Identification of necroptosis-related features in diabetic nephropathy and analysis of their immune microenvironent and inflammatory response.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38020922", "kind": "science-record", "revision": 2, "content_hash": "sha256:0b2f84aa39bf78c8a9831d29b38a7e5f7eeb7273dd7bfa3ddd6212d2ae4a5e89", "created_at": "2026-11-01T19:02:56Z", "updated_at": "2026-11-01T21:32:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of necroptosis-related features in diabetic nephropathy and analysis of their immune microenvironent and inflammatory response.", "doi": "10.3389/fcell.2023.1271145", "pmid": "38020922", "authors": ["Kaibo Hu", "Ruifeng He", "Minxuan Xu", "Deju Zhang", "Guangyu Han", "Shengye Han", "Leyang Xiao", "Panpan Xia", "Jitao Ling", "Tingyu Wu", "Fei Li", "Yunfeng Sheng", "Jing Zhang", "Peng Yu"], "journal": "Frontiers in cell and developmental biology", "year": 2023, "abstract": "Background: Diabetic nephropathy (DN) was considered a severe microvascular complication of diabetes, which was recognized as the second leading cause of end-stage renal diseases. Therefore, identifying several effective biomarkers and models to diagnosis and subtype DN is imminent. Necroptosis, a distinct form of programmed cell death, has been established to play a critical role in various inflammatory diseases. Herein, we described the novel landscape of necroptosis in DN and exploit a powerful necroptosis-mediated model for the diagnosis of DN. Methods: We obtained three datasets (GSE96804, GSE30122, and GSE30528) from the Gene Expression Omnibus (GEO) database and necroptosis-related genes (NRGs) from the GeneCards website. Via differential expression analysis and machine learning, significant NRGs were identified. And different necroptosis-related DN subtypes were divided using consensus cluster analysis. The principal component analysis (PCA) algorithm was utilized to calculate the necroptosis score. Finally, the logistic multivariate analysis were performed to construct the necroptosis-mediated diagnostic model for DN. Results: According to several public transcriptomic datasets in GEO, we obtained eight significant necroptosis-related regulators in the occurrence and progress of DN, including CFLAR, FMR1, GSDMD, IKBKB, MAP3K7, NFKBIA, PTGES3, and SFTPA1 via diversified machine learning methods. Subsequently, employing consensus cluster analysis and PCA algorithm, the DN samples in our training set were stratified into two diverse necroptosis-related subtypes based on our eight regulators' expression levels. These subtypes exhibited varying necroptosis scores. Then, we used various functional enrichment analysis and immune infiltration analysis to explore the biological background, immune landscape and inflammatory status of the above subtypes. Finally, a necroptosis-mediated diagnostic model was exploited based on the two subtypes and validated in several external verification datasets. Moreover, the expression level of our eight regulators were verified in the singe-cell level and glomerulus samples. And we further explored the relationship between the expression of eight regulators and the kidney function of DN. Conclusion: In summary, our necroptosis scoring model and necroptosis-mediated diagnostic model fill in the blank of the relationship between necroptosis and DN in the field of bioinformatics, which may provide novel diagnostic insights and therapy strategies for DN.", "claims": [{"id": "claim-method", "statement": "Finally, we performed multivariate logistics regression to establish our diagnostic model: Diagnostic Score = Σ i (Coefficient i * Expression level of feature i ).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38020922", "locator": "sections[9] \"Construction of a predictive diagnostic model based on the necroptosis-related subtypes of DN\" paragraph 3", "quote": "Finally, we performed multivariate logistics regression to establish our diagnostic model: Diagnostic Score = Σ i (Coefficient i * Expression level of feature i )."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: The original data used in this project can be downloaded in the public database GEO (https://www.ncbi.nlm.nih.gov/geo/). Accession Numbers: GSE96804, GSE30528 and GSE30122.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38020922", "locator": "sections[28] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: The original data used in this project can be downloaded in the public database GEO (https://www.ncbi.nlm.nih.gov/geo/). Accession Numbers: GSE96804, GSE30528 and GSE30122."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Meanwhile, the AUC of our model in the training set was 0.97, which emphasized the powerful diagnostic efficiency (Figure 5L).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38020922", "locator": "sections[21] \"The construction of a diagnostic model based on the two necroptosis-related clusters\" paragraph 6", "quote": "Meanwhile, the AUC of our model in the training set was 0.97, which emphasized the powerful diagnostic efficiency (Figure 5L)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Meanwhile, the AUC of our model in the training set was 0.97, which emphasized the powerful diagnostic efficiency (Figure 5L).", "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:38020922"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE30122"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE30122"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38020922", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcell.2023.1271145", "sha256": "sha256:e66a38fa07929c611274a2dcff58521b237a7edfc1fdb868fa254e50077bb2cd", "bytes": 56326, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcell.2023.1271145", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10661379", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE30122", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30122", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE30122", "version": null, "note": "Publicly available datasets were analyzed in this study. 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