Construction and Validation of Predictive Model to Identify Critical Genes Associated with Advanced Kidney Disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33274190", "kind": "science-record", "revision": 8, "content_hash": "sha256:ab70c60a43695b134ff80497680b476c0edb01b9c47c26f3253d0bff977cf516", "created_at": "2026-11-01T15:41:17Z", "updated_at": "2026-11-01T22:45:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and Validation of Predictive Model to Identify Critical Genes Associated with Advanced Kidney Disease.", "doi": "10.1155/2020/7524057", "pmid": "33274190", "authors": ["Guangda Xin", "Guangyu Zhou", "Wenlong Zhang", "Xiaofei Zhang"], "journal": "International journal of genomics", "year": 2020, "abstract": "BACKGROUND: Chronic kidney disease (CKD) is characterized by progressive renal function loss, which may finally lead to end-stage renal disease (ESRD). The study is aimed at identifying crucial genes related to CKD progressive and constructing a disease prediction model to investigate risk factors.\nMETHODS: GSE97709 and GSE37171 datasets were downloaded from the GEO database including peripheral blood samples from subjects with CKD, ESRD, and healthy controls. Differential expressed genes (DEGs) were identified and functional enrichment analysis. Machine learning algorithm-based prediction model was constructed to identify crucial functional feature genes related to ESRD.\nRESULTS: A total of 76 DEGs were screened from CDK vs. normal samples while 10,114 DEGs were identified from ESRD vs. CDK samples. For numerous genes related to ESRD, several GO biological terms and 141 signaling pathways were identified including markedly upregulated olfactory transduction and downregulated platelet activation pathway. The DEGs were clustering in three modules according to WGCNA access, namely, ME1, ME2, and ME3. By construction of the XGBoost model and dataset validation, we screened cohorts of genes associated with progressive CKD, such as FZD10, FOXD4, and FAM215A. FZD10 represented the highest score (F score = 21) in predictive model.\nCONCLUSION: Our results demonstrated that FZD10, FOXD4, PPP3R1, and UCP2 might be critical genes in CKD progression.", "claims": [{"id": "claim-method", "statement": "After data preprocessing, the limma package [8] was used to screen DEGs between CDK groups and normal groups. Thus, the major DEGs were also identified from ESRD samples compared with CDK samples. In order to reduce the false positives in sequencing results, Benjamini and Hochberg method was used to correct the p values.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33274190", "locator": "sections[4] \"2.2. Screening the DEGs\" paragraph 2", "quote": "After data preprocessing, the limma package [8] was used to screen DEGs between CDK groups and normal groups. Thus, the major DEGs were also identified from ESRD samples compared with CDK samples. In order to reduce the false positives in sequencing results, Benjamini and Hochberg method was used to correct the p values."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The microarray data used to support the findings of this study have been deposited in the GEO repository (GSE97709 and GSE37171).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33274190", "locator": "sections[18] \"Data Availability\" paragraph 2", "quote": "The microarray data used to support the findings of this study have been deposited in the GEO repository (GSE97709 and GSE37171)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33274190"], "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:GSE37171"], "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:GSE37171"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33274190", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2020/7524057", "sha256": 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Thus, the major DEGs were also identified from ESRD samples compared with CDK samples. 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