Mining featured micro ribonucleic acids associated with lung cancer based on bioinformatics

Abstract Background Few genetic markers useful for the screening of lung cancer risk exist. Although related research has shown that certain expression profiles of micro ribonucleic acids ( miRNA s) are different in lung cancer versus the normal lung, such as miR ‐29a and miR ‐29s, the precise molecular mechanism of lung cancer remains obscure. In order to get a better understanding of the pathogenetic mechanism of lung cancer, we analyzed the differentially expressed genes ( DEG s) and identified featured miRNA s in lung cancer tissues. Methods We used the gene expression profile GSE 10072, including 49 gene chips of non‐tumor tissues and 58 gene chips of lung tumor specimens. The DEGs between these two groups were identified by L imma package in R language. The T ar B ase database was used to construct the networks of miRNA regulating DEGs related to lung cancer. After ordering miRNA s regulating DEGs , we further screened featured miRNA s combined with the mi R2D isease database. Results A total of 5572 DEG s were obtained between lung cancer and control specimens. After constructing a miRNA regulatory network, a total of 398 regulations between 57 miRNA s and 321 target genes existed. By intergrating the mi R2D isease database and using a sorting algorithm, a total of six featured miRNAs related to lung cancer were identified, including miR ‐520h, miR ‐133a, miR ‐34, miR ‐103, miR ‐370, and miR ‐148. They might be involved in lung cancer progression by regulating ABCG2 , PKM2 , VAMP2 , GPD1 , MAP3K8 , and DNMT3B , respectively. Conclusion The top 10 significant miRNA s, such as miR ‐520h, miR ‐133a, miR ‐34, and miR ‐103 may be potential therapeutic targets for lung cancer.