Mukhopadhyay Samanwoy, Thatoi Pravat K, Pandey Abhay D, Das Bidyut K, Ravindran Balachandran, Bhattacharjee Samsiddhi, Mohapatra Saroj K
National Institute of Biomedical Genomics, Kalyani, West Bengal, India.
Department of Medicine, Sriram Chandra Bhanja Medical College and Hospital, Cuttack, Odisha, India.
PLoS One. 2017 Feb 15;12(2):e0171689. doi: 10.1371/journal.pone.0171689. eCollection 2017.
Septic shock is a major medical problem with high morbidity and mortality and incompletely understood biology. Integration of multiple data sets into a single analysis framework empowers discovery of new knowledge about the condition that may have been missed by individual analysis of each of these datasets. Electronic search was performed on medical literature and gene expression databases for selection of transcriptomic studies done in circulating leukocytes from human subjects suffering from septic shock. Gene-level meta-analysis was conducted on the six selected studies to identify the genes consistently differentially expressed in septic shock. This was followed by pathway-level analysis using three different algorithms (ORA, GSEA, SPIA). The identified up-regulated pathway, Osteoclast differentiation pathway (hsa04380) was validated in two independent cohorts. Of the pathway, 25 key genes were selected that serve as an expression signature of Septic Shock.
脓毒性休克是一个具有高发病率和死亡率且生物学机制尚未完全明确的重大医学问题。将多个数据集整合到一个单一的分析框架中，有助于发现关于该病症的新知识，而这些知识可能在对每个数据集进行单独分析时被遗漏。我们对医学文献和基因表达数据库进行了电子检索，以选择在脓毒性休克患者的循环白细胞中进行的转录组学研究。对六项选定的研究进行了基因水平的荟萃分析，以确定在脓毒性休克中持续差异表达的基因。随后使用三种不同的算法（ORA、GSEA、SPIA）进行通路水平的分析。所确定的上调通路，破骨细胞分化通路（hsa04380）在两个独立队列中得到了验证。在该通路中，选择了25个关键基因作为脓毒性休克的表达特征。
