Evidence map›Paper›PMID 29464864›Full record

ArticleMolecular oncology2018

Inferences of individual drug responses across diverse cancer types using a novel competing endogenous RNA network.

Yan Zhang, Xin Li, Dianshuang Zhou, Hui Zhi, Peng Wang, Yue Gao, Maoni Guo, Ming Yue, Yanxia Wang, Weitao Shen and 3 more

Open access · goldAbstract read
In one paragraph

Article in Molecular oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
1.3field-weighted citation impact, top 24% 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

22 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  4. Competing endogenous RNAs (ceRNAs) and drug resistance to cancer therapy.Cancer drug resistance (Alhambra, Calif.) · 2024
    Review
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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

13 authors at 1 institution in 1 country.

Yan ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Xin LiCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Dianshuang ZhouCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Hui ZhiCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Peng WangCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Yue GaoCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Maoni GuoCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Ming YueCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Yanxia WangCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Weitao ShenCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Shangwei NingCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Yixue LiCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Xia LiCollege of Bioinformatics Science and Technology, Harbin Medical University, China.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Differences in individual drug responses are an obstacle to progression in cancer treatment, and predicting responses would help to plan treatment. The accumulation of cancer molecular profiling and drug response data provides opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs. This study evaluated drug responses with a competing endogenous RNA (ceRNA) system that depended on competition between diverse RNA species. We identified drug response-related ceRNA (DRCEs) by combining the sequence and expression data of long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA), and the survival data of cancer patients treated with drugs. We constructed a patient-drug two-layer integrated network and used a linear weighting method to predict individual drug responses. DRCEs were found to be significantly enriched in known cancer and drug-associated data resources, involved in biological processes known to mediate drug responses, and correlated to drug activity in cancer cell lines. The dysregulation of DRCE expression influenced drug response-associated functions and pathways, suggesting DRCEs as potential therapeutic targets affecting drug responses. A further case study in breast invasive carcinoma (BRCA) found that DRCE expression was consistent with the drug response pattern and the aberrant expression of the two NEAT1-related DRCEs may lead to poor response to tamoxifen therapy for patients with TP53 mutations. In summary, this study provides a framework for ceRNA-based evaluation of clinical drug responses across multiple cancer types. Understanding the underlying molecular mechanisms of drug responses will allow improved response to chemotherapy and outcomes of cancer treatment.

Indexed as

Biomarkers, PharmacologicalCancer SurvivorsCell Line, TumorComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansKaplan-Meier EstimateMicroRNAsNeoplasmsPrecision MedicinePrognosisProportional Hazards ModelsResearch DesignRNA, Long NoncodingBiomarkers, PharmacologicalMicroRNAsRNA, Long NoncodingRNA, MessengerceRNA networkdrug responsemolecular signaturepan-cancer analysis

Identifiers

PMID29464864
PMCPMC6120231
OpenAlexW2792891798

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.