Microarray analysis of human bone metastases samples
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Capulli Mattia
PROVIDER: S-ECPF-GEOD-34485 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:We performed microarray analysis with two different chip platforms, Affymetrix and Agilent, on bone metastasis samples from breast cancer patients with only bone metastases and metastases in bone and visceral organs.
2011-12-29 | GSE34485 | GEO
Project description:We performed microarray analysis with two different chip platforms, Affymetrix and Agilent, on bone metastasis samples from breast cancer patients with only bone metastases and metastases in bone and visceral organs. We collected bone metastases biopsies from breast cancer patients with only bone metastases and metastases in bone and visceral organs. RNA was extracted from these samples and subjected to microarray analysis with the Agilent platform. Data were compared with a signsture obtained with the Affymetrix platform, to obtain a signature of differentially regulated genes between the two groups of patients This Series represents the Agilent data only. The Affymetrix data are provided in GEO Series GSE11078.
2011-12-29 | E-GEOD-34485 | biostudies-arrayexpress
Project description:At this moment, no method is available to objectively estimate the temperature to which skeletal remains have been exposed during a fire. Estimating this temperature can provide crucial information in a legal investigation. Exposure of bone to heat results in observable and measurable changes, including a change in colour. To determine the exposure temperature of experimental bone samples, heat related changes in colour were systemically studied by means of image analysis. In total 1138 samples of fresh human long bone diaphysis and epiphysis, varying in size, were subjected to heat ranging from room temperature to 900 °C for various durations and in different media. The samples were scanned with a calibrated flatbed scanner and photographed with a Digital Single Lens Reflex camera. Red, Green, Blue values and Lightness, A-, and B-coordinates were collected for statistical analysis. Cluster analysis showed that discriminating thresholds for Lightness and B-coordinate could be defined and used to construct a model of decision rules. This model enables the user to differentiate between seven different temperature clusters with relatively high precision and accuracy. The proposed decision model provides an objective, robust and non-destructive method for estimating the exposure temperature of heated bone samples.
| S-EPMC6586926 | biostudies-literature
Project description:The macula is a unique and important region in the primate retina that achieves high resolution and color vision in the central visual field. We recently reported data obtained from microarray analysis of gene expression in the macula of the human fetal retina (Kozulin et al., Mol Vis 15:45-59, 1). In this paper, we describe the preliminary analyses undertaken to visualize differences and verify comparability of the replicates used in that study, report the differential expression of other gene families obtained from the analysis, and show the reproducibility of our findings in several gene families by quantitative real-time PCR.
| S-EPMC2816828 | biostudies-literature
Project description:DNA methylation profiling of paired samples of non-malignant and malignant prostate tissue obtained from prostatectomy speciments (n=12) and of bone metastasis tissue samples obtained from separate prostate cancer patients (n=70). The Illumina Infinium Methylation EPIC arrays were used to obtain genome-wide DNA methylation profiles across approximately 850,000 CpGs in each sample. The bone metastasis samples were obtained from castration-resistant (n = 52) and untreated (n = 14) prostate cancer patients as well as from prostate cancer patients treated for a short period with castration therapy (n = 4).
2021-07-01 | GSE174613 | GEO
Project description:Large-scale and in-depth characterization of the intestinal microbiota necessitates application of high-throughput 16S rRNA gene-based technologies, such as barcoded pyrosequencing and phylogenetic microarray analysis. In this study, the two techniques were compared and contrasted for analysis of the bacterial composition in three fecal and three small intestinal samples from human individuals. As PCR remains a crucial step in sample preparation for both techniques, different forward primers were used for amplification to assess their impact on microbial profiling results. An average of 7,944 pyrosequences, spanning the V1 and V2 region of 16S rRNA genes, was obtained per sample. Although primer choice in barcoded pyrosequencing did not affect species richness and diversity estimates, detection of Actinobacteria strongly depended on the selected primer. Microbial profiles obtained by pyrosequencing and phylogenetic microarray analysis (HITChip) correlated strongly for fecal and ileal lumen samples but were less concordant for ileostomy effluent. Quantitative PCR was employed to investigate the deviations in profiling between pyrosequencing and HITChip analysis. Since cloning and sequencing of random 16S rRNA genes from ileostomy effluent confirmed the presence of novel intestinal phylotypes detected by pyrosequencing, especially those belonging to the Veillonella group, the divergence between pyrosequencing and the HITChip is likely due to the relatively low number of available 16S rRNA gene sequences of small intestinal origin in the DNA databases that were used for HITChip probe design. Overall, this study demonstrated that equivalent biological conclusions are obtained by high-throughput profiling of microbial communities, independent of technology or primer choice.
