Gene Expression Patterns Distinguish Mortality Risk in Patients with Postsurgical Shock

• Published in: Journal of clinical medicine Vol. 9; no. 5; p. 1276
• Main Authors: Martínez-Paz, Pedro, Aragón-Camino, Marta, Gómez-Sánchez, Esther, Lorenzo-López, Mario, Gómez-Pesquera, Estefanía, López-Herrero, Rocío, Sánchez-Quirós, Belén, de la Varga, Olga, Tamayo-Velasco, Álvaro, Ortega-Loubon, Christian, García-Morán, Emilio, Gonzalo-Benito, Hugo, Heredia-Rodríguez, María, Tamayo, Eduardo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.04.2020; MDPI
• Subjects: Clinical medicine; Clustering; Gene expression; Intensive care; Mortality; Patients; Physiology; Polymerase chain reaction; Sepsis; Software; Survival analysis; Palo Alto California; United States > US; Germany; mortality; microarray; postsurgical shock; transcriptomic profile; biomarker; sepsis
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm9051276

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). , , , and 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.