Identification of novel stem cell markers using gap analysis of gene expression data.
Ontology highlight
ABSTRACT:
SUBMITTER: Krzyzanowski PM
PROVIDER: S-EPMC2375031 | biostudies-literature | 2007
REPOSITORIES: biostudies-literature
Publications
Identification of novel stem cell markers using gap analysis of gene expression data.
Krzyzanowski Paul M PM Andrade-Navarro Miguel A MA
Genome biology 20070101 9
We describe a method for detecting marker genes in large heterogeneous collections of gene expression data. Markers are identified and characterized by the existence of demarcations in their expression values across the whole dataset, which suggest the presence of groupings of samples. We apply this method to DNA microarray data generated from 83 mouse stem cell related samples and describe 426 selected markers associated with differentiation to establish principles of stem cell evolution. ...[more]
Similar Datasets
Project description:(This section will be revisited in good time prior to publication, for final approval) Research within the field of functional genomics provides a more profound understanding of living organisms, by elucidating the interaction between genes and gene products. This is of increasing importance in many fields, and especially within cancer research. With the use of microarray technology, the gene expression of a vast number of genes can be measured simultaneously. This allows us insight into tumor biology, and an opportunity to obtain knowledge of diagnostic and prognostic value, as well as hints towards the causes of cancer. In this study the gene expression in a selection of human neuroendocrine (NE) and non-neuroendocrine tumor cell lines were compared. Both qualitative and quantitative methods were applied to get an insight into the NE tumor biology. NE cells are found in practically every organ and tissue in the body and are assigned to synthesize peptide hormones and take up amino acids and transform these into biogenic amines by carboxylation. NE tumors arise after malignant transformation of neuroendocrine cells and are usually highly differentiated and have a low growth rate, which makes this a high prevalence tumor type. These rather rare tumors characteristically express NE markers, such as chromogranin A, and contains characteristic dense-secretory core granules. By using cDNA microarray analysis, the gene expression profiles of various NE tumor cell lines (e. g. carcinoid, neuroblastoma, medullary thyroid carcinoma and endocrine pancreatic tumor) were compared with those of non-NE tumor cells. As a reference the commercially available Universal human RNA reference (Stratagene) was used. Verification of the results were done by real time RT-PCR analysis after the NE nature of the cell lines was stated by immunohistochemical, electron microscopic and light microscopic examinations. A large number of genes were differentially expressed in NE cell lines vs. the non-NE cell lines, and were found to be of interest in NE tumor biology. Some of these genes were previously known in NE tumor biology, whereas the vast majority of the specified genes are novel in the context of NE tumor biology. Genes with little previous information were also identified and thought to be an asset in the search of new NE markers. A feasible prospective of the study would be expansion with additional samples cell lines and a more thorough verification with real time RT-PCR analysis. This would enhance the credibility and thus the utility of the findings. Future studies on the biological functions of the candidate genes and the mechanisms of their regulation, will hopefully enable suggestions of new NE markers of prognostic or diagnostic value, and eventually new drug targets. Keywords: neuroendocrine, neuroendocrine markers, gene expression profiling, cDNA microarray The hybridisation was performed by an experienced laboratory technician Eli Helge, (Dept. of Laboratory Medicine, Children`s and Women`s health, NTNU) at the microarray core facility. Three separate runs with 10, 11 and 3 slides was needed to obtain the 20 hybridisations (dyeswap x 10 cell lines) planned for this study. Of the 10 cell lines 6 were neuroendocrine and 4 were non-neuroendocrine. For comparison between these two groups the universal human reference RNA (Stratagene) was used as the "control" on each slide. An adapted version of the 3DNA Array 350 protocol (Genisphere) was used. The slides were scanned with a ScanArrayTMExpressHT (Packard BioScience) twice immediately after the hybridisation was completed to avoid photo bleaching of the fluorescent dyes.
| S-ECPF-GEOD-4328 | biostudies-other
Project description:PurposeComparative reanalysis of single-cell transcriptomics data to gain useful novel insights into cancer stem cells (CSCs), which are a rare subset of cells within tumors, characterized by their capability to self-renew and differentiate, and their role in tumorigenicity.Patients and methodsThis project utilized publically available liver single-cell RNA-seq datasets of liver cancer and liver progenitor cell types to demonstrate how shared large amounts of data can generate new and valuable information. The data were analyzed using EdgeR differential expression analysis, with focus on a set of 34 known stemness markers.ResultsWe showed that the expression of stemness markers SOX9, KRT19, KRT7, and CD24, and Yamanaka factors Oct4 and SOX2 in CSCs was significantly elevated relative to progenitor cell types, potentially explaining their increased differentiation and replication potential.ConclusionThese results help to further document the complementary expression changes that give CSCs their distinct phenotypic profile. Our findings have potential significance to advance our knowledge of the important genes relevant to CSCs.
