Evidence map›Paper›PMID 31600288›Full record

ArticlePloS one2019

A proteomic clock for malignant gliomas: The role of the environment in tumorigenesis at the presymptomatic stage.

Le Zheng, Yan Zhang, Shiying Hao, Lin Chen, Zhen Sun, Chi Yan, John C Whitin, Taichang Jang, Milton Merchant, Doff B McElhinney and 5 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 91% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

15 authors at 3 institutions in 2 countries.

Le ZhengDepartment of Cardiothoracic Surgery, Stanford University, Stanford, California, United States of America.
Yan ZhangDepartment of Oncology, the First Hospital of Shijiazhuang, Shijiazhuang, Hebei, China.
Shiying HaoDepartment of Cardiothoracic Surgery, Stanford University, Stanford, California, United States of America.
Lin ChenDepartment of Surgery, Stanford University School of Medicine, Stanford, California, United States of America.
Zhen SunDepartment of Surgery, Stanford University School of Medicine, Stanford, California, United States of America.
Chi YanDepartment of Surgery, Stanford University School of Medicine, Stanford, California, United States of America.
John C WhitinDepartment of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America.
Taichang JangDepartment of Neurology and Neurological Science, Stanford University School of Medicine, Stanford, California, United States of America.
Milton MerchantDepartment of Neurology and Neurological Science, Stanford University School of Medicine, Stanford, California, United States of America.
Doff B McElhinneyDepartment of Cardiothoracic Surgery, Stanford University, Stanford, California, United States of America.
Karl G SylvesterDepartment of Surgery, Stanford University School of Medicine, Stanford, California, United States of America.
Harvey J CohenDepartment of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America.
Lawrence RechtDepartment of Neurology and Neurological Science, Stanford University School of Medicine, Stanford, California, United States of America.
Xiaoming YaoClinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, California, United States of America.
Xuefeng B LingClinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, California, United States of America.ORCID 0000-0002-5386-3884
Stanford University · USLucile Packard Children's Hospital · USFirst Hospital of Shijiazhuang · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malignant gliomas remain incurable with a poor prognosis despite of aggressive treatment. We have been studying the development of brain tumors in a glioma rat model, where rats develop brain tumors after prenatal exposure to ethylnitrosourea (ENU), and there is a sizable interval between when the first pathological changes are noted and tumors become detectable with MRI. Our aim to define a molecular timeline through proteomic profiling of the cerebrospinal fluid (CSF) such that brain tumor commitment can be revealed earlier than at the presymptomatic stage. A comparative proteomic approach was applied to profile CSF collected serially either before, at and after the time MRI becomes positive. Elastic net (EN) based models were developed to infer the timeline of normal or tumor development respectively, mirroring a chronology of precisely timed, "clocked", adaptations. These CSF changes were later quantified by longitudinal entropy analyses of the EN predictive metric. False discovery rates (FDR) were computed to control the expected proportion of the EN models that are due to multiple hypothesis testing. Our ENU rat brain tumor dating EN model indicated that protein content in CSF is programmed even before tumor MRI detection. The findings of the precisely timed CSF tumor microenvironment changes at presymptomatic stages, deviation from the normal development timeline, may provide the groundwork for the understanding of adaptation of the brain environment in tumorigenesis to devise effective brain tumor management strategies.

Indexed as

EnvironmentProteomicsAnimalsBrain NeoplasmsCarcinogenesisDisease Models, AnimalEntropyGliomaKineticsProteomeRatsProteome

Identifiers

PMID31600288
PMCPMC6786640
OpenAlexW2979423967

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.