Evidence map›Paper›PMID 36505340›Full record

ArticleAmerican journal of translational research2022

Immunological classification of glioblastoma and its prognostic implications.

Qiangwei Wang, Weiwei Lin, Tianjian Liu, Jue Hu, Yongjian Zhu

Open access · greenAbstract read
In one paragraph

Article in American journal of translational research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 39% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
  2. Article
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

5 authors at 3 institutions in 1 country.

Qiangwei WangDepartment of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine Hangzhou 310009, Zhejiang, China.
Weiwei LinDepartment of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine Hangzhou 310009, Zhejiang, China.
Tianjian LiuDepartment of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine Hangzhou 310009, Zhejiang, China.
Jue HuSchool of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College Hangzhou 311399, Zhejiang, China.
Yongjian ZhuDepartment of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine Hangzhou 310009, Zhejiang, China.
Zhejiang Meteorological Bureau · CNChinese National Human Genome Center · CNHangzhou Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe progress of immunotherapy for glioblastoma (GBM) is currently slow. To improve immunotherapy, we need a deeper understanding of the immune microenvironment of GBM. Here, we aimed to establish a classification system based on immune expression profile in GBM.

methodsImmune gene expression profiles of 152 patients with GBM from The Cancer Genome Atlas (TCGA) were used to identify subtypes by consensus clustering, and the classification system was reproduced in the two validation datasets (CGGA and GSE16011). Clinical information, molecular characteristics, immune infiltration, and genomic variation were integrated to characterize the subtypes.

resultsTwo distinct immune subtypes in GBM were successfully identified and validated. The Im2 subtype was closely related to

conclusionsOur study confirmed the immune heterogeneity of GBM and might provide valuable classification for immunotherapy.

Indexed as

Glioblastomaimmune classificationsurvivaltumor immunity

Identifiers

PMID36505340
PMCPMC9730108
OpenAlexW4311287540

What OpenQuestion holds

Textmetadata
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.