| S-EPMC8216768 | biostudies-literature
Project description:Glioblastoma (GBM) is a common and malignant tumor with a poor prognosis. Glioblastoma stem cells (GSCs) have been reported to be involved in tumorigenesis, tumor maintenance and therapeutic resistance. Thus, to discover novel candidate therapeutic drugs for anti-GBM and anti-GSCs is an urgent need. We hypothesized that if treatment with a drug could reverse, at least in part, the gene expression signature of GBM and GSCs, this drug may have the potential to inhibit pathways essential in the formation of GBM and thereby treat GBM. Here, we collected 356 GBM gene signatures from public databases and queried the Connectivity Map. We systematically evaluated the in vitro antitumor effects of 79 drugs in GBM cell lines. Of the drugs screened, thioridazine was selected for further characterization because it has potent anti-GBM and anti-GSCs properties. When investigating the mechanisms underlying the cytocidal effects of thioridazine, we found that thioridazine induces autophagy in GBM cell lines, and upregulates AMPK activity. Moreover, LC3-II was upregulated in U87MG sphere cells treated with thioridazine. In addition, thioridazine suppressed GBM tumorigenesis and induced autophagy in vivo. We not only repurposed the antipsychotic drug thioridazine as a potent anti-GBM and anti-GSCs agent, but also provided a new strategy to search for drugs with anticancer and anticancer stem cell properties.
| S-EPMC4669717 | biostudies-literature
Project description:Neuroblastoma is the most common extracranial solid tumor in childhood. Patients in high-risk group often have poor outcomes with low survival rates despite several treatment options. This study aimed to identify a genetic signature from gene expression profiles that can serve as prognostic indicators of survival time in patients of high-risk neuroblastoma, and that could be potential therapeutic targets. RNA-seq count data was downloaded from UCSC Xena browser and samples grouped into Short Survival (SS) and Long Survival (LS) groups. Differential gene expression (DGE) analysis, enrichment analyses, regulatory network analysis and machine learning (ML) prediction of survival group were performed. Forty differentially expressed genes (DEGs) were identified including genes involved in molecular function activities essential for tumor proliferation. DEGs used as features for prediction of survival groups included EVX2, NHLH2, PRSS12, POU6F2, HOXD10, MAPK15, RTL1, LGR5, CYP17A1, OR10AB1P, MYH14, LRRTM3, GRIN3A, HS3ST5, CRYAB and NXPH3. An accuracy score of 82% was obtained by the ML classification models. SMIM28 was revealed to possibly have a role in tumor proliferation and aggressiveness. Our results indicate that these DEGs can serve as prognostic indicators of survival in high-risk neuroblastoma patients and will assist clinicians in making better therapeutic and patient management decisions.
| S-EPMC7679032 | biostudies-literature
Project description:Oligodendrocytes undergo extensive changes as they differentiate from progenitors into myelinating cells. To better understand the molecular mechanisms underlying this transformation, we performed a comparative analysis using gene expression profiling of A2B5+ oligodendrocyte progenitors and O4+ oligodendrocytes. Cells were sort-purified ex vivo from postnatal rat brain using flow cytometry. Using Affymetrix microarrays, 1707 transcripts were identified with a more than twofold increase in expression in O4+ oligodendrocytes. Many genes required for oligodendrocyte differentiation were upregulated in O4+ oligodendrocytes, including numerous genes encoding myelin proteins. Transcriptional changes included genes required for cell adhesion, actin cytoskeleton regulation, and fatty acid and cholesterol biosynthesis. At the O4+ stage, there was an increase in expression of a novel proline-rich transmembrane protein (Prmp). Localized to the plasma membrane, Prmp displays adhesive properties that may be important for linking the extracellular matrix to the actin cytoskeleton. Together, our results highlight the usefulness of this discovery-driven experimental strategy to identify genes relevant to oligodendrocyte differentiation and myelination.
