Evidence map›Paper›PMID 34123852›Full record

ArticleFrontiers in oncology2021

A Prognostic DNA Damage Repair Genes Signature and Its Impact on Immune Cell Infiltration in Glioma.

Guohui Wang, Huandi Zhou, Lei Tian, Tianfang Yan, Xuetao Han, Pengyu Chen, Haonan Li, Wenyan Wang, Zhiqing Xiao, Liubing Hou and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

16 citing papers in PubMed, 20 citations in OpenAlex.

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  16. International journal of molecular sciences · 2021
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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

11 authors at 5 institutions in 2 countries.

Guohui WangDepartment of Radiotherapy, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Huandi ZhouDepartment of Radiotherapy, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Lei TianDepartment of Radiotherapy, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Tianfang YanDepartment of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita, Japan.
Xuetao HanDepartment of Radiotherapy, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Pengyu ChenDepartment of Neurosurgery, Third Affiliated Hospital, Southern Medical University, Guangzhou, China.
Haonan LiDepartment of Radiotherapy, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Wenyan WangDepartment of Radiotherapy, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Zhiqing XiaoDepartment of Radiotherapy, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Liubing HouDepartment of Radiotherapy, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Xiaoying XueDepartment of Radiotherapy, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Second Hospital of Hebei Medical University · CNChina-Japan Friendship Hospital · CNHebei Medical University · CNOsaka University · JPThird Affiliated Hospital of Southern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveGlioma is the most frequent type of malignant cerebral tumors. DNA damage repair genes (DDRGs) play a crucial role in the development of cancer. In this study, we constructed a DDRGs signature and investigated the potential mechanisms involved in this disease.

methodsRNA sequence data, microarray data, and corresponding clinical information of gliomas were downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO). Subsequently, we identified candidate genes by differential analysis and Cox regression analysis. The least absolute shrinkage and selection operator Cox regression model was utilized to construct a DDRGs signature using TCGA training dataset. According to this signature, patients with glioma were divided into low- and high-risk groups. The predictive ability of the signature was validated by prognostic analysis, receiver operating characteristic curves, principal component analysis, and stratification analysis in TCGA testing and CGGA verification datasets. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate the immune microenvironment of glioma. Moreover, we conducted GSEA to determine the functions and pathways in the low- and high-risk groups. Finally, a nomogram was constructed by combining the signature and other clinical features.

resultsA total of 1,431 samples of glioma (592 from TCGA, 686 from the CGGA, and 153 from the GEO) and 23 samples of normal brain tissue from the GEO were analyzed in this study. There were 51 prognostic differentially expressed DDRGs. Additionally, five DDRGs (CDK4、HMGB2、WEE1、SMC3 and GADD45G) were selected to construct a DDRGs signature for glioma, stratifying patients into low- and high-risk groups. The survival analysis showed that the DDRGs signature could differentiate the outcome of the low- and high-risk groups, showing that high-risk gliomas were associated with shorter overall survival. The immune microenvironment analysis revealed that more immunosuppressive cells, such as tumor associated macrophages and regulatory T cells, were recruited in the high-risk group. GSEA also showed that high-risk glioma was correlated with the immune and extracellular matrix pathways.

conclusionThe five DDRGs signature and its impact on the infiltration of immunosuppressive cells could precisely predict the prognosis and provide guidance on the treatment of glioma.

Indexed as

DNA repairgliomaimmune cellsprognosistumor microenvironment

Identifiers

PMID34123852
PMCPMC8193723
OpenAlexW3165902966

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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 OpenQuestion graph.