Evidence map›Paper›PMID 33287223›Full record

ArticleInternational journal of molecular sciences2020

Transcriptome Remodeling in Gradual Development of Inverse Resistance between Paclitaxel and Cisplatin in Ovarian Cancer Cells.

Jolanta Szenajch, Alicja Szabelska-Beręsewicz, Aleksandra Świercz, Joanna Zyprych-Walczak, Idzi Siatkowski, Michał Góralski, Agnieszka Synowiec, Luiza Handschuh

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

9 citing papers in PubMed, 14 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

8 authors at 3 institutions in 1 country.

Jolanta SzenajchLaboratory for Molecular Oncology and Innovative Therapies, Military Institute of Medicine, 04-141 Warsaw, Poland.
Alicja Szabelska-BeręsewiczDepartment of Mathematical and Statistical Methods, Poznań University of Life Sciences, 60-637 Poznań, Poland.ORCID 0000-0002-1806-0891
Aleksandra ŚwierczLaboratory of Genomics, Institute of Bioorganic Chemistry, Polish Academy of Science, 61-704 Poznań, Poland.ORCID 0000-0002-5574-8222
Joanna Zyprych-WalczakDepartment of Mathematical and Statistical Methods, Poznań University of Life Sciences, 60-637 Poznań, Poland.
Idzi SiatkowskiDepartment of Mathematical and Statistical Methods, Poznań University of Life Sciences, 60-637 Poznań, Poland.ORCID 0000-0003-2793-7074
Michał GóralskiLaboratory of Genomics, Institute of Bioorganic Chemistry, Polish Academy of Science, 61-704 Poznań, Poland.
Agnieszka SynowiecLaboratory for Molecular Oncology and Innovative Therapies, Military Institute of Medicine, 04-141 Warsaw, Poland.
Luiza HandschuhLaboratory of Genomics, Institute of Bioorganic Chemistry, Polish Academy of Science, 61-704 Poznań, Poland.
Institute of Bioorganic Chemistry, Polish Academy of Sciences · PLUniversity of Life Sciences in Poznań · PLWojskowy Instytut Medycyny Lotniczej · PL

Funding

National Science Center, Krakow, Poland NN401532740
6 · The paper itself

Abstract

Resistance to anti-cancer drugs is the main challenge in oncology. In pre-clinical studies, established cancer cell lines are primary tools in deciphering molecular mechanisms of this phenomenon. In this study, we proposed a new, transcriptome-focused approach, utilizing a model of isogenic cancer cell lines with gradually changing resistance. We analyzed trends in gene expression in the aim to find out a scaffold of resistance development process. The ovarian cancer cell line A2780 was treated with stepwise increased concentrations of paclitaxel (PTX) to generate a series of drug resistant sublines. To monitor transcriptome changes we submitted them to mRNA-sequencing, followed by the identification of differentially expressed genes (DEGs), principal component analysis (PCA), and hierarchical clustering. Functional interactions of proteins, encoded by DEGs, were analyzed by building protein-protein interaction (PPI) networks. We obtained human ovarian cancer cell lines with gradually developed resistance to PTX and collateral sensitivity to cisplatin (CDDP) (inverse resistance). In their transcriptomes, we identified two groups of DEGs: (1) With fluctuations in expression in the course of resistance acquiring; and (2) with a consistently changed expression at each stage of resistance development, constituting a scaffold of the process. In the scaffold PPI network, the cell cycle regulator-polo-like kinase 2 (PLK2); proteins belonging to the tumor necrosis factor (TNF) ligand and receptor family, as well as to the ephrin receptor family were found, and moreover, proteins linked to osteo- and chondrogenesis and the nervous system development. Our cellular model of drug resistance allowed for keeping track of trends in gene expression and studying this phenomenon as a process of evolution, reflected by global transcriptome remodeling. This approach enabled us to explore novel candidate genes and surmise that abrogation of the osteomimic phenotype in ovarian cancer cells might occur during the development of inverse resistance between PTX and CDDP.

Indexed as

Gene Expression ProfilingTranscriptomeAntineoplastic AgentsCell Line, TumorCells, CulturedCell SurvivalCisplatinComputational BiologyDose-Response Relationship, DrugDrug Resistance, NeoplasmFemaleGene Expression Regulation, NeoplasticHumansOvarian NeoplasmsPaclitaxelAntineoplastic AgentsCisplatinPaclitaxelchondrogenesiscisplatingene expression profileinverse resistancenetwork analysisosteogenesisosteomimicryovarian cancerpaclitaxel

Identifiers

PMID33287223
PMCPMC7730278
OpenAlexW3107935763

What OpenQuestion holds

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LicenceCC BY
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.