Evidence map›Paper›PMID 34489938›Full record

ArticleFrontiers in immunology2021

Immune Infiltrating Cells-Derived Risk Signature Based on Large-scale Analysis Defines Immune Landscape and Predicts Immunotherapy Responses in Glioma Tumor Microenvironment.

Nan Zhang, Hao Zhang, Zeyu Wang, Ziyu Dai, Xun Zhang, Quan Cheng, Zhixiong Liu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
5.4field-weighted citation impact, top 3% 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

39 citing papers in PubMed, 56 citations in OpenAlex.

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  2. Crosstalk Between CTSBAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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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

7 authors at 2 institutions in 1 country.

Nan ZhangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Hao ZhangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Zeyu WangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Ziyu DaiDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Xun ZhangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Quan ChengDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Zhixiong LiuDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Central South University · CNXiangya Hospital Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The glioma tumor microenvironment (TME), composed of several noncancerous cells and biomolecules is known for its complexity of cancer-immune system interaction. Given that, novel risk signature is required for predicting glioma patient responses to immunotherapy. In this study, we systematically evaluated the TME infiltration pattern of 2877 glioma samples. TME phenotypes were determined using the Partitioning Around Medoid method. Machine learning including SVM-RFE and Principal component analysis (PCA) were used to construct a TME scoring system. A total of 857 glioma samples from four datasets were used for external validation of the TME-score. The correlation of TME phenotypes and TME-scores with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in glioma patients was determined. Immunohistochemistry staining for the M2 macrophage marker

Indexed as

ImmunotherapyGene Expression ProfilingGene Expression Regulation, NeoplasticGliomaHumansKaplan-Meier EstimateLymphocytes, Tumor-InfiltratingMachine LearningPrincipal Component AnalysisRiskTumor Microenvironmentgliomasimmune checkpointimmunotherapymachine learningsomatic mutationtumor microenvironment

Identifiers

PMID34489938
PMCPMC8418124
OpenAlexW3193119005

What OpenQuestion holds

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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.