A transcriptional miRNA-gene network associated with lung adenocarcinoma metastasis based on the TCGA database

Lung adenocarcinoma is the most common subtype of non-small cell lung cancer (NSCLC), leading to the largest number of cancer-related deaths worldwide. The high mortality rate may be attributed to the delay of detection. Therefore, it is of great importance to explore the mechanism of lung adenocarcinoma metastasis and the strategy to block metastasis of the disease. We searched and downloaded mRNA and miRNA expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify differences in mRNA and miRNA expression of primary tumor tissues from lung adenocarcinoma that did and did not metastasize. In addition, combined with bioinformatic prediction, we constructed an miRNA-target gene regulatory network. Finally, we employed RT-qPCR to validate the bioinformatic approach by determining the expression of 10 significantly differentially expressed genes which were also putative targets of several dysregulated miRNAs. RT-qPCR results indicated that the bioinformatic approach in our study was acceptable. Our data suggested that some of the genes including PKM2, STRAP and FLT3, may participate in the pathology of lung adenocarcinoma metastasis and could be applied as potential markers or therapeutic targets for lung adenocarcinoma.