Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:29760609", "kind": "science-record", "revision": 8, "content_hash": "sha256:f94c0d5fc275321c1368eab9eaadc158a4b0b2fd13a93ac1376ba3c0463809d3", "created_at": "2026-11-01T15:40:33Z", "updated_at": "2026-11-01T22:43:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma.", "doi": "10.1186/s41065-018-0061-9", "pmid": "29760609", "authors": ["Yue Pan", "Lingyun Lu", "Junquan Chen", "Yong Zhong", "Zhehao Dai"], "journal": "Hereditas", "year": 2018, "abstract": "BACKGROUND: This study aimed to identify potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma by comprehensive bioinformatics analysis.\nMETHODS: Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from GEO database. The differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by R Bioconductor packages. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. The relationships among the DEGs and module in PPI network were analyzed by plug-in NetworkAnalyzer and MCODE seperately. Through the TargetScan and comparing target genes with DEGs, the miRNA-mRNA regulation network was established.\nRESULTS: Totally 346 DEGs and 90 DEMIs were found to be differentially expressed. These DEGs were enriched in biological processes and KEGG pathway of inflammatory immune response. 25 genes in the PPI network were selected as hub genes. Top 10 hub genes were TYROBP, HLA-DRA, VWF, PPBP, SERPING1, HLA-DPA1, SERPINA1, KIF20A, FERMT3, HLA-E. PPI network of DEGs followed a pattern of power law network and met the characteristics of small-world network. MCODE analysis identified 4 clusters and the most significant cluster consisted of 11 nodes and 55 edges. SEPP1, CKS2, TCAP, BPI were identified as the seed genes in their own clusters, respectively. The miRNA-mRNA regulation network which was composed of 89 pairs was established. MiR-210 had the highest connectivity with 12 target genes. Among the predicted target of MiR-96, HLA-DPA1 and TYROBP were the hub genes.\nCONCLUSION: Our study indicated possible differentially expressed genes and miRNA, and microRNA-mRNA negative regulatory networks in osteosarcoma by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of osteosarcoma.", "claims": [{"id": "claim-method", "statement": "Then, the dataset was analyzed by R Bioconductor packages and raw datasets were normalized based on the preprocess Core package and the DEGs and DEMIs were screened out via the limma package through the cut-off criteria of adjusted P-value< 0.01 and |Log2(FC)| > 2.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29760609", "locator": "sections[3] \"Identification of differentially expressed genes and miRNAs from public microarray data\" paragraph 2", "quote": "Then, the dataset was analyzed by R Bioconductor packages and raw datasets were normalized based on the preprocess Core package and the DEGs and DEMIs were screened out via the limma package through the cut-off criteria of adjusted P-value< 0.01 and |Log2(FC)| > 2."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The authors declare that the data supporting the findings of this study are available within the article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29760609", "locator": "sections[15] \"ack\" paragraph 1", "quote": "The authors declare that the data supporting the findings of this study are available within the article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Totally 346 DEGs and 90 DEMIs were found to be differentially expressed.", "kind": "quantitative", "target": {"value": 0.01, "unit": "p-value", "tolerance": null, "source": "Then, the dataset was analyzed by R Bioconductor packages and raw datasets were normalized based on the preprocess Core package and the DEGs and DEMIs were screened out via the limma package through the cut-off criteria of adjusted P-value< 0.01 and |Log2(FC)| > 2."}, "evidence": [{"asset": "source:pmid:29760609", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from GEO database."}, {"asset": "source:pmid:29760609", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by R Bioconductor packages."}, {"asset": "source:pmid:29760609", "locator": "sections[3] \"Identification of differentially expressed genes and miRNAs from public microarray data\" paragraph 2", "quote": "Then, the dataset was analyzed by R Bioconductor packages and raw datasets were normalized based on the preprocess Core package and the DEGs and DEMIs were screened out via the limma package through the cut-off criteria of adjusted P-value< 0.01 and |Log2(FC)| > 2."}, {"asset": "source:pmid:29760609", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Totally 346 DEGs and 90 DEMIs were found to be differentially expressed."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Totally 346 DEGs and 90 DEMIs were found to be differentially expressed.", "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:29760609"], "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:GSE28423"], "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:GSE28423"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:29760609", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s41065-018-0061-9", "sha256": 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