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. Author manuscript; available in PMC: 2019 Sep 1.
Published in final edited form as: Endocrinol Metab Clin North Am. 2018 Sep;47(3):485–504. doi: 10.1016/j.ecl.2018.05.002

Fig. 2.

Fig. 2.

Computational pipeline used to derive a set of marker genes, the NET Marker Panel that identifies GEP-NEN/NET disease in the blood. Step 1: gene coexpression networks inferred from 2 independent data sets (GEP-NEN-A and GEP-NEN-B) are intersected to produce the GEP-NEN network. Step 2: coexpression networks from neoplastic and normal tissue microarray data sets are combined to produce the normal and neoplastic networks. Step 3: links present in normal and neoplastic networks are subtracted from the GEP-NEN network. Step 4: up-regulated genes in both the GEP-NEN-A and GEP-NEN-B data sets (n = 21) are mapped to the consensus GEP-NEN network. Step 5: identification of consistently up-regulated genes in GEP-NEN blood transcriptome and GEP-NEN-A and GEP-NENB data sets, provided 32 putative genes. Step 6: literature curation and cancer mutation database search yielded an additional panel of 22 putative marker genes. A total of 75 marker genes was analyzed prior to delineation of the final NET marker panel. Step 7: the final NETest liquid biopsy includes 51 marker genes that were validated in 3 independent cohorts totaling 193 NETs and 172 controls. RT-PCR, reverse transcription PCR. (Modified from Modlin I, Drozdov I, Kidd M. The identification of gut neuroendocrine tumor disease by multiple synchronous transcript analysis in blood. PLoS One 2013;8:e63364; with permission.)