Abstract 756: A panel of plasma exosomal miRNAs as diagnosis biomarker to distinguish benign and malignant nodules in non-small cell lung cancer
Authors: Zheng Di , Yang Yang , Wu Chunyan , Wang Huizhen , Zhang Jiyang , Liu Shiyi , Xu Xiaoya , Chen Hao , Zhang Dadong , Li Fugen , Ni Jian , Jiang Gening , Xu Jianfang 
Published: September 2020

Purpose: Lung cancer is the most common malignancy with the highest morbidity and mortality worldwide. Low-dose computed tomography (LDCT) is one of the main tools for lung cancer screening and diagnosis. However, high false positive, high cost, over-diagnosis and radiation exposure are major drawbacks to screen pulmonary nodules by LDCT. Thus, a noninvasive diagnostic approach with high specificity and accuracy is badly needed to enhance LDCT. Liquid biopsy represent a valuable non-invasive approach when biopsy or resection is not the first choice. Till now, it is rare to see the studies on exosome-derived miRNAs as early diagnosis biomarkers to distinguish benign and malignant pulmonary nodules using small RNA sequencing. Here, we aimed to explore the diagnostic value of a panel of significantly differential expressed plasma exosomal miRNAs between benign and malignant pulmonary nodule samples in Chinese cohorts.

Materials and Methods: This study was registered at Chinese Clinical Trial Registry (www.chictr.org.cn) with registration number ChiCTR1800019877. Forty-five patients including twenty-six lung adenocarcinoma and nineteen benign nodules with various pathological characteristics were enrolled as a training cohort. A test cohort consisted of sixty-two patients with twenty-four benign nodules patients similar to training cohort and thirty-eight lung adenocarcinoma. Exosomes were precipitated from the plasma, and small RNA sequencing was performed to identify the differential expressed miRNAs. A statistical model consisting of a panel of exosomal miRNAs was trained to discriminate benign nodules from cancerous ones. The model was validated in the independent test cohort, and re-confirmed in an external dataset from another Chinese cohort. Enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also performed.

Results: Characteristic proteins and morphology of exosomes were characterised by western blotting, nanoparticle tracking analysis and scanning electron microscopy. Five differential expressed miRNAs (let-7b-3p, miR-101-3p, miR-125b-5p, miR-150-5p, and miR-3168) with median expression &gt; 50 were selected by LASSO-penalized regression as a linear model to classify samples into benign or maligant groups with 10-fold cross-validation to determine the model parameters. When the specificity set 94.7% and 91.7% for the training and test cohorts, respectively, the model had 57.7% and 57.9% sensitivity in both cohorts. The model was also confirmed in an external dataset with 87.5% specificity and 53.1% sensitivity. The expression of each biomarker in benign, adenocarcinoma in situ/microinvasive adenocarcinoma and invasive adenocarcinoma nodules were gradually altered. Four of the five biomarkers were gradually increased, whereas one miRNA was gradually decreased. GO and KEGG analysis demonstrated that biological process and pathways of the genes targeted by five biomarkers were associated with tumor development.

Conclusions: This study using small RNA sequencing identified five plasma exosome-derived differentially expressed miRNAs as a diagnosis model to distinguish benign and malignant pulmonary nodules, which provides insights into the feasibility of exosomal miRNAs as a novel early diagnosis approach for lung adenocarcinoma.

Citation Format: Di Zheng, Yang Yang, Chunyan Wu, Huizhen Wang, Jiyang Zhang, Shiyi Liu, Xiaoya Xu, Hao Chen, Dadong Zhang, Fugen Li, Jian Ni, Gening Jiang, Jianfang Xu. A panel of plasma exosomal miRNAs as diagnosis biomarker to distinguish benign and malignant nodules in non-small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 756.