ArticleJournal of inflammation research2024
Identification and Analysis of PANoptosis-Related Genes in Sepsis-Induced Lung Injury by Bioinformatics and Experimental Verification.
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 20 citations in OpenAlex.
- Mechanisms and therapeutic strategies of pyroptosis in sepsis‑induced acute lung injury (Review).International journal of molecular medicine · 2026Review
- Identified BIRC5 and HDAC1 as novel diagnostic biomarkers linked to centrosome-immune crosstalk for cutaneous squamous cell carcinoma via machine learning-based multi-omics analysis.Discover oncology · 2026Article
- Utilizing bulk and single-cell RNA sequencing to identify potential biomarkers linked to angiogenesis and integrated stress response in chondrosarcoma.Scientific reports · 2026Article
- Identification and experimental validation of prognostic genes associated with dietary restriction-related signatures in bladder cancer.Frontiers in immunology · 2026Article
- Article
- Caspases and programmed cell death in sepsis: mechanisms, pathophysiology, and therapeutic targets.Frontiers in immunology · 2026Review
- Targeting PANoptosis: a promising therapeutic strategy for ALI/ARDS.Apoptosis : an international journal on programmed cell death · 2025Review
- Identification of IL1R1 as a potential key PANoptosis-related gene in myocardial ischemia-reperfusion injury using machine learning.Scientific reports · 2025Article
- Targeting panoptosis: a narrative review of its therapeutic potential in kidney disease.BMC nephrology · 2025Review
- PANoptosis in Bacterial Infections: A Double-Edged Sword Balancing Host Immunity and Pathogenesis.Pathogens (Basel, Switzerland) · 2025Review
- Multiple Machine Learning Models, Molecular Subtyping and Singlecell Analysis Identify PANoptosis-related Core Genes and their Association with Subtypes in Crohn's Disease.Current medicinal chemistry · 2025Article
- Multi-Dimensional Characterization of Programmed Cell Death Patterns for Prognostic Stratification and Therapeutic Insights in Sepsis.ImmunoTargets and therapy · 2025Article
- Identification and validation of prognostic genes and prognostic models associated with cutaneous melanoma and integrative stress response.Frontiers in immunology · 2025Article
- Identification of PANoptosis Subtypes to Assess the Prognosis and Immune Microenvironment of Lung Adenocarcinoma Patients: A Bioinformatics Combined Machine Learning Study.Current cancer drug targets · 2025Article
- Explore potential immune-related targets of leeches in the treatment of type 2 diabetes based on network pharmacology and machine learning.Frontiers in genetics · 2025Article
- Identification of PANoptosis-related genes as biomarkers in ischemic stroke.Frontiers in neurology · 2025Article
- PANoptosis in Sepsis: A Central Role and Emerging Therapeutic Target.Journal of inflammation research · 2025Review
- Preoperative Albumin to Alkaline Phosphatase Ratio and Inflammatory Burden Index for Rectal Cancer Prognostic Nomogram-Construction: Based on Multiple Machine Learning.Journal of inflammation research · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Sepsis-induced lung injury (SLI) is a serious complication of sepsis. PANoptosis, a novel form of inflammatory programmed cell death that is not yet to be fully investigated in SLI. Our research aims to screen and validate the signature genes of PANoptosis in SLI by bioinformatics and in vivo experiment. Methods: SLI-related datasets were downloaded from NCBI Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) of SLI were identified and intersected with the PANoptosis gene set to obtain DEGs related to PANoptosis (SPAN_DEGs). Then, Protein-Protein Interaction (PPI) network and functional enrichment analysis were conducted based on SPAN_DEGs. SVM-REF, LASSO and RandomForest three algorithms were combined to identify the signature genes. The Nomogram and ROC curves were performed to predict diagnostic value. Immune infiltration analysis, correlation analysis and differential expression analysis were used to explore the immunological characterization, correlation and expression levels of the signature genes. Finally, H&E staining and qRT-PCR were conducted for further verification in vivo experiment. Results: Twenty-four SPAN_DEGs were identified by intersecting 675 DEGs with the 277 PANoptosis genes. Four signature genes (CD14, GSDMD, IL1β, and FAS) were identified by three machine learning algorithms, which were highly expressed in the SLI group, and had high diagnostic value in the diagnostic model. Moreover, immune infiltration analysis showed that most immune cells and immune-related functions were higher in the SLI group than those in the control group and were closely associated with the signature genes. Finally, it was confirmed that the cecum ligation and puncture (CLP) group mice showed significant pathological damage in lung tissues, and the mRNA expression levels of CD14, IL1β, and FAS were significantly higher than the sham group. Conclusion: CD14, FAS, and IL1β may be the signature genes in PANoptosis to drive the progression of SLI and involved in regulating immune processes.
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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.