Genetic alteration and gene expression modulation during cancer progression

Cancer progresses through a series of histopathological stages. Progression is thought to be driven by the accumulation of genetic alterations and consequently gene expression pattern changes. The identification of genes and pathways involved will not only enhance our understanding of the biology of this process, it will also provide new targets for early diagnosis and facilitate treatment design. Genomic approaches have proven to be effective in detecting chromosomal alterations and identifying genes disrupted in cancer. Gene expression profiling has led to the subclassification of tumors. In this article, we will describe the current technologies used in cancer gene discovery, the model systems used to validate the significance of the genes and pathways, and some of the genes and pathways implicated in the progression of preneoplastic and early stage cancer

Computational Methods for the Analysis of Array Comparative Genomic Hybridization

Array comparative genomic hybridization (array CGH) is a technique for assaying the copy number status of cancer genomes. The widespread use of this technology has lead to a rapid accumulation of high throughput data, which in turn has prompted the development of computational strategies for the analysis of array CGH data. Here we explain the principles behind array image processing, data visualization and genomic profile analysis, review currently available software packages, and raise considerations for future software development

Public Databases and Software for the Pathway Analysis of Cancer Genomes

The study of pathway disruption is key to understanding cancer biology. Advances in high throughput technologies have led to the rapid accumulation of genomic data. The explosion in available data has generated opportunities for investigation of concerted changes that disrupt biological functions, this in turns created a need for computational tools for pathway analysis. In this review, we discuss approaches to the analysis of genomic data and describe the publicly available resources for studying biological pathways

Arsenic Biotransformation as a Cancer Promoting Factor by Inducing DNA Damage and Disruption of Repair Mechanisms

Chronic exposure to arsenic in drinking water poses a major global health concern. Populations exposed to high concentrations of arsenic-contaminated drinking water suffer serious health consequences, including alarming cancer incidence and death rates. Arsenic is biotransformed through sequential addition of methyl groups, acquired from s-adenosylmethionine (SAM). Metabolism of arsenic generates a variety of genotoxic and cytotoxic species, damaging DNA directly and indirectly, through the generation of reactive oxidative species and induction of DNA adducts, strand breaks and cross links, and inhibition of the DNA repair process itself. Since SAM is the methyl group donor used by DNA methyltransferases to maintain normal epigenetic patterns in all human cells, arsenic is also postulated to affect maintenance of normal DNA methylation patterns, chromatin structure, and genomic stability. The biological processes underlying the cancer promoting factors of arsenic metabolism, related to DNA damage and repair, will be discussed here

SeeGH – A software tool for visualization of whole genome array comparative genomic hybridization data

BACKGROUND: Array comparative genomic hybridization (CGH) is a technique which detects copy number differences in DNA segments. Complete sequencing of the human genome and the development of an array representing a tiling set of tens of thousands of DNA segments spanning the entire human genome has made high resolution copy number analysis throughout the genome possible. Since array CGH provides signal ratio for each DNA segment, visualization would require the reassembly of individual data points into chromosome profiles. RESULTS: We have developed a visualization tool for displaying whole genome array CGH data in the context of chromosomal location. SeeGH is an application that translates spot signal ratio data from array CGH experiments to displays of high resolution chromosome profiles. Data is imported from a simple tab delimited text file obtained from standard microarray image analysis software. SeeGH processes the signal ratio data and graphically displays it in a conventional CGH karyotype diagram with the added features of magnification and DNA segment annotation. In this process, SeeGH imports the data into a database, calculates the average ratio and standard deviation for each replicate spot, and links them to chromosome regions for graphical display. Once the data is displayed, users have the option of hiding or flagging DNA segments based on user defined criteria, and retrieve annotation information such as clone name, NCBI sequence accession number, ratio, base pair position on the chromosome, and standard deviation. CONCLUSIONS: SeeGH represents a novel software tool used to view and analyze array CGH data. The software gives users the ability to view the data in an overall genomic view as well as magnify specific chromosomal regions facilitating the precise localization of genetic alterations. SeeGH is easily installed and runs on Microsoft Windows 2000 or later environments

Disruption of the Non-Canonical WNT Pathway in Lung Squamous Cell Carcinoma

Disruptions of beta-catenin and the canonical Wnt pathway are well documented in cancer. However, little is known of the non-canonical branch of the Wnt pathway. In this study, we investigate the transcript level patterns of genes in the Wnt pathway in squamous cell lung cancer using reverse-transcriptase (RT)-PCR. It was found that over half of the samples examined exhibited dysregulated gene expression of multiple components of the non-canonical branch of the WNT pathway. In the cases where beta catenin (CTNNB1) was not over-expressed, we identified strong relationships of expression between wingless-type MMTV integration site family member 5A (WNT5A)/frizzled homolog 2 (FZD2), frizzled homolog 3 (FZD3)/dishevelled 2 (DVL2), and low density lipoprotein receptor-related protein 5 (LRP5)/secreted frizzled-related protein 4 (SFRP4). This is one of the first studies to demonstrate expression of genes in the non-canonical pathway in normal lung tissue and its disruption in lung squamous cell carcinoma. These findings suggest that the non-canonical pathway may have a more prominent role in lung cancer than previously reported

DNA Extraction from Paraffin Embedded Material for Genetic and Epigenetic Analyses

