Cancer research technology
Background of Microarray Technology
Microarrays are a dimensional arrangement of samples of biology that are in two
types. The microarray samples are placed into spots of columns and rows that are in
thousands in terms of numbers. The microarray allows the analyst to effective conduct their
research on the genetic materials and the DNA. Watson and Crick in 1953 were the first
scientists to describe the double helix DNA structure. In addition, the separation of the DNA
strands was introduced by the use of molecular hybridization. The process of molecular
hybridization involves the binding of the complementary DNA in a single-strand. The origin
of microarray technology can be traced back to 1975 (Wang, 2015). During the year,
Grunstein and Hogness use the technology to release blotted microbial colonies. In 1979, the
method of colony hybridization was applied by Gergen et al to produce multiple arrays.
Millions of Agar plates were replicated by the scientists by the use of a mechanical 144 pin
to produce arrays.
Modern DNA Array
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The technology of the DNA array grew rapidly in the late 1990s and the early 2000s.
the rapid progress was a result of increased knowledge of the DNA sequences which allows
assures that arrays are made due to the availability of the raw information of genomes. The
technology was also facilitated by the gradual transition of producing arrays using 25-60bp
oligos as compared to the traditional spotting of the long DNA's. The oligos provided three
types of arrays namely in-situ synthesized, spotted and self-assembled arrays. The spotted
arrays are a type of DNA that allows for the very high-density arrays. The in-situ synthesis
arrays involve the production of 10 amino acid peptides (Kang, 2015).
Microarray Technology in Cancer Research
The knowledge of biological systems has been extensively applied in cancer research.
The technology involves the representation of thousands of genes that are attached to the surface of the naturally inert materials. The technology also involves the co-hybridizing of
the RNA labeled in the fluorescent dye. The technology to the analysis of cancer through
technology also involves quantifying the gene expression in FFPE tissues. The traditional
method of analyzing cancer used the collection of DNA probes that were attached to a solid
surface (Shao, Miller & Roulston, 2014). The surfaces through which the DNA probes were
attached included plastic, glass or silicon chips. Besides, microarray technology research in
cancer involves the scanning of the amount of RNA in the genes. The research involves the
estimation and calculation of RNA in every gene.
The technology of cancer research using a microarray involves two stages. The first
technique involves the probe of the double-strand DNA. The method involves the study of
the strand in which the spotted DNA is spotted into slide surfaces. The other technique
involves the single-stranded probe of the oligonucleotide. The oligonucleotides study
involves the depositing pre-synthesized strands onto the slide surface (Baans & Jambek,
Impact of the Microarray on Cancer
The microarray technology assists the researchers to identify the differences in gene
expression which occur between the normal cells and cancer. Many of the researchers use
the microarray technology of 1,160 DNA elements which are used in the demonstration of
transcriptome changes. The study for microarray on cancer approaches are used to profile
the expression patterns genes. The research also involves the development of DNA in tens of
thousands to give data on the levels of non-tumors and tumors tissues. The identification of
the tumors and non-tumors tissues in the DNA assist in the detection of meaningful patterns
in the complex patterns in the gene expression. Besides, microarray technology has been
used in the study of the dramatic change in the cancer cells. The identification of the cancer
cells facilitates an improved classification of tumors. The microarray technology through the classification of tumors enables the cancer researchers to classify the different types of
cancers such as distinguishing between acute lymphoblastic leukemia (ALL) and acute
myeloid leukemia (AML).
The microarray technology is also important in the decipher signal study which is
directly orchestrated by the transcription factor of cancer. Many researchers have applied the
technology also to determine the chip-chip to be used in cancer determination. Therefore, the
technology of microarray advances the understanding based on science on the different
cancer control gene networks and the development of cancer. Moreover, microarray
technology is useful in the determination of genetic and epigenetic cancer cell makeup. The
knowledge of genes has facilitated the identification of small genetic changes. The small
genetic changes resulting in cancer include the single nucleotide polymorphisms arrays in
the tumor cells.
The large genetic abnormalities are also identified by the use of microarray
technology through the study of comparative genomic hybridization. Most of the large
genetic abnormalities are joined with the development of cancer. The detection of cancer
through the array comparative genomic hybridization also assists in the detection of genetic
change due to the deletion of some kilobases of the chromosomes. The process of
identification is not limited to detection of the addition of the chromosome through
duplication. Lastly, the technology is involved in the decoding of the epigenome of the
different cells which are cancers.
Methods used in the Microarray in Cancer Research
Cancer research involves two analyses. The first analysis is the mutation and genotyping, the
process involves the analysis of the RET oligonucleotide for the MEN2A, FMTC and
MEN2a syndromes. The second analysis is the gene expression, which involves the
determination of response to stimuli and phenotype of an individual. The elucidate cellular functions, regulatory mechanisms, and biochemical pathways are reduced by the profile of
gene expression. The analysis of gene expression through cDNA or oligonucleotide
microarray involves class comparison, class discovery and class prediction. The class
comparison involves the study of the different classes of cancer. The class comparison
identifies the different genes expressed among the sample classes that are predetermined.
The class prediction on the other side involves the analysis of the high-density microarrays.
The process involves biological group, prognostic stage and diagnostic category prediction.
Lastly, class discovery involves research that determines the discrete subsets of disease that
is determined through gene expression. The cancer research in the class discovery involves
the cluster analysis which cannot be applied in both the class prediction and comparison.
Microarray and cancer research
The study will focus on the research done by other researchers that are based on the
prostate, oral and breast cancer.
Microarray and prostate cancer
Many researchers are conducting research using microarrays to characterize prostate cancer.
