English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

BENCHMARKS Recently developed technologies such as microarray analysis allow researchers to determine the genome-wide patterns of expressed genes. This information provides insight into complex regulatory networks, enables the identification of new or underexplored biological processes, and implicates genes in various disease processes (1). While mi-croarray analysis provides genome-wide information on relative gene expression, real-time reverse transcription-PCR (RT-PCR) provides quantitative information by the simultaneous measurement of gene expression in many different samples, which makes the technique especially suitable for research questions that require the measurement of expression level changes (2). Compared to conventional quantification methods such as Northern blot analysis, RNase protection assay, or competitive RT-PCR, real-time RT-PCR analysis has the advantages of greater speed, higher throughput, and a higher degree of potential automation (3,4). Nevertheless, all strategies for mRNA quantification require accurate, reproducible normal-ization. For the correct normalization of gene expression analysis, various strategies have been applied, such as counting cells, total RNA quantitation, and rRNA measurement (3). However, internal control genes are most frequently used to normalize mRNA expression in laboratory experiments. The internal control, usually one of the so-called housekeeping genes (5), should not vary between the tissues or cells under investigation or in response to experimental treatment. However, although housekeeping genes are constant in certain cell types, they can vary in other types (6,7), particularly in clinical samples associated with malignant diseases (5). Thus, the selection of proper control genes for clinical patient samples is vital to gene expression analysis. In the current study, we used liver tissues from normal, liver cirrhosis (LC), and hepatocellular carcinoma (HCC) patients to examine the expression patterns of housekeeping genes. Liver cancer is the third most deadly cancer worldwide and fifth in the number of cases (8), but the molecular mechanisms of hepatocarcinogenesis are not well understood. Therefore, the number of studies probing global gene expression profiles of HCC or preneoplastic chronic liver disease has increased exponentially in recent years (9,10), and the identification of the optimal internal controls is necessary for correct gene expression profiling of liver diseases. Table 1 describes the 10 common housekeeping genes and gene-specific primers that were used. To compare the expression levels of each housekeeping gene, we used four different groups of liver tissues: normal liver tissues, LC, nontumor LC tissues from an HCC patient, and carcinoma tissues from an HCC patient. The expression level of the 10 internal control genes was determined by real-time …

Selection of optimal internal controls for gene expression profiling of liver disease