Citric acid, an ovarian cancer metabolite, induces pyroptosis via caspase-4/TXNIP-NLRP3-GSDMD pathway in ovarian cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE189851 | GEO | 2022/08/01
REPOSITORIES: GEO
Similar Datasets
Project description:Citric acid, an ovarian cancer metabolite, induces pyroptosis via caspase-4/TXNIP-NLRP3-GSDMD pathway in ovarian cancer
| PRJNA784786 | ENA
Project description:Carcinoma-associated mesenchymal stem cells (CA-MSCs) are critical stromal progenitor cells within the tumor microenvironment. We previously demonstrated that CA-MSCs differentially express BMP genes, promote tumor cell growth, increase cancer ‘stemness’ and chemotherapy resistance. Here we use RNA sequencing of normal omental MSCs and ovarian CA-MSCs to demonstrate CA-MSCs have global changes in gene expression. Using these expression profiles we create a unique predictive algorithm to classify CA-MSCs. Our classifier, accurately distinguishes normal omental, ovary and bone marrow MSCs from ovarian cancer CA-MSCs. Suggesting broad applicability, the model correctly classifies pancreatic and endometrial cancer CA-MSCs and distinguishes cancer associated fibroblasts (CAFs) from CA-MSCs. Using this classifier, we definitively demonstrate ovarian CA-MSCs arise from tumor mediated reprograming of local tissue MSCs. While cancer cells alone cannot induce a CA-MSC phenotype, the in vivo ovarian tumor micoenvironment (TME) can reprogram omental or ovary MSCs to protumorigenic CA-MSC (classifier score of >0.96). In vitro studies suggest that both tumor secreted factors and hypoxia are critical to induce the CA-MSC phenotype. Interestingly, while the breast cancer TME can reprogram BM MSCs into CA-MSCs, the ovarian TME cannot, demonstrating for the first time that tumor mediated CA-MSC conversion is tissue and cancer type dependent. Together these findings (1) provide a critical tool to define CA-MSCs and (2) highlight cancer cell influence on distinct normal tissues providing powerful insights into the mechanisms underlying cancer specific metastatic niche formation. Carcinoma-associated mesenchymal stem cells (CA-MSCs) are critical stromal progenitor cells within the tumor microenvironment. We previously demonstrated that CA-MSCs differentially express BMP genes, promote tumor cell growth, increase cancer ‘stemness’ and chemotherapy resistance. Here we use RNA sequencing of normal omental MSCs and ovarian CA-MSCs to demonstrate CA-MSCs have global changes in gene expression. Using these expression profiles we create a unique predictive algorithm to classify CA-MSCs. Our classifier, accurately distinguishes normal omental, ovary and bone marrow MSCs from ovarian cancer CA-MSCs. Suggesting broad applicability, the model correctly classifies pancreatic and endometrial cancer CA-MSCs and distinguishes cancer associated fibroblasts (CAFs) from CA-MSCs. Using this classifier, we definitively demonstrate ovarian CA-MSCs arise from tumor mediated reprograming of local tissue MSCs. While cancer cells alone cannot induce a CA-MSC phenotype, the in vivo ovarian tumor micoenvironment (TME) can reprogram omental or ovary MSCs to protumorigenic CA-MSC (classifier score of >0.96). In vitro studies suggest that both tumor secreted factors and hypoxia are critical to induce the CA-MSC phenotype. Interestingly, while the breast cancer TME can reprogram BM MSCs into CA-MSCs, the ovarian TME cannot, demonstrating for the first time that tumor mediated CA-MSC conversion is tissue and cancer type dependent. Together these findings (1) provide a critical tool to define CA-MSCs and (2) highlight cancer cell influence on distinct normal tissues providing powerful insights into the mechanisms underlying cancer specific metastatic niche formation.
