breast cancer research
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2022 ◽  
Vol 146 ◽  
pp. 112526
Author(s):  
Pegah Farhadi ◽  
Reza Yarani ◽  
Elahe Valipour ◽  
Sarah Kiani ◽  
Zohreh Hoseinkhani ◽  
...  

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Qiang Song ◽  
Lu Dou ◽  
Wenjin Zhang ◽  
Yang Peng ◽  
Man Huang ◽  
...  

Abstract Background Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) is the most sensitive technique for evaluating gene expression levels. Choosing appropriate reference genes (RGs) is critical for normalizing and evaluating changes in the expression of target genes. However, uniform and reliable RGs for breast cancer research have not been identified, limiting the value of target gene expression studies. Here, we aimed to identify reliable and accurate RGs for breast cancer tissues and cell lines using the RNA-seq dataset. Methods First, we compiled the transcriptome profiling data from the TCGA database involving 1217 samples to identify novel RGs. Next, ten genes with relatively stable expression levels were chosen as novel candidate RGs, together with six conventional RGs. To determine and validate the optimal RGs we performed qRT-PCR experiments on 87 samples from 11 types of surgically excised breast tumor specimens (n = 66) and seven breast cancer cell lines (n = 21). Five publicly available algorithms (geNorm, NormFinder, ΔCt method, BestKeeper, and ComprFinder) were used to assess the expression stability of each RG across all breast cancer tissues and cell lines. Results Our results show that RG combinations SF1 + TRA2B + THRAP3 and THRAP3 + RHOA + QRICH1 showed stable expression in breast cancer tissues and cell lines, respectively, and that they displayed good interchangeability. We propose that these combinations are optimal triplet RGs for breast cancer research. Conclusions In summary, we identified novel and reliable RG combinations for breast cancer research based on a public RNA-seq dataset. Our results lay a solid foundation for the accurate normalization of qRT-PCR results across different breast cancer tissues and cells.


2021 ◽  
pp. 1-13
Author(s):  
Margaret Rosenzweig ◽  
Lori A. Miller ◽  
Adrian V. Lee ◽  
Steffi Oesterreich ◽  
Humberto E. Trejo Bittar ◽  
...  

Author(s):  
Marta Tamayo-Angorrilla ◽  
Julia López de Andrés ◽  
Gema Jiménez ◽  
Juan Antonio Marchal

2021 ◽  
Author(s):  
Nasim Ebrahimi ◽  
Alireza Nasr Esfahani ◽  
Setare Samizade ◽  
Atena Mansouri ◽  
Masoud Ghanaatian ◽  
...  

2021 ◽  
Author(s):  
Han-Jun Cho ◽  
Eui Seok Jeong

AbstractBreast cancer is a major cause of female death, and various big data analysis methods have been applied to breast cancer. This study lists cases in which big data analysis was applied to breast cancer research. In addition, statistics and percentages from each specific sample were proposed. However, research on the use of big data has a blind spot that relies on sample characteristics. Therefore, before sampling big data, statistical inference should be discussed more precisely through pre-examination and sample statistical errors should be reduced by professional statistical evaluation of the analysis method.


Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4456
Author(s):  
Helga Bergholtz ◽  
Jodi M. Carter ◽  
Alessandra Cesano ◽  
Maggie Chon U Cheang ◽  
Sarah E. Church ◽  
...  

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity.


2021 ◽  
Vol 22 (8) ◽  
pp. 2385-2389
Author(s):  
Miles Grunvald ◽  
Michael Williams ◽  
Ruta Rao ◽  
Cristina O’Donoghue ◽  
Adan Becerra

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