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Significantly high-expressed circFLNA has been found in various cancer cell lines, but not in lung cancer. Therefore, this study aimed to explore the role of circFLNA in the progression of lung cancer. The target gene of circFLNA was determined by bioinformatics and luciferase reporter assay. Viability, proliferation, migration, and invasion of the transfected cells were detected by CCK-8, colony formation, wound-healing, and transwell assays, respectively. A mouse subcutaneous xenotransplanted tumor model was established, and the expressions of circFLNA, miR-486-3p, XRCC1, CYP1A1, and related genes in the cancer cells and tissues were detected by RT-qPCR, Western blot, or immunohistochemistry. The current study found that miR-486-3p was low-expressed in lung cancer. MiR-486-3p, which has been found to target XRCC1 and CYP1A1, was regulated by circFLNA. CircFLNA was located in the cytoplasm and had a high expression in lung cancer cells. Cancer cell viability, proliferation, migration, and invasion were promoted by overexpressed circFLNA, XRCC1, and CYP1A1 but inhibited by miR-486-3p mimic and circFLNA knockdown. The weight of the xenotransplanted tumor was increased by circFLNA overexpression yet reduced by miR-486-3p mimic. Furthermore, miR-486-3p mimic reversed the effect of circFLNA overexpression on promoting lung cancer cells and tumors and regulating the expressions of miR-486-3p, XRCC1, CYP1A1, and metastasis/apoptosis/proliferation-related factors. However, overexpressed XRCC1 and CYP1A1 reversed the inhibitory effect of miR-486-3p mimic on cancer cells and tumors. In conclusion, circFLNA acted as a sponge of miR-486-3p to promote the proliferation, migration, and invasion of lung cancer cells in vitro and in vivo by regulating XRCC1 and CYP1A1.

Background:This study is a retrospective study. The purpose of this study is to construct and validate an early warning model of lung cancer through machine learning. Methods:The CDKN2A gene expression profile and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database and divided into a tumor group and a normal group (n = 57). The top 5 somatic mutation-related genes were extracted from 567 somatic mutation data downloaded from TCGA database using random forest algorithm. Cox proportional hazard model and nomogram were constructed combining CDKN2A, 5 somatic mutation-related genes, gender, and smoking index. Patients were divided into high-risk and low-risk groups according to risk score. The predictability of the model in the prognosis of lung cancer was estimated by Kaplan–Meier survival analysis and receiver operating characteristics curve. Results:We constructed a prognostic model consisting of 5 somatic mutation-related genes (sphingosine 1-phosphate receptor 1 [S1PR1], dedicator of cytokinesis 7 [DOCK7], DEAD-box helicase 4 [DDX4], laminin subunit beta 3 [LAMB3], and importin 5 [IPO5]), cyclin-dependent kinase inhibitor 2A (CDKN2A), gender, and smoking indicators. The high-risk group had a lower overall survival rate compared to the low-risk group (hazard ratio = 2.14, P= 0 .0323). The area under the curve predicted for 3-year, 5-year, and 10-year survival rates are 0.609, 0.673, and 0.698, respectively. The accuracy, sensitivity, and specificity of the model for predicting the 10-year survival rate of lung cancer are 76.19%, 56.71%, and 86.23%. Conclusion:The lung cancer early warning model and nomogram may provide an essential reference for patients with lung cancer management in the clinic.