| S-EPMC1613258 | biostudies-literature
Project description:BackgroundGene expression heterogeneity contributes to development as well as disease progression. Due to technological limitations, most studies to date have focused on differences in mean expression across experimental conditions, rather than differences in gene expression variance. The advent of single cell RNA sequencing has now made it feasible to study gene expression heterogeneity and to characterise genes based on their coefficient of variation.MethodsWe collected single cell gene expression profiles for 32 human and 39 mouse embryonic stem cells and studied correlation between diverse characteristics such as network connectivity and coefficient of variation (CV) across single cells. We further systematically characterised properties unique to High CV genes.ResultsHighly expressed genes tended to have a low CV and were enriched for cell cycle genes. In contrast, High CV genes were co-expressed with other High CV genes, were enriched for bivalent (H3K4me3 and H3K27me3) marked promoters and showed enrichment for response to DNA damage and DNA repair.ConclusionsTaken together, this analysis demonstrates the divergent characteristics of genes based on their CV. High CV genes tend to form co-expression clusters and they explain bivalency at least in part.
| S-EPMC5012374 | biostudies-literature
Project description:BackgroundMetastatic prostate cancer (PCa) has no curative treatment options. Some forms of PCa are indolent and slow growing, while others metastasise quickly and may prove fatal within a very short time. The basis of this variable prognosis is poorly understood, despite considerable research. The aim of this study was to identify markers associated with the progression of PCa.MethodsArtificial neuronal network analysis combined with data from literature and previous work produced a panel of putative PCa progression markers, which were used in a transcriptomic analysis of 29 radical prostatectomy samples and correlated with clinical outcome.ResultsStatistical analysis yielded seven putative markers of PCa progression, ANPEP, ABL1, PSCA, EFNA1, HSPB1, INMT and TRIP13. Two data transformation methods were utilised with only markers that were significant in both selected for further analysis. ANPEP and EFNA1 were significantly correlated with Gleason score. Models of progression co-utilising markers ANPEP and ABL1 or ANPEP and PSCA had the ability to correctly predict indolent or aggressive disease, based on Gleason score, in 89.7% and 86.2% of cases, respectively. Another model of TRIP13 expression in combination with preoperative PSA level and Gleason score was able to correctly predict recurrence in 85.7% of cases.ConclusionThis proof of principle study demonstrates a novel association of carcinogenic and tumourigenic gene expression with PCa stage and prognosis.
| S-EPMC3251845 | biostudies-literature
Project description:Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required.
| S-EPMC2703796 | biostudies-literature
Project description:Natural killer cells (NK cells) are cytotoxic lymphocytes that play an important role in the innate immune system. In particular, it plays a very important defense function against host cells or cancer cells infected with a specific virus. Recent studies have shown that the activity of NK cells is decreased in patients with various carcinomas compared with normal controls, suggesting that the measurement of activity of NK cells in the blood may be helpful in the early diagnosis of cancer. In a recent study analyzing NK cell activity in 762 patients undergoing colonoscopy, NK cell activity showed performance in diagnosing advanced colorectal adenoma and colorectal cancer with sensitivities 42.2% and 85.7%, and specificity 58.3% and 59.5%, respectively. This finding suggests that NK cell activity may be useful as a screening method for colorectal neoplasms. However, as a single test, this diagnostic power is relatively low. On the other hands, another blood-based colorectal cancer screening test that using 29 gene panels algorithm has recently been reported. According to this study, 29 gene panel algorithms (Colox) showed performance in diagnosing advanced colorectal adenoma and colorectal cancer with sensitivity of 55.4% and 79.5% and specificity of both 90.0%, respectively. for diagnosis of advanced adenoma and colorectal cancer, respectively. Although the Colox test seems to be useful for the colorectal cancer screening using blood test, this diagnostic power is relatively low. In order to overcome low diagnostic performance of aforementioned tests (NK activity and Colox) as a single use, combination of individual biomarkers can be a promising alternative. In this regards, the aim of this study was to evaluate the diagnostic value for predicting advanced colorectal neoplasms by combining Colox and NK cell activity indicators.
| 2254885 | ecrin-mdr-crc
Project description:We compared whole genome expression profiles of GSCs with normal human cortex, human neural stem cells (hNSC) from fetal cortex, glioblastoma (GBM) primary, and recurrent tumors to find GSC-specific plasma membrane transcripts. All of the expression profiles were batch normalized by a robust multichip average (RMA) algorithm using Geospiza GeneSifter (PerkinElmer) online microarray database and analysis software. The data was then exported into Microsoft Office Excel 2010 and organized for GSC transcripts with raw intensity values 10 fold or higher over normal brain, hNSCs, GBM primary and recurrent tumor samples. The reverse sorting algorithm was done to obtain downregulated GSC trascripts.
| S-ECPF-GEOD-51822 | biostudies-other