• Hongshuang Dai, Lin Li, Yikun Yang, Huang Chen, Xin Dong, Yousheng Mao, and Yanning Gao.
• State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center; National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
• Ann. Med. 2023 Jan 1; 55 (2): 22410132241013.

ObjectiveTo screen and identify microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) using clinical samples and construct a prediction model for the prognosis of LUAD.Methods160 patient samples were used to screen and identify miRNAs associated with the prognosis of LUAD. Differentially expressed miRNAs were analyzed using gene chip technology. The selected miRNAs were validated using samples from the validation sample group. Cox proportional hazards regression was used to construct the model and Kaplan-Meier was used to plot survival curves. Model power was assessed by testing the prognosis of the constructed model using real-time polymerase chain reaction (RT-PCR) data.ResultsThe data showed that miR-1260b, miR-21-3p and miR-92a-3p were highly expressed in the early recurrence and metastasis group, while miR-2467-3p, miR-4659a-3p, miR-4514, miR-1471 and miR-3621 were lowly expressed. It was further confirmed that miR-21-3p was significantly highly expressed in the early recurrence and metastasis group (p = 0.02). Receiver operating characteristic (ROC) curve results showed cut-off point value of 0.0172, sensitivity of 88.2% and specificity of 100%. The predictive results of the constructed model were in good agreement with the actual prognosis of patients by using the validation sample test (Kappa = 0.426, p < 0.001), with a model sensitivity of 74.4%, a specificity of 68.3%, and an accuracy of 71.3%.ConclusionmiRNAs associated with the prognosis of patients with stage I LUAD were screened and validated, and a risk model for predicting the prognosis of patients was constructed. This model has good consistency with the actual prognosis of patients.

16 keywords...

hide…