Time Series Gene Expression Profiles Analysis Identified Several Potential Biomarkers for Sepsis

Identification of key genes as potential diagnostic biomarkers in sepsis by bioinformatics analysis

Background

Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis.

Methods

Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the "limma" R package. Protein-protein interaction (PPI) network was created using STRING and Cytoscape, and PPI hub genes were identified with the cytoHubba plugin. The PPI hub genes overlapping with the genes in key modules of WGCNA were determined to be the sepsis-related key genes. Subsequently, the key overlapping genes were validated in an external independent dataset and sepsis patients recruited in our hospital. In addition, CIBERSORT analysis evaluated immune cell infiltration and its correlation with key genes.

Results

By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, p = 2e - 19). 293 DEGs were identified in the merged datasets. The PPI network was created, and the CytoHubba was used to calculate the top 20 genes based on four algorithms (Degree, EPC, MCC, and MNC). Ultimately, LTF, LCN2, ELANE, MPO and CEACAM8 were identified as key overlapping genes as they appeared in the PPI hub genes and the key module genes of WGCNA. These sepsis-related key genes were validated in an independent external dataset (GSE131761) and sepsis patients recruited in our hospital. Additionally, the immune infiltration profiles differed significantly between sepsis and non-sepsis critical illness groups. Correlations between immune cells and these five key genes were assessed, revealing that plasma cells, macrophages M0, monocytes, T cells regulatory, eosinophils and NK cells resting were simultaneously and significantly associated with more than two key genes.

Conclusion

This study suggests a critical role of LTF, LCN2, ELANE, MPO and CEACAM8 in sepsis and may provide potential diagnostic biomarkers and therapeutic targets for the treatment of sepsis.

Blood gene expression biomarkers of response to anti-TNF drugs in pediatric inflammatory bowel diseases before initiation of treatment

BACKGROUND/AIMS

Changes in gene expression profiles among individuals with inflammatory bowel diseases (IBDs) could potentially influence the responsiveness to anti-TNF treatment. The aim of this study was to identify genes that could serve as predictors of early response to anti-TNF therapies in pediatric IBD patients prior to the initiation of treatment.

METHODS

We conducted a prospective, longitudinal, and multicenter study, enrolling 24 pediatric IBD patients aged less than 18 years who were initiating treatment with either infliximab or adalimumab. RNA-seq from blood samples was analyzed using the DESeq2 library by comparing responders and non-responders to anti-TNF drugs.

RESULTS

Bioinformatic analyses unveiled 102 differentially expressed genes, with 99 genes exhibiting higher expression in responders compared to non-responders prior to the initiation of anti-TNF therapy. Functional enrichment analyses highlighted defense response to Gram-negative bacteria (FDR = 2.3 ×10-7) as the most significant biological processes, and hemoglobin binding (FDR = 0.002), as the most significant molecular function. Gene Set Enrichment Analysis (GSEA) revealed notable enrichment in transcriptional misregulation in cancer (FDR = 0.016). Notably, 13 genes (CEACAM8, CEACAM6, CILP2, COL17A1, OLFM4, INHBA, LCN2, LTF, MMP8, DEFA4, PRTN3, AZU1, and ELANE) were selected for validation, and a consistent trend of increased expression in responders prior to drug administration was observed for most of these genes, with findings for 4 of them being statistically significant (CEACAM8, LCN2, LTF2, and PRTN3).

CONCLUSIONS

We identified 102 differentially expressed genes involved in the response to anti-TNF drugs in children with IBDs and validated CEACAM8, LCN2, LTF2, and PRTN3. Genes participating in defense response to Gram-negative bacterium, serine-type endopeptidase activity, and transcriptional misregulation in cancer are good candidates for anticipating the response to anti-TNF drugs in children with IBDs.

The novel biomarkers for assessing clinical benefits of continuous renal replacement therapy in pediatric sepsis: a pilot study

BACKGROUND

Continuous renal replacement therapy (CRRT) has been considered as an adjuvant therapy for sepsis. However, the novel biomarker to evaluate the benefits of CRRT is limited. The aim of this study was to explore the novel biomarkers involved in the impact of CRRT in pediatric sepsis.

METHODS

The serum proteomic profiles on the 7th day after CRRT (CRRT 7th day) compared with before CRRT (CRRT 1st day) was determined in 3 children with sepsis as a discovery set. The screened candidates were confirmed in the validation cohort including patients received CRRT (CRRT group) and without CRRT (non-CRRT group). We defined that pediatric sequential organ failure assessment score (pSOFA) in pediatric patients with sepsis decreased by 2 points or more on the CRRT 1st day compared with CRRT initiation as CRRT responders. The changes of serum biomarkers were compared between CRRT responders and CRRT non-responders. Moreover, correlation analysis was further conducted in pediatric sepsis.

