Dataset Information

GATA4 immunolocalization in breast carcinoma as a potent prognostic predictor.

ABSTRACT: Transcriptional GATA factors are known lineage selector genes and regulate a variety of biological processes including specification and differentiation of tissues. In the present study, we examined expression profiles of six GATA factor genes in invasive ductal carcinomas (IDC) of the breast using microarray analysis (n = 20) and found that GATA4 expression was closely correlated with recurrence in patients. Because the significance of GATA4 has remained largely unknown in breast carcinoma, we further immunolocalized GATA4 in ductal carcinoma in situ (DCIS) of the breast (n = 48) and IDC (n = 163). GATA4 immunoreactivity was detected in the nuclei of carcinoma cells and was positive in 27% of DCIS and 31% of IDC cases. GATA4 status was significantly associated with nuclear grade and van Nuys classification in DCIS and was positively associated with distant metastasis, histological grade and HER2 status, but negatively correlated with progesterone receptor labeling index in IDC. Subsequent multivariate analysis demonstrated that GATA4 status was an independent prognostic factor for both disease-free and breast cancer-specific survival of IDC patients. All of these results indicate that GATA4 plays important roles in the progression of breast carcinoma from an early stage and that immunohistochemical GATA4 status is considered a potent prognostic factor in human breast cancer patients.

SUBMITTER: Takagi K

PROVIDER: S-EPMC4317835 | biostudies-other | 2014 May

REPOSITORIES: biostudies-other

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GATA4 immunolocalization in breast carcinoma as a potent prognostic predictor.

Takagi Kiyoshi K Moriguchi Takashi T Miki Yasuhiro Y Nakamura Yasuhiro Y Watanabe Mika M Ishida Takanori T Yamamoto Masayuki M Sasano Hironobu H Suzuki Takashi T

Cancer science 20140319 5

Transcriptional GATA factors are known lineage selector genes and regulate a variety of biological processes including specification and differentiation of tissues. In the present study, we examined expression profiles of six GATA factor genes in invasive ductal carcinomas (IDC) of the breast using microarray analysis (n = 20) and found that GATA4 expression was closely correlated with recurrence in patients. Because the significance of GATA4 has remained largely unknown in breast carcinoma, we ...[more]

PMID: 24862985

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Project description:Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting.To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification.A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina.Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence.The subtypes were significantly associated with RFS (p<0.01), CSS (p<0.01), and OS (p<0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.

Dataset Information

GATA4 immunolocalization in breast carcinoma as a potent prognostic predictor.

Publications

GATA4 immunolocalization in breast carcinoma as a potent prognostic predictor.

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