Evidence map›Paper›PMID 33041828›Full record

ArticleFrontiers in pharmacology2020

Tim-3 Expression and MGMT Methylation Status Association With Survival in Glioblastoma.

Ji Zhang, Ke Sai, Xiao Li Wang, Sheng Quan Ye, Li Jiao Liang, Yi Zhou, Zhi Jie Chen, Wan-Ming Hu, Jian Min Liu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

13 citing papers in PubMed, 20 citations in OpenAlex.

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  6. From glioma gloom to immune bloom: unveiling novel immunotherapeutic paradigms-a review.Journal of experimental & clinical cancer research : CR · 2024
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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

9 authors at 4 institutions in 1 country.

Ji ZhangDepartment of Neurosurgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Ke SaiDepartment of Neurosurgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Xiao Li WangDepartment of General Surgery, Shang Jin Nan Fu Hospital of West China Hospital of Sichuan University, Chengdu, China.
Sheng Quan YeDepartment of Anesthesiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Li Jiao LiangDepartment of Neurosurgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Yi ZhouDepartment of Anesthesiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Zhi Jie ChenDepartment of Neurosurgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Wan-Ming HuDepartment of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Jian Min LiuDepartment of Neurosurgery, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China.
Sun Yat-sen University · CNSun Yat-sen University Cancer Center · CNFirst Affiliated Hospital of Guangzhou University of Chinese Medicine · CNSichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA profound understanding of the molecular landscape of glioblastoma multiforme (GBM) will make it possible to develop better and more intelligent therapies directed toward specific molecular targets and may one day yield better prognostic capabilities. Immune checkpoint molecules have inspired the emergence of immune checkpoint-targeting therapeutic strategies. However, the prognostic significance of the immune checkpoint molecule T cell immunoglobulin mucin-3 (Tim-3) on tumor-infiltrating immune cells (TIICs) and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status has not yet been fully elucidated. We aimed to develop an MGMT promoter methylation status-associated immune prognostic signature for GBM. PATIENTS AND

methodsA total of 84 patients with newly diagnosed GBM were included in this study. MGMT promoter methylation status was retrospectively analyzed, and the expression level of Tim-3 was investigated using immunohistochemistry (IHC). The correlation between Tim-3 expression combined with MGMT promoter methylation status and prognosis was explored.

resultsTim-3 expression varied in GBM patients. Mesenchymal expression of Tim-3 in GBM tissues was present 73.81% (62/84) of patients, and these were subdivided into groups based on low 15.48% (13/84), moderate 7.14% (6/84), or strong expression 51.19% (43/84). Forty-eight patients had tumors that tested positive for MGMT promoter methylation, while the remaining 36 patients tested negative.

conclusionsWe profiled the immune status of MGMT promoter methylation in GBM and established a local immune signature for GBM that could independently identify patients with a favorable prognosis, indicating a relationship between prognosis and GBM immune signature. MGMT promoter methylation with lower Tim-3 expression was significantly associated with better survival.

Indexed as

glioblastoma multiformeimmuneO-6-methylguanine-DNA methyltransferaseprognosisT cell immunoglobulin mucin-3

Identifiers

PMID33041828
PMCPMC7522578
OpenAlexW3092449895

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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.