ArticleScientific reports2024
Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Comprehensive Pan-Cancer Analysis of ABCA1: Insights From Multi-Omics Data and Exploratory Validation in Esophageal Squamous Cell Carcinoma.Cell biochemistry and function · 2026Article
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- Identification and Screening of Lactate-Related Genes as Molecular Markers for Early Diagnosis of Steroid-Induced Osteonecrosis of the Femoral Head.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Sepsis and the immunometabolic inflammatory response.npj metabolic health and disease · 2026Review
- Identification of Glycolysis-Related Signature and Molecular Subtypes in Child Sepsis Through Machine Learning and Consensus Clustering: Implications for Diagnosis and Therapeutics.Molecular biotechnology · 2026Article
- Identification and validation of prognostic genes associated with m6A-regulated programmed cell death in acute lymphoblastic leukemia.Scientific reports · 2025Article
- Exploration of propionate metabolism-related genes to predict prognosis and immunotherapy response in ovarian cancer.Journal of ovarian research · 2025Article
- Comprehensive Analysis of Sialylation-Related Gene Profiles and Their Impact on the Immune Microenvironment in Periodontitis.Inflammation · 2025Article
- Identification of Hub Genes and Key Pathways Associated with Sepsis Progression Using Weighted Gene Co-Expression Network Analysis and Machine Learning.International journal of molecular sciences · 2025Article
- Decoding the Tumor Microenvironment of Myoepithelial Cells in Triple-Negative Breast Cancer Through Single-Cell and Transcriptomic Sequencing and Establishing a Prognostic Model Based on Key Myoepithelial Cell Genes.International journal of genomics · 2025Article
- AI-based prediction of drug-gene interactions modulating tight junction integrity: A deep learning framework highlighting multiple therapeutic targets.Journal of oral biology and craniofacial researchArticle
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4 authors.
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Abstract
Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package "ConsensusClusterPlus" on the bulk RNA-seq data. Hub genes were discerned through the application of the R package "limma" and univariate regression analysis, with the calculation of risk scores carried out using the R package "survminer". To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan-Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages "ESTIMATE" and "GSVA" were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis.
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