Evidence map›Paper›PMID 35117350›Full record

ArticleTranslational cancer research2020

Bioinformatics analysis of microenvironment-related genes associated with radioresistance in glioblastoma.

Yun Liu, Yufei Zhao, Jinmei Fang, Jing Fang, Xiaodong Yuan

Open access · diamondAbstract read
In one paragraph

Article in Translational cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.6field-weighted citation impact, top 26% 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

10 citing papers in PubMed, 14 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Yun LiuDepartment of Radiation Oncology, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Yufei ZhaoDepartment of Radiation Oncology, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Jinmei FangDepartment of Radiation Oncology, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Jing FangDepartment of Radiation Oncology, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Xiaodong YuanOrgan Transplant Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
University of Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImmune and stromal cells are the two major non-tumor cell types in the glioblastoma (GBM) microenvironment, which play critical roles in the prognostic assessment of tumors. Previous findings have identified genes with prognostic value in the GBM microenvironment; however, correlations between microenvironment-related genes and GBM radioresistance remain unclear. Therefore, in this study, we screened for vital microenvironment-related genes associated with radioresistance in GBM.

methodsWe analyzed the data from 348 patients with primary GBM that had undergone radiotherapy (patients with GBM-RT), in The Cancer Genome Atlas database. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was used to calculate stromal and immune scores to identify the differentially expressed genes (DEGs). Functional enrichment analyses and a protein-protein interaction (PPI) network construction were performed. Survival analysis was conducted to determine genes with prognostic value. The Chinese Glioma Genome Atlas (CGGA) cohort was utilized for validation.

resultsThe stromal score was significantly correlated with the prognoses of patients with GBM-RT. Based on the stromal and immune scores, 139 common DEGs involved in inflammation or immune-related activities were identified. We also identified 86 DEGs associated with poor prognosis, which further intersected with the top nodes in the PPI network. Finally, we identified the shared DEGs using the CGGA database and found 10 genes with prognostic value that contributed to GBM radioresistance. These genes included

conclusionsWe identified several genes related to the immune microenvironment that may mediate GBM radioresistance. Our findings provide a theoretical basis for predicting the radioresponse and survival of patients with GBM.

Indexed as

Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE)glioblastoma (GBM)radioresistancetumor microenvironment

Identifiers

PMID35117350
PMCPMC8798100
OpenAlexW3108593458

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.