Diagnostic model constructed by nine inflammation-related genes for diagnosing ischemic stroke and reflecting the condition of immune-related cells

Abstract

BackgroundIschemic cerebral infarction is the most common type of stroke with high rates of mortality, disability, and recurrence. However, the known diagnostic biomarkers and therapeutic targets for ischemic stroke (IS) are limited. In the current study, we aimed to identify novel inflammation-related biomarkers for IS using machine learning analysis and to explore their relationship with the levels of immune-related cells in whole blood samples.MethodsGene expression profiles of healthy controls and patients with IS were download from the Gene Expression Omnibus. Analysis of differentially expressed genes (DEGs) was performed in healthy controls and patients with IS. Single-sample gene set enrichment analysis was performed to calculate inflammation scores, and weighted gene co-expression network analysis was used to analyze genes in significant modules associated with inflammation scores. Key DEGs in significant modules were then analyzed using LASSO regression analysis for constructing a diagnostic model. The effectiveness and specificity of the diagnostic model was verified in healthy controls and patients with IS and with cerebral hemorrhage (CH) using qRT-PCR. The relationship between diagnostic score and the levels of immune-related cells in whole blood were analyzed using Pearson correlations.ResultsA total of 831 DEGs were identified. Both chronic and acute inflammation scores were higher in patients with IS, while 54 DEGs were also clustered in the gene modules associated with chronic and acute inflammation scores. Among them, a total of 9 genes were selected to construct a diagnostic model. Interestingly, RT-qPCR showed that the diagnostic model had better diagnostic value for IS but not for CH. The levels of lymphocytes were lower in blood of patients with IS, while the levels of monocytes and neutrophils were increased. The diagnostic score of the model was negatively associated with the levels of lymphocytes and positively associated with levels of monocytes and neutrophils.ConclusionsTaken together, the diagnostic model constructed using the inflammation-related genes TNFSF10, ID1, PAQR8, OSR2, PDK4, PEX11B, TNIP1, FFAR2, and JUN exhibited high and specific diagnostic value for IS and reflected the condition of lymphocytes, monocytes, and neutrophils in the blood. The diagnostic model may contribute to the diagnosis of IS.

Potential diagnostic biomarkers for immunogenic cell death in elderly female patients with ischemic stroke: identification and analysis

“…Meanwhile, PDK4 has been proposed as a potential biomarker in patients with renal ischemia 31 and is considered an important gene related to cell death in females with IS 32 . Moreover, the diagnostic value of a model that incorporates eight inflammation-related genes, including PDK4 , for the diagnosis of IS was previously reported 33 .…”

Assessing the impact of novel risk loci on Alzheimer’s and Parkinson’s diseases in a Chinese Han cohort

“…TNIP1 , known to induce excessive inflammation, has been previously identified in autoimmune GWAS ( 27 ). Within mouse microglia, TNIP1 is encompassed in a transcription module regulated by BCL3, correlating with prolonged microglial exposure to inflammation and aging ( 28 ).…”

Immune-Related Molecules CD3G and FERMT3: Novel Biomarkers Associated with Sepsis

“…Baghela, et al contributed the data which were acquired from Illumina NexSeq 200 (Homo sapiens; GPL16791) [45]. Before conducting WGCNA, the gene expression data underwent the following preprocessing steps: (I) probes were annotated and translated into gene names by merging information from the GPL16791 platform; (II) probes with null value were removed; (III) expression values of the same genes were merged and averaged; (IV) genes with a mean expression level < 0.5 were removed; (V) the MAD algorithm was employed to screen out genes with the top 4188 variation degrees, and these genes were subjected to WGCNA [46].…”