Cholesterol Lowering in Cancer Prevention and Therapy

The accumulation of cholesterol in cancer cells and tumor tissues promotes cell growth, proliferation, and migration as well as tumor progression. Cholesterol synthesis is catalyzed by a series of enzymatic reactions. Regulation of these key enzymes can control cholesterol synthesis and modulate cellular cholesterol levels in the cells. Meanwhile, controlling cholesterol transportation, absorption, and depletion could also significantly reduce cellular cholesterol levels. The current evidence supports that cholesterol lowering agents, beyond the expected cholesterol-lowering properties, also display an important anticancer activity in reducing cancer cell growth, proliferation and migration, and inducing apoptosis in a variety of cancer cells. Understanding the mechanisms of cholesterol metabolism and cholesterol lowering could potentially benefit cancer patients in cancer prevention and treatment

Lung cancer screening: from imaging to biomarker.

Despite several decades of intensive effort to improve the imaging techniques for lung cancer diagnosis and treatment, primary lung cancer is still the number one cause of cancer death in the United States and worldwide. The major causes of this high mortality rate are distant metastasis evident at diagnosis and ineffective treatment for locally advanced disease. Indeed, approximately forty percent of newly diagnosed lung cancer patients have distant metastasis. Currently, the only potential curative therapy is surgical resection of early stage lung cancer. Therefore, early detection of lung cancer could potentially increase the chance of cure by surgery and underlines the importance of screening and detection of lung cancer. In the past fifty years, screening of lung cancer by chest X-Ray (CXR), sputum cytology, computed tomography (CT), fluorescence endoscopy and low-dose spiral CT (LDCT) has not improved survival except for the recent report in 2010 by the National Lung Screening Trial (NLST), which showed a 20 percent mortality reduction in high risk participants screened with LDCT compared to those screened with CXRs. Furthermore, serum biomarkers for detection of lung cancer using free circulating DNA and RNA, exosomal microRNA, circulating tumor cells and various lung cancer specific antigens have been studied extensively and novel screening methods are being developed with encouraging results. The history of lung cancer screening trials using CXR, sputum cytology and LDCT, as well as results of trials involving various serum biomarkers, are reviewed herein

IJCEP1108001

Abstract: A diagnosis of lung cancer at its early stages is vital for improving the survival rate of patients. MicroRNAs (miRNAs), a family of 19-to 25-nucleotide non-coding small RNAs, are frequently dysregulated in lung cancer. The objective of this study was to investigate the potential of circulating miRNAs for early detection of lung cancer. We searched the published literature for the miRNA microarray data of primary lung cancer and selected 15 miRNAs that were most frequently up-regulated in lung cancer tissues. Total plasma RNA including miRNAs was isolated, polyadenylated and reverse-transcribed into cDNAs. The levels of miRNAs were determined by real-time RT-PCR in 74 lung cancer patients and 68 age-matched cancer-free controls. We found that the levels of miR-155, miR-197, and miR-182 in the plasma of lung cancer including stage I patients were significantly elevated compared with controls (P<0.001). The combination of these 3 miRNAs yielded 81.33% sensitivity and 86.76% specificity in discriminating lung cancer patients from controls. The levels of miR-155 and miR-197 were higher in the plasma from lung cancer patients with metastasis than in those without metastasis (P<0.05) and were significantly decreased in responsive patients during chemotherapy (P<0.001). These results indicate that miR-155, miR-197, and miR-182 can be potential non-invasive biomarkers for early detection of lung cancer

Lipid Metabolism, Apoptosis and Cancer Therapy

Lipid metabolism is regulated by multiple signaling pathways, and generates a variety of bioactive lipid molecules. These bioactive lipid molecules known as signaling molecules, such as fatty acid, eicosanoids, diacylglycerol, phosphatidic acid, lysophophatidic acid, ceramide, sphingosine, sphingosine-1-phosphate, phosphatidylinositol-3 phosphate, and cholesterol, are involved in the activation or regulation of different signaling pathways. Lipid metabolism participates in the regulation of many cellular processes such as cell growth, proliferation, differentiation, survival, apoptosis, inflammation, motility, membrane homeostasis, chemotherapy response, and drug resistance. Bioactive lipid molecules promote apoptosis via the intrinsic pathway by modulating mitochondrial membrane permeability and activating different enzymes including caspases. In this review, we discuss recent data in the fields of lipid metabolism, lipid-mediated apoptosis, and cancer therapy. In conclusion, understanding the underlying molecular mechanism of lipid metabolism and the function of different lipid molecules could provide the basis for cancer cell death rationale, discover novel and potential targets, and develop new anticancer drugs for cancer therapy

