Evidence map›Paper›PMID 37292257›Full record

ArticleMediators of inflammation2023

Focusing on scRNA-seq-Derived T Cell-Associated Genes to Identify Prognostic Signature and Immune Microenvironment Status in Low-Grade Glioma.

Jiayu Wen, Qiaoyi Huang, Jiuxiu Yao, Wei Wei, Zehui Li, Huiqin Zhang, Surui Chang, Hui Pei, Yu Cao, Hao Li

Open access · goldAbstract read
In one paragraph

Article in Mediators of inflammation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 17% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. 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

10 authors at 3 institutions in 1 country.

Jiayu WenWangjing Hospital of China Academy of Chinese Medical Sciences, Beijing 100020, China.
Qiaoyi HuangGraduate College, Beijing University of Chinese Medicine, Beijing 100020, China.
Jiuxiu YaoFirst Clinical College, Shandong University of Traditional Chinese Medicine, Jinan 250011, China.
Wei WeiWangjing Hospital of China Academy of Chinese Medical Sciences, Beijing 100020, China.
Zehui LiDepartment of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing 100089, China.ORCID https://orcid.org/0000-0003-3867-6068
Huiqin ZhangDepartment of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing 100089, China.
Surui ChangDepartment of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing 100089, China.ORCID https://orcid.org/0000-0003-0885-8557
Hui PeiDepartment of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing 100089, China.
Yu CaoDepartment of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing 100089, China.ORCID https://orcid.org/0000-0003-1211-1122
Hao LiWangjing Hospital of China Academy of Chinese Medical Sciences, Beijing 100020, China.ORCID https://orcid.org/0000-0001-8502-2556
Chinese Academy of Medical Sciences & Peking Union Medical College · CNBeijing University of Chinese Medicine · CNShandong University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The clinical outcomes of low-grade glioma (LGG) are associated with T cell infiltration, but the specific contribution of heterogeneous T cell types remains unclear. Method: To study the different functions of T cells in LGG, we mapped the single-cell RNA sequencing results of 10 LGG samples to obtain T cell marker genes. In addition, bulk RNA data of 975 LGG samples were collected for model construction. Algorithms such as TIMER, CIBERSORT, QUANTISEQ, MCPCOUTER, XCELL, and EPIC were used to depict the tumor microenvironment landscape. Subsequently, three immunotherapy cohorts, PRJEB23709, GSE78820, and IMvigor210, were used to explore the efficacy of immunotherapy. Results: The Human Primary Cell Atlas was used as a reference dataset to identify each cell cluster; a total of 15 cell clusters were defined and cells in cluster 12 were defined as T cells. According to the distribution of T cell subsets (CD4+ T cell, CD8+ T cell, Naïve T cell, and Treg cell), we selected the differentially expressed genes. Among the CD4+ T cell subsets, we screened 3 T cell-related genes, and the rest were 28, 4, and 13, respectively. Subsequently, according to the T cell marker genes, we screened six genes for constructing the model, namely, RTN1, HERPUD1, MX1, SEC61G, HOPX, and CHI3L1. The ROC curve showed that the predictive ability of the prognostic model for 1, 3, and 5 years was 0.881, 0.817, and 0.749 in the TCGA cohort, respectively. In addition, we found that risk scores were positively correlated with immune infiltration and immune checkpoints. To this end, we obtained three immunotherapy cohorts to verify their predictive ability of immunotherapy effects and found that high-risk patients had better clinical effects of immunotherapy. Conclusion: This single-cell RNA sequencing combined with bulk RNA sequencing may elucidate the composition of the tumor microenvironment and pave the way for the treatment of low-grade gliomas.

Indexed as

GliomaSingle-Cell Gene Expression AnalysisCD3 ComplexCD4-Positive T-LymphocytesHumansPrognosisSEC Translocation ChannelsTranscription FactorsTumor MicroenvironmentCD3 ComplexSEC61G protein, humanSEC Translocation ChannelsTranscription Factors

Identifiers

PMID37292257
PMCPMC10247320
OpenAlexW4378901413

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.