Lipid metabolism genes in contralateral unaffected breast and estrogen receptor status of breast cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE41400 | GEO | 2013/03/26
SECONDARY ACCESSION(S): PRJNA177679
REPOSITORIES: GEO
Similar Datasets
Project description:The contralateral unaffected breast of women with unilateral breast cancer (cases) is a good model for defining subtype-specific risk since women with ER-negative index primaries are at high risk for subsequent ER-negative primary cancers. We performed random fine needle aspiration (rFNA) of the unaffected breasts of cases; samples from 30 subjects (15 ER-positive and 15 ER-negative cases matched for age, race and menopausal status), were used for Illumina expression array analysis. In this study, we have examined gene expression profiles in random fine needle aspirate (rFNA) samples from the contralateral breasts of women with new unilateral breast cancer (cases) to seek candidate panels of ER-specific risk biomarkers. On a discovery set of 30 women, we have identified gene expression differences in the contralateral breast that associate with ER+ or ER- index primary tumors.
2013-03-26 | E-GEOD-41400 | biostudies-arrayexpress
Project description:Background: We hypothesize that important genomic differences between breast cancer subtypes occur early in carcinogenesis. Therefore, gene expression might distinguish histologically normal breast epithelium (NlEpi) from breasts containing estrogen receptor positive (ER+) compared with estrogen receptor negative (ER-) cancers. Methods: We examined gene expression in 46 cases of microdissected NlEpi from previously untreated women undergoing breast cancer surgery. From 30 age-matched cases (15 ER+, 15 ER-) we used Affymetryix U133A arrays. From 16 independent cases (9ER+, 7 ER-), we validated seven selected genes using qPCR. We then compared gene expression between NlEpi and invasive breast cancer using 4 publicly available datasets. Results: 216 probes (corresponding to 198 unique genes) distinguished the NlEpi from breasts with ER+ (NlEpiER+) compared to ER- cancers (NlEpiER-). These include genes characteristic of ER+ and ER- cancers themselves, (e.g., ESR1, GATA3, and CX3CL1, FABP7, respectively). QPCR validated the microarray results in both a sampling of the 30 original cases (84%) and all of the 16 independent cases (77%). Gene expression in NlEpiER+ and NlEPIERNlEpiER- resembled gene expression in ER+ and ER- cancers, respectively: 36%-53% of the genes or probes examined in each the 4 external datasets overlapped between NlEpi and the corresponding cancer subtype. Conclusions: Gene expression differs in NlEpi of breasts containing ER+ compared to ER- breast cancers. These differences echo differences in ER+ and ER- invasive cancers. Thus, breast cancer subtypes may be detectable before histologic abnormalities. NlEpi gene expression may help define subtype-specific risk signatures, identify initial subtype specific genomic differences, and suggest new targets for subtype-specific prevention and therapy. We determined that 216 probesets significantly differed between histologically normal epithelium from ER+ breast cancer patients and from ER- breast cancer patients, and that gene expression in each type of histologically normal epithelium resembles expression of the corresponding subtype of invasive breast cancer (i.e., ER+ or ER-). These findings suggest that characteristic features of breast cancer subtypes are detectable prior to any histologic abnormality. This suggestion has implications for understanding breast cancer biology and devising new tools for assessing breast cancer risk.
2010-12-07 | GSE21947 | GEO
Project description:Background: We hypothesize that important genomic differences between breast cancer subtypes occur early in carcinogenesis. Therefore, gene expression might distinguish histologically normal breast epithelium (NlEpi) from breasts containing estrogen receptor positive (ER+) compared with estrogen receptor negative (ER-) cancers. Methods: We examined gene expression in 46 cases of microdissected NlEpi from previously untreated women undergoing breast cancer surgery. From 30 age-matched cases (15 ER+, 15 ER-) we used Affymetryix U133A arrays. From 16 independent cases (9ER+, 7 ER-), we validated seven selected genes using qPCR. We then compared gene expression between NlEpi and invasive breast cancer using 4 publicly available datasets. Results: 216 probes (corresponding to 198 unique genes) distinguished the NlEpi from breasts with ER+ (NlEpiER+) compared to ER- cancers (NlEpiER-). These include genes characteristic of ER+ and ER- cancers themselves, (e.g., ESR1, GATA3, and CX3CL1, FABP7, respectively). QPCR validated the microarray results in both a sampling of the 30 original cases (84%) and all of the 16 independent cases (77%). Gene expression in NlEpiER+ and NlEPIERNlEpiER- resembled gene expression in ER+ and ER- cancers, respectively: 36%-53% of the genes or probes examined in each the 4 external datasets overlapped between NlEpi and the corresponding cancer subtype. Conclusions: Gene expression differs in NlEpi of breasts containing ER+ compared to ER- breast cancers. These differences echo differences in ER+ and ER- invasive cancers. Thus, breast cancer subtypes may be detectable before histologic abnormalities. NlEpi gene expression may help define subtype-specific risk signatures, identify initial subtype specific genomic differences, and suggest new targets for subtype-specific prevention and therapy. We determined that 216 probesets significantly differed between histologically normal epithelium from ER+ breast cancer patients and from ER- breast cancer patients, and that gene expression in each type of histologically normal epithelium resembles expression of the corresponding subtype of invasive breast cancer (i.e., ER+ or ER-). These findings suggest that characteristic features of breast cancer subtypes are detectable prior to any histologic abnormality. This suggestion has implications for understanding breast cancer biology and devising new tools for assessing breast cancer risk. 30 total laser capture microdissected histologically normal breast tissue samples were analyzed with Affymetrix HU133A microarrays. All samples were age-matched between histologically normal epithelial samples from ER+ breast cancer patients (n=15) and histologically normal epithelial samples from ER- breast cancer patients (n=15). Sample numbers correspond to individual patient samples. Of the 4 publicly available datasets mentioned above, the only dataset with a GEO number was GSE3494, corresponding to the Miller dataset. The supplementary file below lists the 251 Samples used from the GSE3494 study. We did not reanalyze the data - there was no change made to the Miller dataset; we only used these data for confirmation of our own dataset.
2010-12-07 | E-GEOD-21947 | biostudies-arrayexpress
Project description:Micro RNA (miRNA) profiling of breast cancer subtype was demonstrated using RT-PCR. We present herein two cases with miRNA profiling in estrogen receptor (ER)-positive and -negative breast cancer. The level of miR-181a expression in ER-positive cancer was higher than in ER-negative cancer, while expression of miR-27a, -107 and -195 was lower in ER-positive when compared with ER-negative cancer.
2018-10-13 | GSE121172 | GEO
Project description:Gene and protein expression in the breast varies relative to menopausal status (MS) and menstrual phase (MP), but cancer risk biomarker research using archived benign breast samples is usually uninformed by MS-MP data. We hypothesized that gene expression variation relative to ambient hormones can be exploited to develop markers of MS-MP that can be measured directly in breast tissue. Random fine needle aspiration samples from the unaffected breasts of 18 women were utilized for Illumina microarray analysis. Gene expression was correlated with simultaneous serum levels of estradiol, progesterone, and follicle stimulating hormone (FSH). True MS-MP states were defined by age and serum hormones. Genes highly correlated to serum hormones were used to classify samples into 2 MS groups: premenopause or postmenopause. The premenopausal samples were further classified into 3 MP groups: early follicular, late follicular or luteal.
2014-07-30 | E-GEOD-36318 | biostudies-arrayexpress
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2023-09-12 | GSE205725 | GEO
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