Disease development and progression are characterized by frequent genetic and epigenetic aberrations including chromosomal rearrangements, copy number gains and losses and DNA methylation. Advances in high-throughput, genome-wide profiling technologies, such as microarrays, have significantly improved our ability to identify and detect these specific alterations. However as technology continues to improve, a limiting factor remains sample quality and availability. Furthermore, follow-up clinical information and disease outcome are often collected years after the initial specimen collection. Specimens, typically formalin-fixed and paraffin embedded (FFPE), are stored in hospital archives for years to decades. DNA can be efficiently and effectively recovered from paraffin-embedded specimens if the appropriate method of extraction is applied. High quality DNA extracted from properly preserved and stored specimens can support quantitative assays for comparisons of normal and diseased tissues and generation of genetic and epigenetic signatures 1. To extract DNA from paraffin-embedded samples, tissue cores or microdissected tissue are subjected to xylene treatment, which dissolves the paraffin from the tissue, and then rehydrated using a series of ethanol washes. Proteins and harmful enzymes such as nucleases are subsequently digested by proteinase K. The addition of lysis buffer, which contains denaturing agents such as sodium dodecyl sulfate (SDS), facilitates digestion 2. Nucleic acids are purified from the tissue lysate using buffer-saturated phenol and high speed centrifugation which generates a biphasic solution. DNA and RNA remain in the upper aqueous phase, while proteins, lipids and polysaccharides are sequestered in the inter- and organic-phases respectively. Retention of the aqueous phase and repeated phenol extractions generates a clean sample. Following phenol extractions, RNase A is added to eliminate contaminating RNA. Additional phenol extractions following incubation with RNase A are used to remove any remaining enzyme. The addition of sodium acetate and isopropanol precipitates DNA, and high speed centrifugation is used to pellet the DNA and facilitate isopropanol removal. Excess salts carried over from precipitation can interfere with subsequent enzymatic assays, but can be removed from the DNA by washing with 70% ethanol, followed by centrifugation to re-pellet the DNA 3. DNA is re-suspended in distilled water or the buffer of choice, quantified and stored at -20°C. Purified DNA can subsequently be used in downstream applications which include, but are not limited to, PCR, array comparative genomic hybridization 4 (array CGH), methylated DNA Immunoprecipitation (MeDIP) and sequencing, allowing for an integrative analysis of tissue/tumor samples

Arsenic Exposure and the Induction of Human Cancers

Arsenic is a metalloid, that is, considered to be a human carcinogen. Millions of individuals worldwide are chronically exposed through drinking water, with consequences ranging from acute toxicities to development of malignancies, such as skin and lung cancer. Despite well-known arsenic-related health effects, the molecular mechanisms involved are not fully understood; however, the arsenic biotransformation process, which includes methylation changes, is thought to play a key role. This paper explores the relationship of arsenic exposure with cancer development and summarizes current knowledge of the potential mechanisms that may contribute to the neoplastic processes observed in arsenic exposed human populations

Comparative Risks of Nonsteroidal Anti-Inflammatory Drugs on CKD

BACKGROUND AND OBJECTIVES: There have been doubts about the association between nonsteroidal anti-inflammatory drug use and worsening kidney function, and whether there is a difference between risks of individual nonsteroidal anti-inflammatory drugs is presently unclear. Therefore, this study aimed to evaluate the association between nonsteroidal anti-inflammatory drug exposure and the risk of incident eGFR <60 ml/min per 1.73 m2 and compare the risks between nonsteroidal anti-inflammatory drug subtypes in the Chinese population. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: From 2008 to 2017, a total of 1,982,488 subjects aged 18 years or older with baseline eGFR ≥60 ml/min per 1.73 m2 were enrolled in this retrospective cohort study. Multivariable Cox proportional hazards regression adjusted for each patient's baseline characteristics was adopted to examine the association between nonsteroidal anti-inflammatory drug and incident eGFR <60 ml/min per 1.73 m2 or eGFR decline ≥30% with reference to baseline. RESULTS: After a median follow-up duration of 6.3 (interquartile range, 3.3-9.4) years, 271,848 cases (14%) of incident eGFR <60 ml/min per 1.73 m2 and 388,386 (21%) events of eGFR decline ≥30% were recorded. After adjusting for each patient's baseline characteristics, nonsteroidal anti-inflammatory drug treatment was shown to be associated with a significantly higher risk of incident eGFR <60 ml/min per 1.73 m2 (hazard ratio, 1.71; 95% confidence interval, 1.67 to 1.75) and eGFR decline ≥30% (hazard ratio, 1.93; 95% confidence interval, 1.89 to 1.96) when compared with no nonsteroidal anti-inflammatory drug, with etoricoxib exhibiting the highest risk of eGFR<60 ml/min per 1.73 m2 (hazard ratio, 3.12; 95% confidence interval, 2.69 to 3.62) and eGFR decline ≥30% (hazard ratio, 3.11; 95% confidence interval, 2.78 to 3.48) and ibuprofen displaying the lowest risk of eGFR<60 ml/min per 1.73 m2 (hazard ratio, 1.12; 95% confidence interval, 1.02 to 1.23) and eGFR decline ≥30% (hazard ratio, 1.32; 95% confidence interval, 1.23 to 1.41). CONCLUSIONS: Nonsteroidal anti-inflammatory drug exposure was associated with higher risks of incident eGFR <60 ml/min per 1.73 m2 and eGFR decline ≥30%. Highest risk was observed in etoricoxib users, and lowest risk was with ibuprofen. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_04_28_CJN18501120.mp3