The Potential Diagnostic Value of Immune-Related Genes in Interstitial Fibrosis and Tubular Atrophy after Kidney Transplantation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35755170", "kind": "science-record", "revision": 3, "content_hash": "sha256:30731409d4556d1a119acd691a476149bb00983bb1b50435304e20013db8f2e0", "created_at": "2026-11-01T18:14:47Z", "updated_at": "2026-11-01T21:28:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The Potential Diagnostic Value of Immune-Related Genes in Interstitial Fibrosis and Tubular Atrophy after Kidney Transplantation.", "doi": "10.1155/2022/7212852", "pmid": "35755170", "authors": ["Bin Yang", "Dike Shi", "Yahong Chen", "Yi Zhu"], "journal": "Journal of immunology research", "year": 2022, "abstract": "Background: Inflammation within areas of interstitial fibrosis and tubular atrophy (IF/TA) is associated with kidney allograft failure. The aim of this study was to reveal new diagnostic markers of IF/TA based on bioinformatics analysis.\nMethods: Raw data of IF/TA samples after kidney transplantation and control samples after kidney transplantation were extracted from the Gene Expression Omnibus (GEO) database (GSE76882 and GSE120495 datasets), and genes that were differentially expressed between the two groups (DEGs) were screened. Gene Set Enrichment Analysis (GSEA), ESTIMATE and single sample GSEA (ssGSEA), least absolute shrinkage and selection operator (LASSO) regression analysis, and competing endogenous RNA (ceRNA) network were used to analyze the data.\nResults: The results of GSEA revealed that multiple immune-related pathways were enriched in the IF/TA group, and subsequent immune landscape analysis also showed that the IF/TA group had higher immune and stromal scores and up to 15 types of immune cells occupied them, such as B cells, cytotoxic cells, and T cells. LASSO regression analysis selected 6 (including ANGPTL3, APOH, LTF, FCGR2B, HLA-DQA2, and EGF) out of 14 DE-IRGs as diagnostic genes to construct a diagnostic model. Then, receiver operating characteristic (ROC) curve analysis showed the powerful diagnostic value of the model, and the area under the curve (AUC) of a single diagnostic gene was greater than 0.75. The results of ingenuity pathway analysis (IPA) also indicated that DEGs were involved in the immune system and kidney disease-related pathways. Finally, we found multiple miRNAs that could regulate diagnostic genes from the ceRNA network.\nConclusion: This study identified 6 IF/TA-related genes, which might be used as a new diagnosis model in the clinical practice.", "claims": [{"id": "claim-method", "statement": "The penalized Cox regression model with LASSO penalty was used to select the most useful diagnostic markers among 14 DE-IRGs, and the optimal values of the penalty parameter λ were determined by tenfold cross-validations [10, 11].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35755170", "locator": "sections[7] \"2.5. Selection of Significant Diagnostic Features and Model Construction with the Training Set\" paragraph 2", "quote": "The penalized Cox regression model with LASSO penalty was used to select the most useful diagnostic markers among 14 DE-IRGs, and the optimal values of the penalty parameter λ were determined by tenfold cross-validations [10, 11]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GSE76882 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76882) and GSE120495 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120495) datasets used for the current study period were available in the GEO repository. The data analyzed during the current study were available from the corresponding authors upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35755170", "locator": "sections[25] \"Data Availability\" paragraph 2", "quote": "The GSE76882 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76882) and GSE120495 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120495) datasets used for the current study period were available in the GEO repository. The data analyzed during the current study were available from the corresponding authors upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "As shown in ROC analysis, the AUCs of our model reached 0.821 and 0.757 in the training set and validation set, indicating a satisfactory accuracy of prediction (Figures 3(c) and 3(d)).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35755170", "locator": "sections[17] \"3.4. Identification and Evaluation of IF/TA Diagnostic Genes\" paragraph 2", "quote": "As shown in ROC analysis, the AUCs of our model reached 0.821 and 0.757 in the training set and validation set, indicating a satisfactory accuracy of prediction (Figures 3(c) and 3(d))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "As shown in ROC analysis, the AUCs of our model reached 0.821 and 0.757 in the training set and validation set, indicating a satisfactory accuracy of prediction (Figures 3(c) and 3(d)).", "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:35755170"], "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:GSE120495"], "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:GSE120495"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35755170", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/7212852", "sha256": "sha256:0c6e596c59ebe62edfc0b79f3e6528b431e3c065ea7bceae1ef2d5390b77180a", "bytes": 34221, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/7212852", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9232312", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE120495", "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=GSE120495", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE120495", "version": null, "note": "The GSE76882 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76882) and GSE120495 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120495) datasets used for the current study period were available in the GEO repository. 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