| S-EPMC3067328 | biostudies-literature
Project description:Bone metastasis is a major cause of morbidity within solid tumours of the breast, prostate, lung and kidney. Metastasis to the skeleton is associated with a wide range of complications including bone fractures, spinal cord compression, hypercalcaemia and increased bone pain. Improved treatments for bone metastasis, such as the use of anti-bone resorptive bisphosphonate agents, within post-menopausal women have improved disease-free survival; however, these treatments are not without side effects. There is thus a need for biomarkers, which will predict the risk of developing the spread to bone within these cancers. The application of molecular profiling techniques, together with animal model systems and engineered cell-lines has enabled the identification of a series of potential bone-metastasis biomarker molecules predictive of bone metastasis risk. Some of these biomarker candidates have been validated within patient-derived samples providing a step towards clinical utility. Recent developments in multiplex biomarker quantification now enable the simultaneous measurement of up to 96 micro-RNA/protein molecules in a spatially defined manner with single-cell resolution, thus enabling the characterisation of the key molecules active at the sites of pre-metastatic niche formation as well as tumour-stroma signalling. These technologies have considerable potential to inform biomarker discovery. Additionally, a potential future extension of these discoveries could also be the identification of novel drug targets within cancer spread to bone. This chapter summarises recent findings in biomarker discovery within the key bone metastatic cancers (breast, prostate, lung and renal cell carcinoma). Tissue-based and circulating blood-based biomarkers are discussed from the fields of genomics, epigenetic regulation (micro-RNAs) and protein/cell-signalling together with a discussion of the potential future development of these markers towards clinical development.
| S-EPMC7465268 | biostudies-literature
Project description:mRNA expression of lung adenocarcinoma brain metastases was analyzed as part of an approach to defining the microenvironment
2020-01-03 | E-MTAB-8659 | biostudies-arrayexpress
Project description:Demand for high quality gene expression data has driven the development of revolutionary microarray technologies. The quality of the data is affected by the performance of the microarray platform as well as how the nucleic acid targets are prepared. The most common method for target nucleic acid preparation includes in vitro transcription amplification of the sample RNA. Although this method requires a small amount of starting material and is reported to have high reproducibility, there are also technical disadvantages such as amplification bias and the long, laborious protocol. Using RNA derived from human brain, breast and colon, we demonstrate that a non-amplification method, which was previously shown to be inferior, could be transformed to a highly quantitative method with a dynamic range of five orders of magnitude. Furthermore, the correlation coefficient calculated by comparing microarray assays using non-amplified samples with qRT-PCR assays was approximately 0.9, a value much higher than when samples were prepared using amplification methods. Our results were also compared with data from various microarray platforms studied in the MicroArray Quality Control (MAQC) project. In combination with micro-columnar 3D-Gene™ microarray, this non-amplification method is applicable to a variety of genetic analyses, including biomarker screening and diagnostic tests for cancer.
| S-EPMC3280296 | biostudies-literature
Project description:In biological systems that undergo processes such as differentiation, a clear concept of progression exists. We present a novel computational approach, called Sample Progression Discovery (SPD), to discover patterns of biological progression underlying microarray gene expression data. SPD assumes that individual samples of a microarray dataset are related by an unknown biological process (i.e., differentiation, development, cell cycle, disease progression), and that each sample represents one unknown point along the progression of that process. SPD aims to organize the samples in a manner that reveals the underlying progression and to simultaneously identify subsets of genes that are responsible for that progression. We demonstrate the performance of SPD on a variety of microarray datasets that were generated by sampling a biological process at different points along its progression, without providing SPD any information of the underlying process. When applied to a cell cycle time series microarray dataset, SPD was not provided any prior knowledge of samples' time order or of which genes are cell-cycle regulated, yet SPD recovered the correct time order and identified many genes that have been associated with the cell cycle. When applied to B-cell differentiation data, SPD recovered the correct order of stages of normal B-cell differentiation and the linkage between preB-ALL tumor cells with their cell origin preB. When applied to mouse embryonic stem cell differentiation data, SPD uncovered a landscape of ESC differentiation into various lineages and genes that represent both generic and lineage specific processes. When applied to a prostate cancer microarray dataset, SPD identified gene modules that reflect a progression consistent with disease stages. SPD may be best viewed as a novel tool for synthesizing biological hypotheses because it provides a likely biological progression underlying a microarray dataset and, perhaps more importantly, the candidate genes that regulate that progression.
| S-EPMC3077357 | biostudies-literature