Research Article
Use of Micro RNAs to Screen for Colon Cancer
Farid E Ahmed* and Nancy C Ahmed
Department of Radiation Oncology, GEM Tox Labs, Greenville, NC 27834, USA
*Corresponding author: Farid E Ahmed, Department of Radiation Oncology, Institute for Research in Biotechnology, 2905 South Memorial Drive, Greenville, NC 27834, USA
Published: 17 Jul, 2017
Cite this article as: Ahmed FE, Ahmed NC. Use of Micro
RNAs to Screen for Colon Cancer. Clin
Surg. 2017; 2: 1565.
Abstract
Colon cancer (CC) screening is important for diagnosing early stage for malignancy and therefore
potentially reduces mortality from this disease because the cancer could be cured at the early
disease stage. Early detection is needed if accurate and cost effective diagnostic methods are
available. Mortality from colon cancer malignancy is theoretically preventable through screening.
The Current screening method, the immunological fecal occult blood test, FOBTi, lacks sensitivity
and requires dietary restriction, which impedes compliance. Moreover colonoscopy is invasive and
costly, which decreases compliance, and in certain cases could lead to mortality. Compared to the
FOBT test, a noninvasive sensitive screen that does not require dietary restriction would be more
convenient. Colonoscopy screening is recommended for colorectal cancer (CRC). Although it is a
reliable screening method, colonoscopy is an invasive test, often accompanied by abdominal pain,
has potential complications and has high cost, which has hampered its application worldwide. A
screening approach that uses the relatively stable and non degradable micro RNA molecules when
extracted from either the noninvasive human stool, or the semi-invasive blood samples by available
commercial kits and manipulated thereafter, would be more preferable than a transcriptomic
messenger (m) RNA-, a mutation DNA-, an epigenetic- or a proteomic-based test. That approach
utilizes reverse transcriptase (RT), followed by a modified quantitative real-time polymerase chain
reaction (qPCR). To compensate for exosomal miRNAs that would not be measured, a parallel test
could be performed on stool or plasma's total RNAs, and corrections for exsosomal loss are made
to obtain accurate results. Ultimately, a chip would be developed to facilitate diagnosis, as has been
carried out for the quantification of genetically modified organisms (GMOs) in foods. The gold
standard to which the miRNA test is compared to is colonoscopy. If laboratory performance criteria
are met, a miRNA test in human stool or blood samples based on high through put automated
technologies and quantitative expression measurements currently employed in the diagnostic
clinical laboratory, would eventually be advanced to the clinical setting, making a noticeable impact
on the prevention of colon cancer.
Keywords: Bioinformatics; Diagnosis; Histopathology; Microarrays; QC; RNA; RT-Qpcr;
Statistics
Abbreviations
ACS: American Cancer Society; ANOVA: Analysis of Variance by Statistics; APC: Adenomatous Polyposis Coli Gene; CA: Carcinoembryonic Antigen; CC: Colon Cancer; CP: Comparative Cross Point; CRC: Colorectal Cancer; DMMR: Defective DNA Mismatch Repair; DNMTs: DNA Methylation Enzymes; CRC: Colorectal Cancer; DAVID: Bioinformatics tool Referring to Database for Annotation, Visualization and Integrated Discovery; E: efficiency of the polymerase chain reaction; EDTA: Ethylene Diminetetra Acetic Acid; E-method: another name for the comparative cross point method for polymerase chain reaction quantification; FOBT: Fecal Occult Blood Test; GESS: Gene Expression Statistical System; GMOs: Genetically Modified Organisms; IBD: Inflammatory Bowel Disease; IHC: Immunohistological; LC: Light Cycler Instrument; LCM: Laser Capture Micro dissection; MIQUE: Guidelines on reporting qPCR data known as minimum information for publication of quantitative real-time PCR expression; NCI-EORTC: National Cancer Institute and the European Organization for Research and Treatment of Cancer; NCSS: Statistical Software; NF1A: Nuclear Factor 1A-type protein; pMMR: Proficient in DNA Mismatch Repair; PRoBE: Epidemiological Experimental Random Design; QC: Quality Control; Qpcr: Quantitative Polymerase Chain Reaction; 18s rRNA: Ribosomal Ribonucleic Acid; RT: Reverse Transcription Reaction; SYBR Green, an asymmetrical canine dye for nucleic acids staining; TNM staging, a cancer staging notation system; TPC: Test performance characteristics; UC: Ulcerative Colitis; UTR: the 3’ Untranslated Region of Target Messenger RNA
Introduction
Colon cancer is a disease that is different from rectal cancer [1]. In this article, we have focused on colon cancer (CC) screening, which is the process of looking for the disease in people showing no symptoms for malignancy [1,2]. Regular screening can detect colon cancer at its early stages, when it is most likely curable, because if growing polyps are observed, they can be removed before they have a chance to develop into a full-blown cancer [3]. It should be stressed, however, that none of the tests currently employed on the market is optimal, and they also have poor rates in certain populations. Tests for colon cancer screening fall into two categories [4]: a) In vivo tests that detect both polyps and cancer, and looks at the structure of the colon to find any abnormalities. This is carried out with an X-ray either after ingesting a contrasting liquid, followed by inserting a scope into the rectum (flexible sigmoidoscopy, capsule endoscopy, double contrast barium enema), or in other tests that employs special X-ray imaging such, as CT colonography (virtual colonoscopy). These tests although are invasive, they allow for the removal of polyps when observed, and thus have a role in colon cancer prevention, or b) in vitro tests that generally looks at the genetic material (DNA or RNA) in a noninvasive excrement (stool) or in a semi-invasive body fluid (blood), so that tests with high sensitivity and specificity, capable to function as an acceptable screen for this preventable cancer (e.g., guaiac- and immunological-based FOBTs, and molecular DNA tests in either stool and blood) are developed. These in vitro tests are less invasive and are easier to carry out, but many of them have low sensitivity for polyps’ detection, unless they are further developed and refined [1,4,6,10-15]. Therefore, much effort and expense have been spent during the last 20 years to develop acceptable non-invasive tests. These tests can be used when people exhibit symptoms of colon cancer, or other digestive diseases to check on the progression of the anomalies.
Methods for Colon Cancer Screening
When recommended, screening often begins with fecal occult
blood test, FOBT, which is blood that cannot be seen with the naked
eye in stool [3,5,15]. Many CRCs bleed into the intestinal lumen
because blood vessels at the surface of large polyps or cancer are fragile
and can easily be damaged by the passage of feces, releasing a small
amount of blood into the stool, and FOBT can detect the invisible
occurrence of blood in stool by a chemical reaction. The test cannot
tell if blood is from the colon or from other parts of the digestive
tract (e.g., stomach). Although polyps and cancers cause blood in
stool, other causes of bleeding are ulcers, hemorrhoids, diverticulosis
(tiny pouches that form at weak spots in colon wall), or IBDs (colitis).
Nonetheless, as blood passes through the intestine, it becomes
degraded, and depending upon the site at which the hemorrhage
occurs, blood detected in the stool by FOBT will vary. Thus, FOBT
alone has a limited ability to decrease mortality, as 67% - 85% of
colon cancer patients who undergone FOBT died from the disease,
indicating that its detection does not occur early enough to maximally
affect the overall outcome of the disease, and therefore FOBT is not a
sensitive test since it misses many early stage cancers and adenomas.
Moreover, guaiac FOBT test requires patients to change their diet
before testing, avoid nonsteroidal anti-inflammatory drugs (NSAIDs)
like ibuprofen (Advil), naproxen (Aleve) or aspirin (>1 adult aspirin,
325 mg/day) for 7 days before testing as they cause bleeding, although
Tylenol® can be taken as needed, vitamin C in excess of 250 mg/day
from all sources, and red meats (beef, lamb or liver) for 3 days before
testing, because components of blood in meat could give false positive
results [1,4-6,15-17]. The procedure requires multiple tests to be
repeated every year, potentially reducing compliance [18]. Moreover,
if the test finds blood, a colonoscopy will be required to look for the
source (American Cancer Society, http://www.cancer.org. A more
recent test than the traditional guaiac is fecal immunochemical test
(FIT) or (iFOBT), which reacts to part of the human hemoglobin
protein found in red blood cells. This test is easier to use than guaiac
FOBT because it requires no drug or dietary restrictions, and it is less
likely to react to bleeding from parts of the upper digestive tract (e.g.,
stomach) [4,16]. Because like guaiac FOBT, the FIT will not react to
a non bleeding tumor [17,18] multiple stool samples are required for
testing, and if results are positive, a colonoscopy will also be necessary.
