Extracellular mRNA detected by molecular beacons in tethered lipoplex nanoparticles for diagnosis of human hepatocellular carcinoma.

• Main Authors: Xinmei Wang, Kwang Joo Kwak, Zhaogang Yang, Aili Zhang, Xiaoli Zhang, Rachael Sullivan, Dan Lin, Robert L Lee, Carlos Castro, Kalpana Ghoshal, Carl Schmidt, L James Lee
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2018-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC5991670?pdf=render

Hepatocellular carcinoma (HCC) remains one of the major causes of cancer related deaths. Although ultrasonography (US), computed tomography (CT) and/or high-cost magnetic resonance imaging (MRI) have been shown to improve early detection of liver cancer and mortality rates in high-risk individuals, such imaging based methods are limited by high rates of false positivity leading to unnecessary patient anxiety and invasive procedures. Complementary blood biomarkers could increase the accuracy of early detection. Although Alpha-fetoprotein (AFP) in blood is widely used in HCC screening and diagnosis, the false-negative rate as high as 30% and 40% is found in advanced HCC and early stage HCC respectively. We detected AFP messenger RNA (mRNA) in extracellular vesicles (EVs) in patient plasma using designed molecular beacons and a novel tethered lipoplex nanoparticle (TLN) biochip. Together with glypican-3 (GPC-3) mRNA, another well-known HCC marker, we observed much improved performance of AFP protein-based HCC detection. Comparing normal donors (N = 38) and HCC patients (N = 40), our TLN biochip using EV AFP and GPC-3 mRNAs provided an AUC (area under the ROC curve) of 0.995, better than that of a single marker. This 2-mRNA combination also provided a perfect positive predictive value (PPV = 1) at a negative predictive value (NPV) of 0.95 and 20% prevalence, while the blood AFP protein or plasma EV GPC3 mRNA alone could only provide a PPV of 0.61 and 0.79 respectively at the same conditions. Thus, this facile new method may complement current models for risk stratification in liver cancer screening, therapeutic monitoring, and after-treatment surveillance. However, large scale validation will need to be conducted to confirm its clinical potential.