Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Combined with Multiple Machine Learning Identified a Novel Immune Signature in Diabetic Nephropathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37312900", "kind": "science-record", "revision": 3, "content_hash": "sha256:4564906caa45a5572890682b1e66a23feeec1df5089f9ce064d612165cb003b7", "created_at": "2026-11-01T19:02:03Z", "updated_at": "2026-11-01T22:09:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Combined with Multiple Machine Learning Identified a Novel Immune Signature in Diabetic Nephropathy.", "doi": "10.2147/DMSO.S413569", "pmid": "37312900", "authors": ["Yue-Ling Peng", "Yan Zhang", "Lin Pang", "Ya-Fang Dong", "Mu-Ye Li", "Hui Liao", "Rong-Shan Li"], "journal": "Diabetes, metabolic syndrome and obesity : targets and therapy", "year": 2023, "abstract": "Background: Increasing evidence suggests that immune modulation contributes to the pathogenesis and progression of diabetic nephropathy (DN). However, the role of immune modulation in DN has not been elucidated. The purpose of this study was to search for potential immune-related therapeutic targets and molecular mechanisms of DN.\nMethods: Gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. A total of 1793 immune-related genes were acquired from the Immunology Database and Analysis Portal (ImmPort). Weighted gene co-expression network analysis (WGCNA) was performed for GSE142025, and the red and turquoise co-expression modules were found to be key for DN progression. We utilized four machine learning algorithms, namely, random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), and k-nearest neighbor (KNN), to evaluate the diagnostic value of hub genes. Immune infiltration patterns were analyzed using the CIBERSORT algorithm, and the correlation between immune cell type abundance and hub gene expression was also investigated.\nResults: A total of 77 immune-related genes of advanced DN were selected for subsequent analyzes. Functional enrichment analysis showed that the regulation of cytokine-cytokine receptor interactions and immune cell function play a corresponding role in the progression of DN. The final 10 hub genes were identified through multiple datasets. In addition, the expression levels of the identified hub genes were corroborated through a rat model. The RF model exhibited the highest AUC. CIBERSORT analysis and single-cell sequencing analysis revealed changes in immune infiltration patterns between control subjects and DN patients. Several potential drugs to reverse the altered hub genes were identified through the Drug-Gene Interaction database (DGIdb).\nConclusion: This pioneering work provided a novel immunological perspective on the progression of DN, identifying key immune-related genes and potential drug targets, thus stimulating future mechanistic research and therapeutic target identification for DN.", "claims": [{"id": "claim-method", "statement": "The co-expression network of GSE142025 was analyzed using weighted gene co-expression network analysis (WGCNA) in the R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37312900", "locator": "sections[4] \"Weighted Gene Co-Expression Network Analysis\" paragraph 2", "quote": "The co-expression network of GSE142025 was analyzed using weighted gene co-expression network analysis (WGCNA) in the R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets from the GEO (https://www.ncbi.nlm.nih.gov/geo/) database were analyze d in this study. All data supporting this study are available from the corresponding authors upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37312900", "locator": "sections[23] \"Data Sharing Statement\" paragraph 2", "quote": "Publicly available datasets from the GEO (https://www.ncbi.nlm.nih.gov/geo/) database were analyze d in this study. All data supporting this study are available from the corresponding authors upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "WGCNA identified the turquoise and red modules as positively correlated with advanced diabetic nephropathy, with correlations of 0.82 and 0.76, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37312900", "locator": "sections[14] \"Construction of a Weighted Co-Expression Network and Identification of Key Modules\" paragraph 2", "quote": "According to the heatmap of module-trait relationships, the turquoise module (r = 0.82, P = 7e−10) and the red module (r = 0.76, P = 9e−08) presented a higher positive correlation with advanced DN in comparison to other modules, which suggested that the genes in the turquoise and red modules might play a vital role in the progression of DN to the advanced stage."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "WGCNA identified the turquoise and red modules as positively correlated with advanced diabetic nephropathy, with correlations of 0.82 and 0.76, respectively.", "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:37312900"], "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:GSE142025"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE142025"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37312900", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/DMSO.S413569", "sha256": "sha256:081602ca7e630610fb6e4c8fd4f4eb604519c2c74ea5d031d8a2739864d01f64", "bytes": 49339, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/DMSO.S413569", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10258044", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE142025", "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=GSE142025", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE142025", "version": null, "note": "Publicly available datasets from the GEO (https://www.ncbi.nlm.nih.gov/geo/) database were analyze d in this study. 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The RF model exhibited the highest AU", "the chaga groups, insulin resistances of SD rats were confirmed after eight weeks’ high-fat chow.", "after eight weeks’ high-fat chow. Then, the rats were received a single intraperitoneal injection", "the chaga groups, insulin resistances of SD rats were confirmed after eight weeks’ high-fat c"], "order_basis": "paper_text_order:1648", "id": "assay:3"}, {"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": ["Animal Models, Kidney RNA Extraction and RT‒qPCR In the DN model group and the chaga groups,", "Script RT Master Mix (Takara, Japan). The RT‒qPCR analyzes were performed in triplicate using", "ormed in triplicate using the NovoStart SYBR qPCR SuperMix Plus Kit (Novoprotein, China) and d", "nd detected using an Applied Biosystems 7500 Real-Time PCR System (Thermo Fisher Scientific, USA). β-Ac"], "order_basis": "paper_text_order:2295", "id": "assay:4"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["random forest"], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}