Development of a Novel Biomarker and Stage-Classifier Panel for Treatment and Rapid Identification of Lung Cancer by Blood Tests Utilizing Next-Generation Sequencing, Computational, and In-Vitro Analyses

Overview

Lung cancer is the leading cause of cancer death worldwide. Diagnostic methods including CT-scans are expensive and time-consuming. miRNAs participate in gene-silencing, and can be dysregulated in cancer. Extracellular vesicles (EVs) circulate our bloodstream and participate in cell-cell communication within tumor microenvironments during metastasis. This project developed a novel EV miRNA biomarker panel for various lung cancer histologies and stages, for point-of-care testing. Small RNA libraries of patients’ plasma were quantified by qRT-PCR, underwent next-generation sequencing, and pre-processed. Novel miRNAs with their default parameters and their abundance changes were identified. A heat-map (biomarker panel) displayed the expression of miRNAs with p-values <0.05 and fold-changes >1.5, for Stages I-III of SCC, LUAD, and SCLC. miR-374a-5p, and miR-374b-5p were selected for qRT-PCR validation of the biomarker panel in LUAD EVs. PCR confirmed that miR-374a-5p and miR-374b-5p were downregulated in advanced stages of patient plasma samples. Hundreds of LUAD patient data were extracted from TCGA. miR-374a and miR-374b were downregulated in LUAD tissue samples, while miR-1306 was upregulated. Low levels of miR-374a-5p and miR-374b-5p and high levels of miR-1306-5p resulted in LUAD patients having a lower probability surviving over a period of time. Deep-learning neural networks were trained develop automated diagnosis of lung cancer based on histopathological images from available online datasets, with an accuracy rate greater than 95%. Discovery of these novel plasma and tumor miRNAs via potential simple blood extraction tests can be used in conjunction with automated deep-learning diagnosis for effective and rapid detection of lung cancer. The Effect of Therapeutic Agents on the Installation of GLUT1 Haarika Ayyadevera Little Rock Central High Little Rock, Arkansas Supervising Scientist: Dr. Steven Barger University of Arkansas for Medical Science Glucose is the main source of energy for the brain and the presence of glucose transporters, such as GLUT1, is very critical for brain function. Glucose utilization in the human brain of an Alzheimer’s (AD) patient is very low, and recent evidence indicates that this may result in poor installation of GLUT1 i n the plasma membrane of certain brain cells called astrocytes. Modeling this phenomenon with a strocytes in cell cultures will allow the screening of potential drugs that might restore GLUT1 m embrane installation. As an initial step in developing such a model, we tested the effects on GLUT1 installation in the astrocyte membrane affected by tunicamycin, an inhibitor of the proper trafficking of membrane proteins through the secretory pathway. Primary cultures of astrocytes were treated with tunicamycin, fixed, and stained for GLUT1 by immunocytochemistry. Some cultures were permeabilized with detergent and others were not, so the latter should restrict detection to the GLUT1 on the cell surface. Glial fibrillary acidic protein (GFAP), an i ntracellular protein was detected as a control for permeabilization. This approach revealed d ecreased levels if GLUT1 in the presence of tunicamycin; there was no significant difference in GFAP. These findings indicate that this m ethodology will be useful in testing various agents f or the ability to increase or decrease G LUT1 installation in the plasma membrane of astrocytes. We validated this method with a novel drug developed in lab PNR962 (patent pending) which n ot only reduced ER stress but also improved GLUT1 installation on the astrocyte membrane.

Competition history

• JSHS 2020 Category not listed

Resources

• National JSHS abstract book ↗

Related projects

Closest projects by meaning, across every fair and year in the corpus.