Principal component analysis, hierarchical clustering, and decision tree assessment of plasma mRNA and hormone levels as an early detection strategy for small intestinal neuroendocrine (carcinoid) tumors

Abstract

Incidence of neuroendocrine tumors (NETs) is increasing (approximately 6%/year), but clinical presentation is nonspecific, resulting in delays in diagnosis (5-7 years; approximately 70% have metastases). This reflects absence of a sensitive plasma marker. The aim of this study is to investigate whether detection of circulating messenger RNA (mRNA) alone or in combination with circulating NET-related hormones and growth factors can detect gastrointestinal NET disease. The small intestinal (SI) NET cell line KRJ-I was used to define the sensitivity of real-time polymerase chain reaction (PCR) for mRNA detection in blood. NSE, Tph-1, and VMAT ( 2 ) transcripts were identified from one KRJ-I cell/ml blood. mRNA from the tissue and plasma of SI-NETs (n = 12) and gastric NETs (n = 7), and plasma from healthy controls (n = 9) was isolated and real-time PCR performed. Tph-1 was a specific marker of SI-NETs (58%, p < 0.03) whereas CgA transcripts did not differentiate tumors from controls. Patients with metastatic disease expressed more marker transcripts than localized tumors (75% versus 18%, p < 0.02). Plasma 5-hydroxytryptamine (5-HT), chromogranin A (CgA), ghrelin, and connective tissue growth factor (CTGF) fragments were measured, combined with mRNA levels, and a predictive mathematical model for NET diagnosis developed using decision trees. The sensitivity and specificity to diagnose SI-NETs and gastric NETs were 81.2% and 100%, and 71.4% and 55.6%, respectively. We conclude that mRNA from one NET cell/ml blood can be detected. Circulating plasma Tph-1 is a promising marker gene for SI-NET disease (specificity 100%) while an increased number of marker transcripts (>2) correlated with disease spread. Including NET-related circulating hormones and growth factors in the algorithm increased the sensitivity of detection of SI-NETs from 58 to 82%.