Genomic Expression Profiling and Bioinformatics Analysis of Chronic Recurrent Multifocal Osteomyelitis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33628812", "kind": "science-record", "revision": 8, "content_hash": "sha256:2623192be7efc12f21820c83a92a75130bc2e8e90ac114cc3867b00fd83d8a7c", "created_at": "2026-11-01T15:41:26Z", "updated_at": "2026-11-01T22:46:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Genomic Expression Profiling and Bioinformatics Analysis of Chronic Recurrent Multifocal Osteomyelitis.", "doi": "10.1155/2021/6898093", "pmid": "33628812", "authors": ["Kai Huang", "Bingyuan Lin", "Yiyang Liu", "Qiaofeng Guo", "Haiyong Ren"], "journal": "BioMed research international", "year": 2021, "abstract": "OBJECTIVE: Chronic nonbacterial osteomyelitis (CNO) is an autoinflammatory bone disorder. Its most severe form is referred to as chronic recurrent multifocal osteomyelitis (CRMO). Currently, the exact molecular pathophysiology of CNO/CRMO remains unknown. No uniform diagnostic standard and treatment protocol were available for this disease. The aim of this study was to identify the differentially expressed genes (DEGs) in CRMO tissues compared to normal control tissues to investigate the mechanisms of CRMO.\nMATERIALS: Microarray data from the GSE133378 (12 CRMO and 148 matched normal tissue samples) data sets were downloaded from the Gene Expression Omnibus (GEO) database. DEGs were identified using the limma package in the R software. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis were performed to further investigate the function of the identified DEGs.\nRESULTS: This study identified a total of 1299 differentially expressed mRNAs, including1177 upregulated genes and 122 downregulated genes, between CRMO and matched normal tissue samples. GO analyses showed that DEGs were enriched in immune-related terms. KEGG pathway enrichment analyses showed that the DEGs were mainly related to oxidative phosphorylation, ribosome, and Parkinson disease. Eight modules were extracted from the gene expression network, including one module constituted with immune-related genes and one module constituted with ribosomal-related genes.\nCONCLUSION: Oxidative phosphorylation, ribosome, and Parkinson disease pathways were significantly associated with CRMO. The immune-related genes including IRF5, OAS3, and HLA-A, as well as numerous ribosomal-related genes, might be implicated in the pathogenesis of CRMO. The identification of these genes may contribute to the development of early diagnostic tools, prognostic markers, or therapeutic targets in CRMO.", "claims": [{"id": "claim-method", "statement": "DEGs were identified using the limma package in the R software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33628812", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "DEGs were identified using the limma package in the R software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The microarray data used to support the findings of this study are from the GSE133378 data sets (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133378) and can be downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo) of NCBI. The processed data are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33628812", "locator": "sections[13] \"Data Availability\" paragraph 2", "quote": "The microarray data used to support the findings of this study are from the GSE133378 data sets (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133378) and can be downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo) of NCBI. The processed data are available from the corresponding author upon request."}], "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:33628812"], "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:GSE133378"], "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:GSE133378"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33628812", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2021/6898093", "sha256": "sha256:29d8c00311d452a1791c0c6f5d31989c3bc58ef8ac0c20c198983ca2120d39de", "bytes": 29997, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2021/6898093", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7888306", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE133378", "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=GSE133378", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE133378", "version": null, "note": "The microarray data used to support the findings of this study are from the GSE133378 data sets (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133378) and can be downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo) of NCBI. The processed data are available from the corresponding author upon request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The microarray data used to support the findings of this study are from the GSE133378 data sets (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133378) and can be downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo) of NCBI. 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