Drug Discovery in Spinal Cord Injury With Ankylosing Spondylitis Identified by Text Mining and Biomedical Databases

Wang, Chenfeng; Ma, Hongdao; Wu, Weiqing; Lü, Xin

doi:10.3389/fgene.2022.799970

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…PROS1 expression is positively correlated with neutrophil count and activity and oxidative burst, and is a potential therapeutic target for decompensated cirrhosis and sepsis [44]. PROS1 can be used as a targeted drug for the treatment of in ammatory diseases, such as spinal cord injury and ankylosing spondylitis [45]. In the present study, PROS1 expression was upregulated in the NS group.…”

Section: Signi Cance Of Gene Expression Signatures In Ns Diagnosissupporting

confidence: 55%

Identification and verification of feature biomarkers associated with immune cells in neonatal sepsis

Liao

Xiao

et al. 2022

Preprint

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Background: Neonatal sepsis (NS), a life-threatening condition, is characterized by organ dysfunction and is the most common cause of neonatal death. However, the pathogenesis of NS is unclear and the clinical inflammatory markers currently used are not ideal for diagnosis of NS. Thus, exploring the link between immune responses in NS pathogenesis, elucidating the molecular mechanisms involved, and identifying potential therapeutic targets is of great significance in clinical practice. Herein, we study aimed to explore immune-related genes in NS and identify potential diagnostic biomarkers. Datasets for patients with NS and healthy controls were downloaded from the GEO database; GSE69686 and GSE25504 were used as the analysis and validation datasets, respectively. Differentially expressed genes (DEGs) were identified and Gene set enrichment analysis (GSEA) was performed to determine their biological functions. Composition of immune cells was determined and immune-related genes (IRGs) between the two clusters were identified and their metabolic pathways were determined. Key genes with correlation coefficient > 0.5 and p < 0.05 were selected as screening biomarkers. Logistic regression models were constructed based on the selected biomarkers, and the diagnostic models were validated. Results: Fifty-two DEGs were identified, and GSEA indicated involvement in acute inflammatory response, bacterial detection, and regulation of macrophage activation. Most infiltrating immune cells, including activated CD8+ T cells, were significantly different in patients with NS compared to the healthy controls. Fifty-four IRGs were identified, and GSEA indicated involvement in immune response and macrophage activation and regulation of T cell activation. Diagnostic models of DEGs containing five genes (PROS1, TDRD9, RETN, LOC728401, and METTL7B) and IRG with one gene (NSUN7) constructed using LASSO algorithm were validated using the GPL6947 and GPL13667 subset datasets, respectively. The IRG model outperformed the DEG model. Additionally, statistical analysis suggested that risk scores may be related to gestational age and birth weight, regardless of sex. Conclusions: We identified six IRGs as potential diagnostic biomarkers for NS and developed diagnostic models for NS. Our findings provide a new perspective for future research on NS pathogenesis.

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Section: Signi Cance Of Gene Expression Signatures In Ns Diagnosissupporting

confidence: 55%

Identification and verification of feature biomarkers associated with immune cells in neonatal sepsis

Liao

Xiao

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…PROS1 expression is positively correlated with neutrophil count and activity and oxidative burst, and is a potential therapeutic target for decompensated cirrhosis and sepsis [ 46 ]. PROS1 can be used as a targeted drug for the treatment of inflammatory diseases, such as spinal cord injury and ankylosing spondylitis [ 47 ]. In the present study, PROS1 expression was up-regulated in the NS group.…”

Section: Discussionmentioning

confidence: 99%

Identification and verification of feature biomarkers associated with immune cells in neonatal sepsis

Liao

Xiao

et al. 2023

Eur J Med Res

View full text Add to dashboard Cite

Background Neonatal sepsis (NS), a life-threatening condition, is characterized by organ dysfunction and is the most common cause of neonatal death. However, the pathogenesis of NS is unclear and the clinical inflammatory markers currently used are not ideal for diagnosis of NS. Thus, exploring the link between immune responses in NS pathogenesis, elucidating the molecular mechanisms involved, and identifying potential therapeutic targets is of great significance in clinical practice. Herein, our study aimed to explore immune-related genes in NS and identify potential diagnostic biomarkers. Datasets for patients with NS and healthy controls were downloaded from the GEO database; GSE69686 and GSE25504 were used as the analysis and validation datasets, respectively. Differentially expressed genes (DEGs) were identified and Gene Set Enrichment Analysis (GSEA) was performed to determine their biological functions. Composition of immune cells was determined and immune-related genes (IRGs) between the two clusters were identified and their metabolic pathways were determined. Key genes with correlation coefficient > 0.5 and p < 0.05 were selected as screening biomarkers. Logistic regression models were constructed based on the selected biomarkers, and the diagnostic models were validated. Results Fifty-two DEGs were identified, and GSEA indicated involvement in acute inflammatory response, bacterial detection, and regulation of macrophage activation. Most infiltrating immune cells, including activated CD8 + T cells, were significantly different in patients with NS compared to the healthy controls. Fifty-four IRGs were identified, and GSEA indicated involvement in immune response and macrophage activation and regulation of T cell activation. Diagnostic models of DEGs containing five genes (PROS1, TDRD9, RETN, LOC728401, and METTL7B) and IRG with one gene (NSUN7) constructed using LASSO algorithm were validated using the GPL6947 and GPL13667 subset datasets, respectively. The IRG model outperformed the DEG model. Additionally, statistical analysis suggested that risk scores may be related to gestational age and birth weight, regardless of sex. Conclusions We identified six IRGs as potential diagnostic biomarkers for NS and developed diagnostic models for NS. Our findings provide a new perspective for future research on NS pathogenesis.

show abstract

Drug Discovery in Spinal Cord Injury With Ankylosing Spondylitis Identified by Text Mining and Biomedical Databases

Cited by 2 publications

References 42 publications

Identification and verification of feature biomarkers associated with immune cells in neonatal sepsis

Identification and verification of feature biomarkers associated with immune cells in neonatal sepsis

Identification and verification of feature biomarkers associated with immune cells in neonatal sepsis

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