Development of a prognostic gene signature in pancreatic cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE57495 | GEO | 2015/08/28
SECONDARY ACCESSION(S): PRJNA246550
REPOSITORIES: GEO
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Project description:Microarray analysis of 63 patients with pancreatic cancer tissues resulted in the identification of a 15-gene signature to predict overall survival RNA was extracted from microdissected frozen pancreatic tissues for gene array analysis
2015-08-28 | E-GEOD-57495 | biostudies-arrayexpress
Project description:Background: Mesothelioma is an aggressive, fatal cancer that is inextricably linked to asbestos exposure. Recent trials using a combination of the immune checkpoint inhibitors ipilimumab and nivolumab has significantly improved treatment outcomes, however durable treatment responses remain restricted to a subset of patients (15-20%), highlighting the need to identify strategies that better predict treatment response. Method: Here, we performed RNAseq on a large tumor biobank from genetically diverse mouse model, CC-MexTAg model to compare gene expression profiles of tumors from mice with different overall survival to develop a prognostic gene signature. Results: while the variation in gene expression data of tumors did not associate with 3-fold variation in overall survival of CC-MexTAg mice, we identified two distinct tumor clusters characterized with immune and non-immune phenotypes, in which immune cluster tumours showed the better potential of response to cancer therapies. We used 20 hub genes associated with these tumor phenotypes to develop a 6-gene signature that could predict survival in four independent mesothelioma datasets and showed a potential to respond to cancer immunotherapy.
2024-02-15 | GSE232512 | GEO
Project description:Pancreatic cancer is the fourth leading cancer-related death in United States. The clinical relevance of genomic imbalances in pancreatic cancer is uncertain. We performed array-comparative genomic hybridization (aCGH) in 44 resected pancreatic cancer specimens from a Korean cohort. We observed recurrent copy number gains were observed in chromosome 1q, 11q, 18q11.1-11.2, and 20q13.13; and losses in chromosome 2p, 9p, 10q, 14q, 15q, 18q12.2-23, 19q, 20q11.1, 21p, and 22q. High copy number gains, with log2 ratio >2.0, were observed in 4 cytobands, encoding genes such as G protein-coupled receptor 48, c-myc and gamma-catenin. By comparing the aCGH results of long survivors and short survivors, determined according to the median survival, we observed that loss of cytoband 18q22.3, encoding 5 genes, was the only alteration with significantly different frequencies. The copy number of the CPGL (Carboxypeptidase of glutamate-like) gene in cytoband 18q22.3 was found to be significantly associated with overall survival in univariate analyses (p=0.019 by log-rank test). This was subsequently assessed in the Italian cohort: 41 out of the 61 specimens carried deletion of the CPGL gene. Patients with deletion of the CPGL gene also had shorter overall survival (p=0.003 by log-rank test). Multivariate analysis of the two cohorts combined showed that loss of 18q22.3 / deletion of the CPGL gene was an independent poor prognostic factor for overall survival after adjusting for other factors which were found to impact the outcome (hazard ratio 2.72, p=0.0007). overexpression of the CPGL or its alternating splicing isoform CPGL-B in a pancreatic cancer cell lines resulted in slower proliferation of cells, G1 cell cycle arrest, and reduced migration activity. In conclusion, through aCGH analysis, we identified loss of 18q22.3 / deletion of the CPGL gene as potentially being an independent poor prognostic factor in resected pancreatic cancer. We further identified the CPGL gene as a potential tumor suppressor for pancreatic cancer cell lines. Array-comparative genomic hybridization was performed in 44 resected pancreatic cancer specimen from a Korean cohort. Genomic alteration relating to prognosis of the Korean cohort was further validated in a Italian cohort containing 61 resected pancreatic cancer specimens.
