ArticleFrontiers in physiology2013
Integrative analysis of cancer-related signaling pathways.
Article in Frontiers in physiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Cancer Susceptibility Candidate 9 (CASC9) Promotes Colorectal Cancer Carcinogenesis via mTOR-Dependent Autophagy and Epithelial-Mesenchymal Transition Pathways.Frontiers in molecular biosciences · 2021Article
- Matrix Hyaluronan-CD44 Interaction Activates MicroRNA and LncRNA Signaling Associated With Chemoresistance, Invasion, and Tumor Progression.Frontiers in oncology · 2019Review
- Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information.Computational and mathematical methods in medicine · 2017Article
- How interacting pathways are regulated by miRNAs in breast cancer subtypes.BMC bioinformatics · 2016Article
- Customizing the therapeutic response of signaling networks to promote antitumor responses by drug combinations.Frontiers in oncology · 2014Article
- Further steps toward functional systems biology of cancer.Frontiers in physiology · 2013Article
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Authors and funding
3 authors.
Funding
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Abstract
Identification and classification of cancer types and subtypes is a major issue in current cancer research. Whole genome expression profiling of cancer tissues is often the basis for such subtype classifications of tumors and different signatures for individual cancer types have been described. However, the search for best performing discriminatory gene-expression signatures covering more than one cancer type remains a relevant topic in cancer research as such a signature would help understanding the common changes in signaling networks in these disease types. In this work, we explore the idea of a top down approach for sample stratification based on a module-based network of cancer relevant signaling pathways. For assembly of this network, we consider several of the most established cancer pathways. We evaluate our sample stratification approach using expression data of human breast and ovarian cancer signatures. We show that our approach performs equally well to previously reported methods besides providing the advantage to classify different cancer types. Furthermore, it allows to identify common changes in network module activity of those cancer samples.
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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.