Bioinformatics Analysis of Microarray Data in Myelodysplastic Syndrome Based on Gene Expression Omnibus Database / 中国实验血液学杂志

OBJECTIVE@#To identify the key genes and explore mechanisms in the development of myelodysplastic syndrome (MDS) by bioinformatics analysis.@*METHODS@#Two cohorts profile datasets of MDS were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed gene (DEG) was screened by GEO2R, functional annotation of DEG was gained from GO database, gene ontology (GO) enrichment analysis was performed via Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and key genes were screened by Matthews correlation coefficient (MCC) based on STRING database.@*RESULTS@#There were 112 DEGs identified, including 85 up-regulated genes and 27 down-regulated genes. GO enrichment analysis showed that biological processes were mainly enriched in immune response, etc, cellular component in cell membrane, etc, and molecular function in protein binding, etc. KEGG signaling pathway analysis showed that main gene enrichment pathways were primary immunodeficiency, hematopoietic cell lineage, B cell receptor signaling pathway, Hippo signaling pathway, and asthma. Three significant modules were screened by Cytoscape software MCODE plug-in, while 10 key node genes (CD19, CD79A, CD79B, EBF1, VPREB1, IRF4, BLNK, RAG1, POU2AF1, IRF8) in protein-protein interaction (PPI) network were screened based on STRING database.@*CONCLUSION@#These screened key genes and signaling pathways are helpful to better understand molecular mechanism of MDS, and provide theoretical basis for clinical targeted therapy.

Gestational diabetes: weight gain during pregnancy and its relationship to pregnancy outcomes / 中华医学杂志(英文版)

BACKGROUND@#Weight gain during pregnancy reflects the mother's nutritional status. However, it may be affected by nutritional therapy and exercise interventions used to control blood sugar in gestational diabetes mellitus (GDM). This study aimed to evaluate weight gain during gestation and pregnancy outcomes among women with GDM.@*METHODS@#A retrospective study involving 1523 women with GDM was conducted between July 2013 and July 2016. Demographic data, gestational weight gain (GWG), blood glucose, glycated-hemoglobin level, and maternal and fetal outcomes were extracted from medical records. Relationships between GWG and pregnancy outcomes were investigated using multivariate logistic regression.@*RESULTS@#In total, 451 (29.6%) women showed insufficient GWG and 484 (31.8%) showed excessive GWG. Excessive GWG was independently associated with macrosomia (adjusted odds ratio [aOR] 2.20, 95% confidence interval [CI] 1.50-3.52, P < 0.001), large for gestational age (aOR 2.06, 95% CI 1.44-2.93, P < 0.001), small for gestational age (aOR 0.49, 95% CI 0.25-0.97, P = 0.040), neonatal hypoglycemia (aOR 3.80, 95% CI 1.20-12.00, P = 0.023), preterm birth (aOR 0.45, 95% CI 0.21-0.96, P = 0.040), and cesarean delivery (aOR 1.45, 95% CI 1.13-1.87, P = 0.004). Insufficient GWG increased the incidence of preterm birth (aOR 3.53, 95% CI 1.96-6.37, P < 0.001).@*CONCLUSIONS@#Both excessive and insufficient weight gain require attention in women with GDM. Nutritional therapy and exercise interventions to control blood glucose should also be used to control reasonable weight gain during pregnancy to decrease adverse pregnancy outcomes.