In contrast to FOBTs, minimally invasive procedures could detect
neoplastic lesions. Since > 60% of early lesions seem to arise in the
recto-sigmoid areas of the large intestine, rigid sigmoidoscopy, about
60 cm long, which can only see half the colon, has been routinely
used in the past for screening [19]. Recently, however, there has been
an increase in the number of lesions arising from more proximal
lesions of the colon [6,20-23], requiring the use of flexible, fiber optic
sigmoidoscopies. Although these methods offer a means of removing
neoplastic polyps, they still leave undetected all lesions that are
beyond the reach of the scope (estimated to be between 25% and 34%)
[19]. Double-contrast barium enema (DCBE), also referred to as aircontrast
barium enema, or a barium enema with air contrast and
sometimes known as lower GI series, is basically a type of an X-ray
test in which a chalky liquid (barium sulfate) and air is used to outline
the inner part of the colon and rectum to look for abnormal areas
on X-rays [1-5]. A clear liquid diet is taken for a day or two before
the procedure, and eating or drinking dairy products is avoided the
night before the start of the procedure. The procedure takes about 45
min and does not require sedation. Moreover, the colon and rectum
needs to be cleansed the night before the test by laxative intake,
and/or use of enemas the morning of the exam. At testing, a small
flexible tube is inserted into the rectum, and barium sulfate liquid
is pumped into it in order to partially fill and opens the colon. Air is
then pumped into the colon through the same tube, which may lead
to bloating, cramping and discomfort, in addition to an urge for a
bowel movement. X-ray pictures of colon lining are taken. If polyps
or other suspicious areas are observed, a colonoscopy may also be
needed. The barium could cause constipation for a few days after the
procedure, and there is a small risk due to inflating the colon with air,
which could injure or puncture the colon, in addition to an exposure
to a relatively small amount of radiation [4].
Colonoscopy, based upon the same principles as sigmoidoscopy,
allows visualization of the entire colon. Although it is the gold
standard for CRC screening for the 70 million people older than 50
years of age in the USA, it requires an unpleasant bowel preparation,
the test itself could be uncomfortable, but sedation often helps, and
some people could experience low blood pressure or changes in heart
rhythm during the test due to the sedation, although these side effects
are not serious. If polyps are removed or a biopsy is taken during the
procedure, blood can be observed for a day or two after the test, and
in rare cases when bleeding continues, it could require treatment
[23]. The test costs about $10 billion per year and exceed the physician
capacity to perform this procedure, requires cathartic preparation
and sedating or anesthetizing the patient, and it has an increased risk
of morbidity or mortality due to perforation of the GI [6,23].
Moreover, studies found the range of colonoscopy miss rates for
right-sided colon cancer to be 4.0%, 12% - 13% for adenomatous polyps 6 mm - 9 mm, and 0% - 6% for polyp’s ≥1 cm in diameter [24].
Clearly, a simple, inexpensive, noninvasive, sensitive and specific
screening test is needed to identify people at risk for developing
advanced adenomas (e.g., polyp’s ≥1 cm with high grade dysplasia) or
CRC who would benefit from a subsequent colonoscopy examination.
Virtual colonoscopy (CT colonography) is an advanced type of
computed tomography (CT or CAT) scan of both the colon and
rectum. It involves examination of a computer generated 3D
presentation of the entire GI tract by reconstructing of either a
computerized tomography (CT) or a magnetic resonance imaging.
This test does not require sedation, but it requires bowel preparation
and the use of a tube placed in the rectum --as in barium enema-- to
fill the colon with air, and also the drinking of a contrast solution
before the test in order to tag any remaining stool in the colon or the
rectum. The procedure takes about 10 min, and it is especially useful
for people who do not want to take the more invasive colonoscopy
test. This method detects lesions based on their site, rather than their
histology, and is thus unable to distinguish benign adenoma from an
invasive carcinoma. It was shown in a meta-analysis of 33 studies
involving 6.393 patients that this test has a low sensitivity for polyps
(48% for polyps < 6 mm, 70% for polyps 6 mm - 9 mm and 85% for
polyps >9 mm). Moreover, the test is expensive, and requires the
availability of experts, which could reduce patients’ compliance [25].
CT is still considered as an investigational alternative for
asymptomatic, not at risk individuals, who also expose patients to a
small amount of X-irradiation, and it can also miss the detection of
small lesions [26]. In an effort to find a more pragmatic early
biomarker noninvasive colon cancer detection methods, investigators
have developed many in vitro tests such as epigenetic methylation
marker changes in genes and chromosomal loci in fecal DNA [27],
promoter DNA methylation in stool [28], mutated DNA markers
found in neoplastic cells that are excreted in feces [29,30], or the
minichromosomal maintenance proteins (MCMs) needed for DNA
replication test [3], proteomics’-based approaches in stool or blood
[31], and transcriptomic mRNA-based approaches in stool or blood
[12], or a combination of both genetic, as well as epigenetic tests [32].
Molecular studies have shown the presence of mutations of K-ras in
DNA from stool of patients, but its drawbacks include its expression
by fewer than half of large adenomas and carcinomas. In addition, its
expression in non-neoplastic tissue makes it less than an optimal
molecular marker. Besides, mutations are only found in a portion of
the tumor, making the test to be less sensitive [33]. Mutation of the
adenomatous polyposis coli (APC) gene in stool of patients obtained
by analysis of ductal DNA by PCR of APC gene templates and the
detection of generated abnormal truncated polypeptides by in vitro
transcription and translation of the PCR product has been
demonstrated at early stages of the disease. However, the digital
protein truncation test is not a reliable screening tool because it lacks
specificity (i.e., 5 out of 28 controls were positive for FOBT, and
another 6 showed rectal bleeding) [34]. Since CRCs exhibit genetic
heterogeneity, a multitarget approach that employ mutations in
K-ras, APC and p53; the microsatellite instability marker Bat-26; and
“long” DNA representing DNA of nonapoptotic colonocytes
characteristic of cancer cells exfoliated from neoplasms, but not
normal apoptotic colonocytes, have been looked at and undergone
clinical testing [35]. However, DNA alterations were detected in only
16 of 31 (51.6%) invasive cancer, 29 of 71 (40.8%) invasive cancer plus
adenoma with high-grade dysplasia, and 76 of 418 (18.2%) in patients
with advanced neoplasia (tubular adenoma ≥1 cm in diameter),
polyps with high grade dysplasia, or cancer [29]. Moreover, these tests
are not cost-effective, as screening for multiple mutations is generally
expensive [36]. Preliminary studies suggest that proteomics may
distinguish normal state from adenoma. This approach has, however,
not been evaluated as a noninvasive screening tool, and it is therefore
considered investigational [37,38]. Currently, the markers most often
elevated in advanced CRC are carcinoembryonic antigen (CEA) [39]
and the carbohydrate antigen, which is also called cancer antigen
(CA) 19-9 [40], but neither of these markers has been found to be a
useful, or a reliable diagnostic screen for colorectal cancer. Early
detection would be greatly enhanced if accurate, practical and cost
effective diagnostic biomarkers for CRC were available. However,
despite the advances detailed above, tests now available neither detect
colon cancer in all cases (i.e., have low sensitivity), nor are they highly
specific. Furthermore, these tests are often costly, produce falsepositive
or false-negative results, molecules could be non-stable and
easily fragment in vitro requiring excessive care and special handling
techniques (mRNA molecules), and some methods entail discomfort/
inconvenience to the patients, or could in rare cases result in mortality
(e.g., colonoscopy) [21]; all are factors that could discourage patients’
enthusiasm and/or compliance. Current participation rates in CRC
screening are less than 30% in both genders, compared to screening
for breast and cervical cancer that have rates of 70% to 80%,
respectively [41]. Participation could thus be enhanced by the use of
molecular lab tests that are less uncomfortable, less expensive and
offer greater accuracy (more sensitivity and specificity). However,
larger clinical studies would be needed to corroborate initial test
results. On the other hand, our data and others [11,13,14,42-54] have
shown that quantitative changes in the expression of few miRNA
genes in stool or blood that are associated with colon cancer permit
development of more sensitive and specific CRC molecular markers
than those currently available on the market. In comparison to the
commonly employed FOBT stool test, a noninvasive molecular and
reliable test would particularly be more convenient as there would be
no requirement for dietary restriction, or meticulous collection of
samples, and thus a screening test would be acceptable to a broader
segment of the population. Using stable molecules such as miRNAs
that are not easily degradable when extracted from stool or blood and
manipulated thereafter, a miRNA –approach for colon cancer is thus
preferable to a transcriptomic mRNA-, mutation DNA-, epigeneticor
a proteomic-based test [11,42-56], particularly that we and others
have shown that these stable, nondegradable miRNA molecules can
be easily extracted from stool or from circulation in vitro using
commercially available kits. Advantages and disadvantages of the in
vivo and in vitro tests are presented in (Table 1).
