Evidence map›Paper›PMID 34856955›Full record

ArticleBMC cancer2021

Exosomal microRNA-107 reverses chemotherapeutic drug resistance of gastric cancer cells through HMGA2/mTOR/P-gp pathway.

Lu Jiang, Yan Zhang, Linghui Guo, Chaoyang Liu, Pan Wang, Weihong Ren

Abstract read
In one paragraph

Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

33 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Mechanisms of chemoresistance in gastric cancer: interplay between microRNAs and the tumor microenvironment.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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  16. Exosomal microRNAs: impact on cancer detection, treatment, and monitoring.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
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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.

Lu JiangHenan University of Chinese Medicine, 156 Jinshui East Road, Zhengzhou, 450046, China.
Yan ZhangHenan University of Chinese Medicine, 156 Jinshui East Road, Zhengzhou, 450046, China.
Linghui GuoHenan University of Chinese Medicine, 156 Jinshui East Road, Zhengzhou, 450046, China.
Chaoyang LiuHenan University of Chinese Medicine, 156 Jinshui East Road, Zhengzhou, 450046, China.
Pan WangSchool of Life and Health Sciences, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Ave, Shenzhen, 518172, China.
Weihong RenDepartment of Clinical Laboratory, The First Affiliated Hospital of Henan University of Chinese Medicine, 19 Renmin Road, Zhengzhou, 450000, China. ren_weihong@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRNA cargo in exosomes, especially microRNAs (miRNAs), play an important role in the chemotherapy drug resistance of human cancers. However, the role and mechanism of exosomal miR-107 on multidrug resistance of gastric cancer cells was still not clear. In this study, we sought to explore whether exosomal miR-107 could reverse the resistance of gastric cancer cells to the chemotherapy drugs.

methodsWe extracted exosomes from sensitive (SGC-7901, MGC-803) and resistant (SGC-7901/5-FU) gastric cancer cells by ultracentrifugation and the isolated exosomes were identified using transmission electron microscopy (TEM) and dynamic light scattering analysis (DLS). The expression of miR-107 and high mobility group A2 (HMGA2) were detected by real-time quantitative PCR (RT-qPCR). MTT assay was used to investigate the effect of exosomes on gastric cancer cells growth in vitro. The uptake of exosomes by recipient cells were observed using a fluorescence microscope. The predicted target relationship between miR-107 and HMGA2 was verified by gauss-luciferase reporter assay. The expression of HMGA2, p-mTOR/mTOR, P-gp and other exosomal indicated marker proteins was detected by western blot.

resultsOur results indicated that the isolated exosomes were typically cup-like lipid bilayer membranes structure. SGC-7901/5-FU cells were cross-resistant to chemotherapy drug cisplatin (CDDP), and the sensitive cells-secreted exosomes drastically reversed the resistance of the resistant GC cells to the chemotherapeutic drugs, which was verified by exosomal inhibitor GW4896. Mechanistically, the reversal effect was mainly mediated by exosome-secreted miR-107 through downregulating the expression of target molecular HMGA2 and inhibiting HMGA2/mTOR/P-gp pathway, which were supported by results from luciferase reporter assay and rescue assay.

conclusionsThese findings demonstrated that exosome-transmitted miR-107 significantly enhanced the sensitivity of resistant gastric cancer cells to chemotherapeutic agents by mediating the HMGA2/mTOR/P-gp axis and exosomal miR-107 may be a novel target in gastric cancers treatment.

Indexed as

Antineoplastic AgentsATP Binding Cassette Transporter, Subfamily B, Member 1Cell Line, TumorCisplatinDown-RegulationDrug Resistance, MultipleDrug Resistance, NeoplasmExosomesFluorescent DyesFluorouracilHMGA2 ProteinHumansMicroRNAsMicroscopy, Electron, TransmissionOrganic ChemicalsStomach NeoplasmsAntineoplastic AgentsATP Binding Cassette Transporter, Subfamily B, Member 1CisplatinFluorescent DyesFluorouracilHMGA2 ProteinHMGA2 protein, humanMicroRNAsMIRN107 microRNA, humanMTOR protein, humanOrganic ChemicalsPKH 26TOR Serine-Threonine KinasesExosomal miR-107Gastric cancerHMGA2/mTOR/P-gpReverse drug resistance

Identifiers

PMID34856955
PMCPMC8638432

What OpenQuestion holds

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Registered trials

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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.