ArticleFrontiers in immunology2023
Development of genomic phenotype and immunophenotype of acute respiratory distress syndrome using autophagy and metabolism-related genes.
Article 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 6 papers.
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6 citing papers in PubMed, 11 citations in OpenAlex.
- Rare genetic variant risks in patients with sepsis-associated acute respiratory distress syndrome.Respiratory research · 2026Article
- Identification and validation of aryl hydrocarbon receptor-associated hub genes in ulcerative colitis via integrated bioinformatics analysis.Human genomics · 2025Article
- Clinical Phenotyping in Acute Respiratory Distress Syndrome: Steps Towards Personalized Medicine.Journal of clinical medicine · 2025Article
- Identification and experimental validation of ulcerative colitis-associated hub genes through integrated WGCNA and lysosomal autophagy analysis.Human genomics · 2025Article
- Integrating single-cell sequencing and transcriptome analysis to unravel the mechanistic role of sialylation-related genes in sepsis-induced acute respiratory distress syndrome.Frontiers in immunology · 2025Article
- Autophagy and machine learning: Unanswered questions.Biochimica et biophysica acta. Molecular basis of disease · 2024Review
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9 authors at 2 institutions in 2 countries.
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Abstract
Background: Distinguishing ARDS phenotypes is of great importance for its precise treatment. In the study, we attempted to ascertain its phenotypes based on metabolic and autophagy-related genes and infiltrated immune cells. Methods: Transcription datasets of ARDS patients were obtained from Gene expression omnibus (GEO), autophagy and metabolic-related genes were from the Human Autophagy Database and the GeneCards Database, respectively. Autophagy and metabolism-related differentially expressed genes (AMRDEGs) were further identified by machine learning and processed for constructing the nomogram and the risk prediction model. Functional enrichment analyses of differentially expressed genes were performed between high- and low-risk groups. According to the protein-protein interaction network, these hub genes closely linked to increased risk of ARDS were identified with CytoHubba. ssGSEA and CIBERSORT was applied to analyze the infiltration pattern of immune cells in ARDS. Afterwards, immunologically characterized and molecular phenotypes were constructed according to infiltrated immune cells and hub genes. Results: A total of 26 AMRDEGs were obtained, and CTSB and EEF2 were identified as crucial AMRDEGs. The predictive capability of the risk score, calculated based on the expression levels of CTSB and EEF2, was robust for ARDS in both the discovery cohort (AUC = 1) and the validation cohort (AUC = 0.826). The mean risk score was determined to be 2.231332, and based on this score, patients were classified into high-risk and low-risk groups. 371 differential genes in high- and low-risk groups were analyzed. ITGAM, TYROBP, ITGB2, SPI1, PLEK, FGR, MPO, S100A12, HCK, and MYC were identified as hub genes. A total of 12 infiltrated immune cells were differentially expressed and have correlations with hub genes. According to hub genes and implanted immune cells, ARDS patients were divided into two different molecular phenotypes (Group 1: n = 38; Group 2: n = 19) and two immune phenotypes (Cluster1: n = 22; Cluster2: n = 35), respectively. Conclusion: This study picked up hub genes of ARDS related to autophagy and metabolism and clustered ARDS patients into different molecular phenotypes and immunophenotypes, providing insights into the precision medicine of treating patients with ARDS.
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