RESULTS

A total of 145 differentially expressed proteins were found according to the serum proteomics profiles. By visualizing the interaction between the differential proteins, 6 candidates (Lysozyme C [LYZ], Leucine-rich alpha-2-glycoprotein [LRG1], Fibromodulin [FMOD], Alpha-1-antichymotrypsin [SERPINA3], L-selectin [SELL], Monocyte differentiation antigen CD14 [CD14]) were screened. In the validation cohort, serum levels of LYZ and LRG1 showed a higher trend on the CRRT 7th day than that on the 1st day in the non-CRRT group. However, the changes in levels of LYZ and LRG1 on the 7th day was significant in the CRRT group (p = 0.016, p = 0.009, respectively). Moreover, the levels of LYZ and LRG1 on the CRRT 7th day in the CRRT group were significantly higher than that in the non-CRRT group (p < 0.001, p = 0.025). Decreased levels of CD14 were associated with sepsis recovery, but not associated with CRRT. There were no significantly difference in serum FMOD, SERPINA3, and SELL levels. Importantly, serum LYZ and LRG1 levels changed in CRRT responders, but not CRRT non-responders. Further analysis indicated that serum LYZ levels were correlated to total platelet counts, aspartate aminotransferase (ALT), alanine aminotransferase (AST), and albumin levels, and serum LRG1 level were correlated to total platelet count and TBIL levels on the 1st day in the CRRT group. Protein-protein interaction network analysis displayed that serum LYZ and LRG1 were involved in the process of inflammatory response, leucocytes adhesion to vascular endothelial cell, as well as complement activation.

CONCLUSION

Elevated serum LYZ and LRG1 levels are associated with clinical benefits of CRRT during sepsis.

A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis

BACKGROUND

Immune-related genes (IRGs) remain poorly understood in their function in the onset and progression of sepsis.

METHODS

GSE65682 was obtained from the Gene Expression Omnibus database. The IRGs associated with survival were screened for subsequent modeling using univariate Cox regression analysis and least absolute shrinkage and selection operator in the training cohort. Then, we assessed the reliability of the 7 IRGs signature's independent predictive value in the training and validation cohorts following the creation of a signature applying multivariable Cox regression analysis. After that, we utilized the E-MTAB-4451 external dataset in order to do an independent validation of the prognostic signature. Finally, the CIBERSORT algorithm and single-sample gene set enrichment analysis was utilized to investigate and characterize the properties of the immune microenvironment.

RESULTS

Based on 7 IRGs signature, patients could be separated into low-risk and high-risk groups. Patients in the low-risk group had a remarkably increased 28-day survival compared to those in the high-risk group (P < 0.001). In multivariable Cox regression analyses, the risk score calculated by this signature was an independent predictor of 28-day survival (P < 0.001). The signature's predictive ability was confirmed by receiver operating characteristic curve analysis with the area under the curve reaching 0.876 (95% confidence interval 0.793-0.946). Moreover, both the validation set and the external dataset demonstrated that the signature had strong clinical prediction performance. In addition, patients in the high-risk group were characterized by a decreased neutrophil count and by reduced inflammation-promoting function.

CONCLUSION

We developed a 7 IRGs signature as a novel prognostic marker for predicting sepsis patients' 28-day survival, indicating possibilities for individualized reasonable resource distribution of intensive care unit.

Novel plasma protein biomarkers from critically ill sepsis patients

BACKGROUND

Despite the high morbidity and mortality associated with sepsis, the relationship between the plasma proteome and clinical outcome is poorly understood. In this study, we used targeted plasma proteomics to identify novel biomarkers of sepsis in critically ill patients.

METHODS

Blood was obtained from 15 critically ill patients with suspected/confirmed sepsis (Sepsis-3.0 criteria) on intensive care unit (ICU) Day-1 and Day-3, as well as age- and sex-matched 15 healthy control subjects. A total of 1161 plasma proteins were measured with proximal extension assays. Promising sepsis biomarkers were narrowed with machine learning and then correlated with relevant clinical and laboratory variables.

RESULTS

The median age for critically ill sepsis patients was 56 (IQR 51-61) years. The median MODS and SOFA values were 7 (IQR 5.0-8.0) and 7 (IQR 5.0-9.0) on ICU Day-1, and 4 (IQR 3.5-7.0) and 6 (IQR 3.5-7.0) on ICU Day-3, respectively. Targeted proteomics, together with feature selection, identified the leading proteins that distinguished sepsis patients from healthy control subjects with ≥ 90% classification accuracy; 25 proteins on ICU Day-1 and 26 proteins on ICU Day-3 (6 proteins overlapped both ICU days; PRTN3, UPAR, GDF8, NTRK3, WFDC2 and CXCL13). Only 7 of the leading proteins changed significantly between ICU Day-1 and Day-3 (IL10, CCL23, TGFα1, ST2, VSIG4, CNTN5, and ITGAV; P < 0.01). Significant correlations were observed between a variety of patient clinical/laboratory variables and the expression of 15 proteins on ICU Day-1 and 14 proteins on ICU Day-3 (P < 0.05).

CONCLUSIONS

Targeted proteomics with feature selection identified proteins altered in critically ill sepsis patients relative to healthy control subjects. Correlations between protein expression and clinical/laboratory variables were identified, each providing pathophysiological insight. Our exploratory data provide a rationale for further hypothesis-driven sepsis research.

