ArticleBMC bioinformatics2017
Investigating MicroRNA and transcription factor co-regulatory networks in colorectal cancer.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 50 citations in OpenAlex.
- MicroRNA-Transcription factor regulatory networks in the early strobilar development of Echinococcus granulosus protoscoleces.BMC genomics · 2023Article
- Identification of potential microRNA diagnostic panels and uncovering regulatory mechanisms in breast cancer pathogenesis.Scientific reports · 2022Article
- Article
- Structural and Functional Analyses of Hub MicroRNAs in An Integrated Gene Regulatory Network of Arabidopsis.Genomics, proteomics & bioinformatics · 2022Article
- Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer.BMC cancer · 2021Article
- Investigating potential molecular mechanisms of serum exosomal miRNAs in colorectal cancer based on bioinformatics analysis.Medicine · 2020Article
- Construction of a potential microRNA, transcription factor and mRNA regulatory network in hepatocellular carcinoma.Translational cancer research · 2020Article
- Critical microRNAs and regulatory motifs in cleft palate identified by a conserved miRNA-TF-gene network approach in humans and mice.Briefings in bioinformatics · 2020Article
- MyomirDB: A unified database and server platform for muscle atrophy myomiRs, coregulatory networks and regulons.Scientific reports · 2020Article
- Article
- KRAS Mutation-Responsive miR-139-5p inhibits Colorectal Cancer Progression and is repressed by Wnt Signaling.Theranostics · 2020Article
- MicroRNA-Based Combinatorial Cancer Therapy: Effects of MicroRNAs on the Efficacy of Anti-Cancer Therapies.Cells · 2019Review
- Identification of the lncRNA-miRNA-mRNA network associated with gastric cancer via integrated bioinformatics analysis.Oncology letters · 2019Article
- Significance of miR-27a and miR-31 in early diagnosis and prognosis of colorectal cancer.Oncology letters · 2019Article
- Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types.RNA biology · 2019Article
- MicroRNA-related transcription factor regulatory networks in human colorectal cancer.Medicine · 2019Article
- Network-based identification of critical regulators as putative drivers of human cleft lip.BMC medical genomics · 2019Article
- Regulatory network analysis reveals the oncogenesis roles of feed-forward loops and therapeutic target in T-cell acute lymphoblastic leukemia.BMC medical genomics · 2019Article
- Differential Connectivity in Colorectal Cancer Gene Expression NetworkIranian biomedical journal · 2019Article
- Investigating the Role of MicroRNA and Transcription Factor Co-regulatory Networks in Multiple Sclerosis Pathogenesis.International journal of molecular sciences · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundColorectal cancer (CRC) is one of the most common malignancies worldwide with poor prognosis. Studies have showed that abnormal microRNA (miRNA) expression can affect CRC pathogenesis and development through targeting critical genes in cellular system. However, it is unclear about which miRNAs play central roles in CRC's pathogenesis and how they interact with transcription factors (TFs) to regulate the cancer-related genes.
resultsTo address this issue, we systematically explored the major regulation motifs, namely feed-forward loops (FFLs), that consist of miRNAs, TFs and CRC-related genes through the construction of a miRNA-TF regulatory network in CRC. First, we compiled CRC-related miRNAs, CRC-related genes, and human TFs from multiple data sources. Second, we identified 13,123 3-node FFLs including 25 miRNA-FFLs, 13,005 TF-FFLs and 93 composite-FFLs, and merged the 3-node FFLs to construct a CRC-related regulatory network. The network consists of three types of regulatory subnetworks (SNWs): miRNA-SNW, TF-SNW, and composite-SNW. To enhance the accuracy of the network, the results were filtered by using The Cancer Genome Atlas (TCGA) expression data in CRC, whereby we generated a core regulatory network consisting of 58 significant FFLs. We then applied a hub identification strategy to the significant FFLs and found 5 significant components, including two miRNAs (hsa-miR-25 and hsa-miR-31), two genes (ADAMTSL3 and AXIN1) and one TF (BRCA1). The follow up prognosis analysis indicated all of the 5 significant components having good prediction of overall survival of CRC patients.
conclusionsIn summary, we generated a CRC-specific miRNA-TF regulatory network, which is helpful to understand the complex CRC regulatory mechanisms and guide clinical treatment. The discovered 5 regulators might have critical roles in CRC pathogenesis and warrant future investigation.
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.