Investigation of differentially expressed genes related to cellular senescence between high-risk and non-high-risk groups in neuroblastoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39135779", "kind": "science-record", "revision": 2, "content_hash": "sha256:60324fc13b44206515e582da623c047586fa74986dc2c3671a85802b8f1b3293", "created_at": "2026-11-01T20:29:14Z", "updated_at": "2026-11-01T21:33:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Investigation of differentially expressed genes related to cellular senescence between high-risk and non-high-risk groups in neuroblastoma.", "doi": "10.3389/fcell.2024.1421673", "pmid": "39135779", "authors": ["Xingyu Zhou", "Yuying Wu", "Lan Qin", "Miao Zeng", "Mingying Zhang", "Jun Zhang"], "journal": "Frontiers in cell and developmental biology", "year": 2024, "abstract": "Object: This study aims to identify differentially expressed genes (DEGs) between high-risk and non-high-risk groups in neuroblastoma (NB), construct a prognostic model, and establish a risk score formula.\nMaterials and methods: The NB dataset GSE49710 (n = 498) from the GEO database served as the training cohort to select DEGs between high-risk and non-high-risk NB groups. Cellular senescence-related genes were obtained from the Aging Atlas database. Intersection genes from both datasets were identified as key genes of cellular senescence-related genes (SRGs). A prognostic model was constructed using Univariate Cox regression analysis and the Lasso algorithm with SRGs. Validation was performed using the E-MTAB-8248 cohort (n = 223). The expression levels of AURKA and CENPA were evaluated via RT-qPCR in two clinical NB sample groups.\nResults: Eight SRGs were identified, and a prognostic model comprising five genes related to cellular senescence was constructed. AURKA and CENPA showed significant expression in clinical samples and were closely associated with cellular senescence.\nConclusion: The prognostic model consisted with five cellular senescence related genes effectively predicts the prognosis of NB patients. AURKA and CENPA represent promising targets in NB for predicting cellular senescence, offering potential insights for NB therapy.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was applied to eight SRGs to determine their association with OS. The results, illustrated in the forest plot (Figure 1C), indicated significant correlations for all genes (p < 0.05, Supplementary Table S5). To refine the model, the LASSO algorithm was utilized, resulting in a five-gene model derived from the optimal λ value, depicted in Figures 1D, E.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39135779", "locator": "sections[11] \"3.2 Construction of a prognostic model for cellular senescence-related signatures (SRS)\" paragraph 2", "quote": "Univariate Cox regression analysis was applied to eight SRGs to determine their association with OS. The results, illustrated in the forest plot (Figure 1C), indicated significant correlations for all genes (p < 0.05, Supplementary Table S5). To refine the model, the LASSO algorithm was utilized, resulting in a five-gene model derived from the optimal λ value, depicted in Figures 1D, E."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. The NB gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE49710 (Zhang et al., n = 498) and ArrayExpress (https://www.ebi.ac.uk/biostudies/arrayexpress) under accession number E-MTAB-8248 (Christoph et al., n = 223).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39135779", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. The NB gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE49710 (Zhang et al., n = 498) and ArrayExpress (https://www.ebi.ac.uk/biostudies/arrayexpress) under accession number E-MTAB-8248 (Christoph et al., n = 223)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A five-gene cellular senescence-related prognostic model was constructed and demonstrated prognostic performance in neuroblastoma.", "kind": "quantitative", "target": {"value": 0.0001, "unit": "p-value", "tolerance": null, "source": "Kaplan-Meier survival analysis revealed that patients in the high-risk group experienced significantly shorter OS compared to those in the low-risk group (Figure 4B, log-rank p < 0.0001)."}, "evidence": [{"asset": "source:pmid:39135779", "locator": "sections[11] \"3.2 Construction of a prognostic model for cellular senescence-related signatures (SRS)\" paragraph 2", "quote": "To refine the model, the LASSO algorithm was utilized, resulting in a five-gene model derived from the optimal λ value, depicted in Figures 1D, E."}, {"asset": "source:pmid:39135779", "locator": "sections[11] \"3.2 Construction of a prognostic model for cellular senescence-related signatures (SRS)\" paragraph 6", "quote": "Furthermore, the model demonstrated excellent predictive performance with area under the curve (AUC) values of 0.84, 0.86, and 0.89 for 1-year, 3-year, and 5-year OS respectively, according to time-dependent ROC analysis (Figure 4C)."