Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Identification of Biomarkers in Breast Cancer by Gene Expression Profiling Using Human Tissues.

ABSTRACT: Breast cancer is the second leading cause of death by cancer in women. To identify biomarkers with potential diagnostic and therapeutic utilities in breast cancer, gene expression profiling from real patient tissues were used to discover significantly deregulated genes out of 50,739 genes of human transcriptome. Total RNAs were extracted, and the gene expression profiles of 32 cancerous and normal tissues were established using Agilent gene expression microarray technology. The results were analyzed with Agilent GeneSpring 12.6 software. Here we provide detailed experimental methods and analysis for the microarray data, which have been deposited into Gene Expression Omnibus (GEO) under GSE57297.

SUBMITTER: Fu J

PROVIDER: S-EPMC4226530 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Identification of Biomarkers in Breast Cancer by Gene Expression Profiling Using Human Tissues.

Fu Junjie J Allen William W Xia Amy A Ma Zhuofan Z Qi Xin X

Genomics data 20141201

Breast cancer is the second leading cause of death by cancer in women. To identify biomarkers with potential diagnostic and therapeutic utilities in breast cancer, gene expression profiling from real patient tissues were used to discover significantly deregulated genes out of 50,739 genes of human transcriptome. Total RNAs were extracted, and the gene expression profiles of 32 cancerous and normal tissues were established using Agilent gene expression microarray technology. The results were anal ...[more]

PMID: 25396118

Similar Datasets

Discovery of Gene Regulation Pattern in Lung Cancer by Gene Expression Profiling Using Human Tissues.

Project description:Lung cancer continues to be one the most prevalent and life threatening cancers worldwide. In order to study the gene regulation pattern in lung cancer for new therapeutics discovery, gene expression profiling using human lung cancer tissues was conducted. The gene expression profiles were established using Affymetrix Human Exon 1.0 ST Array with RNA extracts from six clinical patients (five lung cancer samples and one normal control). The raw data were analyzed with Affymetrix Expression Console and Affymetrix Transcriptome Analysis Console 2.0. The regulation of several genes were further validated using real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR). Here we provide detailed experimental methods and analysis for the microarray data, which have been deposited into Gene Expression Omnibus (GEO) under GSE63571.

| S-EPMC4313763 | biostudies-literature

Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data.

Project description:Numerous studies have used microarrays to identify gene signatures for predicting cancer patient clinical outcome and responses to chemotherapy. However, the potential impact of gene expression profiling in cancer diagnosis, prognosis and development of personalized treatment may not be fully exploited due to the lack of consensus gene signatures and poor understanding of the underlying molecular mechanisms.We developed a novel approach to derive gene signatures for breast cancer prognosis in the context of known biological pathways. Using unsupervised methods, cancer patients were separated into distinct groups based on gene expression patterns in one of the following pathways: apoptosis, cell cycle, angiogenesis, metastasis, p53, DNA repair, and several receptor-mediated signaling pathways including chemokines, EGF, FGF, HIF, MAP kinase, JAK and NF-kappaB. The survival probabilities were then compared between the patient groups to determine if differential gene expression in a specific pathway is correlated with differential survival.Our results revealed expression of cell cycle genes is strongly predictive of breast cancer outcomes. We further confirmed this observation by building a cell cycle gene signature model using supervised methods. Validated in multiple independent datasets, the cell cycle gene signature is a more accurate predictor for breast cancer clinical outcome than the previously identified Amsterdam 70-gene signature that has been developed into a FDA approved clinical test MammaPrint.Taken together, the gene expression signature model we developed from well defined pathways is not only a consistently powerful prognosticator but also mechanistically linked to cancer biology. Our approach provides an alternative to the current methodology of identifying gene expression markers for cancer prognosis and drug responses using the whole genome gene expression data.

| S-EPMC2551605 | biostudies-literature

Gene expression profiling of human breast tissue samples using SAGE-Seq.

Project description:We present a powerful application of ultra high-throughput sequencing, SAGE-Seq, for the accurate quantification of normal and neoplastic mammary epithelial cell transcriptomes. We develop data analysis pipelines that allow the mapping of sense and antisense strands of mitochondrial and RefSeq genes, the normalization between libraries, and the identification of differentially expressed genes. We find that the diversity of cancer transcriptomes is significantly higher than that of normal cells. Our analysis indicates that transcript discovery plateaus at 10 million reads/sample, and suggests a minimum desired sequencing depth around five million reads. Comparison of SAGE-Seq and traditional SAGE on normal and cancerous breast tissues reveals higher sensitivity of SAGE-Seq to detect less-abundant genes, including those encoding for known breast cancer-related transcription factors and G protein-coupled receptors (GPCRs). SAGE-Seq is able to identify genes and pathways abnormally activated in breast cancer that traditional SAGE failed to call. SAGE-Seq is a powerful method for the identification of biomarkers and therapeutic targets in human disease.

