Evidence map›Paper›PMID 34115910›Full record

ArticleCancer science2021

Combining MGMT promoter pyrosequencing and protein expression to optimize prognosis stratification in glioblastoma.

Mingxiao Li, Gehong Dong, Weiwei Zhang, Xiaohui Ren, Haihui Jiang, Chuanwei Yang, Xuzhe Zhao, Qinghui Zhu, Ming Li, Hongyan Chen and 3 more

Open access · goldAbstract read
In one paragraph

Article in Cancer science, 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
2.1field-weighted citation impact, top 12% 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, 23 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

13 authors at 1 institution in 1 country.

Mingxiao LiDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Gehong DongDepartment of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Weiwei ZhangDepartment of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Xiaohui RenDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Haihui JiangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Chuanwei YangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Xuzhe ZhaoDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Qinghui ZhuDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Ming LiDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Hongyan ChenDepartment of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Kefu YuDepartment of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Yong CuiDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Lin SongDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0001-5721-274X
Capital Medical University · CN

Funding

National Natural Science Foundation of China 81571632National Natural Science Foundation of China 81771309
6 · The paper itself

Abstract

Pyrosequencing (PSQ) represents the golden standard for MGMT promoter status determination. Binary interpretation of results based on the threshold from the average of several CpGs tested would neglect the existence of the "gray zone". How to define the gray zone and reclassify patients in this subgroup remains to be elucidated. A consecutive cohort of 312 primary glioblastoma patients were enrolled. CpGs 74-81 in the promoter region of MGMT were tested by PSQ and the protein expression was assessed by immunohistochemistry (IHC). Receiver operating characteristic curves were constructed to calculate the area under the curves (AUC). Kaplan-Meier plots were used to estimate the survival rate of patients compared by the log-rank test. The optimal threshold of each individual CpG differed from 5% to 11%. Patients could be separated into the hypomethylated subgroup (all CpGs tested below the corresponding optimal thresholds, n = 126, 40.4%), hypermethylated subgroup (all CpGs tested above the corresponding optimal thresholds, n = 108, 34.6%), and the gray zone subgroup (remaining patients, n = 78, 25.0%). Patients in the gray zone harbored an intermediate prognosis. The IHC score instead of the average methylation levels could successfully predict the prognosis for the gray zone (AUC for overall survival, 0.653 and 0.519, respectively). Combining PSQ and IHC significantly improved the efficiency of survival prediction (AUC: 0.662, 0.648, and 0.720 for PSQ, IHC, and combined, respectively). Immunohistochemistry is a robust method to predict prognosis for patients in the gray zone defined by PSQ. Combining PSQ and IHC could significantly improve the predictive ability for clinical outcomes.

Indexed as

AdolescentAdultAgedBrain NeoplasmsCpG IslandsDNA MethylationDNA Modification MethylasesDNA Repair EnzymesFemaleFollow-Up StudiesGlioblastomaHigh-Throughput Nucleotide SequencingHumansImmunohistochemistryMaleMiddle AgedDNA Modification MethylasesDNA Repair EnzymesMGMT protein, humanTumor Suppressor Proteinsglioblastomagray zoneimmunohistochemistryMGMT promoter methylationpyrosequencing

Identifiers

PMID34115910
PMCPMC8409410
OpenAlexW3172883640

What OpenQuestion holds

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LicenceCC BY-NC
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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.