中文摘要：

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（NR033913, NR038218, NR036512, NR049759, NR033951, NR045408, NR038377 and NR039976） 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.

英文摘要：

AIM: To investigate the expression pattern of plasma long noncoding RNAs (lncRNAs) in Chrohn’s disease (CD) patients.METHODS: Microarray screening and qRT-PCR verification of lncRNAs and mRNAs were performed in CD and control subjects, followed by hierarchy clustering, GO and KEGG pathway analyses. Significantly dysregulated lncRNAs were categorized into subgroups of antisense lncRNAs, enhancer lncRNAs and lincRNAs. To predict the regulatory effect of lncRNAs on mRNAs, a CNC network analysis was performed and cross linked with significantly changed lncRNAs. The overlapping lncRNAs were randomly selected and verified by qRT-PCR in a larger cohort.RESULTS: Initially, there were 1211 up-regulated and 777 down-regulated lncRNAs as well as 1020 up-regulated and 953 down-regulated mRNAs after microarray analysis; a heat map based on these results showed good categorization into the CD and control groups. GUSBP2 and {'type':'entrez-nucleotide','attrs':{'text':'AF113016','term_id':'6642755','term_text':'AF113016'}}AF113016 had the highest fold change of the up- and down-regulated lncRNAs, whereas TBC1D17 and CCL3L3 had the highest fold change of the up- and down-regulated mRNAs. Six (SNX1, CYFIP2, CD6, CMTM8, STAT4 and IGFBP7) of 10 mRNAs and 8 ({'type':'entrez-nucleotide','attrs':{'text':'NR_033913','term_id':'299890801','term_text':'NR_033913'}}NR_033913, {'type':'entrez-nucleotide','attrs':{'text':'NR_038218','term_id':'333360887','term_text':'NR_038218'}}NR_038218, {'type':'entrez-nucleotide','attrs':{'text':'NR_036512','term_id':'302393555','term_text':'NR_036512'}}NR_036512, {'type':'entrez-nucleotide','attrs':{'text':'NR_049759','term_id':'388240827','term_text':'NR_049759'}}NR_049759, {'type':'entrez-nucleotide','attrs':{'text':'NR_033951','term_id':'300068996','term_text':'NR_033951'}}NR_033951, {'type':'entrez-nucleotide','attrs':{'text':'NR_045408','term_id':'354548835','term_text':'NR_045408'}}NR_045408, {'type':'entrez-nucleotide','attrs':{'text':'NR_038377','term_id':'335334999','term_text':'NR_0383