Cancer Medicine by Alexandros G.Sfakianakis,Anapafseos 5 Agios Nikolaos,Crete 72100,Greece,tel :00302841026182 & 00306932607174
Δευτέρα 30 Μαΐου 2016
Weighted gene co-expression network analysis reveals key genes involved in pancreatic ductal adenocarcinoma development
Abstract
Purpose
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Up till now, the patient's prognosis remains poor which, among others, is due to the paucity of reliable early diagnostic biomarkers. In the past, candidate diagnostic biomarkers and therapeutic targets have been delineated from genes that were found to be differentially expressed in normal versus tumour samples. Recently, new systems biology approaches have been developed to analyse gene expression data, which may yield new biomarkers. As of yet, the weighted gene co-expression network analysis (WGCNA) tool has not been applied to PDAC microarray-based gene expression data.
Methods
PDAC microarray-based gene expression datasets, listed in the Gene Expression Omnibus (GEO) database, were analysed. After pre-processing of the data, we built two final datasets, Normal and PDAC, encompassing 104 and 129 patient samples, respectively. Next, we constructed a weighted gene co-expression network and identified modules of co-expressed genes distinguishing normal from disease conditions. Functional annotations of the genes in these modules were carried out to highlight PDAC-associated molecular pathways and common regulatory mechanisms. Finally, overall survival analyses were carried out to assess the suitability of the genes identified as prognostic biomarkers.
Results
Using WGCNA, we identified several key genes that may play important roles in PDAC. These genes are mainly related to either endoplasmic reticulum, mitochondrion or membrane functions, exhibit transferase or hydrolase activities and are involved in biological processes such as lipid metabolism or transmembrane transport. As a validation of the applied method, we found that some of the identified key genes (CEACAM1, MCU, VDAC1, CYCS, C15ORF52, TMEM51, LARP1 and ERLIN2) have previously been reported by others as potential PDAC biomarkers. Using overall survival analyses, we found that several of the newly identified genes may serve as biomarkers to stratify PDAC patients into low- and high-risk groups.
Conclusions
Using this new systems biology approach, we identified several genes that appear to be critical to PDAC development. As such, they may represent potential diagnostic biomarkers as well as therapeutic targets with clinical utility.
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Genomic Characterization of High-Grade Serous Ovarian Cancer: Dissecting Its Molecular Heterogeneity as a Road Towards Effective Therapeutic Strategies
Abstract
High-grade serous ovarian carcinoma (HGSOC) accounts for the majority of the ovarian cancer deaths, but over the last years little improvement in overall survival has been achieved. HGSOC is a molecularly and clinically heterogeneous disease. At genomic level, it represents a C-class malignancy having frequent gene losses (NF1, RB1, PTEN) and gains (CCNE1, MYC). HGSOC shows a simple mutational profile with TP53 nearly always mutated and with other genes mutated at low frequency. Importantly, 50 % of all HGSOCs have genetic features indicating a homologous recombination (HR) deficiency. HR deficient tumors are highly sensitive to PARP inhibitor anticancer agents, which exhibit synthetic lethality with a defective HR pathway. Transcriptionally, HGSOCs can be grouped into different molecular subtypes with distinct biology and prognosis. Molecular stratification of HGSOC based on these genomic features may result in improved therapeutic strategies.
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Anomalous origin of the left coronary artery from the pulmonary artery in children: diagnostic use of multidetector computed tomography
Abstract
Background
Anomalous origin of the left coronary artery from the pulmonary artery is a rare congenital anomaly. It is important to demonstrate the anomalous origin of the left coronary artery and its course before surgery.
Objective
To explore the clinical diagnostic use of multidetector CT coronary angiography in detecting anomalous origin of the left coronary artery from the pulmonary artery in children.
Materials and methods
Nine children (2 boys, 7 girls) ages 2 months to 9 years with surgically confirmed anomalous origin of the left coronary artery from the pulmonary artery were studied. Clinical data, transthoracic echocardiography and CT coronary angiography images were retrospectively analyzed.
Results
Transthoracic echocardiography correctly diagnosed anomalous origin of the left coronary artery from the pulmonary artery in 7 of 9 patients (95% CI: 40–97%). CT coronary angiography revealed the anomalous origin of the left coronary artery in all children (95% CI: 66–100%). In a 4-year-old girl and a 9-year-old girl, CT coronary angiography showed dilation of the right coronary artery and collateral circulation between the right and the left coronary arteries.
Conclusion
CT coronary angiography is a useful method to show the anomalous origin of the coronary artery in children with anomalous origin of the left coronary artery from the pulmonary artery, especially for patients in whom origin of the left coronary artery cannot be detected by transthoracic echocardiography.
from Cancer via ola Kala on Inoreader http://ift.tt/1WUTvO4
via IFTTT
Weighted gene co-expression network analysis reveals key genes involved in pancreatic ductal adenocarcinoma development
Abstract
Purpose
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Up till now, the patient's prognosis remains poor which, among others, is due to the paucity of reliable early diagnostic biomarkers. In the past, candidate diagnostic biomarkers and therapeutic targets have been delineated from genes that were found to be differentially expressed in normal versus tumour samples. Recently, new systems biology approaches have been developed to analyse gene expression data, which may yield new biomarkers. As of yet, the weighted gene co-expression network analysis (WGCNA) tool has not been applied to PDAC microarray-based gene expression data.
Methods
PDAC microarray-based gene expression datasets, listed in the Gene Expression Omnibus (GEO) database, were analysed. After pre-processing of the data, we built two final datasets, Normal and PDAC, encompassing 104 and 129 patient samples, respectively. Next, we constructed a weighted gene co-expression network and identified modules of co-expressed genes distinguishing normal from disease conditions. Functional annotations of the genes in these modules were carried out to highlight PDAC-associated molecular pathways and common regulatory mechanisms. Finally, overall survival analyses were carried out to assess the suitability of the genes identified as prognostic biomarkers.
Results
Using WGCNA, we identified several key genes that may play important roles in PDAC. These genes are mainly related to either endoplasmic reticulum, mitochondrion or membrane functions, exhibit transferase or hydrolase activities and are involved in biological processes such as lipid metabolism or transmembrane transport. As a validation of the applied method, we found that some of the identified key genes (CEACAM1, MCU, VDAC1, CYCS, C15ORF52, TMEM51, LARP1 and ERLIN2) have previously been reported by others as potential PDAC biomarkers. Using overall survival analyses, we found that several of the newly identified genes may serve as biomarkers to stratify PDAC patients into low- and high-risk groups.
Conclusions
Using this new systems biology approach, we identified several genes that appear to be critical to PDAC development. As such, they may represent potential diagnostic biomarkers as well as therapeutic targets with clinical utility.
from Cancer via ola Kala on Inoreader http://ift.tt/25unG0n
via IFTTT
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