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This study aimed to screen lymphatic metastasis-related microRNAs (miRNAs) in lung adenocarcinoma and explore their underlying mechanisms using bioinformatics. The miRNA expression in primary lung adenocarcinoma, matched adjacent non-tumorigenic and lymph node metastasis tissues of patients were profiled via microarray. The screened metastasis-related miRNAs were then validated using quantitative real-time PCR in a second cohort of lung adenocarcinoma patients with lymphatic metastasis. Significance was determined using a paired t-test. Target genes of the metastasis-related miRNAs were predicted using TargetScan, and transcription factors (TFs) were predicted based on the TRANSFAC and ENCODE databases. Furthermore, the related long non-coding RNAs (lncRNAs) were screened with starBase v2.0. The miRNA-TF-mRNA and lncRNA-miRNA-mRNA networks were constructed to determine the key interactions associated with lung adenocarcinoma metastasis. According to the miRNA microarray results, there were 10 miRNAs that were differentially expressed in metastatic tissues compared with primary tumor and adjacent non-tumorigenic tissues. Among them were increased levels of miR-146a-5p, miR-342-3p, and miR-150-5p, which were validated in the second cohort. Based on the miRNA-TF-mRNA network, vascular endothelial growth factor A and transcription factors (TFs) including TP53, SMAD4, and EP300 were recognized as critical targets of the three miRNAs. Interactions involving SNHG16–miR-146a-5p–SMAD4 and RP6-24A23.7–miR-342-3p/miR-150-5p–EP300 were highlighted according to the lncRNA-miRNA-mRNA network. miR-146a-5p, miR-342-3p, and miR-150-5p are lymphatic metastasis-related miRNAs in lung adenocarcinoma. Bioinformatics analyses demonstrated that SNHG16 might inhibit the interaction between miR-146a-5p and SMAD4, while RP6-24A23.7 might weaken miR-342-3p–EP300 and miR-150-5p–EP300 interactions in metastasis.

experimental biology and medicine

Identification of lymph node metastasis-related microRNAs in lung adenocarcinoma and analysis of the underlying mechanisms using a bioinformatics approach