2011-04-21 | E-GEOD-28732 | biostudies-arrayexpress
Project description:MicroRNAs (miRNAs) are short single-stranded RNA molecules that have a critical role in the regulation of gene expression. Alterations in miRNA expression levels have been observed in multiple tumor types and there is clear evidence on their active involvement in cancer development. Here, a comprehensive miRNA expression profiling in 16 pancreatic cancer cell lines and four normal pancreatic samples provided a specific molecular signature for pancreatic cancer and enabled us to identify 72 differentially expressed miRNAs with approximately half of them being up- and half downregulated in cancer cells as compared to normal samples. Of these, miR-31 was selected for further functional analyses based on its interesting M-bM-^@M-^\on-offM-bM-^@M-^] type expression profile, i.e. very low or even absent expression in normal pancreas and in six of the pancreatic cancer samples but extremely high expression in the remaining ten cell lines. Quite unexpectedly, both the inhibition of miR-31 in AsPC-1 and HPAF-II pancreatic cancer cells with high endogenous expression and forced expression of miR-31 in MIA PaCa-2 with low endogenous levels led to reduced cell proliferation, migration and invasion. More importantly, in AsPC-1 cells further enhancement of miR-31 also resulted in reduced cell migration and invasion, implicating that the level of miR-31 is critical for these phenotypes. We also identified novel miR-31 target genes, APBB2 and RSBN1, that might contribute to cancer pathogenesis. This study highlights a specific miRNA expression pattern in pancreatic cancer and reveals that manipulation of miR-31 expression leads to reduced cell migration and invasion in pancreatic cancer. 16 pancreatic cancer cell lines and 4 normal pancreatic RNA samples were hybridized on Agilent vs1 miRNA microarrays. Pool of four normal samples was hybridized on each slide to allow normalization between slides.
2012-03-30 | E-GEOD-28955 | biostudies-arrayexpress
Project description:Close to 50 genetic loci have been associated with type 2 diabetes (T2D), but they explain only 15% of the heritability. In an attempt to identify additional T2D genes, we analyzed global gene expression in human islets from 63 donors.
2012-07-05 | GSE38642 | GEO
Project description:We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.
| S-EPMC8526937 | biostudies-literature
Project description:Background: Recently a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N-) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. Materials and Methods: Gene expression profiling of frozen samples from 198 N- systemically untreated patients was performed at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were performed with the genomic risk and adjusted for the clinical risk, defined by Adjuvant!Online. Results: The actual 5- and 10-year time to distant metastasis (TDM) were 98% (88%-100%) and 94% (83%-98%) respectively for the good profile group and 76% (68%- 82%) and 73% (65%-79%) for the poor profile group. The actual 5- and 10-year overall survival (OS) were 98% (88%-100%) and 87% (73%-94%) respectively for the good profile group and 84% (77%-89%) and 72% (63%-78%) for the poor profile group. We observed a strong time-dependency of this signature, leading to an adjusted HR of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years, and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for TDM and OS respectively. Conclusion: This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases. Experiment Overall Design: dataset of microarray experiments from primary breast tumors used to validate the 76-gene signature (VERIDEX). No replicate, no reference sample.
2008-06-16 | E-GEOD-7390 | biostudies-arrayexpress
Project description:Background: Recently a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N-) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. Materials and Methods: Gene expression profiling of frozen samples from 198 N- systemically untreated patients was performed at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were performed with the genomic risk and adjusted for the clinical risk, defined by Adjuvant!Online. Results: The actual 5- and 10-year time to distant metastasis (TDM) were 98% (88%-100%) and 94% (83%-98%) respectively for the good profile group and 76% (68%- 82%) and 73% (65%-79%) for the poor profile group. The actual 5- and 10-year overall survival (OS) were 98% (88%-100%) and 87% (73%-94%) respectively for the good profile group and 84% (77%-89%) and 72% (63%-78%) for the poor profile group. We observed a strong time-dependency of this signature, leading to an adjusted HR of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years, and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for TDM and OS respectively. Conclusion: This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases. Keywords: disease state analysis
2007-06-11 | GSE7390 | GEO
Project description:Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath [Park, Sunho et al. “An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.” Bioinformatics (2016): 1643-51. doi:10.1093/bioinformatics/btv692] to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated in a prospective manner.
2022-01-01 | GSE183136 | GEO
Project description:The desmoplastic reaction observed in many cancers is a hallmark of disease progression and prognosis, particularly in breast and pancreatic cancer. Stromal-derived extracellular matrix (ECM) is significantly altered in desmoplasia, and as such plays a critical role in driving cancer progression. Using fibroblast-derived matrices (FDMs), we show that cancer cells have increased growth on cancer-associated FDMs, comparing to FDMs derived from non-malignant tissue (normal) fibroblasts. We assess the changes in ECM characteristics from normal to cancer-associated stroma at the primary tumor site. Compositional, structural, and mechanical analyses reveal significant differences, with an increase in abundance of core ECM proteins, coupled with an increase in stiffness and density in cancer-associated FDMs. From compositional changes of FDM we derived a 36-ECM proteins’ signature, which we show largely matching the changes in pancreatic ductal adenocarcinoma (PDAC) tumor and metastases progression, as well as at the transcriptomic level in multiple cancer types in patients, predictive of their survival. Together, our results show relevance of FDMs for cancer modelling and identification of desmoplastic ECM components for further mechanistic studies.
2023-07-17 | PXD042342 | Pride