Unveiling promising breast cancer biomarkers: an integrative approach combining bioinformatics analysis and experimental verification (original) (raw)
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2013
Breast cancer is a serious health concern and still a leading cause of death among women in the world. To explore the complexity of this cancer, we performed microarray analysis on highly selective cancer and normal breast tissues. The aim of this study was to identify differentially expressed genes between both tissues and to elucidate further molecular pathways involved in breast cancer carcinogenesis. Genome-wide expression profiling was performed on fifteen cancer and five normal breast tissues using the Affymetrix GeneChip® Human Gene 1.0 ST array. Supervised hierarchical cluster analysis using filtering parameters of -1.5 to 1.5 fold-change and p-value with False Discovery Rate < 0.05 revealed 404 up-regulated and 463 down-regulated genes. Pathway analysis revealed the significant genes were involved in cell cycle regulation, DNA repair, Hedgehog pathway, histone phosphorylation, TRRAP/Tip60 chromatin remodelling and apoptosis regulation. Among the top 10 significantly over...
International Journal of Molecular Sciences, 2013
Molecular tests predicting the outcome of breast cancer patients based on gene expression levels can be used to assist in making treatment decisions after consideration of conventional markers. In this study we identified a subset of 20 mRNA differentially regulated in breast cancer analyzing several publicly available array gene expression data using R/Bioconductor package. Using RTqPCR we evaluate 261 consecutive invasive breast cancer cases not selected for age, adjuvant treatment, nodal and estrogen receptor status from paraffin embedded sections. The biological samples dataset was split into a training (137 cases) and a validation set (124 cases). The gene signature was developed on the training set and a multivariate stepwise Cox analysis selected five genes independently associated with DFS: FGF18 (HR = 1.13, p = 0.05), BCL2 (HR = 0.57, p = 0.001), PRC1
Gene Expression Profiles Reveal Potential Targets for Breast Cancer Diagnosis and Treatment
Figuring out the molecular mechanisms underlying breast cancer is essential for the diagnosis and treatment of this invasive disorder. Hence it is important to identify the most significant genes correlated with molecular events and to study their interactions in order to identify breast cancer mechanisms. Here we focus on the gene expression profiles, which we have detected in breast cancer. High-throughput genomic innovations such as microarray have helped us understand the complex dynamics of multisystem diseases such as diabetes and cancer. We performed an analysis using microarray datasets by the Networkanalyst bioinformatics tool, based on a random effect model (REM). We achieved pivotal differential expressed genes like ADAMTS5, SCARA5, IGSF10, and C2orf40 that had the most down-regulation, and also COL10A1, COL11A1, and UHRF1 that they had the most up-regulation in four-stage of breast cancer. We used CentiScape and AllegroMCODE plugins in CytoScape software in order to figu...
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.
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.
Gene Expression Profiling for Breast Cancer Prognosis in Chinese Populations
n Abstract: To investigate a quantitative reverse transcription polymerase chain reaction (QRT-PCR) assay different from 21-gene assay which can be used to prognosticate the risk of recurrence in patients with estrogen receptor (ER) positive, lymph node (LN) negative breast cancer. To accurately determine the relationship between the Recurrence Score (RS) derived from our assay and the risk of distant recurrence in Chinese patients with LN negative and positive breast cancer through the analysis of paraffin tissues. We obtained archival paraffin-embedded tissues from patients with invasive breast cancer and varying axillary lymph node involvement. QRT-PCR reaction was performed by using the method of SYBR Green I dye with primers. Expression of the 21-genes was converted to RS by a prespecified algorithm. We then assessed the probability of the test to accurately predict distant recurrence-free survival in this retrospective cohort. Ninety-three patients were eligible based on gene expression profiles. In our population, most breast cancer patients were premenopausal (82.6%), at early stage (93.6%) and ER positive (91.4%). Median follow-up was 65.9 months. The 5-year recurrence-free survival rate for the group was 58.8%. The concordance between the reverse transcription-PCR and immunohistochemical (IHC) measurement for ER, progesterone receptor (PgR), and HER-2 determinations was high and comparable. High RS was predictive of an elevated risk of relapse (p < 0.001). In subgroups of patients, RS had significantly predictive performance both in node-negative (p = 0.009) and node-positive patients (p = 0.038). Multivariable analysis showed that nodal status, adjuvant hormonal therapy and RS were significantly related to prognosis. RS category is a better predictor than the other risk assessment criteria or clinicopatholic features, with which we can determine more accurately the risks for recurrence of various patients. We have established an easy and economical QRT-PCR assay and validated in concordance with IHC measurements for ER, PgR, and HER-2. RS was associated with distant recurrence among Chinese patients with hormone receptor (HR) positive breast cancer. This study may promote the use of RS estimated from the expression of the 21-gene set for prognostication and routine clinical diagnostic application in Chinese populations. n
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...
Oncogene, 2002
Identifying novel and known genes that are dierentially expressed in breast cancer has important implications in understanding the biology of breast tumorigenesis and developing new diagnostic and therapeutic agents. In this study we have combined two powerful technologies, PCRbased cDNA subtraction and cDNA microarray, as a high throughput methodology designed to identify cDNA clones that are breast tumor-and tissue-speci®c and are overexpressed in breast tumors. Approximately 2000 cDNA clones generated from the subtracted breast tumor library were arrayed on the microarray chips. The arrayed target cDNAs were then hybridized with 30 pairs of¯uorescentlabeled cDNA probes generated from breast tumors and normal tissues to determine the tissue distribution and tumor speci®city. cDNA clones showing overexpression in breast tumors by microarray were further analysed by DNA sequencing, GenBank and EST database searches, and quantitative real time PCR. We identi®ed several known genes, including mammaglobin, cytokeratin 19, ®bronectin, and hair-speci®c type II keratin, which have previously been shown to be overexpressed in breast tumors and may play an important role in the malignance of breast. We also discovered B726P which appears to be an isoform of NY-BR-1, a breast tissue-speci®c gene. Two additional clones discovered, B709P and GABA A receptor p subunit, were not previously described for their overexpression pro®le in breast tumors. Thus, combining PCRbased cDNA subtraction and cDNA microarray allowed for an ecient way to identify and validate genes with elevated mRNA expression levels in breast cancer that may potentially be involved in breast cancer progression. These dierentially expressed genes may be of potential utility as therapeutic and diagnostic targets for breast cancer.