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pmid:39763654
Identification and experimental validation of diagnostic and prognostic genes CX3CR1, PID1 and PTGDS in sepsis and ARDS using bulk and single-cell transcriptomic analysis and machine learning.
Jijin Jiang · Yan Chen · Yue Su · Li Zhang · Hao Qian · Xinmiao Song · Jin-Fu Xu
Frontiers in immunology · 2024 doi:10.3389/fimmu.2024.1480542
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Complete file
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Patients suffering both sepsis and ARDS have a poor prognosis and high mortality. However, the mechanisms behind their simultaneous occurrence are unclear.\nMethods: We acquired sepsis and ARDS datasets from GEO and Arrayexpress databases and screened hub genes by WGCNA and machine learning algorithm. For diagnosis and prognosis, ROC curve and survival analysis were used. We performed GO, KEGG, GSEA, immune cell infiltration, drug prediction, molecular docking, transcription factor prediction, and constructed PPI and ceRNA networks to explore these genes and the common mechanisms of sepsis and ARDS. Single-cell data analysis compared immune cell profiles and hub gene localization. Finally, RT-qPCR and H&E staining confirmed the reliability of hub genes using PBMCs samples and mouse models.\nResults: We identified 242 common differentially expressed genes in sepsis and ARDS. WGCNA analysis showed that the turquoise module in GSE95233 is strongly linked to sepsis occurrence and poor prognosis, while the black module in GSE10474 is associated with ARDS. Using WGCNA and three machine learning methods (LASSO, random forest and Boruta), we identified three key genes CX3CR1, PID1 and PTGDS. Models built with them showed high AUC values in ROC curve evaluations and were validated by external datasets, accurately predicting the occurrence and mortality. We further explored the immunological landscape of these genes using immune infiltration and single-cell analysis. Then, the ceRNA, predicted drugs and molecular docking were analyzed. Ultimately, we demonstrated that these genes are expressed differently in human and mouse samples with sepsis and ARDS.\nConclusion: This study identified three molecular signatures (CX3CR1, PID1 and PTGDS) linked to the diagnosis and poor prognosis of sepsis and ARDS, validated by RT-qPCR and H&E staining in both patient and mouse samples. 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The CMap database conducts transcriptome analysis on cell lines treated with various", "igure 1 . First, we obtained sepsis and ARDS RNA-seq datasets from the GEO database ( Table 1 ).", "in cell types identified through single-cell RNA sequencing of sepsis and ARDS. (A) UMAP plot of 18 cell"], "order_basis": "paper_text_order:102", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["profiles and hub gene localization. Finally, RT-qPCR and H&E staining confirmed the reliability o", "r prognosis of sepsis and ARDS, validated by RT-qPCR and H&E staining in both patient and mouse s", "files and hub gene localization. Finally, RT-qPCR and H&E staining confirmed the reliability o", "rognosis of sepsis and ARDS, validated by RT-qPCR and H&E staining in both patient and mouse s"], "order_basis": "paper_text_order:975", "id": "assay:2"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ability of hub genes using PBMCs samples and mouse models. Results: We identified 242 common di", "genes are expressed differently in human and mouse samples with sepsis and ARDS. Conclusion: Th", "RT-qPCR and H&E staining in both patient and mouse samples. This research may be valuable for i", "(K) Serum IL-6 levels in different groups of mice were detected by ELISA (n=3). Data are presented"], "order_basis": "paper_text_order:1063", "id": "assay:3"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Subsets within cell types identified through single-cell RNA sequencing of sepsis and ARDS. (A) UMAP plot of 18 cell", "were used to determine the binding affinity. scRNA-seq data processing and analysis To control for", "ression levels of crucial genes found in the scRNA-seq datasets for sepsis and ARDS. In order to co"], "order_basis": "paper_text_order:5758", "id": "assay:4"}, {"role": "data_generation", "definition_id": "elisa", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["in different groups of mice were detected by ELISA (n=3). Data are presented as the mean ± stan"], "order_basis": "paper_text_order:17331", "id": "assay:5"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We applied three machine learning techniques—LASSO regression, Random Forest, and Boruta—to the GSE65682 and GSE32707 datasets to pinpoint the top predictors for sepsis and ARDS."], "order_basis": "consumes_earlier_output", "id": "assay:6"}]}