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Integrating Multi-omics Data to Dissect Mechanisms of DNA repair Dysregulation in Breast Cancer

View Article: PubMed Central - PubMed

ABSTRACT

DNA repair genes and pathways that are transcriptionally dysregulated in cancer provide the first line of evidence for the altered DNA repair status in tumours, and hence have been explored intensively as a source for biomarker discovery. The molecular mechanisms underlying DNA repair dysregulation, however, have not been systematically investigated in any cancer type. In this study, we performed a statistical analysis to dissect the roles of DNA copy number alteration (CNA), DNA methylation (DM) at gene promoter regions and the expression changes of transcription factors (TFs) in the differential expression of individual DNA repair genes in normal versus tumour breast samples. These gene-level results were summarised at pathway level to assess whether different DNA repair pathways are affected in distinct manners. Our results suggest that CNA and expression changes of TFs are major causes of DNA repair dysregulation in breast cancer, and that a subset of the identified TFs may exert global impacts on the dysregulation of multiple repair pathways. Our work hence provides novel insights into DNA repair dysregulation in breast cancer. These insights improve our understanding of the molecular basis of the DNA repair biomarkers identified thus far, and have potential to inform future biomarker discovery.

No MeSH data available.


A LASSO-based statistical framework to select TFs potentially involved in DNA repair dysregulation.
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f4: A LASSO-based statistical framework to select TFs potentially involved in DNA repair dysregulation.

Mentions: In practice, a major drawback of LASSO is that its result can be heavily affected by an initiating parameter termed regularisation coefficient (λ), whose value needs to be specified for each analysis. This value is typically obtained using cross-validation; however, due to the randomness inherent to the cross-validation process, the estimated optimal λ value for the same analysis can differ across different cross-validation runs, resulting in unstable feature selection results. To overcome this, we developed a secondary feature-selection procedure with the assumption that TFs consistently selected with different λ values are likely to be truly associated with a given repair gene (Fig. 4).


Integrating Multi-omics Data to Dissect Mechanisms of DNA repair Dysregulation in Breast Cancer
A LASSO-based statistical framework to select TFs potentially involved in DNA repair dysregulation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036051&req=5

f4: A LASSO-based statistical framework to select TFs potentially involved in DNA repair dysregulation.
Mentions: In practice, a major drawback of LASSO is that its result can be heavily affected by an initiating parameter termed regularisation coefficient (λ), whose value needs to be specified for each analysis. This value is typically obtained using cross-validation; however, due to the randomness inherent to the cross-validation process, the estimated optimal λ value for the same analysis can differ across different cross-validation runs, resulting in unstable feature selection results. To overcome this, we developed a secondary feature-selection procedure with the assumption that TFs consistently selected with different λ values are likely to be truly associated with a given repair gene (Fig. 4).

View Article: PubMed Central - PubMed

ABSTRACT

DNA repair genes and pathways that are transcriptionally dysregulated in cancer provide the first line of evidence for the altered DNA repair status in tumours, and hence have been explored intensively as a source for biomarker discovery. The molecular mechanisms underlying DNA repair dysregulation, however, have not been systematically investigated in any cancer type. In this study, we performed a statistical analysis to dissect the roles of DNA copy number alteration (CNA), DNA methylation (DM) at gene promoter regions and the expression changes of transcription factors (TFs) in the differential expression of individual DNA repair genes in normal versus tumour breast samples. These gene-level results were summarised at pathway level to assess whether different DNA repair pathways are affected in distinct manners. Our results suggest that CNA and expression changes of TFs are major causes of DNA repair dysregulation in breast cancer, and that a subset of the identified TFs may exert global impacts on the dysregulation of multiple repair pathways. Our work hence provides novel insights into DNA repair dysregulation in breast cancer. These insights improve our understanding of the molecular basis of the DNA repair biomarkers identified thus far, and have potential to inform future biomarker discovery.

No MeSH data available.