Identifying Aging-Related Biomarkers and Immune Infiltration Features in Diabetic Nephropathy Using Integrative Bioinformatics Approaches and Machine-Learning Strategies.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37760894", "kind": "science-record", "revision": 4, "content_hash": "sha256:58f4c3c98aa33f104880b9b91b1a0a2b5bec949d8b477610e8b5f26d4a28520a", "created_at": "2026-11-01T19:02:36Z", "updated_at": "2026-11-01T22:52:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying Aging-Related Biomarkers and Immune Infiltration Features in Diabetic Nephropathy Using Integrative Bioinformatics Approaches and Machine-Learning Strategies.", "doi": "10.3390/biomedicines11092454", "pmid": "37760894", "authors": ["Tao Liu", "Xing-Xing Zhuang", "Jia-Rong Gao"], "journal": "Biomedicines", "year": 2023, "abstract": "BACKGROUND: Aging plays an essential role in the development of diabetic nephropathy (DN). This study aimed to identify and verify potential aging-related genes associated with DN using bioinformatics analysis.\nMETHODS: To begin with, we combined the datasets from GEO microarrays (GSE104954 and GSE30528) to find the genes that were differentially expressed (DEGs) across samples from DN and healthy patient populations. By overlapping DEGs, weighted co-expression network analysis (WGCNA), and 1357 aging-related genes (ARGs), differentially expressed ARGs (DEARGs) were discovered. We next performed functional analysis to determine DEARGs' possible roles. Moreover, protein-protein interactions were examined using STRING. The hub DEARGs were identified using the CytoHubba, MCODE, and LASSO algorithms. We next used two validation datasets and Receiver Operating Characteristic (ROC) curves to determine the diagnostic significance of the hub DEARGs. RT-qPCR, meanwhile, was used to confirm the hub DEARGs' expression levels in vitro. In addition, we investigated the relationships between immune cells and hub DEARGs. Next, Gene Set Enrichment Analysis (GSEA) was used to identify each biomarker's biological role. The hub DEARGs' subcellular location and cell subpopulations were both identified and predicted using the HPA and COMPARTMENTS databases, respectively. Finally, drug-protein interactions were predicted and validated using STITCH and AutoDock Vina.\nRESULTS: A total of 57 DEARGs were identified, and functional analysis reveals that they play a major role in inflammatory processes and immunomodulation in DN. In particular, aging and the AGE-RAGE signaling pathway in diabetic complications are significantly enriched. Four hub DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) were further screened using the interaction network, CytoHubba, MCODE, and LASSO algorithms. The results above were further supported by validation sets, ROC curves, and RT-qPCR. According to an evaluation of immune infiltration, DN had significantly more resting mast cells and delta gamma T cells but fewer regulatory T cells and active mast cells. Four DEARGs have statistical correlations with them as well. Further investigation revealed that four DEARGs were implicated in immune cell abnormalities and regulated a wide range of immunological and inflammatory responses. Furthermore, the drug-protein interactions included four possible therapeutic medicines that target four DEARGs, and molecular docking could make this association practical.\nCONCLUSIONS: This study identified four DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) associated with DN, which might play a key role in the development of DN and could be potential biomarkers in DN.", "claims": [{"id": "claim-method", "statement": "The limma R tool was then used in differential analysis to find the genes that differ between the DN group and the control group [23].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37760894", "locator": "sections[3] \"2.1. Data Preprocessing and Differentially Expressed Genes (DEGs) and Aging-Related Genes (ARGs) Identifying in DN\" paragraph 2", "quote": "The limma R tool was then used in differential analysis to find the genes that differ between the DN group and the control group [23]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used and analyzed to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37760894", "locator": "sections[32] \"Data Availability Statement\" paragraph 2", "quote": "The data used and analyzed to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO regression identified four hub differentially expressed aging-related genes in diabetic nephropathy: CCR2, VCAM1, CSF1R, and ITGAM.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37760894", "locator": "sections[19] \"3.5. Identification of Hub DEARGs with a Least Absolute Shrinkage and Selection Operator (LASSO) Algorithm\" paragraph 2", "quote": "The results of the LASSO regression identified four hub DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) with non-zero regression coefficients and the optimal lambda value of lambda. min = 0.16 (Figure 6C,D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "LASSO regression identified four hub differentially expressed aging-related genes in diabetic nephropathy: CCR2, VCAM1, CSF1R, and ITGAM.", "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:37760894"], "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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RT-qPCR, meanwhile, was used to confirm the hub DEAR", "upported by validation sets, ROC curves, and RT-qPCR. According to an evaluation of immune infilt", "iagnostic significance of the hub DEARGs. RT-qPCR, meanwhile, was used to confirm the hub DEAR", "orted by validation sets, ROC curves, and RT-qPCR. According to an evaluation of immune infilt"], "order_basis": "paper_text_order:1082", "id": "assay:1"}, {"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": ["The limma R tool was then used in differential analysis to find the genes that differ between the DN group and the control group [23]."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}