Evidence map›Paper›PMID 32359215›Full record

ArticleCancer communications (London, England)2020

Association between glioblastoma cell-derived vessels and poor prognosis of the patients.

Xin Mei, Yin-Sheng Chen, Qing-Ping Zhang, Fu-Rong Chen, Shao-Yan Xi, Ya-Kang Long, Ji Zhang, Hai-Ping Cai, Chao Ke, Jing Wang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Cancer communications (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

14 citing papers in PubMed, 20 citations in OpenAlex.

  1. Review
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  8. Biomaterial-basedCancer pathogenesis and therapy · 2023
    Review
  9. Review
  10. Review
  11. Article
  12. Article
  13. Frontiers in oncology · 2021
    Article
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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 2 institutions in 1 country.

Xin MeiDepartment of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Yin-Sheng ChenDepartment of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Qing-Ping ZhangDepartment of Neurosurgery, Huazhong University of Science and Technology Union Shenzhen Hospital, The 6th Affiliated Hospital of Shenzhen University Health Science Center (Shenzhen Nanshan People's Hospital), Shenzhen, Guangdong, 518052, P. R. China.
Fu-Rong ChenDepartment of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Shao-Yan XiDepartment of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Ya-Kang LongDepartment of Molecular Diagnostics, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Ji ZhangDepartment of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Hai-Ping CaiDepartment of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Chao KeDepartment of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Jing WangDepartment of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Zhong-Ping ChenDepartment of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Sun Yat-sen University · CNShenzhen Sixth People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVessels with different microcirculation patterns are required for glioblastoma (GBM) growth. However, details of the microcirculation patterns in GBM remain unclear. Here, we examined the microcirculation patterns of GBM and analyzed their roles in patient prognosis together with two well-known GMB prognosis factors (O

methodsEighty GBM clinical specimens were collected from patients diagnosed between January 2000 and December 2012. The microcirculation patterns, including endothelium-dependent vessels (EDVs), extracellular matrix-dependent vessels (ECMDVs), GBM cell-derived vessels (GDVs), and mosaic vessels (MVs), were evaluated by immunohistochemistry (IHC) and immunofluorescence (IF) staining in both GBM clinical specimens and xenograft tissues. Vascular density assessments and three-dimensional reconstruction were performed. MGMT promoter methylation status was determined by methylation-specific PCR, and IDH1/2 mutations were detected by Sanger sequencing. The relationship between the microcirculation patterns and patient prognosis was analyzed by Kaplan-Meier method.

resultsAll 4 microcirculation patterns were observed in both GBM clinical specimens and xenograft tissues. EDVs were detected in all tissue samples, while the other three patterns were observed in a small number of tissue samples (ECMDVs in 27.5%, GDVs in 43.8%, and MVs in 52.5% tissue samples). GDV-positive patients had a median survival of 9.56 months versus 13.60 months for GDV-negative patients (P = 0.015). In MGMT promoter-methylated cohort, GDV-positive patients had a median survival of 6.76 months versus 14.23 months for GDV-negative patients (P = 0.022).

conclusionGDVs might be a negative predictor for the survival of GBM patients, even in those with MGMT promoter methylation.

Indexed as

AnimalsBrain NeoplasmsCell Line, TumorDNA MethylationDNA Modification MethylasesDNA Repair EnzymesFemaleGlioblastomaHumansIsocitrate DehydrogenaseKaplan-Meier EstimateMaleMice, NudeMiddle AgedMutationNeovascularization, PathologicDNA Modification MethylasesDNA Repair EnzymesIsocitrate DehydrogenaseMGMT protein, humanTumor Suppressor Proteinsendothelial differentiationendothelium-dependent vesselextracellular matrix-dependent vesselGlioblastomaglioblastoma cell-derived vesselMGMT promoter methylationmicrocirculationmosaic vesselprognosisvasculogenic mimicry

Identifiers

PMID32359215
PMCPMC7238665
OpenAlexW3021956547

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.