Gene expression patterns distinguish breast carcinomas from normal breast tissues: The Malaysian context

Whole genome DNA methylation signature of HER2-positive breast cancer

“…Five of these genes, HIST1H4F, GABRA4, WDR69, C1orf114, and CPXM1, have not been previously identified to be involved in any cancer. External validation of the top candidate biomarker genes using a publically available data set 26 showed lowered mean expression in HER2+ breast cancers, statistically significant in INA and FOXC2, consistent with the findings of transcriptional repression as result of hypermethylated gene promoters. 35 A recent 450K based study showed that only 15% of the significantly differentially methylated genes showed a correlated change in mRNA expression, 36 similar to our external validation findings.…”

“…This was followed by external validation of gene expression of these top candidate biomarker genes using gene expression data set from Pau Ni I.B. et al, 26 publically available at the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.gov/geo/). Main steps in the data analysis of 450K array is summarized in Figure 5.…”

“…Gene expression analysis of these genes using a data set from Pau Ni I.B. et al 26 revealed lowered mean expression of most of these genes in HER2+ breast tumors in comparison to normal breast tissues, of which INA and FOXC2 were statistically significant (crude *P value ≤ 0.05). In contrast, when the total cohort of patients was analyzed (HER2+ and HER2 negative) there was no such consistent pattern between the total cohort and normal breast tissues ( Table 3).…”

Keratin 19 expression correlates with poor prognosis in breast cancer

“…As a source of primary patient material three data sets GSE9574 [9], GSE15852 [10] and GSE16873 [11] were analyzed. These datasets include mRNA expression data from 70 normal breast tissue samples and 69 cancer tissue samples.…”

Integrative Gene Selection for Classification of Microarray Data

“…For instance in cancer classification, microarray is used to classify tumor samples into different classes. Previous studies have shown that microarray data can be used to differentiate between normal and cancerous tissues (Alon, et al, 1999;Pau Ni, et al), to classify multiclass cancer subtype (Rifkin, et al, 2003;Yeang, et al, 2001), and even to identify new cancer subtypes (Golub, et al, 1999). Having good cancer classification is crucial in order to give the most effective and cost saving treatments for patients (Soh, et al, 2007).…”