ArticleOncology letters2021
Construction and analysis of competing endogenous RNA network of MCF-7 breast cancer cells based on the inhibitory effect of 6-thioguanine on cell proliferation.
Article in Oncology letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Design, synthesis, molecular modelling and biological evaluation of novel 6-amino-5-cyano-2-thiopyrimidine derivatives as potent anticancer agents against leukemia and apoptotic inducers.Journal of enzyme inhibition and medicinal chemistry · 2024Article
- Ocular pharmacological and biochemical profiles of 6-thioguanine: a drug repurposing study.Frontiers in pharmacology · 2024Article
- Emerging potential of ubiquitin-specific proteases and ubiquitin-specific proteases inhibitors in breast cancer treatment.World journal of clinical cases · 2022Review
- Sequence Requirements for miR-424-5p Regulating and Function in Cancers.International journal of molecular sciences · 2022Review
- The new horizon of biomarker in melanoma patients: A study based on autophagy-related long non-coding RNA.Medicine · 2022Article
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- LINC00324 in cancer: Regulatory and therapeutic implications.Frontiers in oncology · 2022Review
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Previous research has proven that 6-thioguanine (6-TG) inhibits the growth of MCF-7 breast cancer cells. Accumulating evidence indicates that long non-coding (lnc)RNAs are involved in the development of various cancer types as competitive endogenous (ce)RNA molecules. The present study was conducted to investigate the regulatory mechanism underlying the function of lncRNAs as ceRNA molecules in MCF-7 cells and to identify more effective prognostic biomarkers for breast cancer treatment. The expression profiles of lncRNAs in untreated MCF-7 cells and 6-TG-treated MCF-7 cells were compared by RNA-seq. The regulatory associations among lncRNAs, micro (mi)RNAs and mRNAs were analyzed and verified by the TargetScan, miRDB and miRTarBas databases. The ceRNA networks were constructed by Cytoscape. The expression levels of two lncRNAs and two miRNAs in the ceRNA network were measured by reverse transcription-quantitative PCR. The OncoLnc and Kaplan-Meier plotter network databases were utilized to determine the effects of lncRNA and miRNA expression on the survival of patients with breast cancer. A ceRNA network was constructed for MCF-7 breast cancer cells treated with 6-TG, and this network may provide valuable information for further research elucidating the molecular mechanism underlying the effects of 6-TG on breast cancer. Moreover, LINC00324, MIR22HG, miR-370-3p and miR-424-5p were identified as potential prognostic and therapeutic biomarkers for breast cancer.
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Registered trials
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.