ArticleBMC bioinformatics2019
The exploration of disease-specific gene regulatory networks in esophageal carcinoma and stomach adenocarcinoma.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks.eLife · 2025Article
- Learning in Transcriptional Network Models: Computational Discovery of Pathway-Level Memory and Effective Interventions.International journal of molecular sciences · 2022Article
- Next Generation Sequencing for Potential Regulated Genes and Micro-RNAs of Early Growth Response-1 in the Esophageal Squamous Cell Carcinoma.The protein journal · 2022Article
- Identification of potential microRNA diagnostic panels and uncovering regulatory mechanisms in breast cancer pathogenesis.Scientific reports · 2022Article
- Reconstruction of dynamic regulatory networks reveals signaling-induced topology changes associated with germ layer specification.Stem cell reports · 2022Article
- Identification of hub genes related to CD4Frontiers in genetics · 2022Article
- A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris.Computational intelligence and neuroscience · 2022Article
- Oncogenic microRNA-301b regulates tumor repressor dystrobrevin alpha to facilitate cell growth, invasion and migration in esophageal cancer.Esophagus : official journal of the Japan Esophageal Society · 2021Article
- Gene regulatory networks exhibit several kinds of memory: quantification of memory in biological and random transcriptional networks.iScience · 2021Article
- Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFeed-forward loops (FFLs), consisting of miRNAs, transcription factors (TFs) and their common target genes, have been validated to be important for the initialization and development of complex diseases, including cancer. Esophageal Carcinoma (ESCA) and Stomach Adenocarcinoma (STAD) are two types of malignant tumors in the digestive tract. Understanding common and distinct molecular mechanisms of ESCA and STAD is extremely crucial.
resultsIn this paper, we presented a computational framework to explore common and distinct FFLs, and molecular biomarkers for ESCA and STAD. We identified FFLs by combining regulation pairs and RNA-seq data. Then we constructed disease-specific co-expression networks based on the FFLs identified. We also used random walk with restart (RWR) on disease-specific co-expression networks to prioritize candidate molecules. We identified 148 and 242 FFLs for these two types of cancer, respectively. And we found that one TF, E2F3 was related to ESCA, two genes, DTNA and KCNMA1 were related to STAD, while one TF ESR1 and one gene KIT were associated with both of the two types of cancer.
conclusionsThis proposed computational framework predicted disease-related biomolecules effectively and discovered the correlation between two types of cancers, which helped develop the diagnostic and therapeutic strategies of Esophageal Carcinoma and Stomach Adenocarcinoma.
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