ArticleScientific reports2017
Disease biomarker identification from gene network modules for metastasized breast cancer.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The interplay between extracellular matrix remodeling and cellular lipid metabolic reprogramming in cancer: a review.Molecular biology reports · 2025Review
- Exploring the molecular mechanism of estrogen therapy effectiveness after TCRA in IUA patients at single-cell level.Biology direct · 2024Article
- Nine-gene signature and nomogram for predicting survival in patients with head and neck squamous cell carcinoma.Frontiers in genetics · 2022Article
- WDR5 promotes the tumorigenesis of oral squamous cell carcinoma via CARM1/β-catenin axis.Odontology · 2022Article
- ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets.Journal of bioinformatics and computational biology · 2020Article
- Discovery and characterization of functional modules associated with body weight in broilers.Scientific reports · 2019Article
- PNME - A gene-gene parallel network module extraction method.Journal, genetic engineering & biotechnology · 2018Article
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Authors and funding
3 authors.
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Abstract
Advancement in science has tended to improve treatment of fatal diseases such as cancer. A major concern in the area is the spread of cancerous cells, technically refered to as metastasis into other organs beyond the primary organ. Treatment in such a stage of cancer is extremely difficult and usually palliative only. In this study, we focus on finding gene-gene network modules which are functionally similar in nature in the case of breast cancer. These modules extracted during the disease progression stages are analyzed using p-value and their associated pathways. We also explore interesting patterns associated with the causal genes, viz., SCGB1D2, MET, CYP1B1 and MMP9 in terms of expression similarity and pathway contexts. We analyze the genes involved in both the stages- non metastasis and metastatsis and change in their expression values, their associated pathways and roles as the disease progresses from one stage to another. We discover three additional pathways viz., Glycerophospholipid metablism, h-Efp pathway and CARM1 and Regulation of Estrogen Receptor, which can be related to the metastasis phase of breast cancer. These new pathways can be further explored to identify their relevance during the progression of the disease.
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