PDF(Pubmed)
Abstract：
UNASSIGNED: Based on hypothetical hypothyroidism and nonthyroidal illness syndrome (NTIS) gene expression similarities, we decided to compare the patterns of expression of both as models of NTIS. The concordant profile between them may enlighten new biomarkers for NTIS challenging scenarios.
UNASSIGNED: We used Ion Proton System next-generation sequencing to build the hypothyroidism transcriptome. We selected two databanks in GEO2 platform datasets to find the differentially expressed genes (DEGs) in adults and children with sepsis. The ROC curve was constructed to calculate the area under the curve (AUC). The AUC, chi-square, sensitivity, specificity, accuracy, kappa and likelihood were calculated. We performed Cox regression and Kaplan-Meier analyses for the survival analysis.
UNASSIGNED: Concerning hypothyroidism DEGs, 70.42% were shared with sepsis survivors and 61.94% with sepsis nonsurvivors. Some of them were mitochondrial gene types (mitGenes), and 95 and 88 were related to sepsis survivors and nonsurvivors, respectively. BLOC1S1, ROMO1, SLIRP and TIMM8B mitGenes showed the capability to distinguish sepsis survivors and nonsurvivors.
UNASSIGNED: We matched our hypothyroidism DEGs with those in adults and children with sepsis. Additionally, we observed different patterns of hypothyroid-related genes among sepsis survivors and nonsurvivors. Finally, we demonstrated that ROMO1, SLIRP and TIMM8B could be predictive biomarkers in children´s sepsis.
UNASSIGNED: We used Ion Proton System next-generation sequencing to build the hypothyroidism transcriptome. We selected two databanks in GEO2 platform datasets to find the differentially expressed genes (DEGs) in adults and children with sepsis. The ROC curve was constructed to calculate the area under the curve (AUC). The AUC, chi-square, sensitivity, specificity, accuracy, kappa and likelihood were calculated. We performed Cox regression and Kaplan-Meier analyses for the survival analysis.
UNASSIGNED: Concerning hypothyroidism DEGs, 70.42% were shared with sepsis survivors and 61.94% with sepsis nonsurvivors. Some of them were mitochondrial gene types (mitGenes), and 95 and 88 were related to sepsis survivors and nonsurvivors, respectively. BLOC1S1, ROMO1, SLIRP and TIMM8B mitGenes showed the capability to distinguish sepsis survivors and nonsurvivors.
UNASSIGNED: We matched our hypothyroidism DEGs with those in adults and children with sepsis. Additionally, we observed different patterns of hypothyroid-related genes among sepsis survivors and nonsurvivors. Finally, we demonstrated that ROMO1, SLIRP and TIMM8B could be predictive biomarkers in children´s sepsis.
摘要：
■基于假设的甲状腺功能减退症和非甲状腺疾病综合征（NTIS）基因表达相似性，我们决定比较两者作为NTIS模型的表达模式。它们之间的一致概况可能会为NTIS挑战性场景启发新的生物标志物。
■我们使用离子质子系统下一代测序来构建甲状腺功能减退症转录组。我们在GEO2平台数据集中选择了两个数据库，以寻找成人和儿童败血症的差异表达基因（DEGs）。构建ROC曲线以计算曲线下面积(AUC)。AUC,卡方,灵敏度,特异性，准确度,计算kappa和可能性。我们对生存分析进行了Cox回归和Kaplan-Meier分析。
■关于甲状腺功能减退症，70.42%与败血症幸存者共享，61.94%与败血症非幸存者共享。其中一些是线粒体基因类型（mitGenes），95和88与败血症幸存者和非幸存者有关，分别。BLOC1S1，ROMO1，SLIRP和TIMM8BmitGenes显示出区分败血症幸存者和非幸存者的能力。
■我们将我们的甲状腺功能减退DEGs与患有脓毒症的成人和儿童进行了匹配。此外,我们观察到脓毒症幸存者和非幸存者中甲状腺功能减退相关基因的不同模式.最后,我们证明ROMO1,SLIRP和TIMM8B可能是儿童脓毒症的预测生物标志物.
■我们使用离子质子系统下一代测序来构建甲状腺功能减退症转录组。我们在GEO2平台数据集中选择了两个数据库，以寻找成人和儿童败血症的差异表达基因（DEGs）。构建ROC曲线以计算曲线下面积(AUC)。AUC,卡方,灵敏度,特异性，准确度,计算kappa和可能性。我们对生存分析进行了Cox回归和Kaplan-Meier分析。
■关于甲状腺功能减退症，70.42%与败血症幸存者共享，61.94%与败血症非幸存者共享。其中一些是线粒体基因类型（mitGenes），95和88与败血症幸存者和非幸存者有关，分别。BLOC1S1，ROMO1，SLIRP和TIMM8BmitGenes显示出区分败血症幸存者和非幸存者的能力。
■我们将我们的甲状腺功能减退DEGs与患有脓毒症的成人和儿童进行了匹配。此外,我们观察到脓毒症幸存者和非幸存者中甲状腺功能减退相关基因的不同模式.最后,我们证明ROMO1,SLIRP和TIMM8B可能是儿童脓毒症的预测生物标志物.
