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pmid:30643174

Astrocytoma progression scoring system based on the WHO 2016 criteria.

Zhen-Hang Li · Yan-Lei Guan · Qiang Liu · Yao Wang · Run Cui · Yun-Jie Wang

Scientific reports · 2019 doi:10.1038/s41598-018-36471-4

Paper

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Experiment

complete

Data

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partial

Execution

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pmid:30643174
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title
Astrocytoma progression scoring system based on the WHO 2016 criteria.
doi
10.1038/s41598-018-36471-4
pmid
30643174
authors array · 6 items
[0]
Zhen-Hang Li
[1]
Yan-Lei Guan
[2]
Qiang Liu
[3]
Yao Wang
[4]
Run Cui
[5]
Yun-Jie Wang
journal
Scientific reports
year
2019
abstract
Diffuse astrocytoma (including glioblastoma) is morbid with a worse prognosis than other types of glioma. Therefore, we sought to build a progression-associated score to improve malignancy and prognostic predictions for astrocytoma. The astrocytoma progression (AP) score was constructed through bioinformatics analyses of the training cohort (TCGA RNA-seq) and included 18 genes representing distinct aspects of regulation during astrocytoma progression. This classifier could successfully discriminate patients with distinct prognoses in the training and validation (REMBRANDT, GSE16011 and TCGA-GBM Microarray) cohorts (P < 0.05 in all cohorts) and in different clinicopathological subgroups. Distinct patterns of somatic mutations and copy number variation were also observed. The bioinformatics analyses suggested that genes associated with a higher AP score were significantly involved in cancer progression-related biological processes, such as the cell cycle and immune/inflammatory responses, whereas genes associated with a lower AP score were associated with relatively normal nervous system biological processes. The analyses indicated that the AP score was a robust predictor of patient survival, and its ability to predict astrocytoma malignancy was well elucidated. Therefore, this bioinformatics-based scoring system suggested that astrocytoma progression could distinguish patients with different underlying biological processes and clinical outcomes, facilitate more precise tumour grading and possibly shed light on future classification strategies and therapeutics for astrocytoma patients.
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The prognostic differences between patients with high or low expression of a certain gene or score (higher or lower than the median value) were calculated by the Kaplan–Meier method with the two-sided log-rank test using the R “survival” package.
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sections[13] "Statistical analysis" paragraph 2
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The prognostic differences between patients with high or low expression of a certain gene or score (higher or lower than the median value) were calculated by the Kaplan–Meier method with the two-sided log-rank test using the R “survival” package.
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The expression, CNV, somatic mutation and clinical data from 471 cases (training cohort) and 525 GBM cases belonging to TCGA dataset were retrieved from the website http://cancergenome.nih.gov/. The REMBRANDT dataset was retrieved from the website http://caintegrator.nci.nih.gov/rembrandt. The GSE16011 dataset was retrieved from the Gene Expression Omnibus website (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16011). The GSE4271 dataset was retrieved from the Gene Expression Omnibus website (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse4271).
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sections[19] "Availability of Data and Materials" paragraph 2
quote
The expression, CNV, somatic mutation and clinical data from 471 cases (training cohort) and 525 GBM cases belonging to TCGA dataset were retrieved from the website http://cancergenome.nih.gov/. The REMBRANDT dataset was retrieved from the website http://caintegrator.nci.nih.gov/rembrandt. The GSE16011 dataset was retrieved from the Gene Expression Omnibus website (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16011). The GSE4271 dataset was retrieved from the Gene Expression Omnibus website (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse4271).
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Intrinsic Gene Expression Profiles of Gliomas are a Better Predictor of Survival than Histology
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Molecular subclasses of high-grade glioma: prognosis, disease progression, and neurogenesis
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Univariate and multivariate Cox regression analyses in the training cohort.
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Distribution and prognostic value of the AP score among the astrocytoma subgroups
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Univariate and multivariate Cox regression analyses in the training cohort.
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Correlation and prognostic value of each immune cell in the training cohort.
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Association between the AP score and immune cells
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Table 2
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Correlation and prognostic value of each immune cell in the training cohort.
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The expression, CNV, somatic mutation and clinical data from 471 cases (training cohort) and 525 GBM cases belonging to TCGA dataset were retrieved from the website http://cancergenome.nih.gov/. The REMBRANDT dataset was retrieved from the website http://caintegrator.nci.nih.gov/rembrandt. The GSE16011 dataset was retrieved from the Gene Expression Omnibus website (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16011). The GSE4271 dataset was retrieved from the Gene Expression Omnibus website (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse4271).
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The astrocytoma progression (AP) score was constructed through bioinformatics analyses of the training cohort (TCGA RNA-seq) and included 18 genes representing distinct aspects of regulation during astrocytoma progression. This classifier could successfully discriminate patients with distinct prognoses in the training and validation (REMBRANDT, GSE16011 and TCGA-GBM Microarray) cohorts (P < 0.05 in all cohorts) and in different clinicopathological subgroups. Distinct patterns of somatic mutations and copy number variation were also observed. The bioinformatics analyses suggested that genes associated with a higher AP score were significantly involved in cancer progression-related biological processes, such as the cell cycle and immune/inflammatory responses, whereas genes associated with a lower AP score were associated with relatively normal nervous system biological processes. The analyses indicated that the AP score was a robust predictor of patient survival, and its ability to predict astrocytoma malignancy was well elucidated. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["logrank_test"], "assets": ["source:pmid:30643174", "data:geo:GSE16011", "asset:table:pmid:30643174:1", "asset:table:pmid:30643174:2"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE16011"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:13:23Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 3, "usage": {"prompt_tokens": 28586, "completion_tokens": 2575, "cached_tokens": 784}}, "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:30643174", "type": "paper", "label": "pmid:30643174"}, {"id": "source:pmid:30643174", "type": "manuscript", "label": "PMID 30643174 full text"}, {"id": "data:geo:GSE16011", "type": "dataset", "label": "GEO GSE16011"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:30643174", "type": "paper", "label": "pmid:30643174"}, {"id": "source:pmid:30643174", "type": "manuscript", "label": "PMID 30643174 full text"}, {"id": "data:geo:GSE16011", "type": "dataset", "label": "GEO GSE16011"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-30643174", "type": "extraction", "used": ["source:pmid:30643174"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE16011", "type": "accession_resolution", "used": ["data:geo:GSE16011"], "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:30643174", "parent_hash": "sha256:4c1ce697bf95f9057d9a28ef8220b5a79b7e0e881b9cc3321a10325df60f8f90", "fork_of": null, "generation": 6}, "gaps": ["asset:data:geo:GSE16011 licence not read", "asset:data:geo:GSE16011 not downloaded", "asset:data:geo:GSE4271 licence not read", "asset:data:geo:GSE4271 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["logrank_test"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The prognostic differences between patients with high or low expression of a certain gene or score (higher or lower than the median value) were calculated by the Kaplan–Meier method with the two-sided log-rank test using the R “survival” package."], "order_basis": "analysis_only", "id": "assay:1"}]}