[Predictive Value of A miRNA Signature for Distant Metastasis in Lung Cancer]

Lung cancer represents the main cause of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) is the most main subtype. More than half of NSCLC patients have already developed distant metastasis (DM) at the time of diagnosis and have a poor prognosis. Therefore, it is necessary to find new biomarkers for predicting NSCLC DM in order to guide subsequent treatment and thus improve the prognosis of NSCLC patients. Numerous studies have shown that microRNAs (miRNAs) are abnormally expressed in lung cancer tissues and play an important role in tumorigenesis and progression. The aim of this study is to identify differentially expressed miRNAs in lung adenocarcinoma tissues with DM group compared to those with non-distant metastasis (NDM) group, and to construct a miRNA signature for predicting DM of lung adenocarcinoma.

We first obtained miRNA and clinical data for patients with lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database. Subsequently, bioinformatics analysis, which included different R packages, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a range of online analysis tools, was performed to analyze the data.

A total of 12 differentially expressed miRNAs were identified between the DM and NDM groups, and 8 miRNAs (miR-377-5p, miR-381-5p, miR-490-5p, miR-519d-5p, miR-3136-5p, miR-320e, miR-2355-5p, miR-6784-5p) were screened for constructing a miRNA signature. The efficacy of this miRNA signature in predicting DM was good with an area under the curve (AUC) of 0.831. Logistic regression analysis showed that this miRNA signature was an independent risk factor for DM of lung adenocarcinoma. Next, target genes of the eight miRNAs were predicted, and enrichment analysis showed that these target genes were enriched in a variety of pathways, including pathways in cancer, herpes simplex virus I infection, PI3K-Akt pathway, MAPK pathway, Ras pathway, etc. CONCLUSIONS: This miRNA signature has good efficacy in predicting DM of lung adenocarcinoma and has the potential to be a predictor of DM of lung adenocarcinoma.