Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm

Abstract

Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not present with specific symptoms, its diagnosis is often delayed. The lack of diagnostic accuracy results in a non-specific diagnosis, and to date, a standard diagnostic test to detect sepsis in patients remains lacking. Therefore, it is vital to identify sepsis-related diagnostic genes. This study aimed to conduct an integrated analysis to assess the immune scores of samples from patients diagnosed with sepsis and normal samples, followed by weighted gene co-expression network analysis (WGCNA) to identify immune infiltration-related genes and potential transcriptome markers in sepsis. Furthermore, gene regulatory networks were established to screen diagnostic markers for sepsis based on the protein-protein interaction networks involving these immune infiltration-related genes. Moreover, we integrated WGCNA with the support vector machine (SVM) algorithm to build a diagnostic model for sepsis. Results showed that the immune score was significantly lower in the samples from patients with sepsis than in normal samples. A total of 328 and 333 genes were positively and negatively correlated with the immune score, respectively. Using the MCODE plugin in Cytoscape, we identified four modules, and through functional annotation, we found that these modules were related to the immune response. Gene Ontology functional enrichment analysis showed that the identified genes were associated with functions such as neutrophil degranulation, neutrophil activation in the immune response, neutrophil activation, and neutrophil-mediated immunity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed the enrichment of pathways such as primary immunodeficiency, Th1- and Th2-cell differentiation, T-cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity. Finally, we identified a four-gene signature, containing the hub genes LCK, CCL5, ITGAM, and MMP9, and established a model that could be used to diagnose patients with sepsis.

Prediction of Prognosis in Patients with Sepsis Based on Platelet-Related Genes

“…LCK mainly participates in T cell signal transduction and is one of the therapeutic targets for many inflammation-mediated pathological processes [37]. Several studies have demonstrated that LCK is a crucial gene in sepsis patients [38][39][40]. The F13A1 gene encodes coagulation factor XIII A, and F13A1 gene mutations may lead to abnormal coagulation factor XIII function, resulting in coagulation system diseases [41].…”

Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis

“…CCL5 is a chemokine gene clustered on the chromosome 17, which is a potential target for sepsis [ 31 ]. Li M identified a gene signature, containing the hub genes CCL5, and established a model that could be used to diagnose patients with sepsis [ 32 ]. The protein encoded by CCR7 is a member of the G protein coupled receptor family.…”

Applying Transcriptomics for an Enhanced Clinical Research Framework, Implications for an Improved Research Strategy based on an Omics Approach: A Scoping Review

“…Zhang et al also analyzed an adult ICU dataset (5) using WGCNA methodology to highlight significant gene modules in sepsis (6). Also, Li et al used WCGNA to demonstrate inflammation-related genes and to identify transcriptomic markers (7). Furthermore, in this study, WGCNA was integrated with the support vector machine (SVM) algorithm.…”