Construction of a Prognostic Model for Mitochondria and Macrophage Polarization Correlation in Glioma Based on Single-Cell and Transcriptome Sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39491527", "kind": "science-record", "revision": 3, "content_hash": "sha256:583577e270842ddcbfbd240826b7c54a75b8f82c8380a4752e49a3cc2c4dae23", "created_at": "2026-11-01T20:29:44Z", "updated_at": "2026-11-01T22:13:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a Prognostic Model for Mitochondria and Macrophage Polarization Correlation in Glioma Based on Single-Cell and Transcriptome Sequencing.", "doi": "10.1111/cns.70083", "pmid": "39491527", "authors": ["Pengyu Chen", "Heping Wang", "Yufei Zhang", "Siyao Qu", "Yulian Zhang", "Yanbo Yang", "Chuanpeng Zhang", "Kun He", "Hanhan Dang", "Yang Yang", "Shaoyi Li", "Yanbing Yu"], "journal": "CNS neuroscience & therapeutics", "year": 2024, "abstract": "BACKGROUND: Numerous diseases are associated with the interplay of mitochondrial and macrophage polarization. However, the correlation of mitochondria-related genes (MRGs) and macrophage polarization-related genes (MPRGs) with the prognosis of glioma remains unclear. This study aimed to examine this relationship based on bioinformatic analysis.\nMETHODS: Glioma-related datasets (TCGA-GBMLGG, mRNA-seq-325, mRNA-seq-693, GSE16011, GSE4290, and GSE138794) were included in this study. The intersection genes were obtained by overlapping differentially expressed genes (DEGs) from differential expression analysis in GSE16011, key module genes from WGCNA, and MRGs. Subsequently, the intersection genes were further screened to obtain prognostic genes. Following this, a risk model was developed and verified. After that, independent prognostic factors were identified, followed by the construction of a nomogram and subsequent evaluation of its predictive ability. Furthermore, immune microenvironment analysis and expression validation were implemented. The GSE138794 dataset was utilized to evaluate the expression of prognostic genes at a cellular level, followed by conducting an analysis on cell-to-cell communication. Finally, the results were validated in different datasets and tissue samples from patients.\nRESULTS: ECI2, MCCC2, OXCT1, SUCLG2, and CPT2 were identified as prognostic genes for glioma. The risk model constructed based on these genes in TCGA-GBMLGG demonstrated certain accuracy in predicting the occurrence of glioma. Additionally, the nomogram constructed based on risk score and grade exhibited strong performance in predicting patient survival. Significant differences were observed in the proportion of 27 immune cell types (e.g., activated B cells and macrophages) and the expression of 32 immune checkpoints (e.g., CD70, CD200, and CD48) between the two risk groups. Single-cell RNA sequencing showed that CPT2, ECI2, and SUCLG2 were highly expressed in oligodendrocytes, neural progenitor cells, and BMDMs, respectively. The results of cell-cell communication analysis revealed that both oligodendrocytes and BMDMs exhibited a substantial number of interactions with high strength.\nCONCLUSION: This study revealed five genes associated with the prognosis of glioma (ECI2, MCCC2, OXCT1, SUCLG2, and CPT2), providing novel insights into individualized treatment and prognosis.", "claims": [{"id": "claim-method", "statement": "Candidate genes were sequentially included in univariate Cox regression analysis ((HR) ≠ 1 and p < 0.05), proportional hazards (PH) hypothesis testing (p > 0.05), and least absolute shrinkage and selection operator (LASSO) regression analysis to obtain prognostic genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39491527", "locator": "sections[7] \"Screening of Prognostic Genes in Glioma\" paragraph 2", "quote": "Candidate genes were sequentially included in univariate Cox regression analysis ((HR) ≠ 1 and p < 0.05), proportional hazards (PH) hypothesis testing (p > 0.05), and least absolute shrinkage and selection operator (LASSO) regression analysis to obtain prognostic genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available in the University of California Santa Cruz (UCSC) database at http://genome.ucsc.edu These data were derived from the following resources available in the public domain: the University of California Santa Cruz (UCSC) database, http://genome.ucsc.edu Glioma Genome Atlas (CGGA), http://www.cgga.org.cn the Gene Expression Omnibus (GEO) database, https://www.ncbi.nlm.nih.gov/ MitoCarta3.0 database, https://www.broadinstitute.org/mitocarta.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39491527", "locator": "sections[34] \"Data Availability Statement\" paragraph 2", "quote": "The data that support the findings of this study are available in the University of California Santa Cruz (UCSC) database at http://genome.ucsc.edu These data were derived from the following resources available in the public domain: the University of California Santa Cruz (UCSC) database, http://genome.ucsc.edu Glioma Genome Atlas (CGGA), http://www.cgga.org.cn the Gene Expression Omnibus (GEO) database, https://www.ncbi.nlm.nih.gov/ MitoCarta3.0 database, https://www.broadinstitute.org/mitocarta."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This study revealed five genes associated with the prognosis of glioma (ECI2, MCCC2, OXCT1, SUCLG2, and CPT2), providing novel insights into individualized treatment and prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39491527", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "This study revealed five genes associated with the prognosis of glioma (ECI2, MCCC2, OXCT1, SUCLG2, and CPT2), providing novel insights into individualized treatment and prognosis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "This study revealed five genes associated with the prognosis of glioma (ECI2, MCCC2, OXCT1, SUCLG2, and CPT2), providing novel insights into individualized treatment and prognosis.", "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:39491527"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39491527", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1111/cns.70083", "sha256": "sha256:3f44b3a033234ab2699c77b964d6949913c89e909142e03f1e4fe4099c0c6162", "bytes": 48196, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1111/cns.70083", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11532235", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "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": 1.0, "band": "verbatim", "quote": "Candidate genes were sequentially included in univariate Cox regression analysis ((HR) ≠ 1 and p < 0.05), proportional hazards (PH) hypothesis testing (p > 0.05), and least absolute shrinkage and selection operator (LASSO) regression analysis to obtain prognostic genes."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "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": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The data that support the findings of this study are available in the University of California Santa Cruz (UCSC) database at http://genome.ucsc.edu These data were derived from the following resources available in the public domain: the University of California Santa Cruz (UCSC) database, http://genome.ucsc.edu Glioma Genome Atlas (CGGA), http://www.cgga.org.cn the Gene Expression Omnibus (GEO) database, https://www.ncbi.nlm.nih.gov/ MitoCarta3.0 database, https://www.broadinstitute.org/mitocarta."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:39491527"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:37:21Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13067, "completion_tokens": 2650, "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:39491527", "type": "paper", "label": "pmid:39491527"}, {"id": "source:pmid:39491527", "type": "manuscript", "label": "PMID 39491527 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39491527", "type": "paper", "label": "pmid:39491527"}, {"id": "source:pmid:39491527", "type": "manuscript", "label": "PMID 39491527 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39491527", "type": "extraction", "used": ["source:pmid:39491527"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "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:39491527", "parent_hash": "sha256:d4edc1c20cd5664a9c9b830de9e992879d954264db0edc3827d22812c292c522", "fork_of": null, "generation": 2}, "gaps": ["execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["rrelation in Glioma Based on Single-Cell and Transcriptome Sequencing.", "48) between the two risk groups. 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