Development and validation of novel inflammatory response-related gene signature for sepsis prognosis

Comprehensive bioinformatic analysis reveals a fibroblast-related gene signature for the diagnosis of keloids

Aim

A keloid is a fibroproliferative cutaneous disorder secondary to skin injury, caused by an imbalance in fibroblast proliferation and apoptosis. However, the pathogenesis is not fully understood. In this study, candidate genes for keloid were identified and used to construct a diagnostic model.

Methods

Three datasets related to keloids were downloaded from NCBI Gene Expression Omnibus. Fibroblast-related genes were screened, and fibroblast scores for the samples were determined. Then, a weighted gene co-expression network analysis (WGCNA) was used to identify modules and genes associated with keloids and the fibroblast score. Differentially expressed genes (DEGs) between keloid and control samples were identified and compared with fibroblast-related genes and genes in the modules. Overlapping genes were evaluated using functional enrichment analyses. Signature genes were further screened, and a diagnostic model was constructed. Finally, correlations between immune cell frequences and signature genes were analyzed.

Results

In total, 124 fibroblast-related genes were obtained, and the fibroblast score was an effective indicator of the sample type. WGCNA revealed five modules that were significantly correlated with both the disease state and fibroblast scores, including 1760 genes. Additionally, 589 DEGs were identified, including 16 that overlapped with fibroblast-related genes and genes identified in the WGCNA. These genes were related to cell proliferation and apoptosis and were involved in FoxO, Rap1, p53, Ras, MAPK, and PI3K-Akt pathways. Finally, a six fibroblast-related gene signature (CCNB1, EGFR, E2F8, BTG1, TP63, and IGF1) was identified and used for diagnostic model construction. The proportions of regulatory T cells and macrophages were significantly higher in keloid tissues than in controls.

Conclusion

The established model based on CCNB1, EGFR, E2F8, BTG1, TP63, and IGF1 showed good performance and may be useful for keloid diagnosis.

Mesenchymal stem cell-based therapy for paraquat-induced lung injury

Paraquat poisoning results in significant pulmonary damage, but current treatments are only minimally effective in repairing the injured lung tissues. Recent research has highlighted the promise of using stem cell therapy, namely mesenchymal stem cells, as a new method for treating paraquat toxicity. These cells have shown effectiveness in decreasing inflammation, apoptosis, and fibrosis in the mice lungs subjected to paraquat. The therapeutic implications of mesenchymal stem cells are believed to arise from their release of bioactive proteins and their capacity to regulate inflammatory responses. However, additional clinical study is required to validate these therapies' efficacy. This review thoroughly explores the pathophysiology of paraquat poisoning and the properties of mesenchymal stem cells. Additionally, it critically assesses the long-term safety and effectiveness of mesenchymal stem cell therapies, which is crucial for developing more dependable and effective treatment protocols. In summary, although mesenchymal stem cells offer promising prospects for treating lung injuries, more investigations are required to optimize their therapeutic promise and ensure their safe clinical application in the context of paraquat poisoning.

B-lymphocyte-induced maturation protein-1 inhibits inflammation and pyroptosis to alleviate sepsis injury

Liver and lung tissue damage caused by sepsis is still one of the causes of death. B-lymphocyte-induced maturation protein-1 (Blimp-1) has a protective role in inflammation-related disease. However, whether Blimp-1 can regulate cell pyroptosis and affect disease progression in sepsis is still unclear. Animal and cell models were established by the cecal ligation and puncture method and lipopolysaccharides (LPS)-induced RAW 264.7 cells, respectively, and the role of Blimp-1 in regulation inflammatory response and pyroptosis was verified. The changes of inflammation and pyroptosis in liver and lung tissues of septic mice were determined by the addition of TAK-242 (TLR4 inhibitor). Cell pyroptosis and the level of inflammation was detected after Blimp-1 knockdown and TAK-242 treatment in the cell model. The expression of Blimp-1 was continuously increased in a septic mice model. After treatment with TAK-242, the expression of Blimp-1, pyroptosis and inflammatory levels were reduced in mice. In the LPS-induced cell model, cell injury by knockout Blimp-1 was increased, and cell activity was restored after TAK-242 intervention. Overexpression of Blimp-1 relieved LPS-induced cellular inflammatory damage and pyroptosis. Our study had shown that Blimp-1 could improve septic damage by regulating the level of cellular inflammation and pyroptosis in sepsis.

Identification of diagnostic signature, molecular subtypes, and potential drugs in allergic rhinitis based on an inflammatory response gene set

Background

Rhinitis is a complex condition characterized by various subtypes, including allergic rhinitis (AR), which involves inflammatory reactions. The objective of this research was to identify crucial genes associated with inflammatory response that are relevant for the treatment and diagnosis of AR.

