Evidence map›Paper›PMID 36305649›Full record

ArticleInternational journal of cancer2023

New insights into glioma frequency maps: From genetic and transcriptomic correlate to survival prediction.

Hongbo Bao, Peng Ren, Liye Yi, Zhonghua Lv, Wencai Ding, Chenlong Li, Siyang Li, Zhipeng Li, Xue Yang, Xia Liang and 1 more

Open access · hybridAbstract read
In one paragraph

Article in International journal of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 10% of its field
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. An MRI Atlas of Regional Brain Vulnerability to Metastatic Disease.medRxiv : the preprint server for health sciences · 2026
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  5. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Hongbo BaoDepartment of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China.ORCID 0000-0003-1043-6951
Peng RenLaboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China.
Liye YiDepartment of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Zhonghua LvDepartment of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China.
Wencai DingDepartment of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
Chenlong LiDepartment of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China.
Siyang LiLaboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China.
Zhipeng LiLaboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China.
Xue YangDepartment of Information, Harbin Medical University Cancer Hospital, Harbin, China.
Xia LiangLaboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China.
Peng LiangDepartment of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China.ORCID 0000-0002-9394-5016
Harbin Medical University · CNHarbin Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasing evidence indicates that glioma topographic location is linked to the cellular origin, molecular alterations and genetic profile. This research aims to (a) reveal the underlying mechanisms of tumor location predilection in glioblastoma multiforme (GBM) and lower-grade glioma (LGG) and (b) leverage glioma location features to predict prognosis. MRI images from 396 GBM and 190 LGG (115 astrocytoma and 75 oligodendroglioma) patients were standardized to construct frequency maps and analyzed by voxel-based lesion-symptom mapping. We then investigated the spatial correlation between glioma distribution with gene expression in healthy brains. We also evaluated transcriptomic differences in tumor tissue from predilection and nonpredilection sites. Furthermore, we quantitively characterized tumor anatomical localization and explored whether it was significantly related to overall survival. Finally, we employed a support vector machine to build a survival prediction model for GBM patients. GBMs exhibited a distinct location predilection from LGGs. GBMs were nearer to the subventricular zone and more likely to be localized to regions enriched with synaptic signaling, whereas astrocytoma and oligodendroglioma tended to occur in areas associated with the immune response. Synapse, neurotransmitters and calcium ion channel-related genes were all activated in GBM tissues coming from predilection regions. Furthermore, we characterized tumor location features in terms of a series of tumor-to-predilection distance metrics, which were able to predict GBM 1-year survival status with an accuracy of 0.71. These findings provide new perspectives on our understanding of tumor anatomic localization. The spatial features of glioma are of great value in individual therapy and prognosis prediction.

Indexed as

AstrocytomaBrain NeoplasmsGlioblastomaGliomaOligodendrogliomaHumansTranscriptomefrequency mapGBMLGGsurvival predictionsynapse

Identifiers

PMID36305649
PMCPMC10100131
OpenAlexW4307501607

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.