Integrated bioinformatic analysis unveils significant genes and pathways in the pathogenesis of supratentorial primitive neuroectodermal tumor.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:29670360", "kind": "science-record", "revision": 9, "content_hash": "sha256:d621993ee3f0125060b0206da9e7909f43116758ebe2210cd5f4f4b399180195", "created_at": "2026-11-01T15:40:33Z", "updated_at": "2026-11-01T22:43:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated bioinformatic analysis unveils significant genes and pathways in the pathogenesis of supratentorial primitive neuroectodermal tumor.", "doi": "10.2147/OTT.S148776", "pmid": "29670360", "authors": ["Guang-Yu Wang", "Ling Li", "Bo Liu", "Xiao Han", "Chun-Hua Wang", "Ji-Wen Wang"], "journal": "OncoTargets and therapy", "year": 2018, "abstract": "PURPOSE: This study aimed to explore significant genes and pathways involved in the pathogenesis of supratentorial primitive neuroectodermal tumor (sPNET).\nMATERIALS AND METHODS: Gene expression profile of GSE14295 was downloaded from publicly available Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened out in primary sPNET samples compared with normal fetal and adult brain reference samples (sPNET vs fetal brain and sPNET vs adult brain). Pathway enrichment analysis of these DEGs was conducted, followed by protein-protein interaction (PPI) network construction and significant module selection. Additionally, transcription factors (TFs) regulating the common DEGs in the two comparison groups were identified, and the regulatory network was constructed.\nRESULTS: In total, 526 DEGs (99 up- and 427 downregulated) in sPNET vs fetal brain and 815 DEGs (200 up- and 615 downregulated) in sPNET vs adult brain were identified. DEGs in sPNET vs fetal brain and sPNET vs adult brain were associated with calcium signaling pathway, cell cycle, and p53 signaling pathway. CDK1, CDC20, BUB1B, and BUB1 were hub nodes in the PPI networks of DEGs in sPNET vs fetal brain and sPNET vs adult brain. Significant modules were extracted from the PPI networks. In addition, 64 upregulated and 200 downregulated overlapping DEGs were identified in both sPNET vs fetal brain and sPNET vs adult brain. The genes involved in the regulatory network upon overlapping DEGs and the TFs were correlated with calcium signaling pathway.\nCONCLUSION: Calcium signaling pathway and several genes (CDK1, CDC20, BUB1B, and BUB1) may play important roles in the pathogenesis of sPNET.", "claims": [{"id": "claim-method", "statement": "After data preprocessing, we used the bioconductor limma package10 to calculate the level of gene differential expression in the two groups, sPNET vs fetal brain and sPNET vs adult brain. Benjamini–Hochberg (BH)11 multiple testing correction was applied to control the false-positive results, adjusting the raw p-values into false discovery rate (FDR).12 A combination of FDR <0.05 and the |log2 fold change (FC)| ≥2 was used as the stringent cutoff to determine the significance of gene expression difference.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29670360", "locator": "sections[4] \"Data preprocessing and screening of DEGs\" paragraph 3", "quote": "After data preprocessing, we used the bioconductor limma package10 to calculate the level of gene differential expression in the two groups, sPNET vs fetal brain and sPNET vs adult brain. Benjamini–Hochberg (BH)11 multiple testing correction was applied to control the false-positive results, adjusting the raw p-values into false discovery rate (FDR).12 A combination of FDR <0.05 and the |log2 fold change (FC)| ≥2 was used as the stringent cutoff to determine the significance of gene expression difference."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Gene expression profile of GSE14295 (accession number), deposited by Li et al,8 was downloaded from the public repository, Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/).9 The platform is GPL6102, Illumina human-6 v2.0 expression beadchip. This dataset consisted of 33 primary sPNET samples with good RNA quality. Additionally, seven normal fetal and seven adult brain reference samples were also included in GSE14295.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29670360", "locator": "sections[3] \"Microarray data\" paragraph 2", "quote": "Gene expression profile of GSE14295 (accession number), deposited by Li et al,8 was downloaded from the public repository, Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/).9 The platform is GPL6102, Illumina human-6 v2.0 expression beadchip. This dataset consisted of 33 primary sPNET samples with good RNA quality. Additionally, seven normal fetal and seven adult brain reference samples were also included in GSE14295."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used limma with Benjamini–Hochberg false-discovery-rate correction on GSE14295 downloaded from the public Gene Expression Omnibus database and found that the calcium signaling pathway and genes including CDK1, CDC20, BUB1B, and BUB1 may play important roles in sPNET pathogenesis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:29670360", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Gene expression profile of GSE14295 was downloaded from publicly available Gene Expression Omnibus (GEO) database."}, {"asset": "source:pmid:29670360", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Calcium signaling pathway and several genes (CDK1, CDC20, BUB1B, and BUB1) may play important roles in the pathogenesis of sPNET."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study used limma with Benjamini–Hochberg false-discovery-rate correction on GSE14295 downloaded from the public Gene Expression Omnibus database and found that the calcium signaling pathway and genes including CDK1, CDC20, BUB1B, and BUB1 may play important roles in sPNET pathogenesis.", "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:29670360"], "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:GSE14295"], "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": {"fdr_threshold": "with Benjamini–Hochberg false discovery rate correction"}, "inputs": ["data:geo:GSE14295"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:29670360", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/OTT.S148776", "sha256": "sha256:1ff2c6caad49ded6617ae2830c1b29a5dde2fdfaf22474f11289407d6cdbf761", "bytes": 22164, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/OTT.S148776", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC5894672", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE14295", "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=GSE14295", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14295", "version": null, "note": "Gene expression profile of GSE14295 (accession number), deposited by Li et al,8 was downloaded from the public repository, Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/).9 The platform is GPL6102, Illumina human-6 v2.0 expression beadchip. 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Benjamini–Hochberg (BH)11 multiple testing correction was applied to control the false-positive results, adjusting the raw p-values into false discovery rate (FDR).12 A combination of FDR <0.05 and the |log2 fold change (FC)| ≥2 was used as the stringent cutoff to determine the significance of gene expression difference."}}, {"id": "method-2", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-3", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "Pathway enrichment"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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Benjamini–Hochberg (BH)11 multiple testing correction was applied to control the false-positive results, adjusting the raw p-values into false discovery rate (FDR).12 A combination of FDR <0.05 and the |log2 fold change (FC)| ≥2 was used as the stringent cutoff to determine the significance of gene expression difference."], "order_basis": "analysis_only", "id": "assay:1"}]}