Gene Expression Patterns Distinguish Mortality Risk in Patients with Postsurgical Shock

Abstract

Nowadays, mortality rates in intensive care units are the highest of all hospital units. However, there is not a reliable prognostic system to predict the likelihood of death in patients with postsurgical shock. Thus, the aim of the present work is to obtain a gene expression signature to distinguish the low and high risk of death in postsurgical shock patients. In this sense, mRNA levels were evaluated by microarray on a discovery cohort to select the most differentially expressed genes between surviving and non-surviving groups 30 days after the operation. Selected genes were evaluated by quantitative real-time polymerase chain reaction (qPCR) in a validation cohort to validate the reliability of data. A receiver-operating characteristic analysis with the area under the curve was performed to quantify the sensitivity and specificity for gene expression levels, which were compared with predictions by established risk scales, such as acute physiology and chronic health evaluation (APACHE) and sequential organ failure assessment (SOFA). IL1R2, CD177, RETN, and OLFM4 genes were upregulated in the non-surviving group of the discovery cohort, and their predictive power was confirmed in the validation cohort. This work offers new biomarkers based on transcriptional patterns to classify the postsurgical shock patients according to low and high risk of death. The results present more accuracy than other mortality risk scores.

[Retracted] Identification of a Four‐Gene Signature for Diagnosing Paediatric Sepsis

“…Some of the key genes in our study ( ANXA3 and CD177 ) have been previously reported to be involved in sepsis, while GRAMD1C and TIGD3 have not [ 26 – 29 ]. GRAMD1C is a featureless protein belonging to the gram domain protein family [ 30 ].…”

“…In addition, CD177 combined with other genes ( IL1R2 , OLFM4 , and RETN ) has been reported as a potential indicator of prognosis in patients with sepsis. Compared with the Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment scores, CD117 has more advantages in estimating the prognosis of patients [ 29 ].…”

Identification of the TF-miRNA-mRNA co-regulatory networks involved in sepsis

“…The GEO (http://www.ncbi.nlm.nih.gov/geo) database is a public functional genomics data repository of high-throughput gene expression data, chips, and microarrays [12] . Two datasets, GSE131761 [13] and GSE94717 [14] , met our retrieval requirement for this study. The differentially expressed mRNAs and miRNAs between healthy controls and samples collected from patients who experienced sepsis were identified using the web tool named GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r).…”

Neutrophil Phenotypes and Functions in Trauma and Trauma-Related Sepsis

“…The author observed a constant subset of neutrophils that did not express CD177, while simultaneously, neutrophils from septic patients had increased CD177 both mRNA and protein expression (70). Moreover, significantly elevated transcription of CD177 was found in non-survivors compared with survivors after post-surgical shock (71). Increased CD177 expression was also reported in rat lung tissue in cecal ligation and puncture (CLP)-induced sepsis but not in blood or liver (72).…”