Micro RNAs as molecular markers for colon cancer
screening in stool or blood
Stool testing has several advantages over other colon cancer
screening media as it is truly noninvasive and requires no unpleasant
cathartic preparation, formal health care visits, or time away from
work or routine activities [3-6]. Unlike sigmoidoscopy, it reflects
the full length of the colorectum and samples can be taken in a way
that represents both the right, as well as the left side of the colon.
It is also believed that colonocytes are released continuously and
abundantly into the fecal stream [7,8], contrary to situation in
blood --where it is released intermittently-- as in FOBT [9], and
transformed colonoctes produce more RNA than normal ones [10-
14]; therefore, this natural enrichment phenomenon partially obviate
for the need to use a laboratory technique to enrich for tumorigenic
colonocytes. Furthermore, because testing can be performed on mail in-specimens, geographic access to stool screening is unimpeded
[2,16,32]. The American Cancer Society (ACS) (http://www.cancer.
org) has recognized that a promising diagnostic screen for CRC would
be enhanced by employing a molecular-based stool testing. It should
be emphasized that although not all of the shed cells in stool are
derived from a tumor, data published by us and others [11,13,14,44-
56] have indicate that diagnostic miRNA gene expression profiles
are associated with adequate number of exfoliated cancerous cells
and enough transformed RNA is released in the stool, and also the
availability of measurable amount of circulating. MiRNA genes in
blood (either cellular or extra cellularly), which can be determined
quantitatively by a sensitive technique such as PCR in spite of the
presence of bacterial DNA, non-transformed RNA and other
interfering substances. That quantification is feasible because of the
high specificity of PCR primers that are employed in this method,
which overcomes all of these, stated obstacles; hence, the number
of abnormally-shed colonocytes in stool, or total RNA presents in
plasma or serum becomes unlimiting [11-14]. A test that employs
miRNA in stool or blood could also result in a robust screen because
of the durability of the miRNA molecules [11,13,14]. Moreover,
an approach utilizing miRNA genes is more comprehensive and
encompassing than a test that is based on the fragile messenger (m)
RNA [12], for example, because it is based on mechanisms at a higher
level of control. We believe that ultimately the final noninvasive test
in stool or blood will include testing of several miRNA genes that
show increased and decreased expression, and eventually a chip that
contains a combination of these stable molecules will be produced
to simplify testing, as has been developed for the testing of GMOs
in foods [57]. Blood is a body fluid that can be obtained through a
semi-invasive method (skin puncturing) that is commonly used in
the laboratory testing, which makes it logical to employ on routine
bases, and thus it would be attractive to technicians performing lab
tests. However, working with blood for miRNA profiling present
various challenges in purification and molecular characterization. For
example, a naked miRNA molecule would degrade within seconds
of vein puncture due to the presence of high levels of nucleases and
other inhibitory components in blood, which can interfere with
downstream enzymatic reactions, as for example, the common
anticoagulant heparin that coamplify with RNA. Moreover, highquality
RNA preparations found in blood contain contaminants
that inhibit a RT-qPCR reaction if too much sample is used in the
RT preparation [58]. Therefore, it is recommended to use EDTA
or citrate anti-coagulated blood instead of heparin. Circulating
miRNAs, however, have shown stability in several studies resulting
from either the formation of complexes between circulating miRNAs
and specific proteins [59-61], or the miRNAs are contained within
protective circulating exosomes or macrovesicles [62]. Plasma is
preferable to serum when quantifying miRNAs in blood, because
its use minimizes variations caused by differences due to the lack of
clotting factors [63]. For mature miRNAs testing, there are currently
available commercial preparations that save time and provide
the advantage of manufacturer's established validation and QC
standards. For example, a Qiagen buffer (miScript HiSpec Buffer®),
Qiagen, Inc., Frederick, MD, USA, that inhibits the activity of the
tailing reverse-transcription (RT) reaction on templates other than
miRNA-sized templates provides for an exceptionally specific cDNA
synthetic reaction that eliminates background from longer RNA
species. To measure pre-miRNA, however, it would be essential to
use another buffer (miScript HiFlex Buffer®) as the nonbiased reaction
results in an increased background signal from cross reactivity with
sequences from a total RNA preparation, which can be distinguished
by performing a melt curve analysis when carrying out PCR analysis
[64]. Small noncoding RNAs that exhibit little variation in different
cell types (e.g., snoRNAs and snRNAs) are polyadenylated and are
reverse transcribed (RT) in the same way as the small miRNAs and
thereby could serve as controls for variability in sample loading and
real-time RT-PCR efficiency. They are, however, not suited for data
normalization in miRNA profiling experiments because they are not well expressed in serum and plasma samples. Therefore, normalization
by a plate mean (i.e., mean CT value of all the miRNA targets on the
plate), or using a commonly expressed miRNA targets (i.e., only
the targets that are expressed in all samples are used to calculate
the mean value) would be needed for a proper normalization of the
amplification reaction [65]. An extraction protocol for miRNAs in
blood can, however, is challenging. When setting up an extraction
step, there are two options: either extracts the miRNA molecules
from cellular blood components, as whole blood is full of cells that
can be obtained by differential centrifugation followed by isolating
these cells, or from liquid plasma that contains circulating miRNAs.
Attention, however, should be paid to heparin as this anticoagulant
is known to be a strong inhibitor of polymerase in PCR reactions.
There are several collection tubes that contain citrate as anticoagulant
instead of heparin, as those made by Qiagen or Tempus can be used
for the whole blood collection. If the aim is to isolate miRNAs from
plasma, EDTA tubes can be used to collect blood and plasma isolated,
then store at -80°C until ready for extracting the miRNAs, as these
molecules are very stable under standardized laboratory extraction
methods. Extraction can be carried out by modified Trizol method
from Life Technologies, or miRNeasy reagent from Qiagen. Columns
employed in extraction can be clogged and RNA may be lost and/or
degraded; therefore, the integrity of total RNA needs to be checked
on a standard agarose or acrylamide gels, or with an electrophoresis
apparatus, like the Agilent Bioanalyzer. To check if RT-PCR method
works, one should employ another source of RNA, as for example
cells in culture. A RT- qPCR based screening, like hybrid based assays,
however, does need validation. Both Life Technologies Taqman- and
SYBR - based probes (like LNA Universal miRCURY RT micro RNA
PCR assay, made by Exiqon, Woburn, MA) have high specificity
for short miRNAs and both methods showed similar efficiencies,
without the need to design and validate home-made primers.
MiRNA quantification by both methods, however, showed difference
in variability that impact miRNA measurements, and therefore
quantification is influenced by the choice of assay methodology.
Thus, the method used for quantification must be considered when
interpreting analyses of PCR results [66-69].
Our research team [11,13,14] and others [28,42-65,70-85] are
in the opinion that a miRNA approach in tissue, cell lines, stool or
plasma, could meet the criteria for test acceptability by laboratory staff
carrying out these tests, as it is a non- or a minimally-invasive method,
requites at the most 1 g of stool, or < 2 ml of blood (60% of which is
plasma), does not need sampling on consecutive dates, can be sent
by mail in cold packs, able to differentiate between normal subjects
and colon adenomas/carcinomas, has high sensitivity and specificity
for detecting advanced polyps, and can be automated, which makes
it relatively inexpensive and more suited for early detection when
compared to a test such as mutated DNA markers, especially since
plasma is free from interfering clotting products, which are present
in serum, miRNAs are stable in stool and plasma [11-14], and only
500 μl of plasma and 1 gram of stool, is required to perform the assay
using commercially available kits [13,14]. The availability of powerful
approaches for global miRNA characterization such as microarrays
[86] and simple, universally applicable assays for quantification of
miRNA expression such as qPCR [87] and statistical/bioinformatics
methods for data analyses and interpretation [88-90], suggests
that the validation pipeline that often encounters bottlenecks [15]
will be more efficient in this assay. There is a pressing need for
accelerating use of sensitive and stable molecular markers, such as
miRNA molecules, in non- or minimally-invasive media such as
stool and/or blood to improve the detection of CRC [91], particularly
at an early tumor lymph node metastasis (TNM) disease stage
[92,93] while the cancer is still curable. The discovery of small noncoding
protein sequences, 17-27 nucleotides long RNAs, miRNAs,
which regulate cell processes in ~ 30% of mammalian genes by
imperfectly binding to the 3’ un-translated region (UTR) of target
mRNAs resulting in prevention of protein accumulation by either
transcription repression, or by induction of mRNA degradation
[94,95], has opened new opportunities for a non-invasive test for
early diagnosis of many cancers [53,66,70-81]. The latest miRBase
release (v20, June 2013) [http://www.mirbase.org] contains 24,521
21,264 miRNA loci from 206 species to produce 30,424 mature
miRNA products [96]. Each miRNA generally targets hundreds of
conserved mRNAs and several hundreds of non-conserved targets
that operate in a complex regulatory network, and it is predicted that
miRNAs together regulate thousands of human genes [49,54,56].
