Background:　Since　there　are　inextricably　connections　among　molecules　in　the　biological　networks,　it　would　be　a　more　efficient　and　accurate　research　strategy　to　screen　microRNA　(miRNA)　markers　combining　with　miRNA-mRNA　regulatory　networks.　The　independent　regulation　mode　is　more　＂fragile＂　and　＂influential＂　th　an　the　co-regulation　mode.　miRNAs　can　be　used　as　biomarkers　if　they　can　independently　regulate　hub　genes　with　important　roles　in　the　PPI　network,　simultaneously　the　expression　products　of　the　regulated　hub　genes　play　important　roles　in　the　signaling　pathways　of　related　tissue　diseases.　Methods:　We　collected　miRNA　expression　of　non-small　cell　lung　cancer　(NSCLC)　from　The　Cancer　Genome　Atlas　(TCGA)　database　and　the　Gene　Expression　Omnibus　(GEO)　database.　Volcano　plot　and　signal-to-noise　ratio　(SNR)　methods　were　used　to　obtain　significant　differentially　expressed　(SDE)　miRNAs　from　the　TCGA　database　and　GEO　database,　respectively.　A　human　miRNA-mRNA　regulatory　network　was　constructed　and　the　number　of　genes　uniquely　targeted　(NOG)　by　a　certain　miRNA　was　calculated.　The　area　under　the　curve　(AUC)　values　were　used　to　screen　for　clinical　sensitivity　and　specificity.　The　candidate　markers　were　obtained　using　the　criteria　of　the　top　five　maximum　AUC　values　and　NOG　3.　The　protein-protein　interaction　(PPI)　network　was　constructed　and　independently　regulated　hub　genes　were　obtained.　Gene　Ontology　(GO)　analysis　and　KEGG　pathway　analysis　were　used　to　identify　genes　involved　in　cancer-related　pathways.　Finally,　the　miRNA　which　can　independently　regulate　a　hub　gene　and　the　hub　gene　can　participate　in　an　important　cancer-related　pathway　was　considered　as　a　biomarker.　The　AUC　values　and　gene　expression　profile　analysis　from　two　external　GEO　datasets　as　well　as　literature　validation　were　used　to　verify　the　screening　capability　and　reliability　of　this　marker.　Results:　Fifteen　SDE　miRNAs　in　lung　cancer　were　obtained　from　the　intersection　of　volcano　plot　and　SNR　based　on　the　GEO　database　and　the　TCGA　database.　Five　miRNAs　with　the　top　five　maximum　AUC　values　and　NOG　＞=　3　were　screened　out.　A　total　of　61　hub　genes　were　obtained　from　the　PPI　network.　It　was　found　that　the　hub　gene　GTF2F2　was　independently　regulated　by　miR-708-5p.　Further　pathway　analysis　indicated　that　GTF2F2　participates　in　protein　expression　by　binding　with　polymerase　II,　and　it　can　regulate　transcription　and　accelerate　tumor　growth.　Hence,　miR-708-5p　could　be　used　as　a　biomarker.　The　good　screening　capability　and　reliability　of　miR-708-5p　as　a　lung　cancer　marker　were　confirmed　by　AUC　values　and　gene　expression　profiling　of　external　datasets,　and　experimental　literature.　The　potential　mechanism　of　miR-708-5p　was　proposed.　Conclusions:　This　study　proposes　a　new　idea　for　lung　cancer　marker　screening　by　integrating　microRNA　expression,　regulation　network　and　signal　pathway.　miR-7085p　was　identified　as　a　biomarker　using　this　novel　strategy.　This　study　may　provide　some　help　for　cancer　marker　screening.