatures or oncogenesis. A gene set enrichment analysis identified an additional 29 genes that most frequently contributed to the enriched biologic pathways extrapolated from the sequencing data. Unsupervised surrogate variable analysis estimated and adjusted for significant sources of variation not related to metastatic potential and mitigated unwanted noise and batch effects. A differential gene expression analysis identified 20 concordantly differentially expressed genes associated with metastatic status between the two cohorts. Two cohorts were generated with equally balanced metastatic PNET composition (15 (32.6%) vs. To build this model, RNA sequencing data was obtained from the primary tissue of 96 surgically-resected PNETs from various institutions. We used machine learning to develop a predictive model of metastatic potential dependent upon the transcriptomic signature of primary PNET tissue. While many PNETs have the propensity to be indolent, some small tumors display aggressive features with early metastatic potential. Pancreatic neuroendocrine tumors (PNETs) are rare neoplasms that arise from cells in the islets of Langerhans, with surgical resection presently recommended for tumors > 2cm.
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