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.

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.

Analysis of Gene Expression Profiles Reveals Novel Correlations With the Clinical Course of Colorectal Cancer

2007 ◽  
Vol 16 (11) ◽  
pp. 535-548 ◽  
Author(s):  
Duccio Cavalieri ◽  
Piero Dolara ◽  
Enrico Mini ◽  
Cristina Luceri ◽  
Cinzia Castagnini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Clinical Course ◽  
Expression Profiles ◽  
Gene Expression Profiles

Molecular classification of human endometrial cancer based on gene expression profiles from specialized microarrays

2010 ◽  
Vol 110 (2) ◽  
pp. 125-129 ◽  
Author(s):  
YuanYang Yao ◽  
YongHua Chen ◽  
Yue Wang ◽  
XiaoPing Li ◽  
JianLiu Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endometrial Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Classification

scholarly journals Organ-Specific Molecular Classification of Primary Lung, Colon, and Ovarian Adenocarcinomas Using Gene Expression Profiles

2001 ◽  
Vol 159 (4) ◽  
pp. 1231-1238 ◽  
Author(s):  
Thomas J. Giordano ◽  
Kerby A. Shedden ◽  
Donald R. Schwartz ◽  
Rork Kuick ◽  
Jeremy M.G. Taylor ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Organ Specific ◽  
Ovarian Adenocarcinomas

scholarly journals A novel immune subtype classification of ER-positive, PR-negative and HER2-negative breast cancer based on the genomic and transcriptomic landscape

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Peiling Xie ◽  
Rui An ◽  
Shibo Yu ◽  
Jianjun He ◽  
Huimin Zhang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Clinical Outcomes ◽  
Immune Escape ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Characteristics ◽  
Consensus Clustering ◽  
Breast Cancers ◽  
Her2 Breast Cancer

Abstract Background The diversity and plasticity behind ER+/PR−/HER2− breast cancer have not been widely explored. It is essential to identify heterogeneous microenvironment phenotypes and investigate specific genomic events driving the formation of these phenotypes. Methods Based on the immune-related gene expression profiles of 411 ER+/PR−/HER2− breast cancers in the METABRIC cohort, we used consensus clustering to identify heterogeneous immune subtypes and assessed their reproducibility in an independent meta-cohort including 135 patients collected from GEO database. We further analyzed the differences of cellular and molecular characteristics, and potential immune escape mechanism among immune subtypes. In addition, we constructed a transcriptional trajectory to visualize the distribution of individual patient. Results Our analysis identified and validated five reproducible immune subtypes with distinct cellular and molecular characteristics, potential immune escape mechanisms, genomic drivers, as well as clinical outcomes. An immune-cold subtype, with the least amount of lymphocyte infiltration, had a poorer prognosis. By contrast, an immune-hot subtype, which demonstrated the highest infiltration of CD8+ T cells, DCs and NK cells, and elevated IFN-γ response, had a comparatively favorable prognosis. Other subtypes showed more diverse gene expression and immune infiltration patterns with distinct clinical outcomes. Finally, our analysis revealed a complex immune landscape consisting of both discrete cluster and continuous spectrum. Conclusion Overall, this study revealed five heterogeneous immune subtypes among ER+/PR–/HER2− breast cancer, also provided important implications for clinical translations.

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