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Updated: Mar 3, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Disease biomarker identification from gene network modules for metastasized breast cancer
Pooja Sharma1, Dhruba K Bhattacharyya2, Jugal Kalita3
1Tezpur University, Computer Science and Engineering Dept, Tezpur, Assam, 784028, India.
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.
Insights
This study identifies key gene networks and pathways involved in breast cancer metastasis. Understanding these molecular changes offers new targets for improved cancer treatment strategies.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Metastasis, the spread of cancer cells, presents a significant challenge in treating advanced cancers like breast cancer.
- Current treatments for metastatic cancer are often palliative, highlighting the need for novel therapeutic targets.
Purpose of the Study:
- To identify functionally similar gene-gene network modules associated with breast cancer progression.
- To analyze the role of specific causal genes (SCGB1D2, MET, CYP1B1, MMP9) in metastasis.
- To discover novel pathways implicated in the metastasis phase of breast cancer.
Main Methods:
- Analysis of gene-gene network modules during different stages of breast cancer progression.
- Utilizing p-value analysis to assess the significance of identified modules.
- Investigating expression similarity and pathway contexts of key causal genes.
Main Results:
- Identification of gene-gene network modules exhibiting functional similarity across disease stages.
- Detailed analysis of causal genes (SCGB1D2, MET, CYP1B1, MMP9) and their expression patterns.
- Discovery of three new pathways – Glycerophospholipid metabolism, h-Efp pathway, and CARM1 and Regulation of Estrogen Receptor – linked to breast cancer metastasis.
Conclusions:
- The identified gene networks and pathways provide insights into the molecular mechanisms driving breast cancer metastasis.
- The newly discovered pathways represent potential targets for future research and therapeutic development in metastatic breast cancer treatment.
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