Evidence map›Paper›PMID 40089944›Full record

ArticleMedical oncology (Northwood, London, England)2025

LncRNA CCAT1 decreases the sensitivity to doxorubicin in lung cancer cells by regulating miR-181a/CPEB2 axis.

Qi Muge, Yu Qing, Wenshan Bao, Xiangrong Bao, Arong Gaowa, Lanying Chen

Abstract read
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In one paragraph

Article in Medical oncology (Northwood, London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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.

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.

Qi MugeSchool of Pharmacy, Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, China.
Yu QingThe Affiliated Hospital of Inner Mongolia University for Nationalities, Tongliao, 028000, Inner Mongolia, China.
Wenshan BaoThe Affiliated Hospital of Inner Mongolia University for Nationalities, Tongliao, 028000, Inner Mongolia, China.
Xiangrong BaoInner Mongolia University for Nationalities, Tongliao, 028000, China.
Arong GaowaInner Mongolia University for Nationalities, Tongliao, 028000, China.
Lanying ChenNational Engineering Research Center of Traditional Chinese Medicine Solid Preparation Manufacturing Technology, Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, China. chenlanying218@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, long non-coding RNAs have gained an increasing amount of attention in treating lung cancer. However, a full understanding of how CCAT1 lncRNA works against proliferation is not yet available. Therefore, we assess the impact of CCAT1 on the lung cancer cell proliferation, apoptosis, and doxorubicin (DOX) sensitivity, and the involvement of miR-181a/CPEB2 pathway. For this purpose, lung cancer A549 cells were exposed to siRNA against CCAT1 and DOX and cell viability were measured by MTT assay. ELISA was used to evaluate cell apoptosis. The protein and mRNA expression levels of apoptotic markers, miR-181a and CPEB2 were measured by western blot and qRT-PCR. Knock-downing CCAT1 inhibited the cell viability of A549 cells. In addition, si-CCAT1 treatment increased apoptosis in both cell lines via modulating the anti- and pro-apoptotic markers. Si-CCAT1 increased the levels miR-181a and decreased CPEB2 in A549 cells. In conclusion, our study has provided strong evidence that lncRNA CCAT1 decreased the sensitivity to doxorubicin in lung cancer cells by regulating the miR-181a/CPEB2 axis.

Indexed as

Antibiotics, AntineoplasticDoxorubicinDrug Resistance, NeoplasmLung NeoplasmsMicroRNAsRNA-Binding ProteinsRNA, Long NoncodingA549 CellsApoptosisCell ProliferationCell SurvivalGene Expression Regulation, NeoplasticHumansAntibiotics, AntineoplasticCCAT1 long noncoding RNA, humanDoxorubicinMicroRNAsMIrn181 microRNA, humanRNA-Binding ProteinsRNA, Long NoncodingCCAT1lncRNALung cancermiR-181a/CPEB2 pathwaysiRNA Knockdown

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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.