OBJECTIVETo screen differentially expressed genes in hyperplastic scar to explore the pathogenesis of hyperplastic scar and identify new therapeutic targets.

METHODSThree pairs of surgical specimens of hyperplastic scar and adjacent normal skin tissues were collected to investigate the differentially expressed genes in hyperplastic scar using Agilent gene oligonucletide microarray and clustering analysis. DAVID Bioinformatics Resources 6.7 was used for GO analysis and pathway analysis.

RESULTS AND CONCLUSIONDistinctly different gene expression profiles were found between hyperplastic scar tissues and normal skin tissues. Compared with normal skin tissue, hyperplastic scar tissues showed 3142 up-regulated and 2984 down-regulated genes by two folds and 28 up-regulated and 44 down-regulated genes by 5 folds after repeating the experiment once; after repeating the experiment twice, 3004 genes were found up-regulated and 3038 down-regulated by 2 folds and 25 up-regulated and 38 down-regulated by 5 folds in hyperplastic scars. In all the 3 specimens, 1920 genes were up-regulated and 1912 down-regulated by 2 folds and 18 up-regulated and 29 down-regulated by 5 folds. The dysregulated genes in hyperplastic scar were involved in cell cycles, cell proliferation, immune response and cell adhesion (CDKN1C, CDKN2A, CTNNA3, COL6A3, and HOXB4) and in signaling pathway of focal adhesion, TGF-beta signaling pathway, p53 signaling pathway, cell cycle, and tumor-associated pathways (TGFβ1, CDKN1C, CDKN2A, CDC14A , ITGB6, and EGF).

Cicatrix ; genetics ; Cluster Analysis ; Computational Biology ; Down-Regulation ; Gene Expression Profiling ; Humans ; Oligonucleotide Array Sequence Analysis ; Signal Transduction ; Transcriptome ; Up-Regulation

Objective : To explore the association of time in range(TIR) and glucose management indicator ( GMI) with the risk of type 2 diabetic nephropathy (DN) ． Methods : The clinical data of 215 patients with type 2 diabetes mellitus (T2DM) were collected and analyzed．According to the results of estimated glomerular filtration rate (eGFR) and urinary albumin to creatinine ratio( UACR) ，they were divided into 117 patients with T2DM and 98 patients with DN．The clinical data，biochemical indicators and continuous glucose monitoring ( CGM) indicators of the two groups were compared．Logistic regression was used to analyze the influencing factors of DN risk．The predictive value of TIR and GMI on the risk of DN was evaluated by receiver operating characteristic (ROC) curve. Results: There were significant differences in age，duration of diabetes，systolic blood pressure，glycosylated hemoglobin ( HbA1c) ，fasting plasma glucose (FPG) ，2 hour postprandial plasma glucose (2hPG) ，creatinine( Cr) ，UACR, eGFR between the two groups(P＜0. 05) ．There were statistically significant differences between the two groups in the CGM indexes of GMI，mean absolute difference of mean of daily differences ( MODD) ，glucose above target range time(TAR) and TIR(P＜0. 05) ．The results of logistic regression analysis showed that TIR was a protective factor of DN．In the ROC curve analysis of TIR prediction DN，the area under the ROC curve was 0. 718 (95% CI = 0. 648 ～0. 789，P＜0. 001) ，and the Yoden index was 0. 38．At this time，the sensitivity was 66. 7% ，and the specificity was 71. 3%．In the ROC curve analysis of GMI prediction DN，the area under the ROC curve was 0. 701 (95% CI = 0. 629 ～0. 774，P＜0. 001) ，and the Yoden index was 0. 368．At this time，the sensitivity was 63. 3% ， and the specificity was 73. 5%． Conclusion Specifically，lower TIR and higher GMI increase the risk of DN.