Identification of novel prognostic targets in glioblastoma using bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35436915", "kind": "science-record", "revision": 4, "content_hash": "sha256:12f8eb52615e1aca5be01b0ea1216fc8f60f5b5939683b358dd41c2fb7286c66", "created_at": "2026-11-01T18:14:17Z", "updated_at": "2026-11-01T22:06:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of novel prognostic targets in glioblastoma using bioinformatics analysis.", "doi": "10.1186/s12938-022-00995-8", "pmid": "35436915", "authors": ["Xiaofeng Yin", "Quansheng Wu", "Zheng Hao", "Laizhao Chen"], "journal": "Biomedical engineering online", "year": 2022, "abstract": "BACKGROUND: Glioblastoma (GBM) is the most malignant grade of glioma. Highly aggressive characteristics of GBM and poor prognosis cause GBM-related deaths. The potential prognostic biomarkers remain to be demonstrated. This research builds up predictive gene targets of expression alterations in GBM utilizing bioinformatics analysis.\nMETHODS AND RESULTS: The microarray datasets (GSE15824 and GSE16011) associated with GBM were obtained from Gene Expression Omnibus (GEO) database to identify the differentially expressed genes (DEGs) between GBM and non-tumor tissues. In total, 719 DEGs were obtained and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for function enrichment analysis. Furthermore, we constructed protein-protein Interaction (PPI) network among DEGs utilizing Search Tool for the Retrieval of Interacting Genes (STRING) online tool and Cytoscape software. The DEGs of degree > 10 was selected as hub genes, including 73 upregulated genes and 21 downregulated genes. Moreover, MCODE application in Cytoscape software was employed to identify three key modules involved in GBM development and prognosis. Additionally, we used the Gene expression profiling and interactive analyses (GEPIA) online tool to further confirm four genes involving in poor prognosis of GBM patients, including interferon-gamma-inducible protein 30 (IFI30), major histocompatibility complex class II-DM alpha (HLA-DMA), Prolyl 4-hydroxylase beta polypeptide (P4HB) and reticulocalbin-1 (RCN1). Furthermore, the correlation analysis indicated that the expression of IFI30, an acknowledged biomarker in glioma, was positively correlated with HLA-DMA, P4HB and RCN1. RCN1 expression was positively correlated with P4HB and HLA-DMA. Moreover, qRT-PCR and immunohistochemistry analysis further validated the upregulation of four prognostic markers in GBM tissues.\nCONCLUSIONS: Analysis of multiple datasets combined with global network information and experimental verification presents a successful approach to uncover the risk hub genes and prognostic markers of GBM. Our study identified four risk- and prognostic-related gene signatures, including IFI30, HLA-DMA, P4HB and RCN1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of GBM.", "claims": [{"id": "claim-method", "statement": "Spearman’s correlation analysis was carried out to analyze the expression correlation between the four prognostic genes (IFI30, HLA-DMA, P4HB and RCN1) (Fig. 6A).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35436915", "locator": "sections[7] \"Correlation analysis between prognostic genes\" paragraph 2", "quote": "Spearman’s correlation analysis was carried out to analyze the expression correlation between the four prognostic genes (IFI30, HLA-DMA, P4HB and RCN1) (Fig. 6A)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The open-access datasets are available through the following URL: GSE15824 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15824) and GSE16011 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16011). All data generated or analyzed during this study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35436915", "locator": "sections[24] \"Availability of data and materials\" paragraph 2", "quote": "The open-access datasets are available through the following URL: GSE15824 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15824) and GSE16011 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16011). All data generated or analyzed during this study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our study identified four risk- and prognostic-related gene signatures, including IFI30, HLA-DMA, P4HB and RCN1.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35436915", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Our study identified four risk- and prognostic-related gene signatures, including IFI30, HLA-DMA, P4HB and RCN1."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our study identified four risk- and prognostic-related gene signatures, including IFI30, HLA-DMA, P4HB and RCN1.", "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:35436915"], "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:GSE15824"], "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:GSE15824"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35436915", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12938-022-00995-8", "sha256": "sha256:fcae25ff146c81a9a6f2f1dc06e8188988e224fcf4e4cf8ab229b1abadacb061", "bytes": 40985, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12938-022-00995-8", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9014588", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE15824", "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=GSE15824", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE15824", "version": null, "note": "The open-access datasets are available through the following URL: GSE15824 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15824) and GSE16011 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16011). 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METHODS AND RESULTS: The microarray datasets (GSE15824 and GSE16011) associated"], "order_basis": "paper_text_order:405", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["with P4HB and HLA-DMA. Moreover, qRT-PCR and immunohistochemistry analysis further validated the upregulation", "analysis of prognostic genes by qRT-PCR and immunohistochemistry Clinical study has confirmed that IFI30 expr", "unpaired t test. **p < 0.01, ***p < 0.001. D Immunohistochemical analysis of P4HB, HLA-MDA and RCN1 expressio", "rmal brain specimens by qRT-PCR analysis and IHC assay. 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