Impact of propofol versus sevoflurane anesthesia on molecular subtypes and immune checkpoints of glioma during surgery.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37706017", "kind": "science-record", "revision": 2, "content_hash": "sha256:84c9f5ebf56460d7913d33488ae5b25a1ef374fd3a0584d19dd38c3b70a31465", "created_at": "2026-11-01T19:02:32Z", "updated_at": "2026-11-01T21:31:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Impact of propofol versus sevoflurane anesthesia on molecular subtypes and immune checkpoints of glioma during surgery.", "doi": "10.1002/hsr2.1366", "pmid": "37706017", "authors": ["Shenghua Cen", "Guocai Yang", "Hongyan Bao", "Ze Yu", "Lei Liang"], "journal": "Health science reports", "year": 2023, "abstract": "Background: Sevoflurane and propofol are two popular anesthetics used during glioblastoma (GBM) surgery. This investigation compared the molecular subtypes and immune checkpoints of cancer cells following GBM surgery under sevoflurane and propofol anesthesia.\nMethod: The expression profile data and clinical information of glioma samples of different grades were downloaded from The Cancer Genome Atlas database. Weighted gene coexpression network analysis was used to identify hub modules and key genes related to glioma grades (G2 and G3). The GEO database (GSE179004) was used to retrieve glioma surgical specimens with different anesthetic gene expression profiles. The differential expression of immune checkpoint genes under various anesthetic settings was examined using the R-ggplot2.\nResults: Compared to sevoflurane, propofol significantly downregulated SERPINI1 and CAMK2A expression. These are also important factors in glioma grading. Simultaneously, SERPINI1 and CAMK2A were also significantly related to the prognosis of GBM and lower-grade glioma patients and acted as potential tumor suppressors. In addition, propofol increases the expression of the immune checkpoint molecule, PD-L1.\nConclusions: Our study revealed that sevoflurane can more effectively prevent the development of glioma after surgery than propofol, and SERPINI1 can be used as a new independent prognostic factor for glioma.", "claims": [{"id": "claim-method", "statement": "Weighted gene coexpression network analysis was used to identify hub modules and key genes related to glioma grades (G2 and G3).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37706017", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Weighted gene coexpression network analysis was used to identify hub modules and key genes related to glioma grades (G2 and G3)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original data are available upon reasonable request to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37706017", "locator": "sections[31] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The original data are available upon reasonable request to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our study revealed that sevoflurane can more effectively prevent the development of glioma after surgery than propofol, and SERPINI1 can be used as a new independent prognostic factor for glioma.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37706017", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Our study revealed that sevoflurane can more effectively prevent the development of glioma after surgery than propofol, and SERPINI1 can be used as a new independent prognostic factor for glioma."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our study revealed that sevoflurane can more effectively prevent the development of glioma after surgery than propofol, and SERPINI1 can be used as a new independent prognostic factor for glioma.", "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:37706017"], "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:GSE179004"], "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:GSE179004"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37706017", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/hsr2.1366", "sha256": "sha256:ae33f22ace73a833ce410e74c613e4f318c549402a54aa96237cec8e1afffbbd", "bytes": 35054, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1002/hsr2.1366", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10495545", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE179004", "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=GSE179004", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE179004", "version": null, "note": "The original data are available upon reasonable request to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original data are available upon reasonable request to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM5403447", "GSM5403448", "GSM5403449", "GSM5403450", "GSM5403451", "GSM5403452"], "sample_count": 6, "group_sizes": null, "group_sizes_basis": null, "platform": "23126", "taxon": "Homo sapiens", "title": "Expression data from glioblastoma surgical specimens", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE179004 not downloaded", "asset:data:geo:GSE179004 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Cox regression"}}, {"id": "method-2", "family": null, "specific": null, "label": "Weighted gene coexpression network analysis (WGCNA)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Weighted gene coexpression network analysis was used to identify hub modules and key genes related to glioma grades (G2 and G3)."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "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": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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