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Gene expression profiles in breast cancer: breast cancer tissues vs. normal breast tissues

ABSTRACT: To screen candidate methylation markers for early detection of breast cancer, we performed methylated-CpG island recovery assay combined with CpG island array on 61982 CpG sites across 4162 genes in 10 breast tumor tissues and 10 non-tumor breast tissues. We detected 70 significantly hypermethylated genes in breast tumor tissues, including many novel hypermethylated genes such as ITGA4, NFIX, OTX2 and FGF12. Direct bisulfite sequencing showed widespread methylations occurred in intragenic regions of WT1, PAX6 and ITGA4 genes and promoter region of OTX2 in breast cancer tissue. COBRA assay in independent tumor and non-tumor samples confirmed that WT1, OTX2 and PAX6 genes were hypermethylated in breast cancer tissues. To explore the relationship between methylation and gene expression, gene expression profiling analysis was performed in 8 breast tumor tissues and 8 non-tumor breast tissues. We found that some hypermethylated genes in breast cancer were not expressed in breast tissues. RT-PCR assay showed that WT1 and PITX2 were only weakly expressed in the breast tumor tissues and weren’t expressed in most non-tumor breast tissues. OTX2 and PAX6 weren’t expressed in both breast tumor tissues and non-tumor tissues. Unpaired experiments, breast tumor tissues vs. breast non-tumor tissues. Biological replicates: 8 breast cancer tissue replicates, 8 breast non-tumor tissue replicates.

ORGANISM(S): Homo sapiens

SUBMITTER: Weiguo Hong

PROVIDER: S-ECPF-GEOD-33447 | biostudies-other |

REPOSITORIES: biostudies-other

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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.

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Gene expression profiles in breast cancer: breast cancer tissues vs. normal breast tissues

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