Identification of biomarkers, immune infiltration landscape, and treatment targets of ischemia-reperfusion acute kidney injury at an early stage by bioinformatics methods.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35658960", "kind": "science-record", "revision": 4, "content_hash": "sha256:d729165f3c21986b945cae92fa4e80fdad6c01fe7876c945a1c7aea3b0e13c25", "created_at": "2026-11-01T18:14:36Z", "updated_at": "2026-11-01T22:06:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of biomarkers, immune infiltration landscape, and treatment targets of ischemia-reperfusion acute kidney injury at an early stage by bioinformatics methods.", "doi": "10.1186/s41065-022-00236-x", "pmid": "35658960", "authors": ["Ruilian You", "Zhige Heyang", "Yixin Ma", "Peng Xia", "Hua Zheng", "Jianfeng Lin", "Peili Ji", "Limeng Chen"], "journal": "Hereditas", "year": 2022, "abstract": "BACKGROUND: Mechanisms underlying ischemia/reperfusion injury-acute kidney injury (IRI-AKI) are not fully elucidated. We conducted an integrative analysis of IRI-AKI by bioinformatics methods.\nMETHODS: We screened gene expression profiles of the IRI-AKI at early phase from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and enrichment pathways were conducted based on gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and Gene set enrichment analysis (GSEA). Immune cell infiltration analysis was performed to reveal the change of the microenvironment cell types. We constructed protein-protein interaction (PPI), and Cytoscape with plug-ins to find hub genes and modules. We performed robust rank aggregation (RRA) to combine DEGs and analyzed the target genes for miRNA/transcription factor (TF) and drug-gene interaction networks.\nRESULTS: A total of 239 and 384 DEGs were identified in GSE87024 and GSE34351 separately, with the 73 common DEGs. Enrichment analysis revealed that the significant pathways involve mitogen-activated protein kinase (MAPK) signaling, interleukin-17, and tumor necrosis factor (TNF) signaling pathway, etc. RRA analysis detected a total of 27 common DEGs. Immune cell infiltration analysis showed the plasma cells reduced and T cells increased in IRI-AKI. We identified JUN, ATF3, FOS, EGR1, HMOX1, DDIT3, JUNB, NFKBIZ, PPP1R15A, CXCL1, ATF4, and HSPA1B as hub genes. The target genes interacted with 23 miRNAs and 116 drugs or molecular compounds such as curcumin, staurosporine, and deferoxamine.\nCONCLUSION: Our study first focused on the early IRI-AKI adopting RRA analysis to combine DEGs in different datasets. We identified significant biomarkers and crucial pathways involved in IRI-AKI and first construct the immune landscape and detected the potential therapeutic targets of the IRI-AKI by drug-gene network.", "claims": [{"id": "claim-method", "statement": "We used the de-bach effect, the robust rank aggregation, and probabilistic models to integrate sorted lists of different gene expression profiles from the different protocols or measurement platforms.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35658960", "locator": "sections[9] \"Robust rank aggregation (RRA) method to find the DEGs\" paragraph 2", "quote": "We used the de-bach effect, the robust rank aggregation, and probabilistic models to integrate sorted lists of different gene expression profiles from the different protocols or measurement platforms."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analysed during this study are included in this published article and its Supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35658960", "locator": "sections[29] \"Availability of data and materials\" paragraph 2", "quote": "All data generated or analysed during this study are included in this published article and its Supplementary information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "RRA analysis detected a total of 27 common DEGs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35658960", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "RRA analysis detected a total of 27 common DEGs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "RRA analysis detected a total of 27 common DEGs.", "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:35658960"], "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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