Evidence map›Paper›PMID 30816530›Full record

ArticleOncology reports2019

In silico analysis identified miRNA‑based therapeutic agents against glioblastoma multiforme.

Dan-Dan Xiong, Wen-Qing Xu, Rong-Quan He, Yi-Wu Dang, Gang Chen, Dian-Zhong Luo

Open access · hybridAbstract read
In one paragraph

Article in Oncology reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 14% 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.

  1. Pooled it
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  8. Role and Dysregulation of miRNA in Patients with Parkinson's Disease.International journal of molecular sciences · 2022
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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

6 authors at 2 institutions in 1 country.

Dan-Dan XiongDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China.
Wen-Qing XuDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China.
Rong-Quan HeDepartment of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China.
Yi-Wu DangDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China.
Gang ChenDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China.
Dian-Zhong LuoDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China.
First Affiliated Hospital of GuangXi Medical University · CNGuangxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs or miRs) contribute to the development of various malignant neoplasms, including glioblastoma multiforme (GBM). The present study aimed to explore the pathogenesis of GBM and to identify latent therapeutic agents for patients with GBM, based on an in silico analysis. Gene chips that provide miRNA expression profiling in GBM were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEMs) were also determined via the RobustRankAggreg algorithm. The target genes of DEMs were predicted and then intersected with GBM‑associated genes that were collected from the Gene Expression Profiling Interactive Analysis. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the overlapping genes were then performed. Simultaneously, a connectivity map (CMap) analysis was performed to screen for potential therapeutic agents for GBM. A total of 10 DEMs (hsa‑miR‑196a, hsa‑miR‑10b, hsa‑miR‑196b, hsa‑miR‑18b, hsa‑miR‑542‑3p, hsa‑miR‑129‑3p, hsa‑miR‑1224‑5p, hsa‑miR‑876‑3p and hsa‑miR‑770‑5p) were obtained from three GEO gene chips (GSE25631, GSE42657 and GSE61710). Then, 1,720 target genes of the 10 miRNAs and 4,185 differently expressed genes in GBM were collected. By intersecting the aforementioned gene clusters, the present study identified 390 overlapping genes. GO and KEGG analyses of the 390 genes demonstrated that these genes were involved in certain cancer‑associated biological functions and pathways. Eight genes [(GTPase NRas (NRAS), calcium/calmodulin‑dependent protein kinase type II subunit Gamma (CAMK2G), platelet‑derived growth factor receptor alpha (PDGFRA), calmodulin 3 (CALM3), cyclin‑dependent kinase 6 (CDK6), calcium/calmodulin‑dependent protein kinase type II subunit beta (CAMK2B), retinoblastoma‑associated protein (RB1) and protein kinase C beta type (PRKCB)] that were centralized in the glioma pathway were selected for CMap analysis. Three chemicals (W‑13, gefitinib and exemestane) were identified as putative therapeutic agents for GBM. In summary, the present study identified three miRNA‑based chemicals for use as a therapy for GBM. However, more experimental data are needed to verify the therapeutic properties of these latent drugs in GBM.

Indexed as

Gene Expression Regulation, NeoplasticAndrostadienesAntineoplastic AgentsBrain NeoplasmsComputational BiologyDrug DiscoveryGefitinibGene Expression ProfilingGene Regulatory NetworksGlioblastomaHumansMicroRNAsMolecular Docking SimulationMolecular Targeted TherapySulfonamidesTranscriptomeAndrostadienesAntineoplastic AgentsexemestaneGefitinibMicroRNAsN-(4-aminobutyl)-5-chloro-2-naphthalenesulfonamideSulfonamides

Identifiers

PMID30816530
PMCPMC6412522
OpenAlexW2917173367

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.