A Panel of Platelet circRNAs Serve as Biomarker for Lung Cancer Detection

Abstract

Background: Early stages of lung cancer have a better prognosis than later stages. The accuracy of current lung cancer early detection methods is relatively poor. We firstly try to use circRNAs of platelets enable blood-based early detection of lung cancer. Methods: We did a three-stage study that included healthy controls, benign lung nodule, and patients with diagnosed lung cancer from two hospitals in China. We used High-throughput Illumina sequencing analysis to profile circRNAs expression in the 8 patients with lung cancer and 8 non-cancer controls (including healthy controls and benign lung nodule patients). Using a training cohort of patients with lung cancer and non-cancer controls, we built a circRNAs classifier to detect lung cancer. Then, the classifiers’ ability was validated in two independent cohorts of patients and non-cancer controls. We used the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to evaluate diagnostic performance. Findings: Between Aug 1, 2018, and September 31, 2019, we recruited 88 participants to the training cohort, and 694 and 605 participants to the two independent validation cohorts. A signature comprising 23 circRNAs was identified by RNA-Seq analysis in the discovery stage. Then, four circRNAs (CD274, TIMP1, FLNA, ITGA2B) was found to have the possibility to serve as biomarkers for LC detection in the training stage by qRT-PCR. These selected four circRNAs from platelets was identified and subsequently assessed in the two independent validation cohorts. The area under the curve (AUC) for the five circRNAs from platelets (Pcs) were 0.975 in the validation cohort. In the following blinded test including the platelets from benign pulmonary nodule, LC patients and LC patients after surgery, Pcs correctly discriminated the patients with 90.6% sensitivity and 96.3% specificity. Interpretation: CircRNAs from platelets are very promising biomarkers for early stages of lung cancer diagnosis with very high sensitivity and specificity. This novel technology could serve as a Non-invasive, highly accurate diagnostic tool for lung nodule carriers that difficult to distinguish malignance from benign ones. Funding Statement: This work was supported by grants from National Natural Science Foundation of China (81572262), Jiangsu Province’s Key provincial Talents Program (ZDRCA2016028), 333 high class Talented Man Project (BRA2016516) and The Natural Science Foundation of the Jiangsu Higher Education Institution of China (18KJB320006). Declaration of Interests: None declared. Ethics Approval Statement: The study was approved by institutional ethics committee in each study center.