Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35855056", "kind": "science-record", "revision": 4, "content_hash": "sha256:bebdc56691e12670ea4fc783de7a60130f1b99b6b92a1c1b60fae2c8b899cfa9", "created_at": "2026-11-01T18:14:56Z", "updated_at": "2026-11-01T22:07:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes.", "doi": "10.1155/2022/1273153", "pmid": "35855056", "authors": ["Xiaoju Bao", "Jingyue Qiu", "Qin Xuan", "Xinming Ye"], "journal": "Genetics research", "year": 2022, "abstract": "Background: Exercise is a regular behavioral activity that not only helps to lose weight but also reduces the risk of cardiovascular and cerebrovascular diseases. Diabetes is a common disease that plagues human health. It is shown that regular exercise can improve the insulin sensitivity of diabetic patients and have an important function in adjuvant therapy.\nMethods: We downloaded the GSE101931 dataset from the Gene Expression Omnibus (GEO) database, 10 samples were obtained from the GSE101931 dataset, including 5 before exercise and 5 postexercise samples, and GEO2R was used to screen the differentially expressed genes (DEGs) exhibited by a heat map. Then, the enrichment analysis of DEGs in Gene Ontology (GO) function was analyzed by Metascape, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of DEGs was also analyzed by gene set enrichment analysis (GSEA). Next, the protein-protein interaction (PPI) network maps were drawn, and the hub genes were identified through Metascape. Finally, the expressions of the hub genes in the dataset were analyzed.\nResults: Totally, 116 upregulated DEGs and 1017 downregulated DEGs were identified from these data. These DEGs were mainly enriched in the platelet-derived growth factor receptor signaling pathway and mRNA processing. Then, the GSEA analysis showed that 6 KEGG pathways were associated with postexercise prediabetic samples, namely, ABC transporters, focal adhesion, MAPK signaling pathway, prion diseases, melanogenesis, and gap junction. Afterward, three hub genes (HSPA8, STIP1, and HSPH1) were highly expressed after exercise through the box plot analysis.\nConclusion: A myriad of research results confirms that there is a certain connection between exercise and diabetes, which provides a favorable basis for emerging exercise into the treatment of diabetic patients.", "claims": [{"id": "claim-method", "statement": "GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) is a differential expression analysis software for web applications that recognizes DEGs of GEO series samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35855056", "locator": "sections[4] \"2.2. Identification of DEGs\" paragraph 2", "quote": "GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) is a differential expression analysis software for web applications that recognizes DEGs of GEO series samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35855056", "locator": "sections[17] \"Data Availability\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Totally, 116 upregulated DEGs and 1017 downregulated DEGs were identified from these data.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35855056", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Totally, 116 upregulated DEGs and 1017 downregulated DEGs were identified from these data."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Totally, 116 upregulated DEGs and 1017 downregulated DEGs were identified from these data.", "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:35855056"], "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:GSE101931"], "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:GSE101931"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35855056", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/1273153", "sha256": "sha256:85159bee46315d27cd6f7efd5baedf2cb2af246fa0f8013a93621f8b64a65781", "bytes": 25578, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/1273153", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9259242", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE101931", "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=GSE101931", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE101931", "version": null, "note": "The datasets used and/or analyzed during the current study are available from the corresponding author upon request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets used and/or analyzed during the current study are available from the corresponding author upon request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2719035", "GSM2719036", "GSM2719037", "GSM2719038", "GSM2719039", "GSM2719040", "GSM2719041", "GSM2719042", "GSM2719043", "GSM2719044", "GSM2719045", "GSM2719046", "GSM2719047", "GSM2719048", "GSM2719049", "GSM2719050", "GSM2719051", "GSM2719052", "GSM2719053", "GSM2719054", "GSM2719055", "GSM2719056", "GSM2719057", "GSM2719058", "GSM2719059", "GSM2719060", "GSM2719061", "GSM2719062", "GSM2719063", "GSM2719064", "GSM2719065", "GSM2719066", "GSM2719067", "GSM2719068", "GSM2719069", "GSM2719070", "GSM2719071", "GSM2719072"], "sample_count": 38, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "Acute Exercise Bout Effects on GH and IGF1 in Prediabetic and Healthy African Americans", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE101931 not downloaded", "asset:data:geo:GSE101931 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-2", "family": null, "specific": null, "label": "GEO2R differential expression analysis", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) is a differential expression analysis software for web applications that recognizes DEGs of GEO series samples."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: ppi_network."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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