MiRNAs are transcribed as long primary precursor molecules (primiRNA)
that are subsequently processed by the nuclear enzyme
Drosha and other agents to the precursor intermediate miRNA (premiRNA),
which in turn is processed in the cytoplasm by the protein
Dicer to generate the mature single-stranded (ss) miRNA [97].
MiRNA functions have been shown to regulate development [98]
and apoptosis [99], and specific miRNAs are critical in oncogenesis
[51], effective in classifying solid [70-76] and liquid tumors [42,77-
81], and serve as oncogenes or suppressor genes [100]. MiRNA genes
are frequently located at fragile sites, as well as minimal regions of loss
of heterozygosity, or amplification of common breakpoint regions,
suggesting their involvement in carcinogenesis [101]. MiRNAs
have great promise to serve as biomarkers for cancer diagnosis,
prognosis and/or response to therapy [50,52,102]. Profiles of miRNA
expression differ between normal tissues and tumor types, and
evidence suggests that miRNA expression profiles clusters similar
tumor types together more accurately than expression profiles of
protein-coding mRNA genes [10,12,14,103]. Several of the miRNAs
were shown by microarrays and RT-qPCR techniques in cell culture
lines, CRC tissue, stool and blood to be related to colon cancer tumor
genesis [11,13,42,45-48,52,55,56,67,76,94] and ulcerative colitis (UC)
[11]. A study indicated that a combination of mRNA and miRNA
expression signatures represent a broader approach for improving
biomolecular classification of CRC [103]. Another study employing
microarrays and qPCR, in addition to an in situ hybridization test
to assess differential expression in inflammatory bowel disease
(IBD), showed aberrant expression of 11 miRNA in inflamed tissue
and in HT-29 colon adenocarcinoma cells (3 showing significant
decrease and 8 significant increase) [84]. Our work support the
notion that quantitative changes in the expression of a few cell-free
circulatory mature miRNA molecules in stool and plasma that are
associated with colon cancer progression would provide for a more
sensitive and specific biomarker approach than those tests that are
currently available on the market [11,13,20,91]. As colon cancerspecific
miRNAs are identified in stool colonocytes or blood plasma
by microarrays- and qPCR-based approaches as presented in this
review, the validation of novel miRNA/mRNA target pairs within
the pathways of interest could lead to discovery of cellular functions
collectively targeted by differentially expressed miRNAs [103]. For
example, comparison of top 12 pathways affected by colon cancer and
globally targeted by miRNAs over expressed in CRC shows that coexpressed
miRNAs collectively provide for a systemic compensatory
response to the abnormal phenotypic changes in cancer cells by targeting a broad range of signaling pathways affected in that cancer
[88]. Several algorithms such as: Target Scan [http://www.targetscan.
org], DIANA-micro [http://www.diana.pcbi.upenn/edu], miRanda
[1http://www.microrna.org], PicTar [http://pictar.bio.nyu.edu],
EMBL [http://russell.embl-heidelberg.dr], EIMMo [http://www.
mirz.unibas.ch], mirWIP [http://146.189.76.171/guery] and PITA
Top [http://genic.weizmann.ac.il/ pubs/mir07/mir07_data.html.1]
have been used to correlate complementary 2-8 nucleotides seed
sequences of mature miRNAs with target mRNA sequences in the 3’
UTR ends of in order to identify crucial control elements within a very
complex regulatory system [85,87-90] that could be dysfunctional
in CRC [104-107]. These programs differ in their requirement for
base pairing of miRNA and target mRNA genes, and implement
similar but not the same criteria when cross-species conservation is
applied. Therefore, these different programs will invariably generate
different sets of target genes for probably all miRNAs [91]. A study
that examined global expression of 735 miRNAs in 315 samples of
normal colonic mucosa, tubulovillus adenomas, adenocarcinomas
proficient in DNA mismatch repair (pMMR), and defective in DNA
mismatch repair (dMMR) representing sporadic and inherited CRC
stages I-IV [108]. Results showed the following: a) six of the miRNAs
that were differentially expressed in normal and polyps (miR-1, miR-
9, miR-31, miR-99a, miR-135b and miR-137) were also differentially
expressed with a similar magnitude in normal vs. both the pMMR
and dMMR tumors, b) all but one miRNA (miR-99a) demonstrated
similar expression differences in normal versus carcinoma, suggesting
a stepwise progression from normal colon to carcinoma, and
that early tumor changes were important in both the pMMR- and
dMMR-derived cancers, c) several of these miRNAs were linked to
pathways identified for colon cancer, including APC/WNT signaling
and cMYC, and d) four miRNAs (miR-31, miR-224, miR-552 and
miR-592) showed significant expression differences (≥2 fold changes)
between pMMR and dMMR tumors. The above data suggest the
involvement of common biologic pathways in pMMR and dMMR
tumors in spite of the presence of numerous molecular differences
between them, including differences at the miRNA level [108].
Unlike screening for large numbers of mRNA genes, a modest
number of miRNAs is used to differentiate cancer from normal, and
unlike mRNA, miRNAs in stool and blood remain largely intact
and stable for detection [11-14,91]. Therefore, miRNAs are better
molecules to use for developing a reliable noninvasive diagnostic
screen for colon cancer, since we found out that: a) the presence of
Escherichia coli does not hinder detection of miRNA by a sensitive
technique such as qPCR, as the primers employed are selected to
amplify human and not bacterial miRNA genes, and b) the miRNA
expression patterns are the same in primary tumor, or in diseased
tissue, as in stool and blood samples. The gold standard to which the
miRNA test is to compared should be colonoscopy, which is obtained
from patients’ medical records, as well as the cheaper immune
histological (IHC) FOBT screen, currently used in annual checkups,
for comparison with miRNA results [18]. Although exosomal RNA
will be missed [109] when using restricted extraction of total RNA
from blood or stool, a parallel test could also be carried out on the
small total RNA obtained from noninvasive stool or seminvasive
blood samples, and the appropriate corrections for exsosomal loss can
then be made after the tests are completed. A miRNA quantification
workflow is presented in (Figure 1).
NGS, microarray and RT-qPCR tests for quantitative
detection of miRNAs in diversified samples
We have shown that we have been routinely and systematically
able to extract a high quality total RNA containing miRNAs from
a small number of laser capture micro dissected (LCM) cells from
tissue [110], colonocytes isolated from human stool [11,13,91] or
circulating blood [14] using commercially-available kits (RNeasy
isolation Kit®) from Qiagen, Valencia, CA, USA, followed by another
kit from Qiagen “The “Sensiscript RT Kit”.
Next-generation sequencing (NSG) technologies
The 1977 chain-termination method of Sanger, commonly known
as Sanger's dideoxy sequencing [111] has been partly supplanted by
other more cost effective next-generation sequencing technologies
that provide higher throughput, but at the expense of read lengths.
The Sanger method is based on DNA polymerase-dependent
synthesis of a complementary DNA strand in the presence of
2'-deoxynucleotides (dNTPs) and 2',3'-dideoxynucleotides (ddNTPs)
that serve as nonreversible synthesis terminators when ddNTPs are
added to the growing oligonucleotide chains, resulting in truncated
products of varying lengths, which can subsequently be separated by
size on polyacrylamide gel electrophoresis. Advances in fluorescence
detection have allowed for combining the four terminators into one
reaction, using fluorescent dyes of different colors, one for each of the
four ddNTP. Furthermore, the original slab gel electrophoresis was
replaced by capillary gel electrophoresis, enabling better separation.