| S-EPMC2989999 | biostudies-literature

Gene expression profiles in breast cancer: breast cancer tissues vs. normal breast tissues

Project description:To screen candidate methylation markers for early detection of breast cancer, we performed methylated-CpG island recovery assay combined with CpG island array on 61982 CpG sites across 4162 genes in 10 breast tumor tissues and 10 non-tumor breast tissues. We detected 70 significantly hypermethylated genes in breast tumor tissues, including many novel hypermethylated genes such as ITGA4, NFIX, OTX2 and FGF12. Direct bisulfite sequencing showed widespread methylations occurred in intragenic regions of WT1, PAX6 and ITGA4 genes and promoter region of OTX2 in breast cancer tissue. COBRA assay in independent tumor and non-tumor samples confirmed that WT1, OTX2 and PAX6 genes were hypermethylated in breast cancer tissues. To explore the relationship between methylation and gene expression, gene expression profiling analysis was performed in 8 breast tumor tissues and 8 non-tumor breast tissues. We found that some hypermethylated genes in breast cancer were not expressed in breast tissues. RT-PCR assay showed that WT1 and PITX2 were only weakly expressed in the breast tumor tissues and weren’t expressed in most non-tumor breast tissues. OTX2 and PAX6 weren’t expressed in both breast tumor tissues and non-tumor tissues. Unpaired experiments, breast tumor tissues vs. breast non-tumor tissues. Biological replicates: 8 breast cancer tissue replicates, 8 breast non-tumor tissue replicates.

| S-ECPF-GEOD-33447 | biostudies-other

Identification of significant gene regulations for breast cancer from human tissues

Project description:To discover individual genes with potential diagnostic and therapeutic utilities, we use gene expression profiling from real patient tissues to identify significantly regulated genes out of a broad coverage of human transcriptome. We use Agilent gene expression microarray technologies to measure gene expressions of 32 cancerous and normal tissues from breast cancer patients. Total RNAs were extracted and analyzed using Agilent microarray technologies. The whole genome expression profiling has been proformed and several significantly differently expressed genes (such as HS3ST4, MMP1, MMP11) were quatified by qPCR to verify their up/down-regulations, leading to discovery of important genes related to breast cancer metastasis. We examined 32 human breast tissues of three different breast cancer subtypes (including Luminal A, Luminal B, Triple Negative) and normal controls to perform microarray gene expression profiling.

| S-ECPF-GEOD-57297 | biostudies-other

Gene expression profiling of human breast cancer during pregnancy

Project description:Using a dataset of 54 pregnant and 113 age/stage-matched non-pregnant breast cancer patients with complete clinical and survival data; we evaluated the pattern of hot spot somatic mutations and performed transcriptomic profiling using Sequenom® and Affymetrix®, respectively. Breast cancer molecular subtypes were defined using PAM50 and 3-Gene classifiers. We performed Gene set enrichment analysis (GSEA) to evaluate pathways associated with diagnosis during pregnancy. We investigated the differential expression of cancer-related genes and published gene sets according to pregnancy. We finally investigated genes associated with disease-free survival. We identified 65 patients who were diagnosed with breast cancer during pregnancy. Moreover, for each case, two breast cancer patients who were not diagnosed during pregnancy or lactation, but who were matched according to age (±2 years), tumors size, nodal status, year of surgery (±2 years) and whether neoadjuvant chemotherapy was received were selected. Messenger RNA from tumor tissue has been extracted and hybridized on Affymetrix microarrays. However, due to experimental issues, 54 pregnant and 113 non-pregnant patients were remaining and used in our study.

| S-ECPF-GEOD-53031 | biostudies-other

Investigating microRNA-target interaction-supported tissues in human cancer tissues based on miRNA and target gene expression profiling.

