ArticleScientific reports2023
Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Transcription factor ETS1‑mediated ECT2 expression promotes the malignant behavior of prostate cancer cells.Oncology letters · 2024Article
- Review of Molecular Technologies for Investigating Canine Cancer.Animals : an open access journal from MDPI · 2024Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metastasis is a major breast cancer hallmark due to which tumor cells tend to relocate to regional or distant organs from their organ of origin. This study is aimed to decipher the interaction among 113 differentially expressed genes, interacting non-coding RNAs and drugs (614 miRNAs, 220 lncRNAs and 3241 interacting drugs) associated with metastasis in breast cancer. For an extensive understanding of genetic interactions in the diseased state, a backbone gene co-expression network was constructed. Further, the mRNA-miRNA-lncRNA-drug interaction network was constructed to identify the top hub RNAs, significant cliques and topological parameters associated with differentially expressed genes. Then, the mRNAs from the top two subnetworks constructed are considered for transcription factor (TF) analysis. 39 interacting miRNAs and 1641 corresponding TFs for the eight mRNAs from the subnetworks are also utilized to construct an mRNA-miRNA-TF interaction network. TF analysis revealed two TFs (EST1 and SP1) from the cliques to be significant. TCGA expression analysis of miRNAs and lncRNAs as well as subclass-based and promoter methylation-based expression, oncoprint and survival analysis of the mRNAs are also done. Finally, functional enrichment of mRNAs is also performed. Significant cliques identified in the study can be utilized for identification of newer therapeutic interventions for breast cancer. This work will also help to gain a deeper insight into the complicated molecular intricacies to reveal the potential biomarkers involved with breast cancer progression in future.
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Registered trials
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