Evidence map›Paper›PMID 38137905›Full record

ReviewLife (Basel, Switzerland)2023

Non-Coding RNAs and the Development of Chemoresistance to Docetaxel in Prostate Cancer: Regulatory Interactions and Approaches Based on Machine Learning Methods.

Elena Pudova, Anastasiya Kobelyatskaya, Marina Emelyanova, Anastasiya Snezhkina, Maria Fedorova, Vladislav Pavlov, Zulfiya Guvatova, Alexandra Dalina, Anna Kudryavtseva

Abstract readReview
In one paragraph

Review in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Progress of Exosomal LncRNAs in Pancreatic Cancer.International journal of molecular sciences · 2024
    Review
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

9 authors.

Elena PudovaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia.ORCID 0000-0002-5492-1361
Anastasiya KobelyatskayaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia.
Marina EmelyanovaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia.
Anastasiya SnezhkinaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia.ORCID 0000-0002-4421-4364
Maria FedorovaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia.ORCID 0000-0002-6893-4673
Vladislav PavlovEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia.ORCID 0000-0002-2942-6393
Zulfiya GuvatovaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia.ORCID 0000-0003-3494-9807
Alexandra DalinaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia.
Anna KudryavtsevaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia.ORCID 0000-0002-3722-8207

Funding

Russian Science Foundation 22-24-01093
6 · The paper itself

Abstract

Chemotherapy based on taxane-class drugs is the gold standard for treating advanced stages of various oncological diseases. However, despite the favorable response trends, most patients eventually develop resistance to this therapy. Drug resistance is the result of a combination of different events in the tumor cells under the influence of the drug, a comprehensive understanding of which has yet to be determined. In this review, we examine the role of the major classes of non-coding RNAs in the development of chemoresistance in the case of prostate cancer, one of the most common and socially significant types of cancer in men worldwide. We will focus on recent findings from experimental studies regarding the prognostic potential of the identified non-coding RNAs. Additionally, we will explore novel approaches based on machine learning to study these regulatory molecules, including their role in the development of drug resistance.

Indexed as

chemoresistancecircRNAsdeep learningdocetaxelexosomeslncRNAsmachine learningmicroRNAsprostate cancer

Identifiers

PMID38137905
PMCPMC10744715

What OpenQuestion holds

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LicenceCC BY
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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.