Evidence map›Paper›PMID 38612892›Full record

ArticleInternational journal of molecular sciences2024

MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma.

Erdal Tasci, Yajas Shah, Sarisha Jagasia, Ying Zhuge, Jason Shephard, Margaret O Johnson, Olivier Elemento, Thomas Joyce, Shreya Chappidi, Theresa Cooley Zgela and 4 more

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.1field-weighted citation impact, top 9% 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

7 citing papers in PubMed, 7 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

14 authors at 3 institutions in 2 countries.

Erdal TasciRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.ORCID 0000-0001-6754-2187
Yajas ShahCaryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
Sarisha JagasiaRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Ying ZhugeRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Jason ShephardRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Margaret O JohnsonDepartment of Neurosurgery, Duke University, Durham, NC 27710, USA.ORCID 0000-0003-1208-622X
Olivier ElementoCaryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
Thomas JoyceRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Shreya ChappidiRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Theresa Cooley ZgelaRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Mary SproullRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Megan MackeyRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Kevin CamphausenRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Andra Valentina KrauzeRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.ORCID 0000-0003-1634-6877
National Cancer Institute · USCornell University · USDuke University · US

Funding

Radiation Oncology Branch - Radiation ClinicZIDBC010990 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CAMPHAUSEN, KEVIN · 2009 to 2025
$136.0M
The Synergy Between Radiotherapy and Molecularly Targeted AgentsZIASC010373 · NCI · DIVISION OF CLINICAL SCIENCES - NCI · PI CAMPHAUSEN, KEVIN · 2009 to 2025
$22.2M
Radiation Oncology Branch - Radiation ClinicZ01BC010990 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CAMPHAUSEN, KEVIN · 2008 to 2008
$4.8M
Proteogenomic characterization and biomarker discovery in glioblastomaZIABC012093 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI KRAUZE, ANDRA · 2022 to 2025
$847k
Advancing clinically meaningful AI algorithms to improve oncologic outcomesZIABC012096 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI KRAUZE, ANDRA · 2022 to 2025
$847k
Intramural NIH HHS Z01 BC010990Intramural NIH HHS ZID BC010990
6 · The paper itself

Abstract

Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring. MGMT protein expression levels may offer additional insights into the mechanistic understanding of MGMT but, currently, they correlate poorly to promoter methylation. The difficulty of acquiring tumor tissue for MGMT testing drives the need for non-invasive methods to predict MGMT status. Feature selection aims to identify the most informative features to build accurate and interpretable prediction models. This study explores the new application of a combined feature selection (i.e., LASSO and mRMR) and the rank-based weighting method (i.e., MGMT ProFWise) to non-invasively link MGMT promoter methylation status and serum protein expression in patients with GBM. Our method provides promising results, reducing dimensionality (by more than 95%) when employed on two large-scale proteomic datasets (7k SomaScan

Indexed as

Brain NeoplasmsGlioblastomaBlood ProteinsDNA Modification MethylasesDNA Repair EnzymesHumansO(6)-Methylguanine-DNA MethyltransferaseProteomicsTemozolomideTumor Suppressor ProteinsBlood ProteinsDNA Modification MethylasesDNA Repair EnzymesMGMT protein, humanO(6)-Methylguanine-DNA MethyltransferaseTemozolomideTumor Suppressor Proteinsfeature selectionglioblastomamachine learningMGMTpattern recognitionproteinproteomic

Identifiers

PMID38612892
PMCPMC11012706
OpenAlexW4394568449

What Socratic 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 Socratic graph.