The pancreatic ductal adenocarcinoma (PDAC), which represents over 90% of pancreatic cancer cases,
has the highest proliferative and metastatic rate in comparison to other pancreatic cancer compartments. This
study is designed to determine whether small nucleolar RNA, H/ACA box 64 (snoRNA64) is associated with
pancreatic cancer initiation and progression. Gene expression data from the Gene Expression Omnibus (GEO)
repository have shown that snoRNA64 expression is reduced in primary and metastatic pancreatic cancer as
compared to normal tissues based on statistical analysis of the in Silico analysis. Using qPCR techniques,
pancreatic cancer cell lines include PK-1, PK-8, PK-4, and Mia PaCa-2 with different levels of snoRNA64,
including PK-1, PK-8, PK-4, and Mia PaCa-2. The level of expression is correlated with the cell line epithelial
or mesenchymal characteristics. Cell lines displaying epithelial characteristics such as PK-1, PK-8 show high
levels of snoRNA64 meanwhile, cell lines displaying mesenchymal characteristics such as PK-4, Mia PaCa-2
show low levels of snoRNA64. The level of expression is correlated with the cell line epithelial or
mesenchymal characteristics. After knocking down the PK-8 with high snoRNA64 expression, the epithelial
markers E. cadherin (E-cad) and Cytokeratin-8 (CK-8) are decreased, while mesenchymal markers Vimentin
(Vim), Cytokeratin-19 (CK-19), Metalloprotease -2 (MMP-2), and Metalloprotease-3 (MMP-3) are activated.
Those changes suggest that PK-8 responding to the snoRNA64 knock down protocol and increase in
mesenchymal function. Together, snoRNA64 expression may participate in epithelial to mesenchymal
transition (EMT) and mesenchymal to epithelial transition (MET), in which during metastasis these processes
are crucial. In addition, snoRNA64 may be considered as a potential diagnostic biomarker for both early and
invasive stages of PDAC. And due to its gradual expression decreases, it may be considered a barrier in tumor
progression.
—Medical images have recently played a significant role in the diagnosis and detection of various diseases. Medical imaging can provide a means of direct visualization to observe through the human body and notice the small anatomical change and biological processes associated by different biological and physical parameters. To achieve a more accurate and reliable diagnosis, nowadays, varieties of computer aided detection (CAD) and computer-aided diagnosis (CADx) approaches have been established to help interpretation of the medical images. The CAD has become among the many major research subjects in diagnostic radiology and medical imaging. In this work we study the improvement in accuracy of detection of CAD system when comb
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