Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Chromosome compartmentalization alterations in prostate cancer cell lines model disease progression.

ABSTRACT: Prostate cancer aggressiveness and metastatic potential are influenced by gene expression and genomic aberrations, features that can be influenced by the 3D structure of chromosomes inside the nucleus. Using chromosome conformation capture (Hi-C), we conducted a systematic genome architecture comparison on a cohort of cell lines that model prostate cancer progression, from normal epithelium to bone metastasis. We describe spatial compartment identity (A-open versus B-closed) changes with progression in these cell lines and their relation to gene expression changes in both cell lines and patient samples. In particular, 48 gene clusters switch from the B to the A compartment, including androgen receptor, WNT5A, and CDK14. These switches are accompanied by changes in the structure, size, and boundaries of topologically associating domains (TADs). Further, compartment changes in chromosome 21 are exacerbated with progression and may explain, in part, the genesis of the TMPRSS2-ERG translocation. These results suggest that discrete 3D genome structure changes play a deleterious role in prostate cancer progression. .

SUBMITTER: San Martin R

PROVIDER: S-EPMC8669499 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Alterations in chromosome spatial compartmentalization classify prostate cancer progression.

Project description:This SuperSeries is composed of the SubSeries listed below.

2021-05-01 | GSE172099 | GEO

Alterations in chromosome spatial compartmentalization classify prostate cancer progression [array]

Project description:Through a systematic analysis of chromosome conformation capture in a cohort of cells that model cancer progression, San Martin et.al. find that rearrangement of the 3D genome structure in prostate cancer is a potential mechanism for disease exacerbation and that genome-wide compartment identity can classify cancer according to progression

2021-05-01 | GSE172098 | GEO

Alterations in chromosome spatial compartmentalization classify prostate cancer progression [Hi-C]

2021-05-01 | GSE172097 | GEO

Alterations in chromosome spatial compartmentalization classify prostate cancer progression.

Project description:Alterations in chromosome spatial compartmentalization classify prostate cancer progression.

| PRJNA722007 | ENA

Alterations in chromosome spatial compartmentalization classify prostate cancer progression [array]

Project description:Alterations in chromosome spatial compartmentalization classify prostate cancer progression [array]

| PRJNA722012 | ENA

Alterations in chromosome spatial compartmentalization classify prostate cancer progression [Hi-C]

Project description:Alterations in chromosome spatial compartmentalization classify prostate cancer progression [Hi-C]

| PRJNA722011 | ENA

Novel oncogene-induced metastatic prostate cancer cell lines define human prostate cancer progression signatures.

Project description:Herein, murine prostate cancer cell lines, generated via selective transduction with a single oncogene (c-Myc, Ha-Ras, and v-Src), showed oncogene-specific prostate cancer molecular signatures that were recapitulated in human prostate cancer and developed lung metastasis in immune-competent mice. Interrogation of two independent retrospective cohorts of patient samples using the oncogene signature showed an ability to distinguish tumor from normal prostate with a predictive value for prostate cancer of 98% to 99%. In a blinded study, the signature algorithm showed independent substratification of reduced recurrence-free survival by Kaplan-Meier analysis. The generation of new oncogene-specific prostate cancer cell lines that recapitulate human prostate cancer gene expression, which metastasize in immune-competent mice, are a valuable new resource for testing targeted therapy, whereas the molecular signatures identified herein provides further value over current gene signature markers of prediction and outcome.

| S-EPMC3561759 | biostudies-literature

Metabolomic characterization of colorectal cancer cell lines highlighting stage-specific alterations during cancer progression.

Project description:Introduction: Metabolomic studies on various colorectal cancer (CRC) cell lines have improved our understanding of the biochemical events underlying the disease. However, the metabolic profile dynamics associated with different stages of CRC progression is still lacking. Such information can provide further insights into the pathophysiology and progression of the disease that will prove useful in identifying specific targets for drug designing and therapeutics. Thus, our study aims to characterize the metabolite profiles in the established cell lines corresponding to different stages of CRC. Methods: Metabolite profiling of normal colon cell lines (CCD 841 CoN) and CRC cell lines corresponding to different stages, i.e., SW 1116 (stage A), HT 29 and SW 480 (stage B), HCT 15 and DLD-1 (stage C), and HCT 116 (stage D), was carried out using liquid chromatography-mass spectrometry (LC-MS). Mass Profiler Professional and Metaboanalyst 4.0 software were used for statistical and pathway analysis. METLIN database was used for the identification of metabolites. Results: We identified 72 differential metabolites compared between CRC cell lines of all the stages and normal colon cells. Principle component analysis and partial least squares discriminant analysis score plot were used to segregate normal and CRC cells, as well as CRC cells in different stages of the disease. Variable importance in projection score identified unique differential metabolites in CRC cells of the different stages. We identified 7 differential metabolites unique to stage A, 3 in stage B, 5 in stage C, and 5 in stage D. Conclusion: This study highlights the differential metabolite profiling in CRC cell lines corresponding to different stages. The identification of the differential metabolites in CRC cells at individual stages will lead to a better understanding of the pathophysiology of CRC development and progression and, hence, its application in treatment strategies.

