ArticleOncotarget2017
Identification of cancer prognosis-associated functional modules using differential co-expression networks.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- TransMarker: Unveiling dynamic network biomarkers in cancer progression through cross-state graph alignment and optimal transport.PLoS computational biology · 2025Article
- Identification of hub genes distinguishing subtypes in endometrial stromal sarcoma through comprehensive bioinformatics analysis.Scientific reports · 2024Article
- Lack of expression of miR-29a/b1 impairs bladder function in male mice.Disease models & mechanisms · 2023Article
- A computational approach to generate highly conserved gene co-expression networks with RNA-seq data.STAR protocols · 2022Article
- Changes in gene-gene interactions associated with cancer onset and progression are largely independent of changes in gene expression.iScience · 2021Article
- Assisted differential network analysis for gene expression data.Genetic epidemiology · 2021Article
- m6A RNA methylation regulators can contribute to malignant progression and impact the prognosis of bladder cancer.Bioscience reports · 2019Article
- Upregulation of HOXA11 during the progression of lung adenocarcinoma detected via multiple approaches.International journal of molecular medicine · 2018Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
The rapid accumulation of cancer-related data owing to high-throughput technologies has provided unprecedented choices to understand the progression of cancer and discover functional networks in multiple cancers. Establishment of co-expression networks will help us to discover the systemic properties of carcinogenesis features and regulatory mechanisms of multiple cancers. Here, we proposed a computational workflow to identify differentially co-expressed gene modules across 8 cancer types by using combined gene differential expression analysis methods and a higher-order generalized singular value decomposition. Four co-expression modules were identified; and oncogenes and tumor suppressors were significantly enriched in these modules. Functional enrichment analysis demonstrated the significantly enriched pathways in these modules, including ECM-receptor interaction, focal adhesion and PI3K-Akt signaling pathway. The top-ranked miRNAs (mir-199, mir-29, mir-200) and transcription factors (
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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.