A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock

Abstract

Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25-50%. The present study explored the mechanisms of PSS using four microarray datasets (GSe26378, GSe26440, GSe13904 and GSe4607) that were obtained from the Gene Expression Omnibus database. Based on the MetaDE package, the consistently differentially expressed genes (DEGs) in the four datasets were screened. Using the WGCNA package, the disease-associated modules and genes were identified. Subsequently, the optimal feature genes were further selected using the caret package. Finally, a support vector machine (SVM) classifier based on the optimal feature genes was built using the e1071 package. Initially, there were 2,699 consistent DEGs across the four datasets. From the 10 significantly stable modules across the datasets, four stable modules (including the magenta, purple, turquoise and yellow modules), in which the consistent DEGs were significantly enriched (P<0.05), were further screened. Subsequently, six optimal feature genes (including cysteine rich transmembrane module containing 1, S100 calcium binding protein A9, solute carrier family 2 member 14, stomatin, uridine phosphorylase 1 and utrophin) were selected from the genes in the four stable modules. Additionally, an effective SVM classifier was constructed based on the six optimal genes. The SVM classifier based on the six optimal genes has the potential to be applied for PSS diagnosis. This may improve the accuracy of early PSS diagnosis and suggest possible molecular targets for interventions.

Analysis of signature genes and association with immune cells infiltration in pediatric septic shock

“…Consistent with our results, Guoli et al. also found that UPP1 was highly expressed in children with septic shock ( 30 ). These suggested that UPP1 may involve in young sepsis patients.…”

“…Mike et al reported that the expression of UPP1 was up-regulate in young sepsis rat when compared with aged sepsis rat (29). Consistent with our results, Guoli et al also found that UPP1 was highly expressed in children with septic shock (30). These suggested that UPP1 may involve in young sepsis patients.…”

A Gene Modular Approach to a Temporal Meningococcal Septic Shock Transcriptome, Enabling Clinical Phenotype Genotype Association

“…Moreover, WGCNA applied to sepsis may show potential beyond traditional clinical biomarkers. For example, LONG et al combined WGCNA with a machine learning algorithm and applied this work ow to three publicly available sepsis datasets [23]. Then by applying arti cial intelligence concepts to WGCNA, Long et al presented a diagnostic classi er with the potential for early diagnostic bene t.…”

Application of a Gene Modular Approach for Clinical Phenotype Genotype Association and Sepsis Prediction using Machine Learning in Meningococcal Sepsis

“…The dataset contained 29 instances of survival class (23) and non-survival class (6), which was an unequal distribution of classes. In machine learning, unequal data distribution is one of the major causes of decreasing accuracy of classi cation models.…”