scholarly journals Glut-1 Expression Correlates with Basal-like Breast Cancer

2011 ◽  
Vol 4 (6) ◽  
pp. 321-327 ◽  
Author(s):  
Yaser R. Hussein ◽  
Sudeshna Bandyopadhyay ◽  
Assaad Semaan ◽  
Quratulain Ahmed ◽  
Bassam Albashiti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Glut 1 ◽  
Basal Like Breast Cancer

scholarly journals FOXC1 Activates Smoothened-Independent Hedgehog Signaling in Basal-like Breast Cancer

Cell Reports ◽  
2015 ◽  
Vol 13 (5) ◽  
pp. 1046-1058 ◽  
Author(s):  
Bingchen Han ◽  
Ying Qu ◽  
Yanli Jin ◽  
Yi Yu ◽  
Nan Deng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Hedgehog Signaling ◽  
Basal Like Breast Cancer

scholarly journals Expression of human endogenous retrovirus-K is strongly associated with the basal-like breast cancer phenotype

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Gary L. Johanning ◽  
Gabriel G. Malouf ◽  
Xiaofeng Zheng ◽  
Francisco J. Esteva ◽  
John N. Weinstein ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Endogenous Retrovirus ◽  
Human Endogenous Retrovirus ◽  
Basal Like Breast Cancer ◽  
Cancer Phenotype

scholarly journals Migrations of cancer cells through the lens of phylogenetic biogeography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Antonia Chroni ◽  
Sayaka Miura ◽  
Olumide Oladeinde ◽  
Vivian Aly ◽  
Sudhir Kumar
Keyword(s):  
Breast Cancer ◽  
Cell Migration ◽  
Molecular Evolution ◽  
Simulated Data ◽  
Malignant Cells ◽  
Founder Effects ◽  
Initial Tumor ◽  
Variation Data ◽  
Basal Like Breast Cancer ◽  
Tumor Genome

AbstractMalignant cells leave their initial tumor of growth and disperse to other tissues to form metastases. Dispersals also occur in nature when individuals in a population migrate from their area of origin to colonize other habitats. In cancer, phylogenetic biogeography is concerned with the source and trajectory of cell movements. We examine the suitability of primary features of organismal biogeography, including genetic diversification, dispersal, extinction, vicariance, and founder effects, to describe and reconstruct clone migration events among tumors. We used computer-simulated data to compare fits of seven biogeographic models and evaluate models’ performance in clone migration reconstruction. Models considering founder effects and dispersals were often better fit for the clone phylogenetic patterns, especially for polyclonal seeding and reseeding of metastases. However, simpler biogeographic models produced more accurate estimates of cell migration histories. Analyses of empirical datasets of basal-like breast cancer had model fits consistent with the patterns seen in the analysis of computer-simulated datasets. Our analyses reveal the powers and pitfalls of biogeographic models for modeling and inferring clone migration histories using tumor genome variation data. We conclude that the principles of molecular evolution and organismal biogeography are useful in these endeavors but that the available models and methods need to be applied judiciously.

scholarly journals A clinically relevant gene signature in triple negative and basal-like breast cancer

2011 ◽  
Vol 13 (5) ◽  
Author(s):  
Achim Rody ◽  
Thomas Karn ◽  
Cornelia Liedtke ◽  
Lajos Pusztai ◽  
Eugen Ruckhaeberle ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative ◽  
Gene Signature ◽  
Basal Like Breast Cancer ◽  
Relevant Gene

scholarly journals A siRNA screen to re-activate senescence in basal-like breast cancer

2015 ◽  
Vol 26 ◽  
pp. iii15
Author(s):  
M. Moore ◽  
M. Philpott ◽  
C. Bishop
Keyword(s):  
Breast Cancer ◽  
Sirna Screen ◽  
Basal Like Breast Cancer

scholarly journals Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

2020 ◽  
Author(s):  
Rong Jia ◽  
Zhongxian Li ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Yujie Weng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Survival Data ◽  
Breast Cancer Subtypes ◽  
Differentially Expressed ◽  
Expression Data ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Basal Like Breast Cancer

Abstract Background Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. Methods We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. Results We identified 1,114 differentially expressed genes in luminal A breast cancer and 1,042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7 , KIF18A , STIL , and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. Conclusions NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

scholarly journals Cyclin E1 is a shared biomarker of subsets of high grade serous ovarian cancer (HGSOC) and basal like breast cancer (BLBC)

2019 ◽  
Vol 30 ◽  
pp. ix86
Author(s):  
D. Aziz ◽  
C. Lee ◽  
V. Chin ◽  
K. Fernandez ◽  
D. Etemadmoghadam ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Cyclin E1 ◽  
Basal Like Breast Cancer
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