Evidence map›Paper›PMID 37637066›Full record

ArticleFrontiers in oncology2023

Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel.

Andra Valentina Krauze, Michael Sierk, Trinh Nguyen, Qingrong Chen, Chunhua Yan, Ying Hu, William Jiang, Erdal Tasci, Theresa Cooley Zgela, Mary Sproull and 4 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

9 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 1 institution in 1 country.

Andra Valentina KrauzeRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States.
Michael SierkComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States.
Trinh NguyenComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States.
Qingrong ChenComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States.
Chunhua YanComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States.
Ying HuComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States.
William JiangRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States.
Erdal TasciRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States.
Theresa Cooley ZgelaRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States.
Mary SproullRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States.
Megan MackeyRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States.
Uma ShankavaramRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States.
Daoud MeerzamanComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States.
Kevin CamphausenRadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States.
National Cancer Institute · US

Funding

Radiation Oncology Branch - Radiation ClinicZIDBC010990 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CAMPHAUSEN, KEVIN · 2009 to 2025
$136.0M
Radiation Oncology Branch - Radiation ClinicZ01BC010990 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CAMPHAUSEN, KEVIN · 2008 to 2008
$4.8M
Intramural NIH HHS Z01 BC010990Intramural NIH HHS ZID BC010990
6 · The paper itself

Abstract

Background: Glioblastomas (GBM) are rapidly progressive, nearly uniformly fatal brain tumors. Proteomic analysis represents an opportunity for noninvasive GBM classification and biological understanding of treatment response. Purpose: We analyzed differential proteomic expression pre vs. post completion of concurrent chemoirradiation (CRT) in patient serum samples to explore proteomic alterations and classify GBM by integrating clinical and proteomic parameters. Materials and methods: 82 patients with GBM were clinically annotated and serum samples obtained pre- and post-CRT. Serum samples were then screened using the aptamer-based SOMAScan® proteomic assay. Significant traits from uni- and multivariate Cox models for overall survival (OS) were designated independent prognostic factors and principal component analysis (PCA) was carried out. Differential expression of protein signals was calculated using paired t-tests, with KOBAS used to identify associated KEGG pathways. GSEA pre-ranked analysis was employed on the overall list of differentially expressed proteins (DEPs) against the MSigDB Hallmark, GO Biological Process, and Reactome databases with weighted gene correlation network analysis (WGCNA) and Enrichr used to validate pathway hits internally. Results: 3 clinical clusters of patients with differential survival were identified. 389 significantly DEPs pre vs. post-treatment were identified, including 284 upregulated and 105 downregulated, representing several pathways relevant to cancer metabolism and progression. The lowest survival group (median OS 13.2 months) was associated with DEPs affiliated with proliferative pathways and exhibiting distinct oppositional response including with respect to radiation therapy related pathways, as compared to better-performing groups (intermediate, median OS 22.4 months; highest, median OS 28.7 months). Opposite signaling patterns across multiple analyses in several pathways (notably fatty acid metabolism, NOTCH, TNFα Conclusion: Distinct proteomic alterations with hallmarks of cancer, including progression, resistance, stemness, and invasion, were identified in serum samples obtained from GBM patients pre vs. post CRT and corresponded with clinical survival. The proteome can potentially be employed for glioma classification and biological interrogation of cancer pathways.

Indexed as

classificationgenomicgliomaproteomicradiation

Identifiers

PMID37637066
PMCPMC10448824
OpenAlexW4385740174

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.