Evidence map›Paper›PMID 39068393›Full record

ArticleBMC immunology2024

Histone acetylation risk model predicts prognosis and guides therapy selection in glioblastoma: implications for chemotherapy and anti-CTLA-4 immunotherapy.

Xingyi Jin, Zhigang Qin, Hang Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xingyi JinNeurosurgery Department, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China.
Zhigang QinNeurosurgery Department, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China.
Hang ZhaoNeurosurgery Department, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China. zhaohang@jlu.edu.cn.

Funding

Natural Science Foundation of Jilin Province 20200201604JC
6 · The paper itself

Abstract

backgroundGlioblastoma is characterized by high aggressiveness, frequent recurrence, and poor prognosis. Histone acetylation-associated genes have been implicated in its occurrence and development, yet their predictive ability in glioblastoma prognosis remains unclear.

resultsThis study constructs a histone acetylation risk model using Cox and LASSO regression analyses to evaluate glioblastoma prognosis. We assessed the model's prognostic ability with univariate and multivariate Cox regression analyses. Additionally, immune infiltration was evaluated using ESTIMATE and TIMER algorithms, and the SubMAP algorithm was utilized to predict responses to CTLA4 inhibitor. Multiple drug databases were applied to assess drug sensitivity in high- and low-risk groups. Our results indicate that the histone acetylation risk model is independent and reliable in predicting prognosis.

conclusionsLow-risk patients showed higher immune activity and longer overall survival, suggesting anti-CTLA4 immunotherapy suitability, while high-risk patients might benefit more from chemotherapy. This model could guide personalized therapy selection for glioblastoma patients.

Indexed as

CTLA-4 AntigenGlioblastomaHistonesImmunotherapyAcetylationAgedBiomarkers, TumorBrain NeoplasmsFemaleHumansImmune Checkpoint InhibitorsMaleMiddle AgedPrognosisBiomarkers, TumorCTLA-4 AntigenCTLA4 protein, humanHistonesImmune Checkpoint InhibitorsCTLA4GlioblastomaHistone acetylationPersonalized treatmentTopotecan

Identifiers

PMID39068393
PMCPMC11282667

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