Serum S100A12 levels are correlated with clinical severity in patients with dermatomyositis-associated interstitial lung disease

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Associations of Serum S100A12 With Severity and Prognosis in Patients With Community-Acquired Pneumonia: A Prospective Cohort Study

Background

Previous studies indicated the calcium-binding protein S100A12 to be involved in the pathophysiology of pulmonary inflammatory diseases. However, the role of S100A12 has remained elusive in patients with community-acquired pneumonia (CAP). Therefore, the purpose of this prospective cohort study was to evaluate the association between serum S100A12 with severity and prognosis in CAP patients.

Methods

Two groups with either 239 CAP patients or 239 healthy controls were enrolled in our study. Fasting blood and clinical characteristics were collected. On admission, serum S100A12 was measured using enzyme-linked immunosorbent assay (ELISA).

Results

Serum S100A12 was increased in CAP patients compared to control subjects. Furthermore, serum S100A12 was elevated according to the severity of CAP. Correlative analysis suggested that the level of serum S100A12 was associated with blood routine indices, renal function markers, inflammatory cytokines and other clinical parameters among CAP patients. Additionally, linear and logistical regression analyses indicated that serum S100A12 was positively associated with CAP severity scores in CAP patients. In addition, the association of high serum S100A12 and prognosis was accessed using a follow-up research. Elevated serum S100A12 on admission increased the risk of death and hospital stay in CAP patients during hospitalization.

Conclusions

Elevated serum S100A12 on admission is positively associated with the severity and adverse prognosis in CAP patients, suggesting that S100A12 may involve in the pathophysiological process of CAP. The titre of serum S100A12 may be used as a biomarker for diagnosis and prognosis among CAP patients.

Comprehensive Analysis of the Systemic Transcriptomic Alternations and Inflammatory Response during the Occurrence and Progress of COVID-19

The pandemic of the coronavirus disease 2019 (COVID-19) has posed huge threats to healthcare systems and the global economy. However, the host response towards COVID-19 on the molecular and cellular levels still lacks full understanding and effective therapies are in urgent need. Here, we integrate three datasets, GSE152641, GSE161777, and GSE157103. Compared to healthy people, 314 differentially expressed genes were identified, which were mostly involved in neutrophil degranulation and cell division. The protein-protein network was established and two significant subsets were filtered by MCODE: ssGSEA and CIBERSORT, which comprehensively revealed the alternation of immune cell abundance. Weighted gene coexpression network analysis (WGCNA) as well as GO and KEGG analyses unveiled the role of neutrophils and T cells during the progress of the disease. Based on the hospital-free days after 45 days of follow-up and statistical methods such as nonnegative matrix factorization (NMF), submap, and linear correlation analysis, 31 genes were regarded as the signature of the peripheral blood of COVID-19. Various immune cells were identified to be related to the prognosis of the patients. Drugs were predicted for the genes in the signature by DGIdb. Overall, our study comprehensively revealed the relationship between the inflammatory response and the disease course, which provided strategies for the treatment of COVID-19.

Investigation of a Hypoxia-Immune-Related Microenvironment Gene Signature and Prediction Model for Idiopathic Pulmonary Fibrosis

Background

There is growing evidence found that the role of hypoxia and immune status in idiopathic pulmonary fibrosis (IPF). However, there are few studies about the role of hypoxia and immune status in the lung milieu in the prognosis of IPF. This study aimed to develop a hypoxia-immune-related prediction model for the prognosis of IPF.

Methods

Hypoxia and immune status were estimated with microarray data of a discovery cohort from the GEO database using UMAP and ESTIMATE algorithms respectively. The Cox regression model with the LASSO method was used for identifying prognostic genes and developing hypoxia-immune-related genes. Cibersort was used to evaluate the difference of 22 kinds of immune cell infiltration. Three independent validation cohorts from GEO database were used for external validation. Peripheral blood mononuclear cell (PBMC) and bronchoalveolar lavage fluid (BALF) were collected to be tested by Quantitative reverse transcriptase-PCR (qRT-PCR) and flow cytometry from 22 clinical samples, including 13 healthy controls, six patients with non-fibrotic pneumonia and three patients with pulmonary fibrosis.

Results

Hypoxia and immune status were significantly associated with the prognosis of IPF patients. High hypoxia and high immune status were identified as risk factors for overall survival. CD8+ T cell, activated CD4+ memory T cell, NK cell, activated mast cell, M1 and M0 macrophages were identified as key immune cells in hypoxia-immune-related microenvironment. A prediction model for IPF prognosis was established based on the hypoxia-immune-related one protective and nine risk DEGs. In the independent validation cohorts, the prognostic prediction model performed the significant applicability in peripheral whole blood, peripheral blood mononuclear cell, and lung tissue of IPF patients. The preliminary clinical specimen validation suggested the reliability of most conclusions.

Conclusions

The hypoxia-immune-based prediction model for the prognosis of IPF provides a new idea for prognosis and treatment.

High Serum S100A12 Levels Predict Poor Outcome After Acute Primary Intracerebral Hemorrhage

OBJECTIVE

Intracerebral hemorrhage (ICH) triggers an inflammatory cascade that damages brain tissues and worsens functional outcome. S100A12 functions to promote brain inflammation. We aimed to investigate the relationship between serum S100A12 levels and functional outcome in ICH patients.

METHODS

Serum S100A12 levels were measured in 101 ICH patients hospitalized within 24 h after symptom onset. Poor functional outcome was defined as a modified Rankin scale of 3 or greater at 3 months after stroke. Early neurologic deterioration was defined as an increase of ≥4 points in the National Institutes of Health Stroke Scale (NIHSS) score or death at 24 hours from symptoms onset.

RESULTS

High serum S100A12 levels were independently correlated with NIHSS score (t = 5.384, P < 0.001), hematoma volume (t = 4.221, P < 0.001) and serum C-reactive protein levels (t = 5.068, P < 0.001). Serum S100A12 levels were substantially higher in patients with a poor outcome (median, 66.5 versus 37.7 ng/mL; P < 0.001) or early neurological deterioration (median, 76.5 versus 40.1 ng/mL; P < 0.001) than in the other remainders, independently predicted a poor outcome (odds ratio, 1.035; 95% confidence interval, 1.007-1.064; P = 0.015) and early neurologic deterioration (odds ratio,1.032; 95% confidence interval, 1.003-1.060; P = 0.027), and significantly discriminated a poor outcome (area under curve, 0.794; 95% confidence interval, 0.702-0.868) and early neurologic deterioration (area under curve, 0.760; 95% confidence interval, 0.664-0.839) under receiver operating characteristic curve.

CONCLUSION

High serum S100A12 levels at admission are highly associated with the extent of inflammatory response, severity, a poor functional outcome and early neurologic deterioration in ICH patients, substantializing serum S100A12 as a promising prognostic biomarker for ICH.