Free Radical-Associated Gene Signature Predicts Survival in Sepsis Patients.
Anlin FengMarissa D PokharelYing LiangWenli MaSaurabh AggarwalStephen M BlackTing WangPublished in: International journal of molecular sciences (2024)
Sepsis continues to overwhelm hospital systems with its high mortality rate and prevalence. A strategy to reduce the strain of sepsis on hospital systems is to develop a diagnostic/prognostic measure that identifies patients who are more susceptible to septic death. Current biomarkers fail to achieve this outcome, as they only have moderate diagnostic power and limited prognostic capabilities. Sepsis disrupts a multitude of pathways in many different organ systems, making the identification of a single powerful biomarker difficult to achieve. However, a common feature of many of these perturbed pathways is the increased generation of reactive oxygen species (ROS), which can alter gene expression, changes in which may precede the clinical manifestation of severe sepsis. Therefore, the aim of this study was to evaluate whether ROS-related circulating molecular signature can be used as a tool to predict sepsis survival. Here we created a ROS-related gene signature and used two Gene Expression Omnibus datasets from whole blood samples of septic patients to generate a 37-gene molecular signature that can predict survival of sepsis patients. Our results indicate that peripheral blood gene expression data can be used to predict the survival of sepsis patients by assessing the gene expression pattern of free radical-associated -related genes in patients, warranting further exploration.
Keyphrases
- gene expression
- end stage renal disease
- newly diagnosed
- acute kidney injury
- chronic kidney disease
- ejection fraction
- intensive care unit
- peritoneal dialysis
- prognostic factors
- emergency department
- healthcare
- peripheral blood
- machine learning
- dna damage
- genome wide
- transcription factor
- free survival
- acute care
- genome wide identification