Additionally, capillary electrophoresis was replaced by capillary
arrays, allowing many in vivo amplified fragments samples cloned into
bacterial hosts to be analyzed in parallel. Moreover, the development
of linear polyacrlamide and polydimethylacrilamide allowed the reuse
of capillaries in multiple electrophoretic runs, thereby increasing the
sequencing efficiency. These and other advances of the sequencing
technology have contributed to the relatively low error rate, long read
length and robustness of modern Sanger sequencers. For example,
the high throughput automated Sanger sequent instrument from
Applied Biosystems (ABI 37730xl) has a 96 capillary array format
that produces ≥900 PHRED 20 bp (a measure of the quality of
identification of the nucleobases generated by sequencing) per read,
for up to 96 kb, for a 3 h run [112].
The 454Roche instrument was the first next generation sequencer
released to the market that circumvents the lengthy, labor intensive
and error-prone technology by using in vitro DNA amplification
known as emulsion PCR, where individual DNA fragment-carying
streptavidin beads, obtained by the shearing the DNA and attaching
the fragments to beads using adapters, which are captured into separate emulsion droplets that act as individual amplification
reactors, producing ~ 107 clonal copies of a unique DNA template
per bead. Each template-containing bead is then transferred into
a well of a picotiter plate, which allows hundreds of thousands of
clonally related templates of pyrosequencing reactions to be carried
out in parallel, increasing sequencing output [114]. The sequence of
DNA template is determined by a pyrogram, which corresponds to
the correct order of chemiluminescently incorporated nucleotide as
the signal intensity is proportional to the amount of pyrophosphate
released. The pyrosequencing approach is prone to errors resulting
from incorrectly estimating the length of homopolymeric sequence
stretches (or indels). The Roche 454 platform, considered the most
widely used next generation sequencing technology, is capable of
generating 80 Mb - 120 Mb of sequence in 200 bp - 300 bp reads in
a 4 h run [112]. The Illumina/Solexa approach achieves cloning-free
DNA amplification by attaching a ssDNA fragment to a solid surface,
known as a single molecule array, or free cell, and performing solidphase
bridge amplification of single molecule DNA templates in
which one end of single DNA molecule is attached to a solid surface
by an adapter; the molecule is subsequently bend over and hybridized
to complementary adapters, creating a bridge, which serves as a
template for the synthesis of complementary strands. Following the
amplification, a flow cell containing more than 40 million clusters,
each cluster composed of ~ 1000 clonal copies of a single template
molecule is produced. Templates are sequenced in massively parallel
manner using a DNA sequencing-by-synthesis approach that employs
reversible terminators with removable fluorescent moieties and DNA
polymerases capable of incorporating these terminators into growing
oligonucleotide chains. The terminators are labeled with fluors of
four different colors to distinguish among the different bases at the
given sequence position, and the template sequence of each cluster
is deduced by reading off the color at each successive nucleotide
addition step. Although Illumina technology seems more effective
at sequencing homopolymeric stretches than pyrosequencing, it
produces shorter sequence reeds, and thus cannot resolve short
sequence repeats. Moreover, substitution errors have been noted
in this platform due to the use of modified DNA polymerases and
reversible terminators. The 1G Illumina genome analyzers generates
35 bp reads per run in 2 days to 3 days [115]. Massively parallel
sequencing (MPS) by hybridization-ligation supported in the
oligonucleotide ligation and detection system SOLiD from Applied
Biosystem is based on the polony sequencing technique [116].
Libraries begins with an emulsion PCR single-molecule amplification
step, followed by transfer of the products onto a glass surface where
sequencing occurs by sequential rounds of hybridization and ligation
with 16 dinucleotide combinations labeled by four different fluor
dyes. Each position is probed twice and the identity of the nucleotide
is determined by analyzing the color resulting from two successive
ligation reactions. The two base encoding schemes allow the
distinction between a sequencing error and a polymorphism (an error
would be detected in only one reaction, whereas a polymorphism
would be detected in both). The 1-3 GB SoLiD generates 35 bp reads
per an 8 day run [114] (Table 2). Illustrates available DNA sequencing
technologies.
Microarray technologies
For microarray studies, we employed Affymetrix Gene Chip Micro
3.0 Array (Affymetrix, Inc, Santa Clara, CA, USA), which provides
for 100% miRBase v17 coverage [http://ww.mirbase.org[] by a onecolor
approach. The microarray contains 16,772 entries representing
hairpin precursor, expressing 19,724 mature miRNA products in 153
species, and provides >3 log dynamic range, with 95% reproducibility
and 85% transcript detection at 1.0 amol for a total RNA input of 100
ng. Global microarray expression studies have shown similarity in
expression between stool, plasma and tissue [117]. Microarray studies
in stool samples obtained from fifteen individuals (three controls, and
three each with TNM stage 0-1, stage 2, stage 3, and stage 4 colon
cancer) showed 202 preferentially expressed miRNA genes that
were either increased (141 miRNAs), or decreased (61 miRNAs) in
expression [13]. A scatter plot comparing low dose microarray data
to the control group presented in (Figure 2) shows a multigroup
plot comparing miRNA-193a-3p to internal standard 18S rRNA in healthy normal control and the four TNM colon cancer groups
(stages 0 to IV). To be able to screen several miRNA genes using the
proposed PCR technology in a sequence-specific manner, in which a
cDNA preparation can assay for a specific miRNA, we have employed
in our work [11,13,14,91] a sequence-specific stem-loop RT primers
designed to anneal to the 3’-end of a mature miRNA, which result
in better specificity and sensitivity compared to conventional linear
ones [118]. This step was followed by a SYBR Green®-based real-time
qPCR analysis in which a forward primer specific to the 5’-end of
the miRNA, a universal reverse primer specific for the stem-loop
RT primer sequence, and a 5’-nuclease hydrolysis probe-TaqMan
minor grove binding (MGB) probe --matching part of the miRNA
sequence and part of the RT primer sequence-- was employed in our
Lab, using a standard TaqMan PCR kit from Applied Biosystems
on a Roche’s Light Cycler (LC) 480 instrument, which employed
the E-method [119] to calculate the relative expression of miRNA
genes in modified RT-qPCR studies. It should be emphasized that
the Roche’s LC-480 PCR instrument [120] employs a non userinfluenced
method for high throughput measurements, using
second derivative calculations and double corrections [121]. One
correction utilizes the expression levels of a housekeeping gene of an
experiment as an internal standard, which results in reduced error
due to sample preparation and handling, and the second correction
uses reference expression level of the same housekeeping gene for
the analyzed expression in colonocytes or plasma, which avoids the
variation of the results due to the variability of the housekeeping
gene in each sample, especially in experiments that employ different
treatments [122]. We conducted a stem-loop RT-TaqMan® minor
groove binding (MGB) probes, followed by a modified qPCR
expression assay on 20 selected mature miRNAs in stool [13] and on
15 mature miRNAs in blood [14] that involved amplification of the
gene of interest (target) and a second control sequence (reference)
also called an external standard, which amplified with equal efficacy as the target gene, in the same capillary, a procedure known as
multiplex PCR. Quantification of the target was made by comparison
of the intensity of the products. A suitable reference gene has been
the housekeeping pseudo gene-free 18S ribosomal (r) RNA gene
that was used as a normalization standard because of the absence
of pseudogenes and the weak variation in its expression [123]. This
selection has obviated the need to use normalization strategies
such as plate mean (a mean CT value of all miRNA targets on the
plate), a panel of invariant miRNAs [44], or commonly expressed
miRNA targets [64]. A software to find a normalizer such as Norm
Finder [www.mld.dk/publicationnormfinder.htm], which is run as a
template within Microsoft Excel® can also be used. For a more focused
approach employing PCR on selected number of miRNA genes, we
used miRNA stem-loop RT primers [118] for specific miRNA species
to be tested, to make a copy of ss-DNA [11,13,14] for real-time PCR
expression measurements. Our RT-MGBPCR results in stool taken
from 60 healthy controls and various stages of colon cancer patients
are tabulated in (Table 3) and represented graphically by a scatter plot
in (Figure 3). They show that expression of 12 miRNAs (miR-7, miR-
17, miR-20a, miR-21, miR-92a, miR-96, miR-106a, miR-134, miR-
183, miR-196a, miR-199a-3p and miR214) had increased in stool of
patients with colon cancer, and that later TNM stages exhibited a
greater increased than did adenomas. On the other hand, expression
of eight miRNAs (miR-9, miR-29b, miR-127-5p, miR-138, miR-
143, miR-146a, miR-222 and miR-938) was decreased in stool of
patients with colon cancer that became more pronounced from early
to later TNM stages (stages I to IV) [13]. A volcano plot depiction
of quantification of mature miRNA by a stem-loop, TaqMan® MGB
probes qPCR miRNA expression analysis of human stool for TNM
group I using a Qiagen Corporation program [121] for colon cancer
TNM (stages 0-I) is presented in (Figure 4). Of the selected 15
miRNAs that exhibited quantifiable preferential expression by qPCR
in plasma, and have also been shown to be related to colon cancer
carcinogenesis, nine of them (miR-7, miR-17-3p, miR-20a, miR-
21, miR-92a, miR-96, miR-183, miR196a and miR-214) exhibited
increased expression in plasma (and also in tissue) of patients with
CRC, and later TNM carcinoma stages exhibited a more increased
expression than did adenomas. On the other hand, six of the selected
miRNAs (miR-124, miR-127-3p, miR-138, miR-143, miR-146a and
miR-222) exhibited reduced expression in plasma (and also in tissue)
of patients with colon cancer, with the reduction becoming more
pronounced during progression from early to later TNM carcinoma
stages [14]. The PCR stool data on 60 samples are tabulated in (Table
3) and presented graphically in (Figure 3) using a scatter plot, and
also in (Figure 4) employing a volcano plot exhibits minimal variance
within groups resulting in low p-values calculated using 2(-dCT) (SD
of 0.015275 or 0.025166 is minimal, or raw CT values is only ~ 0.03
for three replicates). The 95% CT for group 4 was between 134.39
and 135.63, an indication of a slight variation between groups.
