RESUMO
Purpose: Sepsis is an organ dysfunction with high mortality. Early identification, diagnosis, and effective treatment of sepsis are beneficial to the survival of patients. This study aimed to find potential diagnosis and immune-related genes, and drug targets, which could provide novel diagnostic and therapeutic markers for sepsis. Patients and Methods: The GSE69063, GSE154918 and GSE28750 datasets were integrated to evaluate immune infiltration and identify differentially expressed genes (DEGs) and immune-related genes. Weighted gene co-expression network analysis (WGCNA) was applied to find the hub module related to immune score and sepsis. Immune-related key genes were screened out by taking interaction of DEGs, immune-related genes, and genes in hub module. Protein-protein interaction (PPI) analysis was used to further screen immune-related hub genes, followed by construction of a diagnostic model based on immune-related hub genes. Functional analysis and drug prediction of immune-related hub genes were, respectively, performed by David software and DGIdb database, followed by expression validation by reverse transcriptase polymerase chain reaction (RT-PCR). Results: Totally, 93 immune-related key genes were identified between 561 DEGs, 1793 immune-related genes and 12,459 genes in the hub module of WGCNA. Through PPI analysis, a total of 5 diagnose and immune-related hub genes were further obtained, including IL7R, IL10, CD40LG, CD28 and LCN2. Relationship pairs between these 5 genes and immune cell were identified, including LCN2/IL7R/CD28-activated dendritic cell and IL10-immature B cell. Based on pharmacogenomics, 17 candidate drugs might interact with IL 10, including CYCLOSPORINE. Six candidate drugs might interact with CD28 and 11 with CD40LG, CD40LG and CD28 were drug targets of ALDESLEUKIN. Four significantly enriched signaling pathways were identified, such as T cell receptor signaling pathway, NF-kappa B signaling pathway and JAK-STAT signaling pathway. Conclusion: The 5-gene diagnostic model could be used to diagnose and guide clinical immunotherapy for sepsis.
RESUMO
Background: Promoter methylation of glutathione-S-transferase p1 (GSTP1) is related to the occurrence of prostate cancer (PCa), but reports are inconsistent about the accuracy of GSTP1 promoter methylation in PCa diagnosis and prognosis. Therefore, we systematically evaluated the diagnostic and prognostic value of GSTP1 promoter methylation in PCa. Methods: The PubMed, EMBASE, Web of Science, and PMC databases were searched for all relevant studies from the date of inception to November 31, 2021. We compared differences in the incidence of GSTP1 promoter methylation in cfDNA between prostate cancer patients and controls. The odds ratio (OR) and hazard ratio (HR) were used as effect sizes, and the result of each effect size is expressed as a 95% confidence interval (95% CI). Results: Our meta-analysis showed that the combined sensitivity and specificity of GSTP1 promoter methylation in cfDNA for the diagnosis of prostate cancer were 0.37 (95% CI = 0.23, 0.53) and 0.97 (95% CI = 0.88, 0.99), respectively. The area under the curve (AUC) with 95% CI was 0.78 (95% CI = 0.75, 0.82). For prognostic variables, hypermethylation of GSTP1 was associated with shorter survival in PCa (HR = 2.57, 95% CI = 1.30, 5.10), with statistical significance in between-study heterogeneity (I 2 = 72%, P=0.006). The results of the subgroup analysis indicated that the heterogeneity of studies may be due to differences in the observed indicators. Conclusions: The results of the meta-analysis substantiate the high specificity of promoter methylation of GSTP1 in cfDNA for the diagnosis of prostate cancer, and it may be used to more precisely evaluate the prognosis of patients with prostate cancer. It may be helpful for the early detection of prostate cancer, but it still must be combined with traditional prostate-specific antigen (PSA) or other methylated genes to accomplish this goal.
