Evidence map›Paper›PMID 34298634›Full record

ArticleCancers2021

Large-Scale Transcriptomics-Driven Approach Revealed Overexpression of

Maxim Sorokin, Mikhail Raevskiy, Alja Zottel, Neja Šamec, Marija Skoblar Vidmar, Alenka Matjašič, Andrej Zupan, Jernej Mlakar, Maria Suntsova, Denis V Kuzmin and 2 more

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2021. 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
4.0field-weighted citation impact, top 4% 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, 46 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

12 authors at 4 institutions in 3 countries.

Maxim SorokinEuropean Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, 1000 Brussels, Belgium.
Mikhail RaevskiyMoscow Institute of Physics and Technology, National Research University, 141700 Moscow, Russia.ORCID 0000-0002-6218-5480
Alja ZottelMedical Centre for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia.ORCID 0000-0002-8587-3390
Neja ŠamecMedical Centre for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia.ORCID 0000-0002-1291-1931
Marija Skoblar VidmarInstitute of Oncology, 1000 Ljubljana, Slovenia.
Alenka MatjašičInstitute of Pathology, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia.
Andrej ZupanInstitute of Pathology, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia.ORCID 0000-0003-3394-7690
Jernej MlakarInstitute of Pathology, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia.
Maria SuntsovaWorld-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 119991 Moscow, Russia.
Denis V KuzminMoscow Institute of Physics and Technology, National Research University, 141700 Moscow, Russia.
Anton BuzdinEuropean Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, 1000 Brussels, Belgium.
Ivana JovčevskaMedical Centre for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia.ORCID 0000-0002-0418-2986
University of Ljubljana · SIMoscow Institute of Physics and Technology · RUInstitute of Oncology Ljubljana · SISechenov University · RU

Funding

Javna Agencija za Raziskovalno Dejavnost RS BI-RU 19/20-005Javna Agencija za Raziskovalno Dejavnost RS Z3-1869Ministry of Science and Higher Education of the Russian Federation World-Class Research Centers "Digital biodesign and personalized healthcare" No 075-15-2020-926OmicsWay research program in oncology
6 · The paper itself

Abstract

Glioblastoma is the most common and malignant brain malignancy worldwide, with a 10-year survival of only 0.7%. Aggressive multimodal treatment is not enough to increase life expectancy and provide good quality of life for glioblastoma patients. In addition, despite decades of research, there are no established biomarkers for early disease diagnosis and monitoring of patient response to treatment. High throughput sequencing technologies allow for the identification of unique molecules from large clinically annotated datasets. Thus, the aim of our study was to identify significant molecular changes between short- and long-term glioblastoma survivors by transcriptome RNA sequencing profiling, followed by differential pathway-activation-level analysis. We used data from the publicly available repositories The Cancer Genome Atlas (TCGA; number of annotated cases = 135) and Chinese Glioma Genome Atlas (CGGA; number of annotated cases = 218), and experimental clinically annotated glioblastoma tissue samples from the Institute of Pathology, Faculty of Medicine in Ljubljana corresponding to 2-58 months overall survival (n = 16). We found one differential gene for long noncoding RNA

Indexed as

CRNDEglioblastomalong-term survivalnoncoding RNARNA sequencing

Identifiers

PMID34298634
PMCPMC8303503
OpenAlexW3182836237

What Socratic holds

Textmetadata
LicenceCC BY
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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.