EMBRYO QUALITY PREDICTIVE MODELS BASED
ON CUMULUS CELLS GENE EXPRESSION
Devjak R, Burnik Papler T, Verdenik I, Fon Tacer K, Vrtačnik Bokal E
*Corresponding Author: Rok Devjak, M.D., Ph.D., Division of Medical Oncology, Institute of Oncology,
Zaloška 2, 1000 Ljubljana, Slovenia. Tel: +386-1-5879-282; Fax: +386-1-5879-303. E-mail: rdevjak@onko-i.si
page: 5
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DISCUSSION
In the present study, we analyzed CC expression
of AMHR2, LIF, SERPINE2, VEGFC and FSHR from
oocytes that developed into either high quality or low
quality embryos. In our previous study [10], selected
genes were shown to be well differentiated between
immature MI and mature MII oocytes, according to
CC expression. In this study, we use them as potential
biomarkers of embryo quality. Only AMHR2 and LIF
were shown to be significant and were used in our
prediction models. In either the binary logistic model
or decision tree model, the predictive power was the
best when both genes were used simultaneously.
In recent years, many studies have been performed
to analyze CC gene expression in association
with various endpoints: oocyte maturity, embryo development
and pregnancy [8,9,16,18]. Since CC is an
easily accessible material that is normally discarded
during the IVF cycle, it represents a good biological
material for research and hopefully, someday, also for
diagnostic purposes. In previous studies on CC gene
expression, many genes were shown to be differentially
expressed between observed groups of oocytes,
but not many were tested for predictive power [19].
As a main characteristic of a good diagnostic test is
high predictive power, the AUC value (defined by
high sensitivity and specificity), only genes which
would yield a good predictive power, whether alone
or in combination with other genes in repeated trials,
would be suitable for potential CC gene expression
diagnostic testing.
The AMH works through its receptor AMHR2,
being the highest in the preantral follicle for their
recruitment, and during follicle maturation it gradually
diminishes [20-22]. It has also been proved that
AMHR2 CC level decreases with the level of oocyte
maturity and in the same manner is expressed by
AMH in CC [10,23]. In this study, the CC expression
of AMHR2 was significantly negatively correlated
with embryo quality. This again indicates that oocyte
maturity is a prerequisite for high quality embryo development.
In this study two models were constructed
with AMHR2 for high quality embryo prediction, and
both showed similar predictive power. In comparison
to models based on LIF CC expression, AMHR2-
based models show better predictive power, which
can be well-explained by higher CC expression differences
between high and low quality embryos in
AMHR2 compared to LIF.
The connection of LIF with reproduction was
shown in the study where LIF was abundantly expressed
in the uterine endometrial glands on day 4 of
pregnancy [24]. Namely, the p53 protein regulates the
LIF expression and sufficient LIF levels are crucial
for embryo implantation [25,26]. In addition, the role
of LIF in reproduction is not only in implantation but
also in CC expansion. In the study of LIF function in
in vitro maturated human and mice cumulus-oophorus
complex, it was proven that LIF supple-mentation
induced cumulus expansion in both settings [17].
LIF also plays a role in blastocyst formation, where
the group of bovine cumulus-oocyte complexes that
were incubated with LIF yielded higher blastocyst
development compared to the control group without
LIF [27]. In our study LIF CC expression showed significance between CC of high quality and low quality
embryos only when a subgroup with high AMHR2
CC expression was observed. Therefore, using LIF
alone in the model results in lower predictive power
than AMHR2 but when used together, LIF improves
the prediction value of AMHR2. Logistic regression
represents the gold standard for constructing prediction
models in biomedical studies. As its statistic is
based on logistic regression, for each attribute the
model computes a coefficient and combines them
into one prediction variable. These are used for the
receiver-operator curve (ROC) and computing AUC
values, but as such, a prediction variable has no informational
value for to user (i.e. clinician). Predictive
data mining has become one of the essential tools
for the researcher in medicine [28]. One of these
techniques is also the decision tree, where a decision
is made in each node, according to the value and
predictive power of the variable. At the end (leaves)
the probability of an event is given. Decision trees
are usually represented with diagrams but can also
be with “if sentences.” Diagrams make the decision
tree model easy to interpret, therefore, a model with
more informational value for the user.
In our study, both types of models, binary logistic
and decision trees, resulted in similar AUC values,
indicating that the type of the models used does not
improve the predictive power. Analyzing the decision
tree diagram of the model with AMHR2 and LIF leads
to the conclusions that first, when both AMHR2 and
LIF are low, there is a high possibility of the development
of high quality embryos; second, when AMHR2
is high and LIF is low, there is a high possibility of
developing low quality embryos, and third, all other
combinations of AMHR2 and LIF expression result in
the equal possibility of developing of high quality or
low quality embryos. An equal chance of developing
a high quality or low quality embryo upon AMHR2
and LIF expression actually means that the model is
unable to predict the outcome, and this group contained
exactly half of all observed embryos. Additional
biomarkers would probably improve prediction
for this group of embryos, or some other factors exist
which we currently did not take into consideration,
e.g. the quality of spermatozoa.
A logistic regression of CC expression was also
used for constructing predictive models in the study
by McKenzie et al. [29]. In their study, the expression
values of hyaluronic acid synthase 2 (HAS2), cyclooxygenase
2 (PTGS2) and gremlin (GREM1) were used for constructing regression models for oocyte maturity,
oocyte fertilization and embryo quality. Regression
models for embryo quality yielded an AUC of 0.76,
0.76 and 0.81 for HAS2, PTGS2 and GREM1, respectively.
Combining PTGS2 and GREM1 only slightly
improved the predictive power (AUC 0.82 vs. 0.81).
Besides PTGS2, the study by Wathlet et al. [30] also
used six other genes and tested them for predictive
power of cleavage stage embryo prediction and pregnancy
prediction. Among the tested genes, the best
cleavage stage embryo prediction relied on TRPM7 and
ITPKA, but the AUC value was not calculated. Another
prognostic model for pregnancy was published by Iager
et al. [31], where 12 genes previously recognized by
microarray, were tested by qPCR for their predictive
power. They used a “signal to noise” ratio to assess the
predictive value of a gene using weighted voting. The
AUC value for pregnancy prediction was 0.76 ± 0.08.
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