However, because the raw CT variations are low, even the slightest
changes resulted in significant p-values; for example, miR-193a-5p
was induced in different groups by between two to 134-fold. It should
be emphasized that there was been no need to use receiver operating
characteristic (ROC) curves because the difference in miRNA
expression between healthy individuals and patients with colon
cancer, and among stages of cancer was large and informative. For
example, the presented data can be compared to that which would
be obtained from a group of students where half are 1st graders and
the other half are high school students (although we have considered
more groups, the idea can still be exemplified with just two groups).
To separate these groups, we would use height as a measurement (in
our experimental work we used gene expression). It turns out that the
shortest high school student is a lot taller than the tallest 1st grader
and all those above are high school students. Specificity, sensitivity
and area under the curve are all 100%. When we use weight (in our
work, a different expression) we get the same results: the lightest high
school student is a lot heavier than the heaviest 1st grader. We can use
other measures, such as shoe size or reading level, and again we get
the same result.
Thus, our results are in general agreement with what has been
reported in the literature for the expression of these miRNAs in
tissue, blood, stool of colon cancer patients, and cells in culture
[42-46,48,50,52,54-56,67,76,103]. This indicates that the choice of
carefully selected set of miRNAs can distinguish between non-colons
from colon cancer, and can even separate different TNM stages. A
miRNA expression index similar to that developed for mRNA [124]
or a complicate multivariate statistical analysis [125] was therefore
not necessary in this case in order to reach conclusions from these
data. The initial number of miRNA genes (whether 15 or twenty)
could be refined by validation studies to a much lower number
(or even a single miRNA molecule) if the data pans out in a larger
epidemiologically randomized study [126] that employs a prospective specimen collection retrospective blinded evaluation (PRoBE) design
for randomized selection of control subjects and case patients from
a consented cohort population, to avoid bias and to ensure that
biomarker selection and outcome assessment will not influence
each other, in order to have a statistical confidence in data outcome.
The validated miRNA biomarkers can then be placed on a chip to
facilitate screening, as has been done for the testing of genetically
modified organisms in food [57] to facilitate and automate studying
miRNA expression. It is necessary to clearly understand the normal,
healthy functions of the human body, and their value ranges (e.g. with
respect to age, sex, environment), in order to more thoroughly detect
what is abnormal by studying human tissue/blood/stool from healthy
donors and patients. Such studies need high quality samples from
large numbers of subjects --in the hundreds to thousands designed by
an appropriate epidemiological method that employs a randomized
unbiased PRoBE design of hundreds to thousands of control subjects
and case patients from a consented cohort population [127].
Method for PCR quantification, normalization and quality
control issues
The comparative cross point (CP) value (or E-method) [119] was
employed, utilizing the Light Cycler (LC) Quantification Software™,
Version v4.0 [120] for Roche LC PCR instruments (Mannheim,
Germany) for the semi-quantitative PCR analysis. The method
employs standard curves in which the relative target concentrations
is a function of the difference between crossing points (or cycle
numbers) as calculated by the second derivative maximum [121],
in which the Cycler’s software algorithm identifies the first turning
point of the graph showing fluorescence vs. cycle number to calculate
the expression of miRNA genes automatically without user’s input,
with a high sensitivity and specificity. A CP value corresponds to the
cycle number at which each well has the same kinetic properties. The
CP method corresponds to the 2-ΔΔCT method [128] used by other
PCR instruments, although the latter method produces reliable
quantitative results only if the efficiency [E=10-1/slope] of the PCR
assay for both target and reference genes are identical and equal to 2
(i.e., doubling of molecules in each amplification cycle); for example
if well A1 has a CP value of 15 and well A2 has a CP value of 16, we
deduce that there was twice as much of the gene of interest in well A1.
A 10-fold difference is shown by a difference of ~ 3.3 CP value. It is
not possible to compare these values between different primer pairs.
The CP method compensates for difference in target and reference
gene amplification efficiency either within an experiment, or between
experiments.
It is also essential to normalize the data to a “reference”
housekeeping internal standard gene (e.g., endogenous reference
genes RNU6 genes RNU6A and RNU6B, SNORD genes SNORD43,
SNORD44, SNORD48, SNORA74A) or miRNA normalizers (e.g.,
miRNA 16, miRNA-191), or in some cases against several standards
because the total input amount may vary from sample to sample when
doing relative quantification. To ensure that miRNA quantification
is not affected by the technical variability that may be introduced at
different analysis steps, synthetic nonhuman spike-in miRNA have
been used to monitor RNA purification and RT efficiencies. The C.
elegans cel-miR-39, cel-miR-54, the synthetic miRNAs Quanto ECI
and Quanto EC2, and the simian virus gene SV40 have been used;
these exogenous miRNA are usually added to samples before the RT
step to avoid differences in template quality, or affect the efficiency of
the RT reaction, and can eliminate deviation of the results, making
results reliable, but does not correct for sampling deviation or quality
of tissues, body fluid or extracellular vesicle samples. It has been
proposed that the best normalization strategy is the one that employs
a combination of exogenous and endogenous control miRNAs
because this compensates for differences in miRNA recovery and
cDNA synthesis among samples 128]. Some studies used absolute
data normalization and calculated miRNA expression using standard
curves developed by synthetic miRNA and melting curves normalized
per nanogram of the total input RNA for miRNA-221 and miRNA-
18a in 40 pairs of CRC tissue and 595 stool samples, a technical
detection limits of 2 copies for miRNA-221 resulted in a Cq value of
42, and a technical detection limit of 5 copies for miRNA-18a resulted
in a Cq value of 47, which were all assigned a value of 0, similar
samples with no amplification of miRNA-221 or miRNA-18a [129].
It should note, however, that values of CQ >40 are unreliable [128].
Absolute normalization method is thus considered to be reliable only
for samples with good RNA quality [130]. To report “fold change”
results, the LC software incorporates all those factors. The CP
method can normalize for run-to-run differences, as those caused by
variations in reagent chemistry. For such normalization, one of the
relative standards must be designated a “calibrator” for the target and
for the reference genes, which can be any of our healthy control stool
sample. These calibrator(s) can then be used repeatedly in subsequent
runs to guarantee a common reference point, allowing for comparison
of all experiments within the series. If necessary, the 2-ΔΔCT can be
calculated by instrument’s software if samples are properly labeled;
the 2-ΔΔCT calculations can also be set up manually. To determine fold
change for a particular unknown cancer stool or blood sample that
has a target gene CP value of 10, one needs three additional values:
a) The reference gene CP value of that same unknown stool sample/
cancer stool sample, b) the target gene CP for the calibrator sample/
normal stool, and c) the reference gene CP for the calibrator sample/
normal stool or blood [131]. In all PCR reactions, strict attention
must be given to quality control (QC) procedures, and as the field
has matured, guidelines on reporting qPCR data known as minimum
information for publication of quantitative real-time PCR expression
(MIQUE) has also been implemented by us [132] in order to ensure
the uniformity, reproducibility and reliability of the PCR reaction
and data integrity.