Candesartan Reduces Neuronal Apoptosis Caused by Ischemic Stroke via Regulating the FFAR1/ITGA4 Pathway

Ischemic stroke (IS) is a general term for necrosis of brain tissue caused by stenosis, occlusion of arteries supplying blood to the brain (carotid artery and vertebral artery), and insufficient blood supply to the brain. Cerebral ischemia is the main kind of IS causing cell damage. However, the underlying mechanism still needs to be clarified further. In this study, it was demonstrated that FFAR1 was a hub gene in IS. The expression of FFAR1 was increased in PC12 cells with OGD/R treatment. FFAR1 deficiency inhibited cell viability and induced cell apoptosis, which was reversed by FFAR1 overexpression. Moreover, candesartan, as a compound targeting FFAR1, facilitated cell viability and reduced cell apoptosis. The expression of ITGA4 was also high in OGD/R-PC12 cells as FFAR1. Furthermore, FFAR1 deficiency retarded the increasing of cell viability and inhibition of cell apoptosis by downregulation of Bax and Cleaved Caspase-3 in OGD/R-PC12 cells with candesartan treatment. In conclusion, candesartan may regulate neuronal apoptosis through FFAR1/ITGA4 axis.

Bioinformatics analysis of potential pathogenesis and risk genes of immunoinflammation-promoted renal injury in severe COVID-19

Renal injury secondary to COVID-19 is an important factor for the poor prognosis of COVID-19 patients. The pathogenesis of renal injury caused by aberrant immune inflammatory of COVID-19 remains unclear. In this study, a total of 166 samples from 4 peripheral blood transcriptomic datasets of COVID-19 patients were integrated. By using the weighted gene co-expression network (WGCNA) algorithm, we identified key genes for mild, moderate, and severe COVID-19. Subsequently, taking these genes as input genes, we performed Short Time-series Expression Miner (STEM) analysis in a time consecutive ischemia-reperfusion injury (IRI) -kidney dataset to identify genes associated with renal injury in COVID-19. The results showed that only in severe COVID-19 there exist a small group of genes associated with the progression of renal injury. Gene enrichment analysis revealed that these genes are involved in extensive immune inflammation and cell death-related pathways. A further protein-protein interaction (PPI) network analysis screened 15 PPI-hub genes: ALOX5, CD38, GSF3R, LGR, RPR1, HCK, ITGAX, LYN, MAPK3, NCF4, SELP, SPI1, WAS, TLR2 and TLR4. Single-cell sequencing analysis indicated that PPI-hub genes were mainly distributed in neutrophils, macrophages, and dendritic cells. Intercellular ligand-receptor analysis characterized the activated ligand-receptors between these immune cells and parenchyma cells in depth. And KEGG enrichment analysis revealed that viral protein interaction with cytokine and cytokine receptor, necroptosis, and Toll-like receptor signaling pathway may be potentially essential for immune cell infiltration leading to COVID-19 renal injury. Finally, we validated the expression pattern of PPI-hub genes in an independent data set by random forest. In addition, we found that the high expression of these genes was correlated with a low glomerular filtration rate. Including them as risk genes in lasso regression, we constructed a Nomogram model for predicting severe COVID-19. In conclusion, our study explores the pathogenesis of renal injury promoted by immunoinflammatory in severe COVID-19 and extends the clinical utility of its key genes.

Identification of hub genes for adult patients with sepsis via RNA sequencing

To screen out potential prognostic hub genes for adult patients with sepsis via RNA sequencing and construction of a microRNA-mRNA-PPI network and investigate the localization of these hub genes in peripheral blood monocytes. The peripheral blood of 33 subjects was subjected to microRNA and mRNA sequencing using high-throughput sequencing, and differentially expressed genes (DEGs) and differentially expressed microRNAs (DEMs) were identified by bioinformatics. Single-cell transcriptome sequencing (10 × Genomics) was further conducted. Among the samples from 23 adult septic patients and 10 healthy individuals, 20,391 genes and 1633 microRNAs were detected by RNA sequencing. In total, 1114 preliminary DEGs and 76 DEMs were obtained using DESeq2, and 454 DEGs were ultimately distinguished. A microRNA-mRNA-PPI network was constructed based on the DEGs and the top 20 DEMs, which included 10 upregulated and 10 downregulated microRNAs. Furthermore, the hub genes TLR5, FCGR1A, ELANE, GNLY, IL2RB and TGFBR3, which may be associated with the prognosis of sepsis, and their negatively correlated microRNAs, were analysed. The genes TLR5, FCGR1A and ELANE were mainly expressed in macrophages, and the genes GNLY, IL2RB and TGFBR3 were expressed specifically in T cells and natural killer cells. Parallel analysis of mRNAs and microRNAs in patients with sepsis was demonstrated to be feasible using RNA-seq. Potential hub genes and microRNAs that may be related to sepsis prognosis were identified, providing new prospects for sepsis treatment. However, further experiments are needed.