- Poster Presentation
- Open Access
Gene expression profiling in whole-blood samples from postmenopausal women exposed to hormone replacement therapy
© BioMed Central 2005
- Published: 17 June 2005
- Hormone Replacement Therapy
- Postmenopausal Hormone Therapy
- Hormone Replacement Therapy User
Accumulating evidence on postmenopausal hormone therapy confirms the deleterious effects on risk of breast cancer or stroke and questions the positive effects on quality of life and coronary disease risk. A large-scale gene expression study may present promising new insights into the effect of hormone replacement therapy (HRT). Our study explores the gene expression profile from whole-blood total RNA, which is an important and relatively unexplored issue in human biology.
In early autumn 2003, 500 women participating in the Norwegian Women and Cancer (NOWAC) cohort were randomized and invited to give blood samples (393 returned the questionnaire). Blood samples were collected in a PAXgene tube in late autumn 2003 and a short questionnaire additional to those previously given in the NOWAC study was completed. In our study, 100 women (50 HRT users and 50 non-HRT users) born between 1943 and 1949 with normal body mass index (18.5 < BMI < 29.9 kg/m2) and no other medication use were selected. After extraction amplification and labelling of the samples, we hybridized them overnight to Agilent Human 1A oligoarrays (G4110b; Agilent Technologies, Palo Alto, CA, USA) containing 22,153 features representing 20,173 unique genes.
Genes identified by t test with P < 0.03 (n = 253) were used to build a classifier using the nearest shrunken centroids method. Results did not reveal any distinct gene list that predicts accurately HRT exposure (error rate = 0.42). We performed a new analysis including, among HRT users, only women who were using continuous combined treatment (ethinylestradiol and norethisterone acetate). The performance of the classifier (i.e. 98 genes) improved (error rate = 0.25). The specificity (78.7%) was slightly better than the sensitivity (68.0%).
We then tested the significant changes in a single gene by different methods like the t test, significance analysis of microarrays controlling for the false discovery rate and Bayesian ANOVA analysis, which balance the false discovery rate and the true positive rate. Only few expression alterations of minor magnitude caused by HRT could be detected in whole-blood total RNA. The weak signals of exposure-induced changes in whole blood made it very difficult, or even impossible, to identify single genes statistically significant with the background of thousands of individuals genes tested simultaneously.
Instead, we focused on identifying significant pattern changes of biologic process in genes identified from the t test, using the annotations defined by the Gene Ontology Consortium.
Mixed cell types in whole blood made it more difficult to observe differences in gene expression profiles. According to the little amplitude of expression alterations observed in whole blood, large sample sizes are needed to conduct global expression profiling. Although one gene change may be small and difficult to detect accurately in a significant test, significant enrichments in the biologic process of genes with small changes after HRT use have been assessable.