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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.

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