Evidence mapPaperPMID 41760070Full record

ArticleMedicine2026

Establishment of a novel gene panel for prognosis assessment in patients with lower grade glioma.

Bin Zhou, Jianxiong He, Yu Yang, Wei Luo, Bin Xi

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In one paragraph

Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Bin ZhouDepartment of Neurosurgery, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China.
Jianxiong HeDepartment of Neurosurgery, Jiangxi Chest Hospital, Nanchang, Jiangxi, People's Republic of China.
Yu YangDepartment of Neurosurgery, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China.
Wei LuoDepartment of Neurosurgery, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China.
Bin XiDepartment of Neurosurgery, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China.ORCID 0000-0001-9101-4587

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lower-grade gliomas (LGGs) encompass a diverse group of primary brain tumors and are associated with poor survival outcomes, particularly among young adults. This study aimed to develop a novel approach for accurately predicting prognosis in LGG patients.

methodsUsing gene expression and clinical data from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, we identified and validated genes with prognostic significance. We then constructed a 71-gene prognostic score and developed a corresponding nomogram. These tools were evaluated for their association with overall survival (OS) and relapse-free survival (RFS) in LGG patients. Additionally, hierarchical clustering of the 71 genes was performed to identify distinct patient subgroups with unique clinical characteristics.

resultsThe 71-gene score was found to be a significant and independent predictor of poor OS and RFS in LGG patients, regardless of clinicopathological features. Hierarchical clustering revealed 3 distinct patient subgroups. Notably, tumors in Cluster 2 were characterized by higher tumor grade, more frequent radiation therapy, and worse OS and RFS compared to those in Clusters 1 and 3. Furthermore, the 71-gene nomogram, which incorporates survival-related clinical variables, showed high predictive accuracy for OS and for 3- and 5-year survival rates, with area under the curve values of 0.83, 0.88, and 0.86, respectively.

conclusionsThe 71-gene nomogram shows significant potential to enhance prognostic prediction in LGG, offering a valuable and reliable tool for clinicians and researchers.

Indexed as

Brain NeoplasmsGliomaAdultBiomarkers, TumorFemaleGene Expression ProfilingHumansMaleMiddle AgedNeoplasm GradingNomogramsPrognosisBiomarkers, Tumorgene signaturelower grade gliomanomogramoverall survivalrelapse-free survival

Identifiers

PMID41760070
PMCPMC12956227

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.