Abstract 867: Identifying plasma exosomal small non-coding RNAs as potential biomarkers for lung cancer early detection
Authors: Fu Yuanyuan , Nasu Masaki , Jijiwa Mayumi , Chen Yu , Gao Zitong , Ibrahim Isam , Deng Youping 
Published: April 2023

Abstract
Introduction: Lung cancer is the second most common cancer and the leading cause of cancer death in the US and worldwide. The National Lung Screening Trial (NLST) through thoracic low-dose computed tomography (LDCT) scans has shown that screening decreases mortality by 20%, but nearly 40% of patients in the NLST had a positive screen at one point, with 96.4% of these being false positive. It is urgent to develop new non-invasive methods with high efficacy, such as blood molecular biomarkers as companion diagnostic tools to complement LDCT screening. Recent studies have shown that small non-coding RNAs (ncRNAs) play important roles in cancer development, and exosomes are actively secreted extracellular vesicles that contain proteins, lipids, and nucleic acids, including miRNAs and other classes of small ncRNAs. Our study aimed to screen and identify noval plasma exosomal small ncRNAs for lung cancer early detection.
Methods: A supersensitive human plasma exosome isolation and purification as well as exosome small RNA sequencing were conducted on 233 human subjects including 117 lung cancer early-stage patients and 116 non-cancer subjects (54 benign and 62 normal samples). Sequencing reads were first processed by trimming off the adapters and low-quality bases using cutadapt. Following trimming, the sequences were aligned to the human genome (Genome Reference Consortium GRCh38) and mapped to different small ncRNA databases using STAR. Read counts for each RNA category (miRNA, piRNA, snRNA, snoRNA, tRNA, tsRNA) were calculated from the mapping results. Differential gene expression analysis was performed by the DESeq2 bioconductor package. The genes with Benjamini and Hochberg q-value &lt; 0.05 were called differentially expressed. Based on the differential gene lists, diagnostic models were built by 10-fold cross validated Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression.
Results: A total of 5470 small ncRNAs were initially identified, including 1094 miRNA, 2172 piRNA, 1185 snRNA, 376 snoRNA, 582 tRNA, 61 tsRNA. Using DESeq2 with an FDR q-value &lt; 0.05 with a cut off of 1.5-fold change, we found 508 small ncRNAs significantly differentially expressed between cancer and non-cancer groups, consisting of 231 small ncRNAs up-regulated and 277 down-regulated in tumor. By diagnostic modeling, a set of 16 small ncRNAs were identified as promising biomarkers, which could differentiate patients with cancer from non-cancer with an area under the receiver operating (ROC) curve (AUC) of 96.71%. Using the same sixteen-smallRNA signature to distinguish cancer from benign, the AUC achieved 97.23%; while the same panel could distinguish cancer from normal with an AUC of 96.26%.
Conclusion: This preliminary study demonstrated the potential diagnostic value of the exosomal small ncRNA biomarkers in lung cancer early detection. The results will be evaluated by more cases in the future study.
Citation Format: Yuanyuan Fu, Masaki Nasu, Mayumi Jijiwa, Yu Chen, Zitong Gao, Isam Ibrahim, Youping Deng. Identifying plasma exosomal small non-coding RNAs as potential biomarkers for lung cancer early detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 867.