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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.1
- 수록면
- 116 - 128 (13page)
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Purpose Chemotherapy targets all rapidly growing cells, not only cancer cells, and thus is often associated with unpleasant side effects. Therefore, examination of the chemosensitivity based on genotypes is needed in order to reduce the side effects.
Materials and Methods Various computational approaches have been proposed for predicting chemosensitivity based on gene expression profiles. A linear regression model can be used to predict the response of cancer cells to chemotherapeutic drugs, based on genomic features of the cells, and appropriate sample size for this method depends on the number of predictors.
We used principal component analysis and identified a combined gene expression profile to reduce the number of predictors Results The coefficients of determinanation (R2) of prediction models with combined gene expression and several independent gene expressions were similar. Corresponding F values, which represent model significances were improved by use of a combined gene expression profile, indicating that the use of a combined gene expression profile is helpful in predicting drug sensitivity. Even better, a prediction model can be used even with small samples because of the reduced number of predictors.
Conclusion Combined gene expression analysis is expected to contribute to more personalized management of breast cancer cases by enabling more effective targeting of existing therapies.
This procedure for identifying a cell-type-specific gene expression profile can be extended to other chemotherapeutic treatments and many other heterogeneous cancer types.
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참고문헌 (22)
참고문헌 신청
/ / BreastCancerTrials.org / Breast-CancerTrials.org
Cohen J / 1988 / Statistical power analysis for the behavioral sciences / Lawrence Erlbaum Associates
De Abreu FB / 2014 / Person-alized therapy for breast cancer / Clin Genet 86 : 62 ~ 67
DeSantis C / 2014 / Breast cancer statistics, 2013 / CA Cancer J Clin 64 : 52 ~ 62
Felding-Habermann B / 2011 / PD03-07:Breast cancer heterogeneity and treatment resistance: clues from metaplastic tumors / Thirty-Fourth Annual CTRCAACR San Antonio Breast Cancer
Cohen J / 1988 / Statistical power analysis for the behavioral sciences / Lawrence Erlbaum Associates
De Abreu FB / 2014 / Person-alized therapy for breast cancer / Clin Genet 86 : 62 ~ 67
DeSantis C / 2014 / Breast cancer statistics, 2013 / CA Cancer J Clin 64 : 52 ~ 62
Felding-Habermann B / 2011 / PD03-07:Breast cancer heterogeneity and treatment resistance: clues from metaplastic tumors / Thirty-Fourth Annual CTRCAACR San Antonio Breast Cancer
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