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Stratified pathway analysis to identify gene sets associated with oral contraceptive use and breast cancer.

Pang H, Zhao H - Cancer Inform (2014)

Bottom Line: We hypothesize that combining pathway and patient clinical information can more effectively identify relevant pathways pertinent to specific patient subgroups, leading to better diagnosis and treatment.In contrast to analysis using all the patients, this more focused analysis has the potential to reveal subgroup-specific pathways that may lead to more biological insights into disease etiology and treatment response.As an illustration, the power of our approach is demonstrated through its application to a breast cancer dataset in which the patients are stratified according to their oral contraceptive use.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA. ; School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.

ABSTRACT
Cancer biomarker discovery can facilitate drug development, improve staging of patients, and predict patient prognosis. Because cancer is the result of many interacting genes, analysis based on a set of genes with related biological functions or pathways may be more informative than single gene-based analysis for cancer biomarker discovery. The relevant pathways thus identified may help characterize different aspects of molecular phenotypes related to the tumor. Although it is well known that cancer patients may respond to the same treatment differently because of clinical variables and variation of molecular phenotypes, this patient heterogeneity has not been explicitly considered in pathway analysis in the literature. We hypothesize that combining pathway and patient clinical information can more effectively identify relevant pathways pertinent to specific patient subgroups, leading to better diagnosis and treatment. In this article, we propose to perform stratified pathway analysis based on clinical information from patients. In contrast to analysis using all the patients, this more focused analysis has the potential to reveal subgroup-specific pathways that may lead to more biological insights into disease etiology and treatment response. As an illustration, the power of our approach is demonstrated through its application to a breast cancer dataset in which the patients are stratified according to their oral contraceptive use.

No MeSH data available.


Related in: MedlinePlus

Top overlapped pathways for non-users (top) and users (bottom) of oral contraceptives.Notes: Hexagon shaped are genes. Dark red as most important, white as least important.
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f1-cin-suppl.4-2014-073: Top overlapped pathways for non-users (top) and users (bottom) of oral contraceptives.Notes: Hexagon shaped are genes. Dark red as most important, white as least important.

Mentions: Figure 1 contains two plots, each showing the set of pathways listed in Table 3 as well as the corresponding important genes. The top and bottom halves of Figure 1 correspond to non-users and users of oral contraceptives, respectively. The genes are hexagon shaped and are shaded according to their discriminative power in distinguishing PR+/PR− samples, with darker red indicating more discriminative power. Clearly as an overview of the plot, we can see that users of oral contraceptives have darker red genes than non-users of oral contraceptives. Moreover, it is important to note that there are genes that are important for distinguishing PR status in non-users but not in users of oral contraceptives and vice versa.


Stratified pathway analysis to identify gene sets associated with oral contraceptive use and breast cancer.

Pang H, Zhao H - Cancer Inform (2014)

Top overlapped pathways for non-users (top) and users (bottom) of oral contraceptives.Notes: Hexagon shaped are genes. Dark red as most important, white as least important.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4263464&req=5

f1-cin-suppl.4-2014-073: Top overlapped pathways for non-users (top) and users (bottom) of oral contraceptives.Notes: Hexagon shaped are genes. Dark red as most important, white as least important.
Mentions: Figure 1 contains two plots, each showing the set of pathways listed in Table 3 as well as the corresponding important genes. The top and bottom halves of Figure 1 correspond to non-users and users of oral contraceptives, respectively. The genes are hexagon shaped and are shaded according to their discriminative power in distinguishing PR+/PR− samples, with darker red indicating more discriminative power. Clearly as an overview of the plot, we can see that users of oral contraceptives have darker red genes than non-users of oral contraceptives. Moreover, it is important to note that there are genes that are important for distinguishing PR status in non-users but not in users of oral contraceptives and vice versa.

Bottom Line: We hypothesize that combining pathway and patient clinical information can more effectively identify relevant pathways pertinent to specific patient subgroups, leading to better diagnosis and treatment.In contrast to analysis using all the patients, this more focused analysis has the potential to reveal subgroup-specific pathways that may lead to more biological insights into disease etiology and treatment response.As an illustration, the power of our approach is demonstrated through its application to a breast cancer dataset in which the patients are stratified according to their oral contraceptive use.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA. ; School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.

ABSTRACT
Cancer biomarker discovery can facilitate drug development, improve staging of patients, and predict patient prognosis. Because cancer is the result of many interacting genes, analysis based on a set of genes with related biological functions or pathways may be more informative than single gene-based analysis for cancer biomarker discovery. The relevant pathways thus identified may help characterize different aspects of molecular phenotypes related to the tumor. Although it is well known that cancer patients may respond to the same treatment differently because of clinical variables and variation of molecular phenotypes, this patient heterogeneity has not been explicitly considered in pathway analysis in the literature. We hypothesize that combining pathway and patient clinical information can more effectively identify relevant pathways pertinent to specific patient subgroups, leading to better diagnosis and treatment. In this article, we propose to perform stratified pathway analysis based on clinical information from patients. In contrast to analysis using all the patients, this more focused analysis has the potential to reveal subgroup-specific pathways that may lead to more biological insights into disease etiology and treatment response. As an illustration, the power of our approach is demonstrated through its application to a breast cancer dataset in which the patients are stratified according to their oral contraceptive use.

No MeSH data available.


Related in: MedlinePlus