ArticleBiometrics2015
miRNA-target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer.
Article in Biometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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.
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Who cites it
14 citing papers in PubMed.
- RNACOREX - RNA coregulatory network explorer and classifier.PLoS computational biology · 2025Article
- Using prior-data conflict to tune Bayesian regularized regression models.Statistics and computing · 2025Article
- NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction.Briefings in bioinformatics · 2024Article
- Tutorial on survival modeling with applications to omics data.Bioinformatics (Oxford, England) · 2024Article
- Attempts to Understand Oral Mucositis in Head and Neck Cancer Patients through Omics Studies: A Narrative Review.International journal of molecular sciences · 2023Review
- Bayesian integrative analysis and prediction with application to atherosclerosis cardiovascular disease.Biostatistics (Oxford, England) · 2022Article
- Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population.PloS one · 2022Article
- Bayesian graphical models for modern biological applications.Statistical methods & applications · 2022Review
- Construction of five microRNAs prognostic markers and a prognostic model for clear cell renal cell carcinoma.Translational cancer research · 2021Article
- MicroRNA‑200a promotes esophageal squamous cell carcinoma cell proliferation, migration and invasion through extensive target genes.Molecular medicine reports · 2020Article
- SIFORM: shared informative factor models for integration of multi-platform bioinformatic data.Bioinformatics (Oxford, England) · 2016Article
- Joint Bayesian variable and graph selection for regression models with network-structured predictors.Statistics in medicine · 2016Article
- Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence.Journal of the American Statistical Association · 2016Article
- The Application of Bayesian Methods in Cancer Prognosis and Prediction.Cancer genomics & proteomicsReview
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The availability of cross-platform, large-scale genomic data has enabled the investigation of complex biological relationships for many cancers. Identification of reliable cancer-related biomarkers requires the characterization of multiple interactions across complex genetic networks. MicroRNAs are small non-coding RNAs that regulate gene expression; however, the direct relationship between a microRNA and its target gene is difficult to measure. We propose a novel Bayesian model to identify microRNAs and their target genes that are associated with survival time by incorporating the microRNA regulatory network through prior distributions. We assume that biomarkers involved in regulatory networks are likely associated with survival time. We employ non-local prior distributions and a stochastic search method for the selection of biomarkers associated with the survival outcome. We use KEGG pathway information to incorporate correlated gene effects within regulatory networks. Using simulation studies, we assess the performance of our method, and apply it to experimental data of kidney renal cell carcinoma (KIRC) obtained from The Cancer Genome Atlas. Our novel method validates previously identified cancer biomarkers and identifies biomarkers specific to KIRC progression that were not previously discovered. Using the KIRC data, we confirm that biomarkers involved in regulatory networks are more likely to be associated with survival time, showing connections in one regulatory network for five out of six such genes we identified.
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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.