Observational studyFrontiers in immunology2023
Six potential biomarkers in septic shock: a deep bioinformatics and prospective observational study.
Observational study in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 48 citations in OpenAlex.
- CEBPD-MMP8 Axis Contributes to Cardiomyocyte Injury in Septic Shock.Applied biochemistry and biotechnology · 2026Article
- An explainable predictive machine learning model of oxaliplatin induced peripheral neuropathy based on clinical data: a retrospective single center.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Development of a machine learning-based model for predicting postoperative survival in gastric cancer.World journal of gastrointestinal surgery · 2026Article
- N-myc and STAT interactor is a novel biomarker for predicting the severity and clinical outcome of sepsis: a prospective research.Frontiers in cellular and infection microbiology · 2026Article
- Multi-omics and machine learning reveal LYZ and ISG15 as diagnostic and therapeutic targets in autoimmune-mediated chronic kidney disease.Frontiers in immunology · 2026Article
- Sepsis biomarkers: recent advances and future perspectives.Frontiers in immunology · 2026Review
- Identification of MMP8, DDX24, RNASE2, and EMB as a novel diagnostic gene panel for sepsis: a transcriptome-based modeling study.Frontiers in immunology · 2026Article
- The protective role of MDL-1 in sepsis-induced lung injury: insights from a murine CLP model.Frontiers in immunology · 2026Article
- Integrative Multi-Omics Analysis Unveils Biomarkers Linking the Gut Microbiota, Blood Metabolites, and Recurrent Pregnancy Loss.International journal of women's health · 2026Article
- Identification of potential biomarkers related to mannose metabolism in keloids: analysis of integrated bulk RNA-seq and scRNA-seq.Frontiers in immunology · 2026Article
- Angiopoietin-2 Surpasses Soluble Thrombomodulin and Syndecan-1 in Characterizing Septic Shock and Predicting Mortality--A Prospective Observational Study.Journal of inflammation research · 2026Article
- Article
- Exploring putative links between gut microbiota and allergic rhinitis: insights from Mendelian randomization and multi-transcriptome integration.AMB Express · 2025Article
- The Interleukin Network in Sepsis: From Cytokine Storm to Clinical Applications.Diagnostics (Basel, Switzerland) · 2025Review
- CYSTM1 is a potential diagnostic and prognostic biomarker and correlated with immune infiltrates in hepatocellular carcinoma.Journal of gastrointestinal oncology · 2025Article
- Bioinformatics identification of key genes correlating NOD1 and Endoplasmic Reticulum stress in Hepatitis B virus-induced acute liver failure.Scientific reports · 2025Article
- Article
- Automated machine learning model for predicting anastomotic strictures after esophageal cancer surgery: a retrospective cohort study.Surgical endoscopy · 2025Article
- Development and application of an early prediction model for risk of bloodstream infection based on real-world study.BMC medical informatics and decision making · 2025Article
- Neutrophil extracellular traps-related genes contribute to sepsis-associated acute kidney injury.BMC nephrology · 2025Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Septic shock occurs when sepsis is related to severe hypotension and leads to a remarkable high number of deaths. The early diagnosis of septic shock is essential to reduce mortality. High-quality biomarkers can be objectively measured and evaluated as indicators to accurately predict disease diagnosis. However, single-gene prediction efficiency is inadequate; therefore, we identified a risk-score model based on gene signature to elevate predictive efficiency. Methods: The gene expression profiles of GSE33118 and GSE26440 were downloaded from the Gene Expression Omnibus (GEO) database. These two datasets were merged, and the differentially expressed genes (DEGs) were identified using the limma package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed. Subsequently, Lasso regression and Boruta feature selection algorithm were combined to identify the hub genes of septic shock. GSE9692 was then subjected to weighted gene co-expression network analysis (WGCNA) to identify the septic shock-related gene modules. Subsequently, the genes within such modules that matched with septic shock-related DEGs were identified as the hub genes of septic shock. To further understand the function and signaling pathways of hub genes, we performed gene set variation analysis (GSVA) and then used the CIBERSORT tool to analyze the immune cell infiltration pattern of diseases. The diagnostic value of hub genes in septic shock was determined using receiver operating characteristic (ROC) analysis and verified using quantitative PCR (qPCR) and Western blotting in our hospital patients with septic shock. Results: A total of 975 DEGs in the GSE33118 and GSE26440 databases were obtained, of which 30 DEGs were remarkably upregulated. With the use of Lasso regression and Boruta feature selection algorithm, six hub genes ( Conclusions:
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.