Lung cancer is the leading cause of cancer deaths worldwide. While the implementation of lung cancer screening for non-small cell lung cancer (NSCLC) subtypes has brought significant hope to this disease, very limited options exist for the early detection of small cell lung cancer (SCLC) SCLC carries a 5-year survival rate of only 7% and despite the development of novel targeted therapies and early detection for NSCLC, no such advances have been achieved in SCLC. A gap in our current approach to lung cancer detection and treatment has been that informative and reliable biomarkers for the detection and surveillance of lung cancer have remained elusive. MicroRNAs (miRNAs) have emerged as viable biomarkers in body fluids thus, providing an excellent means to achieve non-invasive assays for early cancer detection. Furthermore, miRNA expression in circulation appears to be compartment specific. While the majority of miRNAs are intracellular, a significant number of miRNAs have been observed outside of cells, including in various bodily fluids. The origin, applications and potential functionality of RNAs in circulation are the sources of intriguing questions. Obtaining a detailed RNA spectrum in plasma would shed some light on this matter. We have taken a multidisciplinary approach to the investigation of circulating RNA transcripts that integrates expertise in miRNA biology, nanoengineering, lung cancer and bioinformatics. We have developed a simple tethered Cationic Lipoplex Nanoparticle (tCLN) biochip with pre-loaded molecular beacons (MBs) in the lipoplex nanoparticles as probes to capture and detect targeted miRNAs and mRNAs in human plasma without any need of pre- or post-sample treatment. We have successfully demonstrated the ability to assess both exosomal miRNAs and mRNAs using both Next Generation Sequencing and our tCLN biochip in cohorts of control smokers and patients with early stage NSCLC. Our primary objectives are to extend these novel findings by (1) Test and validate the utility of measurement of ASCL1 and DLL3 in the early detection of SCLC in a retrospective and prospective study with network samples (2) Develop A Panel of Comprehensive EV RNA Candidates using nest generation sequencing and q-RT-PCR (3) Develop an optimized EV nanochip based RNA Classifier for early SCLC detection and (4) Validate the optimized EV RNA Classifier by using the multiplex TLN array biochips in independent, blinded case control studies at the OSU James Cancer Hospital and from the SCLC consortium.