Statistical methods and bio-informatics analyses
In genomics work, it is important to have an understanding
of statistics and bioinformatics to appreciate and make sense
of generated data [133]. First, power analysis could be used for
estimating sample size for a study [134]. Moreover, power analysis,
as well as first and second order validation studies could be carried
out to access the degree of separation and reproducibility of the data
[135]. If the difference in miRNA gene expression between healthy
and cancer patients and among the stages is found to be large and
informative for multiple miRNA genes, suggesting that classification
procedures could be based on values exceeding a threshold, then
a sophisticated classification would not be needed to distinguish
between the study data. However, if inconsistent differences on
large samples are found, then predictive classification methods can
be employed [13]. Programs supplied by Qiagen Corporation can be
used free of charge to analyze, normalize and graph molecular data
(http://pcrdataanalysis.sabiosciences/com). The goal in predictive
classification will be to assign cases to predefined classes based on
information collected from the cases. In the simplest setting, the
classes (i.e., tumors) are labeled cancerous and non-cancerous.
Statistical analyses for predictive classification of the information collected (i.e., microarrays and qPCR on miRNA genes) attempt
to approximate an optimal classifier. Classification can be linear,
nonlinear, or nonparametric [133,135]. The miRNA expression data
could be analyzed first with parametric statistics such as Student t-test
or analysis of variance (ANOVA) if data distribution is random, or
with nonparametric Kruskall-Wallis, Mann-Whitney and Fisher exact
tests if distribution is not random [133,136]. If needed, complicated
models as multivariate analysis and logistic discrimination [137,138]
could also be employed. False positive discovery rates (expected
portion of incorrect assignment among the expected assignments)
could also be assessed by statistical methods [139-141], as it could
reflect on the effectiveness of the test, because of the need to do follow
up tests on false positives. The number of optimal miRNA genes
(whether 20 or less) to achieve an optimum gene panel for predicting
carcinogenesis in stool will need to be established by statistical
methods. For the corrected index, cross-validation could be used to:
protect against over fitting, address the difficulties with using the data
to both fit and assess the fit of the model, and determine the number of
samples needed for a cancer study, where the expected proportion of
genes’ expression common to two independently randomly selected
samples is estimated to be between 20% and 50% [142]. Efron and
Tibshirani [143] suggested dividing the data into 10 equal parts and
using one part to assess the model produced by the other nine; this
is repeated for each of the 10 parts. Cross-validation provides a more
realistic estimate of the misclassification rate. The area under the ROC
curves, [in which sensitivity is plotted as a function of (1 - specificity),
are used to generally describe the trade-off between sensitivity and
specificity [144]. Principal component analysis (PCA) method [145],
which is a multivariate dimension reduction technique, could also
be used to simplify grouping of genes that show aberrant expression
from those not showing expression, or a much reduced expression. In
cases where several genes by themselves appear to offer distinct and
clear separation between control or cancer cases in stool samples, a
PMI may thus not be needed.
If the miRNA gene panel (or a derived PMI) is found to be better
than existing screening methods, then all of the data generated can
be used to assess the model so over-fitting is not a concern. The level
of gene expression could be displayed in a database using parallel
coordinate plots [146,147] produced by the lattice package in R
(version 2.9.0. The R Foundation for Statistical Computing [http://
cran.r-project.org] and S-plus software (Insightful Corporation,
Seattle, WA). Other packages such as GESS (Gene Expression
Statistical System) published by NCSS [http://www.ncss.com] could
also are employed, as needed. Bioinformatics analysis using the basic
Target Scan algorithm for up-regulated and down regulated mRNAs
genes has been employed. The program yielded 21 mRNA genes
encoding different cell regulatory functions. The first 12 of these
mRNAs were found with the DAVID program [148] to be active in
the nucleus and related to transcriptional control of gene regulation.
For down regulated miRNAs, the DAVID algorithm found the first
four of these mRNAs to be clustered in cell cycle regulation categories
[12].
Tumor heterogeneity due to mismatch DNA repair
To add another level of complexity to colon cancer, colon tumors
have shown differential expression of miRNAs depending on their
mismatch repair status. MiRNA expression in colon tumors has
exhibited an epigenetic component, and altered expression due
to mismatch repair may reflect a reversion to regulatory programs
characteristic of undifferentiated proliferative developmental states
[149]. MiRNAs also undergo epigenetic inactivation [150], and
miRNA expression in CRC has been associated with MSI subgroups
[151,152]. MiRNAs may regulate chromatin structure by regulating
key histone modification; for example, cartilage-specific miR-140
targets histone deacetylase 4 in mice [153], and miRNAs may be
involved in meiotic silencing of unsynapsed chromatin in mice [154].
In addition, DNA methylation enzymes DNMT1, 3a and 3b were
predicted to be potential miRNA targets [155]. Moreover, a specific
group of miRNAs (epi-miRNAs), miR-107, -124a, -127, directly
target effectors of the epigenetic machinery such as DNMTs, histone
deacetylases and polycomb repressive complex genes, and indirectly
affect the expression of suppressor genes [156-158]. In addition to
negatively regulating target mRNA; miRNAs are regulated by other
factors. For example, c-myc activate transcription of miR-17-92
cluster that has a role in angiogenesis [159], and TFs NFI-A and
C/EBPα compete for binding to miR-223 promoter decreasing and
increasing miR-223 expression, respectively [160]. MiR-223 also
participates in its own feedback, and favors the C/EBPα binding by
repressing the NFI-A translation. Many of the miRNAs located in
the introns of protein-coding genes are co-regulated with their host
gene [161]. The challenge now is to identify those driver methylation
changes that are thought to be critical for the process of tumor
initiation, progression or metastasis, and distinguish these changes
from methylation changes that are merely passenger events that
accompany the transformation process but that have no effect per se
on carcinogenesis.
Test performance characteristics (TPC) of the miRNA
approach
Cytological methods carried out on purified colonocytes
employing Giemsa staining [162] as described for CRC, showed
a sensitivity for detecting tumor cells in smears of 80%, which is
slightly better than that reported earlier (i.e. about 78%) [163,164].
Numerical underpinning of the miRNAs as a function of total RNA
was carried out on colonocytes isolated from stool [165] before any
preservative was added to five healthy control samples, and five
TNM stage IV colon cancer samples, extracting total RNA from
them and determining the actual amount of total RNA per stool
sample, and from the average CP values, taking into account that
some exsosomal RNA will not be released from purified colonocytes
into stool, and arbitrarily corrected for that effect [166]. It is evident
from data shown in (Table 4) that an average CP value for stage IV
colon carcinoma of 21.90 is invariably different from a CP value of
26.05 for healthy controls. Test performance characteristics (TPC)
of the miRNA approach obtained by the CP values of the miRNA
genes calculated from stool colonocyte samples of normal healthy
individuals and patients with colon cancer were compared to the
commonly used FOBT test and with colonoscopy results obtained
from patients’ medical records in 60 subjects (20 control subjects and
40 colon cancer patients with various TNM stages). The data showed
high correlation with colonoscopy results obtained from patients’
medical records for the controls and colon cancer patients studied.
Discussion and Recommendations
The innovation of employing a miRNA approach for colon
cancer screening lies in the exploratory use of an affordable,
quantitative miRNA expression profiling of few of these molecules
in noninvasive stool or semi invasive blood samples, whose extracted
fragile total RNA can been stabilized in the laboratories shortly after
stool collection or blood drawing by commercially available kits so it does not ever fragment, followed by global miRNA expression, then
quantitative standardized analytical real-time qPCR tests on fewer
selected genes that are neither labor intensive, nor requires extensive
sample preparation, in order to develop a panel of few novel miRNA
genes for the diagnostic screening of early left and right sporadic colon
cancer more economically, and with higher sensitivity and specificity
than any other colon cancer screening test currently available on the
market. RT-qPCR has been the subject of considerable controversy.
