ArticleScientific reports2020
Co-expression based cancer staging and application.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Loss of long-range co-expression is a common feature in cancer.NPJ systems biology and applications · 2026Article
- Stage-specific coexpression network analysis of Myc in cohorts of renal cancer.Scientific reports · 2023Article
- Gene coexpression network analysis identifies hubs in hepatitis B virus-associated hepatocellular carcinoma.Journal of the Chinese Medical Association : JCMA · 2022Article
- Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning.Frontiers in genetics · 2022Article
- Changes in gene-gene interactions associated with cancer onset and progression are largely independent of changes in gene expression.iScience · 2021Article
- Gene Co-Expression in Breast Cancer: A Matter of Distance.Frontiers in oncology · 2021Article
- RNA-Seq-Based Breast Cancer Subtypes Classification Using Machine Learning Approaches.Computational intelligence and neuroscience · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample. The R source code used in this study is available at GitHub (https://github.com/yxchspring/CECS).
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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 Socratic graph.