ArticleGenome medicine2014
Transcriptome-wide signatures of tumor stage in kidney renal clear cell carcinoma: connecting copy number variation, methylation and transcription factor activity.
Article in Genome medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Extending differential gene expression testing to handle genome aneuploidy in cancer.PLoS computational biology · 2026Article
- Malignant features related PRDX1 associated with osimertinib sensitivity of EGFR-mutant lung adenocarcinoma.International journal of medical sciences · 2025Article
- Article
- Article
- Analysis of healthy and tumour DNA methylation distributions in kidney-renal-clear-cell-carcinoma using Kullback-Leibler and Jensen-Shannon distance measures.IET systems biology · 2017Article
- Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis.Molecular medicine reports · 2017Article
- The pan-cancer pathological regulatory landscape.Scientific reports · 2016Article
- Integrating Multi-omics Data to Dissect Mechanisms of DNA repair Dysregulation in Breast Cancer.Scientific reports · 2016Article
- A Protective Role for Androgen Receptor in Clear Cell Renal Cell Carcinoma Based on Mining TCGA Data.PloS one · 2016Article
- A Five-Gene Signature Predicts Prognosis in Patients with Kidney Renal Clear Cell Carcinoma.Computational and mathematical methods in medicine · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundComparative analysis of expression profiles between early and late stage cancers can help to understand cancer progression and metastasis mechanisms and to predict the clinical aggressiveness of cancer. The observed stage-dependent expression changes can be explained by genetic and epigenetic alterations as well as transcription dysregulation. Unlike genetic and epigenetic alterations, however, activity changes of transcription factors, generally occurring at the post-transcriptional or post-translational level, are hard to detect and quantify.
methodsHere we developed a statistical framework to infer the activity changes of transcription factors by simultaneously taking into account the contributions of genetic and epigenetic alterations to mRNA expression variations.
resultsApplied to kidney renal clear cell carcinoma (KIRC), the model underscored the role of methylation as a significant contributor to stage-dependent expression alterations and identified key transcription factors as potential drivers of cancer progression.
conclusionsIntegrating copy number, methylation, and transcription factor activity signatures to explain stage-dependent expression alterations presented a precise and comprehensive view on the underlying mechanisms during KIRC progression.
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Registered trials
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