AIM: To investigate the expression pattern of plasma long noncoding RNAs(lnc RNAs) in Chrohn’s disease(CD) patients.METHODS: Microarray screening and q RT-PCR verification of lnc RNAs and m RNAs were performed in CD and control subjects, followed by hierarchy c l u s t e r i n g, G O a n d K E G G p a t h w a y a n a l y s e s. Significantly dysregulated lnc RNAs were categorized into subgroups of antisense lnc RNAs, enhancer lnc RNAs and linc RNAs. To predict the regulatory effect of lnc RNAs on m RNAs, a CNC network analysis was performed and cross linked with significantly changed lnc RNAs. The overlapping lnc RNAs were randomly selected and verified by q RT-PCR in a larger cohort. RESULTS: Initially, there were 1211 up-regulated and 777 down-regulated lnc RNAs as well as 1020 up-regulated and 953 down-regulated m RNAs after microarray analysis; a heat map based on these results showed good categorization into the CD and control groups. GUSBP2 and AF113016 had the highest fold change of the up- and down-regulated lnc RNAs, whereas TBC1D17 and CCL3L3 had the highest foldchange of the up- and down-regulated m RNAs. Six(SNX1, CYFIP2, CD6, CMTM8, STAT4 and IGFBP7) of 10 m RNAs and 8(NR_033913, NR_038218, NR_036512, NR_049759, NR_033951, NR_045408, NR_038377 and NR_039976) of 14 lnc RNAs showed the same change trends on the microarray and q RT-PCR results with statistical significance. Based on the q RT-PCR verified m RNAs, 1358 potential lnc RNAs with 2697 positive correlations and 2287 negative correlations were predicted by the CNC network. CONCLUSION: The plasma lnc RNAs profiles provide preliminary data for the non-invasive diagnosis of CD and a resource for further specific lnc RNA-m RNA pathway exploration. AIM: To investigate the expression pattern of plasma long noncoding RNAs (lnc RNAs) in Chrohn’s disease (CD) patients. METHODS: Microarray screening and q RT-PCR verification of lnc RNAs and m RNAs were performed in CD and control subjects, followed by hierarchy clustering, GO and KEGG pathway analyzes a. Significantly dysregulated lnc RNAs were categorized into subgroups of antisense lnc RNAs, enhancer lnc RNAs and linc RNAs. To predict the regulatory effect of lnc RNAs on m RNAs, a CNC network analysis was performed and cross linked with significantly altered lnc RNAs. The overlapping lnc RNAs were randomly selected and verified by q RT-PCR in a larger cohort. RESULTS: Initially, there were 1211 up-regulated and 777 down- regulated lnc RNAs as well as 1020 up-regulated and 953 down-regulated m RNAs after microarray analysis; a heat map based on these results showed good categorization into the CD and control groups. GUSBP2 and AF113016 had the highest fold chang Six (SNX1, CYFIP2, CD6, CMTM8, STAT4 and IGFBP7) of 10 m RNAs and TBC1D17 and CCL3L3 had the highest foldchange of the up- and down-regulated m RNAs and 8 (NR_033913, NR_038218, NR_036512, NR_049759, NR_033951, NR_045408, NR_038377 and NR_039976) of 14 lnc RNAs showed the same change trends on the microarray and q RT-PCR results with statistical significance. Based on the q RT-PCR verified m RNAs , 1358 potential lnc RNAs with 2697 positive correlations and 2287 negative correlations were predicted by the CNC network. CONCLUSION: The plasma lnc RNAs profiles provide preliminary data for the non-invasive diagnosis of CD and a resource for further lnc RNA-m RNA pathway exploration.