Cholesterol Lowering Therapies and Drugs

Using natural products and developing pharmaceutical drugs are emerging topics to reduce blood cholesterol levels for preventing heart disease and stroke. Covering recent progresses in cholesterol-lowering drugs and therapy, this book describes the natural and pharmaceutical products that are in clinical uses to lower cholesterol and lipids and compares these drugs in responses to different diseases such as homozygous familial hypercholesterolemia, atherosclerosis, cardiovascular disease, and cancer. The relationship between ethnicity and cholesterol-lowering drug responses is also reviewed. Each chapter is a building block for the book, but each individual chapter is also a complete subject package for the readers. Researchers from basic and clinic science interested in lipid and cholesterol metabolism, regulation, and lowering will find this book very useful. Features: - Up-to-date information of the molecular mechanisms of cholesterol lowering, the drugs from natural and pharmaceutical products, and their associated therapeutic strategies in human diseases. - Discussion of the pathogenesis of several human diseases, which are associated with high cholesterol levels and evaluation of the results of different cholesterol-lowering drug treatment in these diseases. - Discussion of the combinations of cancer chemotherapy and cholesterol lowering in potential cancer treatment and cancer prevention by cholesterol-lowering drugs. - Critical analysis of the effect of ethnicity on responses to cholesterol-lowering drug therapy leading to rational dose adjustment of cholesterol-lowering drugs for different people use

Correction: Gaussian decomposition of high-resolution melt curve derivatives for measuring genome-editing efficiency.

[This corrects the article DOI: 10.1371/journal.pone.0190192.]

Gaussian decomposition of high-resolution melt curve derivatives for measuring genome-editing efficiency.

We describe a method for measuring genome editing efficiency from in silico analysis of high-resolution melt curve data. The melt curve data derived from amplicons of genome-edited or unmodified target sites were processed to remove the background fluorescent signal emanating from free fluorophore and then corrected for temperature-dependent quenching of fluorescence of double-stranded DNA-bound fluorophore. Corrected data were normalized and numerically differentiated to obtain the first derivatives of the melt curves. These were then mathematically modeled as a sum or superposition of minimal number of Gaussian components. Using Gaussian parameters determined by modeling of melt curve derivatives of unedited samples, we were able to model melt curve derivatives from genetically altered target sites where the mutant population could be accommodated using an additional Gaussian component. From this, the proportion contributed by the mutant component in the target region amplicon could be accurately determined. Mutant component computations compared well with the mutant frequency determination from next generation sequencing data. The results were also consistent with our earlier studies that used difference curve areas from high-resolution melt curves for determining the efficiency of genome-editing reagents. The advantage of the described method is that it does not require calibration curves to estimate proportion of mutants in amplicons of genome-edited target sites

Temperature-dependent quenching of fluorescence of free and dsDNA-bound fluorophore and its correction.

<p>(A) Plot of first-order polynomial curve fit of raw RFU vs. temperature in no template controls (NTC). The equation shown in the plot is the mean ± SD of six different sample slopes and constants. (B) The unprocessed high-resolution melting profile (blue trace) and the extrapolation from first-order polynomial curve fitting of the post-melt curve region (red dashed line) from an amplicon of an unedited target site. (C) High-resolution melting profile of background subtracted RFU (BcRFU, blue trace) and that of ‘unquenched’ or fluorescence-compensated BcRFU (FcRFU, green trace) from an unedited target site. The red dashed line shows extrapolation of pre-melt region from first-order polynomial curve fitting of BcRFU and depicts the predicted BcRFU in the absence of DNA melting. D) Comparison of first-order polynomial curve fitting of post-melt and pre-melt portions of melting curves. Normalized data were used to enable plotting of the two sets of data.</p

3-GD of first derivative of high-resolution melt curves for estimation of mutant percentage in genome-edited samples.

<p>gDNA was isolated from HEK293T cells transfected with F8-S2 targeting RGENs or CCR5 targeting TALENs and PCR amplified using corresponding primer pairs to obtain high resolution melt curve data (Materials and Methods). 3-GD curve fitting was done on first derivative melt curves using CurveExpert Professional and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190192#pone.0190192.e013" target="_blank">Eq 12</a> as described in Materials and Methods. The individual Gaussians-g1(x) (purple dashed line), g2(x) (brown dashed line) and g3(x) (green dashed line) and their sum- g1(x)+ g2(x) + g3(x) (red solid line) were overlaid over the first derivative melt curve (blue dots). GD of F8-S2 is shown in A and of CCR5 in B. Table C shows the parameters (weights, centers and SDs) of 3-GD. The parameters that were fixed from GD of mocks and those that were set free during 3-GD of edited samples are shown in the Comments column. The g1 weight (w₁) represents the mutation frequencies in the amplicons of genome-edited F8-S2 and CCR5 target sites, respectively.</p