Experimental verification and identifying biomarkers related to insomnia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38090272", "kind": "science-record", "revision": 4, "content_hash": "sha256:aed18279e88c26f662aa1e7f5c5a7029533369429991ad83e3d7b5397c93177d", "created_at": "2026-11-01T19:03:01Z", "updated_at": "2026-11-01T22:10:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Experimental verification and identifying biomarkers related to insomnia.", "doi": "10.3389/fneur.2023.1189076", "pmid": "38090272", "authors": ["Qianfei Wang", "Dong Liu", "Tianci Gao", "Yulei Tao", "Xin Li", "Yuan Liu", "Zhiliang Liu", "Jianqiang Mei", "Fenqiao Chen"], "journal": "Frontiers in neurology", "year": 2023, "abstract": "Introduction: Insomnia is the most common form of sleep deprivation (SD) observed in clinics. Although there are differences between insomnia and SD, they have similar symptoms and the same animal model. Currently, there is a lack of microarray data on insomnia. Therefore, for now, we are going to apply the SD data to insomnia. Although many studies have explained the possible mechanisms associated with insomnia, no previous studies have considered the key genes associated with insomnia or the relationship between insomnia and immune cells. In this study, we analyzed the relationship between key genes and immune cells by identifying biomarkers for the diagnosis of insomnia. Next, we verified the efficacy of these biomarkers experimentally.\nMethods: First, we downloaded four microarrays (GSE11755, GSE12624, GSE28750, and GSE48080) from the Gene Expression Omnibus (GEO) database, which included data from 239 normal human blood samples and 365 blood specimens from patients with SD. Then, we analyzed two groups of differentially expressed genes (DEGs) and used Support Vector Machine Recursive Feature Elimination (SVM-RFE) analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to investigate these key genes. Next, we used CIBERSORT to investigate the composition of 22 immune cell components of key genes in SD patients. Finally, the expression levels of key biomarkers in sleep-deprived patients were examined by quantitative real-time polymerase chain reaction (qRT-PCR).\nResults: A total of 50 DEGs were identified: six genes were significantly upregulated, and 44 genes were significantly downregulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that Salmonella infection, NOD-like receptor (NLR) signaling pathway, Kaposi sarcoma-associated herpesvirus infection, and Th17 cell differentiation were significant. Based on machine learning, we identified C2CD2L, SPINT2, APOL3, PKNOX1, and A2M as key genes for SD; these were confirmed by receiver operating characteristic (ROC) analysis. Immune cell infiltration analysis showed that C2CD2L, SPINT2, APOL3, PKNOX1, and A2M were related in different degrees to regulatory T cells (Tregs), follicular T helper cells, CD8 cells, and other immune cells. The qRT-PCR experiments confirmed that the expression levels of C2CD2L concurred with the results derived from machine learning, but PKNOX1 and APOL3 did not.\nDiscussion: In summary, we identified a key gene (C2CD2L) that may facilitate the development of biomarkers for insomnia.", "claims": [{"id": "claim-method", "statement": "The t-test was used to compare gene expression levels between insomnia samples and normal samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38090272", "locator": "sections[10] \"2.8 Statistical analysis\" paragraph 2", "quote": "The t-test was used to compare gene expression levels between insomnia samples and normal samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38090272", "locator": "sections[20] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 50 DEGs were identified: six genes were significantly upregulated, and 44 genes were significantly downregulated.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38090272", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 50 DEGs were identified: six genes were significantly upregulated, and 44 genes were significantly downregulated."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 50 DEGs were identified: six genes were significantly upregulated, and 44 genes were significantly downregulated.", "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:38090272"], "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:GSE82114"], "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:GSE82114"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38090272", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fneur.2023.1189076", "sha256": 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