While the technique is considered the gold standard for quantifying
gene expression in a cell, tissue or body fluid/excrement, there are so
many variables involved that different labs could perform the same
experiment and end up with different results. Moreover, although a
study may produce a statistically significant result, it's hard to know if
that result is truly valid or if the data might have been skewed due to
a technical error. Therefore, in 2009, a group of researchers published
guidelines to help scientists publish data that are both accurate
and reproducible. These guidelines are known as “The Minimum
Information for Publication of Quantitative Real-Time PCR
Experiments (MIQE)”. They address several key aspects of qPCR,
including sample quality control, assay design, PCR efficiency, and
normalization. A paper that attempted to identify a set of suitable,
reliable reference genes for several different human cancer cell lines
and to determine whether or not MIQE guidelines are followed,
reported that in many of the studies important data are missing,
as many publications do not report the efficiency of their reference
genes or their qPCR data, and that only 30% - 40% of published
studies that investigated reference genes actually followed the MIQE
guidelines [132]. Moreover, as the newest incarnation of PCR, digital
pCR or dPCR, is now being used by an increasing number of labs
to provide for broader quantification, a new set of MIQE guidelines
geared to the specific concerns of this brand-new version of PCR
have recently been published [167]. It is noteworthy to point out
that since the discovery of miRNA in 1993, investigators working
in cancer research paid attention to these regulatory molecules and
attempted to develop minimally-invasive markers to diagnose this
disease. Although methods that employ PCR in stool and blood
samples are currently in the forefront of the quantitative methods to
develop reliable screening markers, a chip that contain a combination
of these genes could be produced to simplify testing, as has been
accomplished in testing of genetically modified organisms in foods
[57]. MiRNAs are interesting biomarkers that are stable, amplifiable,
and functionally important, have ample information content, play a
significant role in gene regulation, and the expression profiles of the
miRNAs annotated in miRBase release 20, June 2013 was 24, 5211
loci in 206 species using small RNA deep sequencing 800 validated
molecules allows for distinguishing malignant and non-malignant
tissue, as well as distinguishing different tumor entities [96]. Most
circulating miRNAs are associated with Argonaute2, which is part of
the RISC silencing complex. But whether these circulating miRNAs
come from normal tissue or tumor tissue and how they are released
into body fluids - through cell death or some other process - are
mostly unanswered questions. In healthy tissue, evidence indicates
that cells release miRNAs, both in vesicles and in protein complexes,
which can then act as intercellular signaling molecules. When taken
up by a recipient cell, miRNAs could modulate their gene expression.
In tumor tissue cells they promote a microenvironment that helps
the tumor survive, giving tumors a selective advantage. However, it
is not known what is the balance between passive release by various
ways, and release that is programmed within the cell, as for example,
immune cells. Many circulating miRNAs linked to solid tumors are
also expressed in blood cells. The source of miRNAs is not important,
provided they are validated as markers. What has been a challenge
is to establish standardized protocols for extracting and quantifying
circulating miRNAs, as the technology keeps developing and
improving; however, it is expected that in 5 to 10 years, we'll have
worked out the best way to quantitative miRNAs in blood and other
body fluids. Because results for many tumor markers have not been
adequately reported, this anomaly has led to difficulty in interpreting
research data and inability to compare published work from different
sources, guidelines for carrying out tumor marker studies in a
transparent fashion and for adequately reporting research findings
have been jointly published by the USA National Cancer Institute and
the European Organization for Research and Treatment of Cancer
(NCI-EORTC) [168] so that researchers could have confidence in
outcome and could repeat these data using the published methods. It
is envisioned that eventually a micro fluidic device of an implantable
biosensor platform that is simple in design, durable in performance
and easy to use will be produced, where by an individual takes
noninvasive stool or semi-invasive blood samples at home and inserts
them into it for assay of colon cancer disease markers. Identification
of early stage disease biomarkers combined with a realistic awareness
of self and sustained discipline for good and improved health would
allow the individual to take preventative actions quickly, which will
help prevent the spread of this cancer.
The following recommendations are considered important and
represent a summary of how we envision miRNAs to influence colon
cancer development and progression:
1. It is necessary to thoroughly understand the normal,
healthy functions of the human body, and their value ranges (e.g. with
respect to age, sex), in order to more rapidly detect what is abnormal.
By studying human tissue/blood/stool from healthy donors and
patients. Such studies need high quality samples from large numbers
of subjects (in the hundreds to thousands) selected by an appropriate
epidemiological design to facilitate reaching meaningful conclusions.
2. When carrying out biological studies, it is essential to select
the number of subjects by an epidemiologically-acceptable approach,
and to have an adequate number of samples (in the hundreds to
thousands) to be able to carry out a thoughtful analyses, and to be
able to reach meaningful conclusions.
3. In its application as a screening approach, global miRNA
profiling by a high throughput omic method such as next generation
sequencing (NGS) and microarrays, followed by real-time qPCR, as
well as digital PCR (dPCR) should be looked at as an expedition into
the terra incognita of molecular diagnosis to identify novel genes,
mechanisms and/or pathways in which a stimuli, whether genetic or
environmental, exerts a change on the physiology of the cell.
4. MiRNA profiling is limited by available cells, which could
be obtained by noninvasive methods, genetic heterogeneity of the
tested population, and environmental factors such as diverse life
styles and nutritional habitats.
5. MiRNA quantification can be influenced by the choice
of methodology, which must be considered when interpreting the
miRNA analysis results.
6. Because array data often underestimate the magnitude of
change in miRNA level, it would be essential to use an independent
confirmatory method such RT-qPCR, or Northern blotting, to check
the magnitude of miRNA level of the identified target gene(s), as the magnitude of the change in the miRNA level depends on a variety of
parameters, particularly the employed normalization method.
7. It is essential to normalize PCR data to a reference
standard(s) using either endogenous standards, or exogenous
miRNA, but preferably a combination of both.
8. To avoid errors due to exosomal RNA loss using restricted
extraction of total RNA from stool or blood, a parallel test should also
be carried out on total RNA obtained from stool or plasma samples,
and appropriate corrections for exsosomal loss need to be made.
9. Although it is mainly used now as a basic science tool,
global miRNA gene expression is moving from laboratories to largescale
clinical trials as a diagnostic tool to describe a pathophysiologic
condition, or even allow clinical states to be determined in diseases
such as cancer.
10. MIQE guidelines, which address several key aspects of
qPCR, including sample quality control, assay design, PCR efficiency,
and normalization were published in 2009 to help scientists publish
data that are both accurate and reproducible, which have been
followed recently by similar guidelines for digital PCR.
11. Acquiring a signature of several miRNAs will provide
useful information for the clinicians to make decision on personalized
management of the disease, instead of a single miRNA marker.
12. Although our results show that several miRNA genes can be
used to discriminate noninvasively healthy individuals from patients
with colon cancer, it would, however, be necessary to conduct a
prospective randomized validation study using the methods that we
have outlined herein, but on a much larger number of individuals to
have a statistical confidence in data outcome.
13. Effort is needed to identify driver methylation changes
believed to be critical to the process of tumor initiation, progression
or metastasis, and distinguish these from methylated changes that are
passenger events, accompanying the transformation process but have
no effect per se on carcinogenesis.
14. Guidelines for carrying out tumor marker studies in a
transparent fashion and for adequately reporting research findings
have been jointly published by the US National cancer institute and
the European Organization for Research and treatment of cancer
(NCI-EORTC).
Figure 1
Figure 1
Experimental work flow for the quantification of miRNA molecules.
Figure 2
Figure 2
A multi group plot comparing miRNA-199a-3p to normalization
standard 18S rRNA in healthy controls and the four studied colon cancer
groups, TNM stages 0 to 4.
Figure3
Figure 3
MicroRNA expression in stool samples taken from 60 healthy and
colon cancer individuals. The stage of cancer is indicated by the bottom row
of the panel. There were 20 normal healthy individuals, and 40 with colon
cancer (TNM stages 0 to 4). Instances of high expression appear on the right
and those with low expression on the left. Expression by stem-loop RT-minor
grove binding qPCR was measured by the CP or the E-method on a Roche
LightCycler® 480 PCR instrument. Scales were chosen so the minimum
values line up on the “Min” mark labeled at top left of the panel. The same is
true for the maximum values, which line up under the mark labeled “Max” at
top right of the panel.
Figure 4
Figure 4
A volcano plot depiction of quantification of mature miRNA by
a stem-loop, TaqMan® MGB probes qPCR miRNA expression analysis of
human stool for TNM group I using a Qiagen Corporation program [115] for
colon cancer TNM stages 0-I.
Table 1
Table 1
Comparison of Tests Employed or Contemplated for Premalignant†& Malignant Colon Cancer Screening.
Table 2
Table 2
Available DNA sequencing technologies.
Table 3
Table 3
Stem-loop RT, TaqManÒ MGB probes qPCR miRNA expression in stool from normal individuals & colon cancer patients.
Table 4
Table 4
Numerical Underpinning of miRNA markers as a function of total RNA.
Acknowledgment
We express my gratitude to Drs. Paul Vos and Clark Jeffries for insight in biostatistics and bioinformatics, respectively. This work has been supported by NIH Grant 1R43-CA144823-A1-01 from the Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.; the State of North Carolina SBIR/STTR Matching Funds Program, Grant # G30433001211SBIR from Office of Science and technology, Raleigh, NC, U.S.A.; and additional operating funds from GEM Tox Labs, Institute for Research in Biotechnology, Greenville, NC, USA.
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