Identification and validation of biomarkers based on cellular senescence in mild cognitive impairment.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37187578", "kind": "science-record", "revision": 3, "content_hash": "sha256:ba665c34c4e18a86414b73330775f77e0fed2d453137cc11e30cf87f83bbdd89", "created_at": "2026-11-01T19:01:52Z", "updated_at": "2026-11-01T22:09:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of biomarkers based on cellular senescence in mild cognitive impairment.", "doi": "10.3389/fnagi.2023.1139789", "pmid": "37187578", "authors": ["Songmei Ma", "Tong Xia", "Xinyi Wang", "Haiyun Wang"], "journal": "Frontiers in aging neuroscience", "year": 2023, "abstract": "Background: Mild cognitive impairment (MCI), a syndrome defined as decline of cognitive function greater than expected for an individual's age and education level, occurs in up to 22.7% of elderly patients in United States, causing the heavy psychological and economic burdens to families and society. Cellular senescence (CS) is a stress response that accompanies permanent cell-cycle arrest, which has been reported to be a fundamental pathological mechanism of many age-related diseases. This study aims to explore biomarkers and potential therapeutic targets in MCI based on CS.\nMethods: The mRNA expression profiles of peripheral blood samples from patients in MCI and non-MCI group were download from gene expression omnibus (GEO) database (GSE63060 for training and GSE18309 for external validation), CS-related genes were obtained from CellAge database. Weighted gene co-expression network analysis (WGCNA) was conducted to discover the key relationships behind the co-expression modules. The differentially expressed CS-related genes would be obtained through overlapping among the above datasets. Then, pathway and GO enrichment analyses were performed to further elucidate the mechanism of MCI. The protein-protein interaction network was used to extract hub genes and the logistic regression was performed to distinguish the MCI patients from controls. The hub gene-drug network, hub gene-miRNA network as well as transcription factor-gene regulatory network were used to analyze potential therapeutic targets for MCI.\nResults: Eight CS-related genes were identified as key gene signatures in MCI group, which were mainly enriched in the regulation of response to DNA damage stimulus, Sin3 complex and transcription corepressor activity. The receiver operating characteristic curves of logistic regression diagnostic model were constructed and presented great diagnostic value in both training and validation set.\nConclusion: Eight CS-related hub genes - SMARCA4, GAPDH, SMARCB1, RUNX1, SRC, TRIM28, TXN, and PRPF19 - serve as candidate biomarkers for MCI and display the excellent diagnostic value. Furthermore, we also provide a theoretical basis for targeted therapy against MCI through the above hub genes.", "claims": [{"id": "claim-method", "statement": "To effectively differentiate the MCI from controls, the logistic regression diagnostic model was constructed using the glm function in R language (Friedman et al., 2010). The gene expression was seen as continuous variable and sample type as binary classification variable. The stepwise regression method was used to screen the variables with p < 0.05, and the final adjusted model was established based on the screened variables.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37187578", "locator": "sections[8] \"Construction and validation of the logistic regression diagnostic model\" paragraph 2", "quote": "To effectively differentiate the MCI from controls, the logistic regression diagnostic model was constructed using the glm function in R language (Friedman et al., 2010). The gene expression was seen as continuous variable and sample type as binary classification variable. The stepwise regression method was used to screen the variables with p < 0.05, and the final adjusted model was established based on the screened variables."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found in the NCBI database, the links are: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63060 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18309.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37187578", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found in the NCBI database, the links are: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63060 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18309."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Eight cellular-senescence-related hub genes were identified as candidate biomarkers for MCI with excellent diagnostic value.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37187578", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Eight CS-related hub genes – SMARCA4, GAPDH, SMARCB1, RUNX1, SRC, TRIM28, TXN, and PRPF19 – serve as candidate biomarkers for MCI and display the excellent diagnostic value."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Eight cellular-senescence-related hub genes were identified as candidate biomarkers for MCI with excellent diagnostic value.", "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:37187578"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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