The research involves publication of gene expression profile for the disease. The
publications indicate that microarray technology was used as the gene discovery tool. The
tool analyzed the genetic markers that differentiates between the cancerous prostate and
normal tissues. Research using the spotted membrane used microarray technology to analyze
the cancerous cells and normal tissues.
Microarray and oral cancer
Research on oral cancer has been done by the use of cDNA microarrays. The microarray
technology and complementary DNA subtraction have been used by several kinds of
research to analyze the probability of chances for the occurrence of genes resulting in oral
cancer. The research establishes nine genes that show the significant result in the potential occurrence of tumor markers. Also, there is research that was carried out to determine the
gene expression profile resulting to change to squamous cell carcinoma in the gullet. The
results indicated that different gene expression results in esophageal cancer.
Microarray and Breast Cancer
Microarray technology has been used in the establishment of different classes of breast
cancer. The microarray technology accurately classifies estrogen receptor-positive and the
estrogen receptor-negative cancer. Besides, research by Veer et al studied 177 samples using
microarray gene expression (Kennedy Babu, Raja & Smiley, 2018). The research profiled
prognostic samples and compared them with known prognostic tumors resulting in breast
cancer. The results and findings were used are used to estimate and predetermine the risks
associated with recurrences of cancer.
Design and interpretation of Microarrays
Research cancer through the microarray involves annotation, experimental design,
quality metrics, and normalization and filtering. Annotation is one of the crucial informatics
steps that are done before any single microarray is hybridized. The data should be updated as
an outdated annotation gives wrong data set. Many researchers are investing in microarray
technologies to acquire data on the causes of cancers.
Experimental design involves two considerations. The first design is a general
consideration, the most important stage in microarray research is the experimental design
which is applied in the cancer clinical research. The bases for the good experimental design
is the appropriate scaring and hypothesis (Hamilton, 2017). The two parameters are
powerful tools during experimental design as it provides for availability and determination
of the type of cancer to be profiled. The conditions for the experiment should be guided by the set of statistical principles of sampling. In addition, the research should take care during
the process of experimentation to ensure that there is a balance between the population and
the sample to be used. The experimental design should also consider the extraneous factors
that affect gene expression. A cancer researcher should ensure that they identify the value of
the normal tissue during the stage of deciding on the appropriate experimental setup to
adopt. Identifying the value of the tissues reduces the chances of wrongly grouping the
genes as they vary across different types of normal tissues.
The second method of experimental design is sampling numbers. Sampling numbers
involves deciding on the number of samples to be profiled. The process of determining the
sample is very important. Although the determination of the sample crucial, the
determination of the sample number is very hand. The cancer research using microarray
technology involves tens of thousands of genes which makes the use of traditional methods
of gene calculation almost impossible (Ruskin, 2016). The research can adopt Tibshirani’s
sample size estimation method which does not incorporate assumptions such as equal
The third method of designing and interpreting the microarray data on cancer research
is quality metrics. Quality metrics involves analysis of the slides after the process of
scanning and hybridization of the genes. The method involves the visual inspection of the
gene’s image for gross defection. The quality analysis should be able to point the pass and
fail through the use of chips. The quality metrics allow for an overall assessment of the
chips to give reliable information to the outliers.
Normalizing and filtering
After the assurance of the slide quality, the research data undergoes the process of
normalization. The normalization process occurs before the valid comparison of the population and the different samples. Normalization occurs to ensure that the biasedness of
factors such as a difference in the labeling efficiencies, fluctuating hybridization and RNA
materials that may manifest in the slides do not occur. The cancer researchers mostly apply
the pre-chip normalization because it systematically balances the slide's biasedness.
Moreover, the process of normalization reduces the intensity-dependent issues in the
microarray pin groupings in the slides (Ruskin, 2016). A researcher in cancer research
applying the technology of microarray should incorporate normalization before any
comparison of the samples.
The next stage in data analysis involved in the microarray is the filtering of the
unwanted genes. The cancer researcher can remove the genes that are of no interest in the
process of further examination. The genes that are of less interest are filtered out as they as
their presence would result in high variability of the results given. Most of the researcher
uses a rule of eliminating the genes that possess an intensity of fewer than two times the
average required. The second way of filtering the genes in a given strand is assigning
percentages to establish the cutoff point (Hamilton, 2017). The last step in designing and
interpreting the microarray results are analyzing. The analyzing of the microarray data
ensures that the different genes are differently expressed.
The popularity of microarray technologies is increasing steadily in the research of cancers.
The technology has become popular due to the drastic change in the accumulation of
epigenetic and genetic variations. Services such as diagnosis of cancer have incorporated the
use of microarray. A comprehensive analysis of the genes remains one of the crucial practice
in the research of cancer. The technology faces some challenges such as high cost and
experimental protocols applicable. The technology will form the future research, diagonalizing and prognosis. The diagnosis of cancer will involve the use of oligonucleotide
while radiotherapy or chemotherapy will be predetermined through gene expression. The
research should utilize the bioinformatics that is available to establish the usefulness of the
technologies of microarrays.
Baans, O., & Jambek, A. (2019). Background correction method for DNA microarray
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Kang, J. (2015). Clinical Implications of Microarray in Cancer
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Kennedy Babu, D., Raja, J., & Smiley, S. (2018). Identification of Therapeutic
Targets and Biomarker for Breast Cancer using Microarray Data Mining. International
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Shao, L., Miller, S., & Roulston, D. (2014). Genome-Wide Microarray Analysis
Improves Routine Diagnosis of Hematological Malignancies. Cancer Genetics, 207(6), 284.
Wang, D. (2015). A Liposome-Based Approach to the Integrated Multi-Component
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