GPX3 and GSTT1 as biomarkers related to oxidative stress during renal ischemia reperfusion injuries and their relationship with immune infiltration.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37033969", "kind": "science-record", "revision": 4, "content_hash": "sha256:a03136c7cc717a0f00c57aa8dacaf4cd2cb578834a7d485cad07bfc8a92f078c", "created_at": "2026-11-01T19:01:41Z", "updated_at": "2026-11-01T22:51:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "GPX3 and GSTT1 as biomarkers related to oxidative stress during renal ischemia reperfusion injuries and their relationship with immune infiltration.", "doi": "10.3389/fimmu.2023.1136146", "pmid": "37033969", "authors": ["Jun Pei", "Xiaomao Tian", "Chengjun Yu", "Jin Luo", "Jie Zhang", "Yi Hua", "Guanghui Wei"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Background: Renal ischemia reperfusion injuries (IRIs) are very common in clinical diagnoses and treatments, which are a common cause of impaired renal functions, worsening pathological damage, affecting disease progression and hindering recovery. Renal IRIs are an inflammatory disease mediated by the adaptive and innate immune system. There is a complex interaction between oxidative stress and immune cell infiltration. Therefore, we aimed to determine biomarkers associated with oxidative stress during renal IRIs and their relationship with immune cell infiltration.\nMethod: A differential gene expression analysis was made based on the GSE148420 dataset from the NCBI Gene Expression Comprehensive Database (GEO) combined with 92 oxidative-stress (OS)-related genes identified in the Molecular Signatures Database. Then we identified differentially-expressed genes (DEOSGs) associated with oxidative stress, which were used for gene ontology (GO) and a Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. At the same time, we used PPI protein interaction networks and Lasso regression analysis to identify key genes, which were verified by the validation sets GSE58438 and GSE71647, as well as Western Blot detection on rat renal IRI models. At the same time, PAS staining, HE staining and immunohistochemistry were used to detect tissue damage and expression of markers related to oxidative stress during renal ischemia-reperfusion. Single-gene enrichment analysis (GSEA) was used to further clarify the underlying biological functions of key genes. Cibersort was used to analyze the immune cell infiltration during renal IRI and the correlation of key genes with immune cells. At the same time, we constructed a network of transcription-factor (TF)-Hub genes and miRNA-Hub genes. DGIDB was used to predict drugs and molecular compounds that might interact with the Hub genes.\nResults: Compared with the control group, a total of 5456 differential genes (DEGs) were measured in the renal IRI group, 2486 of which were upregulated and 2970 were down-regulated. Among them, we found 30 DEGs (DEOSGs) associated with oxidative stress. The results of GO and KEGG enrichment analysis showed that these DEOSGs were mainly enriched in glutathione metabolism, the response to oxidative stress stimulation, the regulation of T cell activation and apoptosis signaling pathways. Through a protein interaction network (PPI) and a LASSO regression analysis, a total of two Hub genes were identified, namely GPX3 and GSTT1, which were validated through external validation sets and animal experiments. Through pathological methods, we found that the pathological damage of renal tissue and the expression of oxidative stress markers increased after renal ischemia-reperfusion. The results of GSEA showed that the Hub genes were related to oxidative stress pathways, apoptosis signaling pathways and immune-response-related signaling pathways. An immunoinfiltration correlation analysis showed that genes GPX3 and GSTT1 were significantly positively correlated with plasma cells and macrophage M0, while were negatively correlated with monocytes and macrophages M1 and M2. Using the Strust, Starbase and DGIDB database, we predicted that 81 transcription factors, 49 miRNAs and 13 drug or molecular compounds might interact with the Hub genes.\nConclusion: Through a comprehensive analysis of gene expression, our findings may provide new potential biomarkers for the pathogenesis of renal IRIs and a reliable basis for its early diagnosis as well as treatment.", "claims": [{"id": "claim-method", "statement": "Through the Limma R software package, the classical Bayesian algorithm was used to calculate the differentially-expressed genes (DEGs) in the dataset GSE148420.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37033969", "locator": "sections[4] \"Identification and analysis of differential genes associated with oxidative stress (DEOSGs)\" paragraph 2", "quote": "Through the Limma R software package, the classical Bayesian algorithm was used to calculate the differentially-expressed genes (DEGs) in the dataset GSE148420."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37033969", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Differential-expression analysis of GSE148420 identified 30 oxidative-stress-related DEOSGs, whose intersection with LASSO-selected genes yielded GPX3 and GSTT1 as the hub genes associated with renal ischemia-reperfusion injury.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37033969", "locator": "sections[4] \"Identification and analysis of differential genes associated with oxidative stress (DEOSGs)\" paragraph 2", "quote": "Through the Limma R software package, the classical Bayesian algorithm was used to calculate the differentially-expressed genes (DEGs) in the dataset GSE148420."}, {"asset": "source:pmid:37033969", "locator": "sections[4] \"Identification and analysis of differential genes associated with oxidative stress (DEOSGs)\" paragraph 2", "quote": "The intersection of DEGs and oxidative-stress-related genes (OSs) obtained in the Molecular Signatures Database is defined as oxidative-stress-related differential genes (DEOSGs) by us."}, {"asset": "source:pmid:37033969", "locator": "sections[17] \"Identification of hub genes based on PPI and LASSO regression\" paragraph 4", "quote": "After intersecting the key genes of PPI with Lasso regression screening, the intersection genes: GSTT1 and GPX3, were finally determined as the most diagnostically-valuable Hub genes ( Figure 3E )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Differential-expression analysis of GSE148420 identified 30 oxidative-stress-related DEOSGs, whose intersection with LASSO-selected genes yielded GPX3 and GSTT1 as the hub genes associated with renal ischemia-reperfusion injury.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37033969"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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At the same time, PAS stai", "a total of 10 adult male Sprague-Dawley (SD) rats from the Laboratory Animal Center of Chongqi", "h were raised under the same conditions. All rats underwent right kidney removal and were rand", "schemia treatment. After 24h, both groups of rats died suddenly under anesthesia, and their le"], "order_basis": "paper_text_order:1337", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["the same time, PAS staining, HE staining and immunohistochemistry were used to detect tissue damage and expres"], "order_basis": "paper_text_order:1407", "id": "assay:2"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ion, the regulation of T cell activation and apoptosis signaling pathways. Through a protein intera", "s were related to oxidative stress pathways, apoptosis signaling pathways and immune-response-relat", "well as regulation of T cell activation and apoptosis signaling pathways ( Figure 2D ). Among the", "aspase and FAS pathway were key pathways for apoptosis in tissue cells; the 41BB, TCR and IL2RB pat"], "order_basis": "paper_text_order:2456", "id": "assay:3"}, {"role": "data_generation", "definition_id": "clinical_cohort", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["ys of DEOSGs. Establishment of animal models We enrolled a total of 10 adult male Sprague-Dawley (SD)"], "order_basis": "paper_text_order:5091", "id": "assay:4"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Through the Limma R software package, the classical Bayesian algorithm was used to calculate the differentially-expressed genes (DEGs) in the dataset GSE148420."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}