scholarly journals Gene expression profiles and molecular classification to predict distant metastasis and tamoxifen-resistant breast cancer

2005 ◽  
Vol 7 (S1) ◽  
Author(s):  
JGM Klijn ◽  
EMJJ Berns ◽  
J Martens ◽  
MPHM Jansen ◽  
D Atkins ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Distant Metastasis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Tamoxifen Resistant

Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer

The Lancet ◽  
2005 ◽  
Vol 365 (9460) ◽  
pp. 671-679 ◽  
Author(s):  
Yixin Wang ◽  
Jan GM Klijn ◽  
Yi Zhang ◽  
Anieta M Sieuwerts ◽  
Maxime P Look ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Lymph Node ◽  
Distant Metastasis ◽  
Primary Breast Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Node Negative

scholarly journals A Robust Gene selection Method for Microarray-based Cancer Classification

2010 ◽  
Vol 9 ◽  
pp. CIN.S3794 ◽  
Author(s):  
Xiaosheng Wang ◽  
Osamu Gotoh
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Information Gain ◽  
Expression Profiles ◽  
Feature Selection Method ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Selection Method ◽  
Chi Square

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.

scholarly journals Diversity of Breast Carcinoma: Histological Subtypes and Clinical Relevance

2015 ◽  
Vol 8 ◽  
pp. CPath.S31563 ◽  
Author(s):  
Jaafar Makki
Keyword(s):  
Gene Expression ◽  
Breast Carcinoma ◽  
Clinical Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Developed Countries ◽  
Breast Cancers ◽  
Wide Scope ◽  
Histological Subtypes

Mammary carcinoma is the most common malignant tumor in women, and it is the leading cause of mortality, with an incidence of ≥1,000,000 cases occurring worldwide annually. It is one of the most common human neoplasms, accounting for approximately one-quarter of all cancers in females worldwide and 27% of cancers in developed countries with a Western lifestyle. They exhibit a wide scope of morphological features, different immunohistochemical profiles, and unique histopathological subtypes that have specific clinical course and outcome. Breast cancers can be classified into distinct subgroups based on similarities in the gene expression profiles and molecular classification.

Gene expression profiles in premenopausal women with HR+ HER2− early breast cancer

2020 ◽  
Vol 138 ◽  
pp. S76
Author(s):  
H. Ni ◽  
A. Kurt ◽  
J. Kumbrink ◽  
A. Seiler ◽  
D. Mayr ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Early Breast Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Premenopausal Women
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