| S-EPMC8022234 | biostudies-literature

Alterations in Tumor Aggression Following Androgen Receptor Signaling Restoration in Canine Prostate Cancer Cell Lines.

Project description:In prostate cancer (PCa), androgens upregulate tumorigenesis, whereas in benign tissue, the revival of androgen receptor (AR) signaling suppresses aggressive behaviors, suggesting therapeutic potential. Dogs, natural PCa models, often lack AR in PCa. We restored AR in dog PCa to investigate resultant characteristics. Three AR-null canine PCa lines (1508, Leo, 1258) were transfected with canine wild-type AR and treated with dihydrotestosterone (DHT). In 1508, AR restoration decreased clonogenicity (p = 0.03), viability (p = 0.004), migration (p = 0.03), invasion (p = 0.01), and increased expression of the tumor suppressor NKX3.1, an AR transcriptional target (p = 0.001). In Leo, AR decreased clonogenicity (p = 0.04) and the expression of another AR transcriptional target FOLH1 (p < 0.001) and increased the expression of NKX3.1 (p = 0.01). In 1258, AR increased migration (p = 0.006) and invasion (p = 0.03). Epithelial-mesenchymal transition (EMT) marker (Vimentin, N-cadherin, SNAIL1) expression increased with AR restoration in Leo and 1258 but not 1508; siRNA vimentin knockdown abrogated AR-induced 1258 migration only. Overall, 1508 showed AR-mediated tumor suppression; AR affected proliferation in Leo but not migration or invasion; and EMT and AR regulated migration and invasion in 1258 but not proliferation. This study highlights the heterogeneous nature of PCa in dogs and cell line-specific effects of AR abrogation on aggressive behaviors.

| S-EPMC11354774 | biostudies-literature

Delineating genetic alterations for tumor progression in the MCF10A series of breast cancer cell lines.

Project description:To gain insight into the role of genomic alterations in breast cancer progression, we conducted a comprehensive genetic characterization of a series of four cell lines derived from MCF10A. MCF10A is an immortalized mammary epithelial cell line (MEC); MCF10AT is a premalignant cell line generated from MCF10A by transformation with an activated HRAS gene; MCF10CA1h and MCF10CA1a, both derived from MCF10AT xenografts, form well-differentiated and poorly-differentiated malignant tumors in the xenograft models, respectively. We analyzed DNA copy number variation using the Affymetrix 500 K SNP arrays with the goal of identifying gene-specific amplification and deletion events. In addition to a previously noted deletion in the CDKN2A locus, our studies identified MYC amplification in all four cell lines. Additionally, we found intragenic deletions in several genes, including LRP1B in MCF10CA1h and MCF10CA1a, FHIT and CDH13 in MCF10CA1h, and RUNX1 in MCF10CA1a. We confirmed the deletion of RUNX1 in MCF10CA1a by DNA and RNA analyses, as well as the absence of the RUNX1 protein in that cell line. Furthermore, we found that RUNX1 expression was reduced in high-grade primary breast tumors compared to low/mid-grade tumors. Mutational analysis identified an activating PIK3CA mutation, H1047R, in MCF10CA1h and MCF10CA1a, which correlates with an increase of AKT1 phosphorylation at Ser473 and Thr308. Furthermore, we showed increased expression levels for genes located in the genomic regions with copy number gain. Thus, our genetic analyses have uncovered sequential molecular events that delineate breast tumor progression. These events include CDKN2A deletion and MYC amplification in immortalization, HRAS activation in transformation, PIK3CA activation in the formation of malignant tumors, and RUNX1 deletion associated with poorly-differentiated malignant tumors.

| S-EPMC2821407 | biostudies-literature

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