Pancreatic cancer
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1007691 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25715621_1.fastq.gz | Fastqsanger.gz | |||
| SRR25715621_2.fastq.gz | Fastqsanger.gz | |||
| SRR25715622_1.fastq.gz | Fastqsanger.gz | |||
| SRR25715622_2.fastq.gz | Fastqsanger.gz | |||
| SRR25715623_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 32 |
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Project description:This is the experiment set used for a paper written in collaboration with Hopkins. It compares genes that are differentially expressed between normal pancreas, pancreatic cell lines and pancreatic adenocarcinoma. Pancreatic cancer is the fifth leading cause of cancer death in the United States. We used cDNA microarrays to analyze global gene expression patterns in 14 pancreatic cancer cell lines, 17 resected infiltrating pancreatic cancer tissues, and 5 samples of normal pancreas to identify genes that are differentially expressed in pancreatic cancer. We found more than 400 cDNAs corresponding to genes that were differentially expressed in the pancreatic cancer tissues and cell lines as compared to normal pancreas. These genes that tended to be expressed at higher levels in pancreatic cancers were associated with a variety of processes, including cell-cell and cell-matrix interactions, cytoskeletal remodeling, proteolytic activity, and Ca(++) homeostasis. Two prominent clusters of genes were related to the high rates of cellular proliferation in pancreatic cancer cell lines and the host desmoplastic response in the resected pancreatic cancer tissues. Of 149 genes identified as more highly expressed in the pancreatic cancers compared with normal pancreas, 103 genes have not been previously reported in association with pancreatic cancer. The expression patterns of 14 of these highly expressed genes were validated by either immunohistochemistry or reverse transcriptase-polymerase chain reaction as being expressed in pancreatic cancer. The overexpression of one gene in particular, 14-3-3 sigma, was found to be associated with aberrant hypomethylation in the majority of pancreatic cancers analyzed. The genes and expressed sequence tags presented in this study provide clues to the pathobiology of pancreatic cancer and implicate a large number of potentially new molecular markers for the detection and treatment of pancreatic cancer. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Using regression correlation
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Project description:The paper describes a model on the size of pancreatic tumour.
Created by COPASI 4.25 (Build 207)
This model is described in the article:
Modeling Pancreatic Cancer Dynamics with Immunotherapy
Xiaochuan Hu, Guoyi Ke and Sophia R.-J. Jang
Bulletin of Mathematical Biology (2019) 81:1885–1915
Abstract:
We develop a mathematical model of pancreatic cancer that includes pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor- suppressing cytokines to investigate the effects of immunotherapies on patient survival. The model is first validated using the survival data of two clinical trials. Local sen- sitivity analysis of the parameters indicates there exists a critical activation rate of pro-tumor cytokines beyond which the cancer can be eradicated if four adoptive trans- fers of immune cells are applied. Optimal control theory is explored as a potential tool for searching the best adoptive cellular immunotherapies. Combined immunother- apies between adoptive ex vivo expanded immune cells and TGF-β inhibition by siRNA treatments are investigated. This study concludes that mono-immunotherapy is unlikely to control the pancreatic cancer and combined immunotherapies between anti-TGF-β and adoptive transfers of immune cells can prolong patient survival. We show through numerical explorations that how these two types of immunotherapies are scheduled is important to survival. Applying TGF-β inhibition first followed by adoptive immune cell transfers can yield better survival outcomes.
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2024-09-02 | BIOMD0000000744 | BioModels