2018-08-17 | GSE118624 | GEO
Project description:Ovarian cancer develops early intra-peritoneal metastasis establishing a pro-tumorigenic tumor microenvironment (TME) through reprogramming normal mesenchymal stem cells into carcinoma-associated mesenchymal stem cells (CA-MSCs). CA-MSCs are the stromal stem cell of the TME, supporting cancer growth, increasing desmoplasia, angiogenesis and chemotherapy resistance. We demonstrate epigenetic rewiring drives CA-MSC formation via enhancer-enriched DNA hypermethylation, altered chromatin accessibility and differential histone modifications inducing a partial mesenchymal to epithelial transition (MET) increasing tumor cell adhesion. Direct CA-MSC:tumor cell interactions, confirmed in patient ascites, facilitate ovarian cancer metastasis through co-migration. WT1, a developmental mediator of MET, and EZH2, mediate CA-MSC epigenetic reprogramming. WT1 overexpression induces CA-MSC conversion while WT1 knock-down, in combination with EZH2 inhibition, blocks CA-MSC formation. EZH2 inhibition subsequently decreases intra-abdominal metastasis.
2020-12-22 | GSE138072 | GEO
Project description:The biology of small cell ovarian carcinoma of the hypercalcemic type (SCCOHT), which is a rare and aggressive form of ovarian cancer, is poorly understood. Tumourigenicity, in vitro growth characteristics, genetic and genomic anomalies, and sensitivity to standard and novel chemotherapeutic treatments were investigated in the unique SCCOHT cell line, BIN-67, to provide further insight in the biology of this rare type of ovarian cancer. Chromosomal anomalies in BIN-67 cells were inferred using the Infinium? genotyping technology with the HumanHap300-Duo Genotyping BeadChip (Illumina, San Diego, CA, USA). This BeadChip contains about 318,000 genetic markers within approximately a 5 kb median SNP spacing. Genotyping and imaging using BeadStudio Data Analysis software (Illumina, San Diego, CA, USA) were performed at the McGill University and Genome Quebec Innovation Centre (Montreal, QC).
2013-09-24 | E-MTAB-1584 | biostudies-arrayexpress
Project description:The paper describes a model on the detection of cancer based on cancer and immune biomarkers.
Created by COPASI 4.25 (Build 207)
This model is described in the article:
Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach
Raluca Eftimie and and Esraa Hassanein
J Transl Med (2018) 16:73
Abstract:
Background: Early cancer diagnosis is one of the most important challenges of cancer research, since in many can- cers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers.
Methods: Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection.
Results: We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solu- tion diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolu- tion of cancer biomarkers, thus allowing us to make predictions on cancer detection times.
Conclusions: Combining cancer and immune biomarkers could improve cancer detection times, and any predic- tions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.
Keywords: Ovarian cancer, Mathematical model, CA-125 biomarker, IL-7 biomarker, Cancer detection times
This model is hosted on BioModels Database and identified by: MODEL1907050002.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.
Please refer to CC0 Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000741 | BioModels
Project description:The biology of small cell ovarian carcinoma of the hypercalcemic type (SCCOHT), which is a rare and aggressive form of ovarian cancer, is poorly understood. Tumourigenicity, in vitro growth characteristics, genetic and genomic anomalies, and sensitivity to standard and novel chemotherapeutic treatments were investigated in the unique SCCOHT cell line, BIN-67, to provide further insight in the biology of this rare type of ovarian cancer. Microarray expression analysis was performed using the GeneChip? Human Genome U133 Plus 2.0 Array (Affymetrix?, Santa Clara, CA) with total RNA from BIN-67 cells. Hybridization and scanning were performed at the McGill University and Genome Quebec Innovation Centre (Montreal, QC). Gene expression levels were determined from the scanned images using Affymetrix? Microarray Suite (MAS) version 5.0 software expression algorithm (Affymetrix?, Santa Clara, CA). Gene expression profiles were compared with Affymetrix U133 Plus 2.0 (MAS5.0) generated expression profiles (similarly normalized) from 10 human normal ovarian surface epithelial (OSE) cell brushings available in the ArrayExpress database (www.ebi.ac.uk/arrayexpress/), accession number E-GEOD-18520.
2013-09-24 | E-MEXP-3872 | biostudies-arrayexpress
Project description:SF3B1 inhibitor pladienolide B induces pyroptosis of ovarian cancer cells
| PRJNA918583 | ENA
Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.
2017-06-01 | GSE79517 | GEO
Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.
2017-06-01 | GSE79516 | GEO
Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.
2017-06-01 | GSE79498 | GEO