Graph-Theoretic Definition of Neuroendocrine Disease – A Tumor Specific Mathematical Toolbox for Assessing Neoplastic Behaviour

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Introduction: Widespread availability of high throughput screens has led to the rapid adaptation of mathematics in biomedicine.

Aim(s): Holistically evaluate the pathobiology of GEP-NENs and model disease processes using Eulerian concepts.

Materials and methods: Public microarray collections: NEN tissue (n = 15), NEN peripheral blood (n = 7), and adenocarcinomas (n = 363). Bayesian modelling: reverse-engineer intracellular signalling networks. Hub genes identified using degree (number of interactions) and betweenness (number of shortest paths). A random forest algorithm was used to assess hub gene expression in 130 blood samples (NENs: n = 63) and to differentiate healthy controls and GEP-NENs. The model was validated in two independent sets (Set 1 [n = 115, NENs: n = 72]; Set 2 [n = 120, NENs: n = 58]). Comparison with CgA (ELISA) was undertaken in 176 samples (NENs: n = 81).

Conference: 13th Annual ENETSConcerence (2016)

Presenting Author: Drozdov I

Authors: Drozdov I, Kidd M, Modlin I,

Keywords: NETest, biomarker, algorithm,

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