ArticlePloS one2017
Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 28 citations in OpenAlex.
- Deep Learning for Predicting Late-Onset Breast Cancer Metastasis: The Single-Hyperparameter Grid Search (SHGS) Strategy for Meta-Tuning a Deep Feed-Forward Neural Network.Bioengineering (Basel, Switzerland) · 2025Article
- Metastasis-associated lung adenocarcinoma transcript 1 molecular mechanisms in gastric cancer progression.World journal of gastrointestinal oncology · 2023Review
- Expression of Selected miRNAs in Normal and Cancer-Associated Fibroblasts and in BxPc3 and MIA PaCa-2 Cell Lines of Pancreatic Ductal Adenocarcinoma.International journal of molecular sciences · 2023Article
- Evaluation of the Relative Frequency of Epstein-Barr Virus Infection in Patients with Recurrent Breast Cancer Compared with Patients with Nonrecurrent Breast Cancer.Advanced biomedical research · 2023Article
- Imaging genetic association analysis of triple-negative breast cancer based on the integration of prior sample information.Frontiers in genetics · 2023Article
- A review of causal discovery methods for molecular network analysis.Molecular genetics & genomic medicine · 2022Review
- Deep Learning and Machine Learning with Grid Search to Predict Later Occurrence of Breast Cancer Metastasis Using Clinical Data.Journal of clinical medicine · 2022Article
- Nerve growth factor receptor increases the tumor growth and metastatic potential of triple-negative breast cancer cells.Oncogene · 2021Article
- Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasis.BMC bioinformatics · 2020Article
- Starvation stress attenuates the miRNA-target interaction in suppressing breast cancer cell proliferation.BMC cancer · 2020Article
- Frequency of MicroRNA Response Elements Identifies Pathologically Relevant Signaling Pathways in Triple-Negative Breast Cancer.iScience · 2020Article
- Data Mining and Expression Analysis of Differential lncRNA ADAMTS9-AS1 in Prostate Cancer.Frontiers in genetics · 2019Article
- Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites.International journal of molecular sciences · 2018Article
- Article
- Machine Learning and Radiogenomics: Lessons Learned and Future Directions.Frontiers in oncology · 2018Review
- The fundamental role of miR-10b in metastatic cancer.American journal of cancer research · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe dysregulation of microRNAs (miRNAs) alters expression level of pro-oncogenic or tumor suppressive mRNAs in breast cancer, and in the long run, causes multiple biological abnormalities. Identification of such interactions of miRNA-mRNA requires integrative analysis of miRNA-mRNA expression profile data. However, current approaches have limitations to consider the regulatory relationship between miRNAs and mRNAs and to implicate the relationship with phenotypic abnormality and cancer pathogenesis. METHODOLOGY/
findingsWe modeled causal relationships between genomic expression and clinical data using a Bayesian Network (BN), with the goal of discovering miRNA-mRNA interactions that are associated with cancer pathogenesis. The Multiple Beam Search (MBS) algorithm learned interactions from data and discovered that hsa-miR-21, hsa-miR-10b, hsa-miR-448, and hsa-miR-96 interact with oncogenes, such as, CCND2, ESR1, MET, NOTCH1, TGFBR2 and TGFB1 that promote tumor metastasis, invasion, and cell proliferation. We also calculated Bayesian network posterior probability (BNPP) for the models discovered by the MBS algorithm to validate true models with high likelihood. CONCLUSION/SIGNIFICANCE: The MBS algorithm successfully learned miRNA and mRNA expression profile data using a BN, and identified miRNA-mRNA interactions that probabilistically affect breast cancer pathogenesis. The MBS algorithm is a potentially useful tool for identifying interacting gene pairs implicated by the deregulation of expression.
Indexed as
Identifiers
What OpenQuestion holds
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.