}, {"asset": "source:pmid:39135779", "locator": "sections[12] \"3.3 Validation of the prognostic model in an independent cohort\" paragraph 2", "quote": "Kaplan-Meier survival analysis revealed that patients in the high-risk group experienced significantly shorter OS compared to those in the low-risk group (Figure 4B, log-rank p < 0.0001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A five-gene cellular senescence-related prognostic model was constructed and demonstrated prognostic performance in neuroblastoma.", "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:39135779"], "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:E-MTAB-8248"], "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:E-MTAB-8248"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39135779", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcell.2024.1421673", "sha256": "sha256:c6ff18b83feba356f841ee594cde77e44e766417e4ea1753ff2ca1c52f798cf2", "bytes": 34022, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcell.2024.1421673", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11317289", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:E-MTAB-8248", "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=E-MTAB-8248", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "E-MTAB-8248", "version": null, "note": "Publicly available datasets were analyzed in this study. The NB gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE49710 (Zhang et al., n = 498) and ArrayExpress (https://www.ebi.ac.uk/biostudies/arrayexpress) under accession number E-MTAB-8248 (Christoph et al., n = 223).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. The NB gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE49710 (Zhang et al., n = 498) and ArrayExpress (https://www.ebi.ac.uk/biostudies/arrayexpress) under accession number E-MTAB-8248 (Christoph et al., n = 223).", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": null, "resolution_checked_at": "2026-10-31T18:29:04Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE49710", "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=GSE49710", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE49710", "version": null, "note": "Publicly available datasets were analyzed in this study. The NB gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE49710 (Zhang et al., n = 498) and ArrayExpress (https://www.ebi.ac.uk/biostudies/arrayexpress) under accession number E-MTAB-8248 (Christoph et al., n = 223).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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"asset:data:geo:GSE49710 licence not read"]}, "analysis": {"state": "complete", "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": 1.0, "band": "verbatim", "quote": "Univariate Cox regression analysis was applied to eight SRGs to determine their association with OS. The results, illustrated in the forest plot (Figure 1C), indicated significant correlations for all genes (p < 0.05, Supplementary Table S5). To refine the model, the LASSO algorithm was utilized, resulting in a five-gene model derived from the optimal λ value, depicted in Figures 1D, E."}}, {"id": "method-2", "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": 0.8, "band": "high", "quote": "Pathway Enrichment"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "cox_ph", "expected_value": 0.0001, "unit": "p-value", "tolerance": null, "state": "complete", "note": "The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold."}], "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": ["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. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE49710", "E-MTAB-8248"], "resolved_accessions": ["GSE49710", "E-MTAB-8248"], "statements": ["Publicly available datasets were analyzed in this study. The NB gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE49710 (Zhang et al., n = 498) and ArrayExpress (https://www.ebi.ac.uk/biostudies/arrayexpress) under accession number E-MTAB-8248 (Christoph et al., n = 223)."], "basis": "The paper names GSE49710, E-MTAB-8248 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:39135779", "data:geo:E-MTAB-8248"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:E-MTAB-8248"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:28:15Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10742, "completion_tokens": 2436, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:39135779", "type": "paper", "label": "pmid:39135779"}, {"id": "source:pmid:39135779", "type": "manuscript", "label": "PMID 39135779 full text"}, {"id": "data:geo:E-MTAB-8248", "type": "dataset", "label": "GEO E-MTAB-8248"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39135779", "type": "paper", "label": "pmid:39135779"}, {"id": "source:pmid:39135779", "type": "manuscript", "label": "PMID 39135779 full text"}, {"id": "data:geo:E-MTAB-8248", "type": "dataset", "label": "GEO E-MTAB-8248"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39135779", "type": "extraction", "used": ["source:pmid:39135779"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-E-MTAB-8248", "type": "accession_resolution", "used": ["data:geo:E-MTAB-8248"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:39135779", "parent_hash": "sha256:6f84ba2011fb1e6ed9f25afb0a4cf820ceb986492a5911673b635fd58329bee2", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:E-MTAB-8248 licence not read", "asset:data:geo:E-MTAB-8248 not downloaded", "asset:data:geo:GSE49710 licence not read", "asset:data:geo:GSE49710 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["levels of AURKA and CENPA were evaluated via RT-qPCR in two clinical NB sample groups. Results: E", "els of AURKA and CENPA were evaluated via RT-qPCR in two clinical NB sample groups. Results: E", "ence quantitative polymerase chain reaction (RT-qPCR) Sixteen children with NB, treated surgicall", "g method, followed by reverse transcription. RT-qPCR was performed according to standard protocol"], "order_basis": "paper_text_order:899", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Univariate Cox regression analysis was applied to eight SRGs to determine their association with OS. The results, illustrated in the forest plot (Figure 1C), indicated significant correlations for all genes (p < 0.05, Supplementary Table S5). To refine the model, the LASSO algorithm was utilized, resulting in a five-gene model derived from the optimal λ value, depicted in Figures 1D, E."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}