Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer (original) (raw)
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Gene expression profile in breast cancer comprising predictive markers for metastatic risk
Genetics and Molecular Research, 2015
Quantitative multiplex reverse transcriptase-polymerase chain reaction was developed for the simultaneous detection of multiple-gene expression levels of formalin-fixed, paraffin-embedded breast cancer samples. Candidate genes were selected from previous microarray data relevant to breast cancer markers that had the potential to serve as predictive markers for metastatic risk. This multiplex gene set included 11 candidate and 3 housekeeping genes, and the aim was to predict breast cancer progression based on lymph node involvement status. Our study demonstrated that the system generated a good standard curve fit (R 2 = 0.9901-0.9998) correlated with RNA concentration. The multiplex gene expression profile indicated significantly downregulated levels of G protein-coupled receptor kinase interacting ArfGAP 2 (GIT2) and mitochondrial transcription termination factor (MTERF) ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (3): 10929-10936 (2015) genes in a lymph node-positive group of patients, with P values of 0.004 and 0.038, respectively. Therefore, this in-house method using multiple genes of interest might be an alternative tool for prediction of breast cancer metastasis.
Proceedings of the National Academy of Sciences, 2003
Comprehensive gene expression patterns generated from cDNA microarrays were correlated with detailed clinico-pathological characteristics and clinical outcome in an unselected group of 99 node-negative and node-positive breast cancer patients. Gene expression patterns were found to be strongly associated with estrogen receptor (ER) status and moderately associated with grade, but not associated with menopausal status, nodal status, or tumor size. Hierarchical cluster analysis segregated the tumors into two main groups based on their ER status, which correlated well with basal and luminal characteristics. Cox proportional hazards regression analysis identified 16 genes that were significantly associated with relapse-free survival at a stringent significance level of 0.001 to account for multiple comparisons. Of 231 genes previously reported by others [van't Veer, L. J., et al. (2002) Nature 415, 530 -536] as being associated with survival, 93 probe elements overlapped with the set of 7,650 probe elements represented on the arrays used in this study. Hierarchical cluster analysis based on the set of 93 probe elements segregated our population into two distinct subgroups with different relapse-free survival (P < 0.03). The number of these 93 probe elements showing significant univariate association with relapse-free survival (P < 0.05) in the present study was 14, representing 11 unique genes. Genes involved in cell cycle, DNA replication, and chromosomal stability were consistently elevated in the various poor prognostic groups. In addition, glutathione S-transferase M3 emerged as an important survival marker in both studies. When taken together with other array studies, our results highlight the consistent biological and clinical associations with gene expression profiles.
International Journal of Cancer, 2008
Gene expression profiles were studied by microarray analysis in 2 sets of archival breast cancer tissues from patients with distinct clinical outcome. Seventy-seven differentially expressed genes were identified when comparing 30 cases with relapse and 30 cases without relapse within 72 months from surgery. These genes had a specific ontological distribution and some of them have been linked to breast cancer in previous studies: AIB1, the two keratin genes KRT5 and KRT15, RAF1, WIF1 and MSH6. Seven out of 77 differentially expressed genes were selected and analyzed by qRT-PCR in 127 cases of breast cancer. The expression levels of 6 upregulated genes (CKMT1B, DDX21, PRKDC, PTPN1, SLPI, YWHAE) showed a significant association to both disease-free and overall survival. Multivariate analysis using the significant factors (i.e., estrogen receptor and lymph node status) as covariates confirmed the association with survival. There was no correlation between the expression level of these genes and other clinical parameters. In contrast, SERPINA3, the only downregulated gene examined, was not associated with survival, but correlated with steroid receptor status. An indirect validation of our genes was provided by calculating their association with survival in 3 publicly available microarray datasets. CKMT1B expression was an independent prognostic marker in all 3 datasets, whereas other genes confirmed their association with disease-free survival in at least 1 dataset. This work provides a novel set of genes that could be used as independent prognostic markers and potential drug targets for breast cancer.
A gene-expression signature as a predictor of survival in breast cancer
… England Journal of …, 2002
Among the 295 patients, 180 had a poorprognosis signature and 115 had a good-prognosis signature, and the mean (±SE) overall 10-year survival rates were 54.6±4.4 percent and 94.5±2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6±4.5 percent in the group with a poor-prognosis signature and 85.2±4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome.
Biomedicines
Breast cancer is one of the most prevalent types of cancer diagnosed globally and continues to have a significant impact on the global number of cancer deaths. Despite all efforts of epidemiological and experimental research, therapeutic concepts in cancer are still unsatisfactory. Gene expression datasets are widely used to discover the new biomarkers and molecular therapeutic targets in diseases. In the present study, we analyzed four datasets using R packages with accession number GSE29044, GSE42568, GSE89116, and GSE109169 retrieved from NCBI-GEO and differential expressed genes (DEGs) were identified. Protein–protein interaction (PPI) network was constructed to screen the key genes. Subsequently, the GO function and KEGG pathways were analyzed to determine the biological function of key genes. Expression profile of key genes was validated in MCF-7 and MDA-MB-231 human breast cancer cell lines using qRT-PCR. Overall expression level and stage wise expression pattern of key genes...
Expression profiling to predict outcome in breast cancer: the influence of sample selection
Breast cancer research : BCR, 2003
Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-alpha status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-alpha-positive and estrogen receptor-alpha-negative tumors.
Interrogating differences in expression of targeted gene sets to predict breast cancer outcome
BMC Cancer, 2013
Background Genomics provides opportunities to develop precise tests for diagnostics, therapy selection and monitoring. From analyses of our studies and those of published results, 32 candidate genes were identified, whose expression appears related to clinical outcome of breast cancer. Expression of these genes was validated by qPCR and correlated with clinical follow-up to identify a gene subset for development of a prognostic test. Methods RNA was isolated from 225 frozen invasive ductal carcinomas,and qRT-PCR was performed. Univariate hazard ratios and 95% confidence intervals for breast cancer mortality and recurrence were calculated for each of the 32 candidate genes. A multivariable gene expression model for predicting each outcome was determined using the LASSO, with 1000 splits of the data into training and testing sets to determine predictive accuracy based on the C-index. Models with gene expression data were compared to models with standard clinical covariates and models ...