Identification and validation of a novel mitochondrion-related gene signature for diagnosis and immune infiltration in sepsis

Abstract

Background: Owing to the complex pathophysiological features and heterogeneity of sepsis, current diagnostic methods are not sufficiently precise or timely, causing a delay in treatment. It has been suggested that mitochondrial dysfunction plays a critical role in sepsis. However, the role and mechanism of mitochondria-related genes in the diagnostic and immune microenvironment of sepsis have not been sufficiently investigated.

Methods: Mitochondria-related differentially expressed genes (DEGs) were identified between human sepsis and normal samples from GSE65682 dataset. Least absolute shrinkage and selection operator (LASSO) regression and the Support Vector Machine (SVM) analyses were carried out to locate potential diagnostic biomarkers. Gene ontology and gene set enrichment analyses were conducted to identify the key signaling pathways associated with these biomarker genes. Furthermore, correlation of these genes with the proportion of infiltrating immune cells was estimated using CIBERSORT. The expression and diagnostic value of the diagnostic genes were evaluated using GSE9960 and GSE134347 datasets and septic patients. Furthermore, we established an in vitro sepsis model using lipopolysaccharide (1 µg/mL)-stimulated CP-M191 cells. Mitochondrial morphology and function were evaluated in PBMCs from septic patients and CP-M191 cells, respectively.

Results: In this study, 647 mitochondrion-related DEGs were obtained. Machine learning confirmed six critical mitochondrion-related DEGs, including PID1, CS, CYP1B1, FLVCR1, IFIT2, and MAPK14. We then developed a diagnostic model using the six genes, and receiver operating characteristic (ROC) curves indicated that the novel diagnostic model based on the above six critical genes screened sepsis samples from normal samples with area under the curve (AUC) = 1.000, which was further demonstrated in the GSE9960 and GSE134347 datasets and our cohort. Importantly, we also found that the expression of these genes was associated with different kinds of immune cells. In addition, mitochondrial dysfunction was mainly manifested by the promotion of mitochondrial fragmentation (p<0.05), impaired mitochondrial respiration (p<0.05), decreased mitochondrial membrane potential (p<0.05), and increased reactive oxygen species (ROS) generation (p<0.05) in human sepsis and LPS-simulated in vitro sepsis models.

Conclusion: We constructed a novel diagnostic model containing six MRGs, which has the potential to be an innovative tool for the early diagnosis of sepsis.

Keywords: bioinformatics; diagnostic biomarkers; immune infiltration; mitochondrion; sepsis.