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pmid:35111402
Exploring the potential biomarkers for prognosis of glioblastoma via weighted gene co-expression network analysis.
Mengyuan Zhang · Zhike Zhou · Zhouyang Liu · Fangxi Liu · Chuansheng Zhao
PeerJ · 2022 doi:10.7717/peerj.12768
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Complete file
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Therefore, there is an urgent need to screen GBM biomarkers to prove the therapeutic effect and improve the prognosis.\nRESULTS: We extracted data from GBM patients from the Gene Expression Integration Database (GEO), analyzed differentially expressed genes in GEO and identified key modules by weighted gene co-expression network analysis (WGCNA). GSE145128 data was obtained from the GEO database, and the darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r = - 0.62, p = 0.01). We performed enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to reveal the interaction activity in the selected modules. Then Kaplan-Meier survival curve analysis was used to extract genes closely related to GBM prognosis. We used Kaplan-Meier survival curves to analyze the 139 genes in the darkturquoise module, identified four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM. Low expression of DARS/GDI2/TRUB1 and high expression of P4HA2 had a poor prognosis. Finally, we used tumor genome map (TCGA) data, verified the characteristics of hub genes through Co-expression analysis, Drug sensitivity analysis, TIMER database analysis and GSVA analysis. We downloaded the data of GBM from the TCGA database, the results of co-expression analysis showed that DARS/GDI2/P4HA2/TRUB1 could regulate the development of GBM by affecting genes such as CDC73/CDC123/B4GALT1/CUL2. Drug sensitivity analysis showed that genes are involved in many classic Cancer-related pathways including TSC/mTOR, RAS/MAPK.TIMER database analysis showed DARS expression is positively correlated with tumor purity (cor = 0.125, p = 1.07e-02)), P4HA2 expression is negatively correlated with tumor purity (cor =-0.279, p = 6.06e-09). Finally, GSVA analysis found that DARS/GDI2/P4HA2/TRUB1 gene sets are closely related to the occurrence of cancer.\nCONCLUSION: We used two public databases to identify four valuable biomarkers for GBM prognosis, namely DARS/GDI2/P4HA2/TRUB1, which have potential clinical application value and can be used as prognostic markers for GBM.", "claims": [{"id": "claim-method", "statement": "We used Kaplan-Meier survival curves to analyze the 139 genes in the darkturquoise module, identified four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35111402", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We used Kaplan-Meier survival curves to analyze the 139 genes in the darkturquoise module, identified four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data (TCGA search terms/accession numbers) are available in the Supplementary File.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35111402", "locator": "sections[24] \"Additional Information and Declarations\" paragraph 4", "quote": "The raw data (TCGA search terms/accession numbers) are available in the Supplementary File."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r = − 0.62, p = 0.01), and four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM were identified.", "kind": "quantitative", "target": {"value": 0.01, "unit": "p-value", "tolerance": null, "source": "GSE145128 data was obtained from the GEO database, and the darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r = − 0.62, p = 0.01)."}, "evidence": [{"asset": "source:pmid:35111402", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "GSE145128 data was obtained from the GEO database, and the darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r = − 0.62, p = 0.01)."}, {"asset": "source:pmid:35111402", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We used Kaplan-Meier survival curves to analyze the 139 genes in the darkturquoise module, identified four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r = − 0.62, p = 0.01), and four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM were identified.", "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:35111402"], "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. 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