Background: Sepsis is a life-threatening multiple-organ dysfunction caused by a dysregulated host response to infection and is the leading cause of death in non-cardiac intensive care facilities. Early reliable prediction of sepsis outcomes leads to cost-efficient resource allocation and therapeutic strategies. However, there are still no reliable markers to predict the outcome of patients at the initial stage of sepsis. Analyzing transcription profiles enables researchers to predict early outcomes using transcripts and their expression patterns. Transcriptomic profiling of septic patients has been done recently; however, analysis of prognostic outcomes is still scarce. Objective: This study aimed to determine transcriptional indicators that may be useful in the prognosis of the severity of sepsis. Methods: This is a prospective cohort study of Filipino patients admitted for sepsis at the national tertiary referral hospital in Manila, Philippines. We conducted differentially expressed gene analysis, network analyses, and area under the curve study of publicly available datasets of surviving vs. non-surviving sepsis patients to identify candidate prognosticator markers. Quantitative PCR was used to characterize the expression of each marker. A model using ordinal logistic regression analysis was done to determine which among the markers can best predict the outcome of sepsis severity. Results: We identified ACTB, RAC1, STAT3, and UBQLN1 as candidate mRNA prognosticators. The expression of STAT3, a gene involved in immunosuppression, is inversely correlated with the severity of sepsis. Conclusion Transcriptomic markers such as STAT3 can predict the severity of patients with sepsis. Early detection of its inverse expression may prompt early and more aggressive management of patients.
sepsis ; STAT3 ; data mining ; transcriptomics