Evidence map›Paper›PMID 35117343›Full record

ArticleTranslational cancer research2020

Identification of potential microRNAs in glioblastoma using bioinformatic analysis and prognostic evaluation.

Zhenwei Sun, Yongquan Zhao, Xuan Ding, Deguang Xing, Chengwei Wang, Xiaofei Wang

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
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Article in Translational cancer research, 2020. 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
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0citing papers in PubMed
–field-weighted citation impact, top 85% 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, 1 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

6 authors at 2 institutions in 1 country.

Zhenwei SunDepartment of Neurosurgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Yongquan ZhaoDepartment of Neurosurgery, People's Hospital of Dongying District, Dongying, China.
Xuan DingDepartment of Neurosurgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Deguang XingDepartment of Neurosurgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Chengwei WangDepartment of Neurosurgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Xiaofei WangDepartment of Neurosurgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Second Hospital of Shandong University · CNDongyang People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGlioblastoma (GB) is the most common and aggressive brain and central nervous system malignancy. MicroRNAs (miRNAs) have been demonstrated to be predictors of prognostic outcomes, playing an important role in the pathogenesis and progression of GB. We aim to identify the potential miRNAs in GB.

methodsGSE103228 was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed miRNAs (DE-miRNAs) using the Student's

resultsA total of 49 DE-miRNAs were identified in GB including 30 down-regulated miRNAs and 19 up-regulated miRNAs. Our analysis predicted 1,118 and 1,063 potential target genes from the top three most up-regulated and down-regulated DE-miRNAs, respectively, that were enriched in several GB-related pathways including the cancer pathway. ACTB and MYC were considered to be hub genes in our PPI networks.

conclusionsMiR-218-5p and miR-148a-3p regulated most of the hub genes and miR-148a-3p appeared to be a prognostic biomarker.

Indexed as

bioinformatic analysisGene Expression Omnibus (GEO)Glioblastoma (GB)hub genemiRNA

Identifiers

PMID35117343
PMCPMC8798749
OpenAlexW3110737273

What OpenQuestion holds

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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.