Project description:UnlabelledRecent studies have revealed that a small non-coding RNA, microRNA (miRNA) down-regulates its mRNA targets. This effect is regarded as an important role in various biological processes. Many studies have been devoted to predicting miRNA-target interactions. These studies indicate that the interactions may only be functional in some specific tissues, which depend on the characteristics of an miRNA. No systematic methods have been established in the literature to investigate the correlation between miRNA-target interactions and tissue specificity through microarray data. In this study, we propose a method to investigate miRNA-target interaction-supported tissues, which is based on experimentally validated miRNA-target interactions. The tissue specificity results by our method are in accordance with the experimental results in the literature.Availability and implementationOur analysis results are available at http://tsmti.mbc.nctu.edu.tw/ and http://www.stat.nctu.edu.tw/hwang/tsmti.html.

| S-EPMC3995724 | biostudies-literature

Gene expression profiling of triple negative breast cancer, normal ductal cells, and normal tissues

Project description:To identify novel molecular targets for triple negative breast cancer (TNBC), we have employed whole genome microarray expression profiling. We purified 30 surgically resected breast cancer tissue diagnosed triple negative by means of immunohistochemical staining and 13 normal mammary ductal cells with lasermicrobeam microdissection system (PALM MicroBeam, Carl Zeiss MicroImaging Co., Ltd), performed whole human genome microarray, and compared gene expression levels of TNBC, normal mammary ductal cells, and normal vital organs to develop molecular targets with a minimum risk. Gene expression levels of 30 TNBC, 13 normal mammary ductal cells, and 4 normal vital tissues were evaluated. to clarify the molecular mechanism involved in TNBC, we analyzed gene expression profiles of 30 TNBC as well as 13 normal epithelial ductal cells purified by laser microbeam microdissection, and identified 301 transcripts that were significantly up-regulated and 321 transcripts that were significantly down-regulated in TNBC. In addition, gene-expression profiles analysis of normal human vital organs including heart, lung, liver, and kidney allowed us to identify 90 cancer-specific genes involved in breast carcinogenesis such as NEK2, PBK, DTL, MELK and GPSM2. Among them, we focused on cell cycle regulators, asp (abnormal spindle) homolog, microcephaly associated (Drosophila) (ASPM) and centromere protein K (CENPK) as novel therapeutic targets for TNBC.

| S-ECPF-GEOD-38959 | biostudies-other

Comparative gene expression profiling of normal and human colorectal adenomatous tissues.

Project description:Adenomatous colorectal polyps are the precursors of the majority of colorectal cancers. Investigation into the gene expression changes in the progression of colorectal adenoma may offer potential targets for the development of novel diagnostic strategies. Previous gene expression studies have generally been based on a limited number of cases or only focused on a single or a few genes. The present study aimed to identify molecular characteristics of colorectal adenoma through analysis of pathways and gene ontology. The study identified 808 upregulated and 857 downregulated genes. Among the 40 pathways enriched with differentially-expressed genes, the Staphylococcus aureus infection pathway and the intestinal immune network for immunoglobulin A production pathway were identified as the most statistically noteworthy pathways at the early stage for colorectal tumorigenesis (P<0.05). These results provide new understanding of colorectal adenoma pathogenesis, with the hope of offering theoretical support for future therapeutic studies.

| S-EPMC4186588 | biostudies-literature

Gene expression profiling identifies Fibronectin 1 and CXCL9 as candidate biomarkers for breast cancer screening.

Project description:BACKGROUND: There is a need to develop blood-based bioassays for breast cancer (BC) screening. In this study, differential gene expression between BC samples and benign tumours was used to identify candidate biomarkers for blood-based screening. METHODS: We identified two proteins (Fibronectin 1 and CXCL9) from a gene expression data set that included 120 BC samples and 45 benign lesions. These proteins fulfil the following criteria: differential gene expression between cancer and benign lesion, protein released in the extracellular medium and stable in the serum, commercially available ELISA kit, ELISA accuracy in a feasibility study. Protein concentrations were determined by ELISA. Blood samples were from normal volunteers (n=119) and early BC patients (n=133). RESULTS: Seventy-three per cent of patients had cT1-T2 tumour. Patients had higher CXCL9 and Fibronectin 1 concentrations than volunteers. CXCL9 mean concentration was 851 and 635 pg ml(-1) for patients and volunteers respectively (P=0.013). CXCL9 concentration was significantly higher in patients with estrogen receptor (ER)-negative compared with volunteers (P=0.003), data consistent with gene expression profile. Fibronectin 1 mean concentration was 190 microg ml(-1) for patients and 125 microg ml(-1) for volunteers (P<0.001). Areas under the curve for BC diagnosis were 0.78 and 0.62 for Fibronectin 1 and CXCL9 respectively. A combined score including Fibronectin 1 and CXCL9 dosages presented 53% of sensitivity and 98% of specificity. Similar performances were observed for ER-negative tumours. CONCLUSIONS: This study suggests that Fibronectin 1/CXCL9 dosage in serum could screen a significant rate of BC, including ER-negative, and that differential gene expression analysis is a good approach to select candidate biomarkers to set up blood assays cancer screening.

| S-EPMC2822945 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data