Identification of Nine mRNA Signatures for Sepsis Using Random Forest

Abstract

Sepsis has high fatality rates. Early diagnosis could increase its curating rates. There were no reliable molecular biomarkers to distinguish between infected and uninfected patients currently, which limit the treatment of sepsis. To this end, we analyzed gene expression datasets from the GEO database to identify its mRNA signature. First, two gene expression datasets (GSE154918 and GSE131761) were downloaded to identify the differentially expressed genes (DEGs) using Limma package. Totally 384 common DEGs were found in three contrast groups. We found that as the condition worsens, more genes were under disorder condition. Then, random forest model was performed with expression matrix of all genes as feature and disease state as label. After which 279 genes were left. We further analyzed the functions of 279 important DEGs, and their potential biological roles mainly focused on neutrophil threshing, neutrophil activation involved in immune response, neutrophil-mediated immunity, RAGE receptor binding, long-chain fatty acid binding, specific granule, tertiary granule, and secretory granule lumen. Finally, the top nine mRNAs (MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A) associated with sepsis were considered as signatures for distinguishing between sepsis and healthy controls. Based on 5-fold cross-validation and leave-one-out cross-validation, the nine mRNA signature showed very high AUC.

The ‘analysis of gene expression and biomarkers for point-of-care decision support in Sepsis‘ study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination

“…neutrophils. Providing external validation to our findings, several genes, such as CD177, ARG1 (arginase), MMP9, OLAH and ADM have been described previously as having important inflammatory roles in sepsis ( 16 , 29 , 41 , 42 , 44 , 51 – 71 ). ARG1 in particular has been identified by other groups as a good biomarker for sepsis diagnosis ( 64 ), specifically associated with neutrophil activity ( 72 ) a component of which may be from a myeloid-derived suppressor cell (MDSC) phenotype ( 60 ).…”

“…With improvements in RNA extraction methodologies, there has been a renewed focus toward cellular transcriptomic analysis in sepsis. Several groups have published similar studies ( 16 , 17 , 20 – 24 , 29 , 30 , 33 , 37 , 40 – 44 ) with various biomarker configurations in clinical validation or development ( 12 , 15 – 18 , 21 , 24 , 45 – 49 ). Despite considerable advances, the field is still considered to be evolving and ‘significant work is needed to identify the optimal combinations of biomarkers that can augment diagnosis, treatment, and influence good patient outcomes’ ( 50 ).…”

“…For biomarker selection, variables were ranked on decrease in accuracy and Gini scores. The Gini score indicated how often a random sample from the test set would be incorrectly categorized as having good or poor prognosis if the samples were randomly distributed ( 27 – 29 ).…”

Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker

“…However, IL-6 presents its own challenges due to the lack of standardization and high susceptibility to other influences, which can make interpretation difficult 15 . Efforts have been made to further reduce this time lag, such as using RNA levels as biomarkers 16 , 17 and leveraging single-cell sequencing to identify sepsis-specific immunologic signatures 18 . However, the extensive time required for RNA sequencing and analysis limits its immediate use as a biomarker.…”