Methods

We acquired the AR-related expression datasets (GSE75011 and GSE50223) from the Gene Expression Omnibus (GEO) database. In GSE75011, we compared the gene expression profiles between the HC and AR groups and identified differentially expressed genes (DEGs). By intersecting these DEGs with inflammatory response-related genes (IRGGs), resulting in the identification of differentially expressed inflammatory response-related genes (DIRRGs). Afterwards, we utilized the protein-protein interaction (PPI) network, machine learning algorithms, namely least absolute shrinkage and selection operator (LASSO) regression and random forest, to identify the signature markers. We employed a nomogram to evaluate the diagnostic effectiveness of the method, which has been confirmed through validation using GSE50223. qRT-PCR was used to confirm the expression of diagnostic genes in clinical samples. In addition, a consensus clustering method was employed to categorize patients with AR. Subsequently, extensive investigation was conducted to explore the discrepancies in gene expression, enriched functions and pathways, as well as potential therapeutic drugs among these distinct subtypes.

Results

A total of 22 DIRRGs were acquired, which participated in pathways including chemokine and TNF signaling pathway. Additionally, machine learning algorithms identified NFKBIA, HIF1A, MYC, and CCRL2 as signature genes associated with AR's inflammatory response, indicating their potential as AR biomarkers. The nomogram based on feature genes could offer clinical benefits to AR patients. We discovered two molecular subtypes, C1 and C2, and observed that the C2 subtype exhibited activation of immune- and inflammation-related pathways.

Conclusions

NFKBIA, HIF1A, MYC, and CCRL2 are the key genes involved in the inflammatory response and have the strongest association with the advancement of disease in AR. The proposed molecular subgroups could provide fresh insights for personalized treatment of AR.

A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts

BACKGROUND

High throughput gene expression profiling is a valuable tool in providing insight into the molecular mechanism of human diseases. Hypoxia- and lactate metabolism-related genes (HLMRGs) are fundamentally dysregulated in sepsis and have great predictive potential. Therefore, we attempted to build an HLMRG signature to predict the prognosis of patients with sepsis.

METHODS

Three publicly available transcriptomic profiles of peripheral blood mononuclear cells from patients with sepsis (GSE65682, E-MTAB-4421 and E-MTAB-4451, total n = 850) were included in this study. An HLMRG signature was created by employing Cox regression and least absolute shrinkage and selection operator estimation. The CIBERSORT method was used to analyze the abundances of 22 immune cell subtypes based on transcriptomic data. Metascape was used to investigate pathways related to the HLMRG signature.

RESULTS

We developed a prognostic signature based on five HLMRGs (ERO1L, SIAH2, TGFA, TGFBI, and THBS1). This classifier successfully discriminated patients with disparate 28-day mortality in the discovery cohort (GSE65682, n = 479), and consistent results were observed in the validation cohort (E-MTAB-4421 plus E-MTAB-4451, n = 371). Estimation of immune infiltration revealed significant associations between the risk score and a subset of immune cells. Enrichment analysis revealed that pathways related to antimicrobial immune responses, leukocyte activation, and cell adhesion and migration were significantly associated with the HLMRG signature.

CONCLUSIONS

Identification of a prognostic signature suggests the critical role of hypoxia and lactate metabolism in the pathophysiology of sepsis. The HLMRG signature can be used as an efficient tool for the risk stratification of patients with sepsis.

Analysis of lactate metabolism-related genes and their association with immune infiltration in septic shock via bioinformatics method

Background: Lactate, as an essential clinical evaluation index of septic shock, is crucial in the incidence and progression of septic shock. This study aims to investigate the differential expression, regulatory relationship, clinical diagnostic efficacy, and immune infiltration of lactate metabolism-related genes (LMGs) in septic shock. Methods: Two sepsis shock datasets (GSE26440 and GSE131761) were screened from the GEO database, and the common differentially expressed genes (DEGs) of the two datasets were screened out. LMGs were selected from the GeneCards database, and lactate metabolism-related DEGs (LMDEGs) were determined by integrating DEGs and LMGs. Protein-protein interaction networks, mRNA-miRNA, mRNA-RBP, and mRNA-TF interaction networks were constructed using STRING, miRDB, ENCORI, and CHIPBase databases, respectively. Receiver operating characteristic (ROC) curves were constructed for each of the LMDEGs to evaluate the diagnostic efficacy of the expression changes in relation to septic shock. Finally, immune infiltration analysis was performed using ssGSEA and CIBERSORT. Results: This study identified 10 LMDEGs, including LDHB, STAT3, LDHA, GSR, FOXM1, PDP1, GCDH, GCKR, ABCC1, and CDKN3. Enrichment analysis revealed that DEGs were significantly enriched in pathways such as pyruvate metabolism, hypoxia pathway, and immune-inflammatory pathways. PPI networks based on LMDEGs, as well as 148 pairs of mRNA-miRNA interactions, 243 pairs of mRNA-RBP interactions, and 119 pairs of mRNA-TF interactions were established. ROC curves of eight LMDEGs (LDHA, GSR, STAT3, CDKN3, FOXM1, GCKR, PDP1, and LDHB) with consistent expression patterns in two datasets had an area under the curve (AUC) ranging from 0.662 to 0.889. The results of ssGSEA and CIBERSORT both showed significant differences in the infiltration of various immune cells, including CD8 T cells, T regulatory cells, and natural killer cells, and LMDEGs such as STAT3, LDHB, LDHA, PDP1, GSR, FOXM1, and CDKN3 were significantly associated with various immune cells. Conclusion: The LMDEGs are significantly associated with the immune-inflammatory response in septic shock and have a certain diagnostic accuracy for septic shock.