Bioinformatics Analysis and Experimental Validation of Endoplasmic Reticulum Stress-Related Genes in Osteoporosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39582915", "kind": "science-record", "revision": 4, "content_hash": "sha256:3f1b13dceee7d90246d9abf773a62f85bfeef2903b0ab445bc7b3e4ac1f4f5a9", "created_at": "2026-11-01T20:29:50Z", "updated_at": "2026-11-01T22:55:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics Analysis and Experimental Validation of Endoplasmic Reticulum Stress-Related Genes in Osteoporosis.", "doi": "10.2147/IJGM.S486776", "pmid": "39582915", "authors": ["Yong Zheng", "Yonggui Luo", "Kuihan Tang"], "journal": "International journal of general medicine", "year": 2024, "abstract": "Background: Endoplasmic reticulum stress (ERS) is closely associated with Osteoporosis (OP). In order to explore the role of ERS related genes in OP and its molecular mechanism.\nMethods: OP-related transcriptome data were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to screen OP-related genes. Differentially expressed ERS-related genes (DE-ERSGs) between OP and controls were identified by overlapping OP-related, differentially expressed genes (DEGs), and ERS-related genes. ERS-related genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore their functions. Receiver operating characteristic (ROC) curves assessed the diagnostic value of DE-ERSGs, and comparative toxicogenomics database (CTD) was used to predict targeting agents for key DE-ERSGs. Finally, biomarker expression was verified by real time quantitative polymerase chain reaction (RT-qPCR).\nResults: A total of 10 DE-ERSGs were screened in OP patients. GO and KEGG analyses indicated their enrichment in Alcoholic liver disease, Endometrial cancer, and Glycerolipid metabolism. ROC curve analysis revealed that RPN2, FOXO3, ERGIC2, and MYO9A had significant diagnostic value, thus being identified as key DE-ERSGs. Moreover, the key DE-ERSGs-drug interaction network showed that some drugs such as bisphenol A, Cisplatin, Cyclosporine, and Valproic Acid might play roles by targeting key DE-ERSGs in OP. The expression validation analysis of key DE-ERSGs revealed that RPN2, ERGIC2, and MYO9A was significantly expressed in the GSE62402. Ultimately, The blood samples RT-qPCR verification results show that RPN2, ERGIC2, and MYO9A were significantly lower in OP samples compared to normal samples (p < 0.05), whereas there was no difference in the expression levels of FOXO3.\nConclusion: RPN2, FOXO3, ERGIC2 and MYO9A as the biomarkers associated with ERS in OP by bioinformatics analysis, which may provide new biological targets for clinical treatment.", "claims": [{"id": "claim-method", "statement": "Firstly, differentially expressed genes (DEGs) between high BMD samples and low BMD samples were detected in the GSE56815 dataset utilizing the “limma” R package (version 3.46.0)14 with an adjusted p<0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39582915", "locator": "sections[5] \"Differential Expression Analysis\" paragraph 2", "quote": "Firstly, differentially expressed genes (DEGs) between high BMD samples and low BMD samples were detected in the GSE56815 dataset utilizing the “limma” R package (version 3.46.0)14 with an adjusted p<0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets (GSE56815 and GSE62402) analyzed during the current study are available in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39582915", "locator": "sections[23] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets (GSE56815 and GSE62402) analyzed during the current study are available in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 10 DE-ERSGs were identified by intersecting differentially expressed genes, key module genes, and endoplasmic reticulum stress-related genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39582915", "locator": "sections[13] \"Identification of DE-ERSGs\" paragraph 2", "quote": "Then, 10 DE-ERSGs were obtained for subsequent analysis by intersecting DEGs, key module genes, and ERSGs (Figure 2C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 10 DE-ERSGs were identified by intersecting differentially expressed genes, key module genes, and endoplasmic reticulum stress-related genes.", "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:39582915"], "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:GSE56815"], "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:GSE56815"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39582915", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJGM.S486776", "sha256": 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Results: A total of 10 DE-ERSGs were scree", "the GSE62402. Ultimately, The blood samples RT-qPCR verification results show that RPN2, ERGIC2,", "e quantitative polymerase chain reaction (RT-qPCR). Results: A total of 10 DE-ERSGs were scree", "e GSE62402. Ultimately, The blood samples RT-qPCR verification results show that RPN2, ERGIC2,"], "order_basis": "paper_text_order:1055", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Firstly, differentially expressed genes (DEGs) between high BMD samples and low BMD samples were detected in the GSE56815 dataset utilizing the “limma” R package (version 3.46.0)14 with an adjusted p<0.05."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}