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PRAE024 PROGESTERONE RECEPTOR MODEL 1

Analysis of Possible role of the Progesterone Receptor in Breast

Cancer Aetiology and Therapy Based on a Boolean Model

Generation and Simulation using in-silico Methods

Ali Emhimed

University of Central Lancashire

05-09-2021

Introduction

Progesterone is a steroidal hormone that is essential for female reproductive functions, for

example, progesterone levels elevate during pregnancy (González-Orozco & Camacho-Arroyo,

2019; Coomarasamy et al., 2019). Moreover, progesterone has essential functions in women’s

menstrual cycle regulation (Reynolds et al., 2018). However, progesterone is being used very

frequently as a drug agent, or exogenous factor, for contraception purposes, as well as miscarriage

prevention (Harland et al., 2018; Czyzyk et al., 2017). Alongside, progesterone becomes a vital

drug agent in the in-vitro fertilisation process (Massin, 2017).

As the use of progesterone is increasing, there are much researches on the adverse effects

of progesterone, especially the exogenous progesterone, which makes the progesterone level above

the normal levels (Liang et al., 2018). Also, the rates of breast cancer, ovarian cancer, and cervical

ABSTRACT

Progesterone hormone is one of the steroidal sex hormones that regulate

reproductive functions in women. The synthetic or exogenous forms of the progesterone

hormone are extensively used for medical purposes; the progesterone hormone bounds

to the progesterone receptor that is regulated by the progesterone receptor gene. Breast

cancer research identified exogenous progesterone as a risk factor for breast cancer.

However, the available evidence is conflicting in regards to the role of progesterone in

breast cancer; the progesterone hormone receptor acts as an activator or inhibitor for

tumours. This research aims to simulate via the PRAE024 model the changes that happen

to the cellular network based on the PRAE024 model built by the author consisting of 24

nodes via 69 interactions under two conditions: in the presence and absence of the

progesterone receptor. This research shows that the absence of the progesterone receptor

node causes many changes at the cellular level using in-silico methods. Results showed

that the absence of the progesterone receptor in the cellular network simulated in the

PRAE024 model causes significant changes in MAPK1 and MAPK3 genes activity. The

findings of this project can be used in building a better understanding of breast cancer

aetiology, pathophysiology, and therapy.

Keywords: Progesterone receptor, bioinformatics, computational biology, breast cancer,

LSSA, dependency matrix, Boolean modelling.

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PRAE024 PROGESTERONE RECEPTOR MODEL 2

cancer are increasing, aligning with the very increased worldwide use of contraceptives containing

progesterone and progesterone drugs in supporting pregnancy. Breast cancer is the second type of

cancer-causing death among women, and risk factors for it include genetic and environmental

factors (Sun et al., 2017). Notably, the progesterone receptor (PGR) is the protein expressed by

the PGR gene, which the progesterone hormone binds to exert physiological actions (Wu et al.,

2018). In accordance with Kurozumi et al. (2017), PGR is a potent prognostic indicator for

evaluating the long-term prognosis of specific types of breast cancer (ER-positive/HER2-negative

breast cancer). Alongside, Kurozami et al. (2017) stated that the PGR status is a powerful method

for selecting patients with a poor prognosis for this specific type of breast cancer.

Previous biological research suggested that progesterone receptor (PGR) testing is an

optional breast cancer diagnostic method; hence progesterone receptor gene expression is a method

for diagnosing breast cancer (Allison et al., 2020). Moreover, researchers in breast cancer linked

synthetic progesterone to breast cancer risk (Trabert et al., 2020). Besides, Progesterone receptor

membrane component 1 (PRGMC1) was found to cause breast cancer progression since it

regulates lipid homeostasis and drives oncogenic signalling pathways as ERα expression

(Asperger et al., 2020). Therefore, a closer investigation and research about progesterone receptor

in the human body and how it influences other reactions in the body is required.

Research by Check (2017) had stated that progesterone receptor plays a significant role in

the prognosis of specific types of cancers; these are key issues stated by Check (2017) regarding

progesterone receptors and cancer: Firstly, breast, endometrial, prostate, and thyroid cancers

contain nuclear progesterone receptors. Secondly, membrane progesterone receptors may

contribute to the proliferation of cancer cells by influencing paracrine factors. Thirdly, when

cancers contain nuclear progesterone receptors, it is predicted that those cancerous cells have

tumour virulence which aids in treatment selection. Fourthly, the progesterone receptor produced

the progesterone-induced blocking factor, which inhibits immune surveillance. Fifthly,

progesterone receptors can provide significant palliation of advanced cancers by suppressing

specific chemical/biological components production.

Bioinformatics and computational biology are applying biological principles using vast

databases that contain data as interactions in the human body by computational methods as

algorithms and simulation. Bioinformatics groups many fields of sciences together as computer

science, biology, mathematics, chemistry, biophysics, engineering, biochemistry, and statistics.

Computational biology offers the possibility of integrating the detailed knowledge of PGR

signalling to generate models that can be used to gain insight into how the network functions

following a loss of PGR function (Bakker et al., 2017). Bioinformatics enables researchers to make

biological models to simulate biological processes without making in-vivo or in-vitro laboratory

trials. Hence, computational biology and bioinformatics help biologists and scientists in solving

problems using computer science (Peters et al., 2018). This project is an in-silico computational

biology experiment that used in-silico methods (Ruiz et al., 2017).

This research aims to find the role of progesterone receptor (PGR) in the human body and

how it correlates to other proteins/genes which interact with it either as a first layer interaction or

a second layer interacts by building a Boolean model. First layer interactions happen between the

progesterone receptor itself and another protein. In comparison, second layer interactions occur

between two proteins that have other first layer interactions with the progesterone receptor. Hence,

when changes between the presence of progesterone receptor and absence of it in the model

(PRAE024) are tracked, a better understanding of the role that progesterone receptor (PGR) in

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PRAE024 PROGESTERONE RECEPTOR MODEL 1 Analysis of Possible role of the Progesterone Receptor in Breast Cancer Aetiology and Therapy Based on a Boolean Model Generation and Simulation using in-silico Methods Ali Emhimed University of Central Lancashire 05-09-2021 ABSTRACT Progesterone hormone is one of the steroidal sex hormones that regulate reproductive functions in women. The synthetic or exogenous forms of the progesterone hormone are extensively used for medical purposes; the progesterone hormone bounds to the progesterone receptor that is regulated by the progesterone receptor gene. Breast cancer research identified exogenous progesterone as a risk factor for breast cancer. However, the available evidence is conflicting in regards to the role of progesterone in breast cancer; the progesterone hormone receptor acts as an activator or inhibitor for tumours. This research aims to simulate via the PRAE024 model the changes that happen to the cellular network based on the PRAE024 model built by the author consisting of 24 nodes via 69 interactions under two conditions: in the presence and absence of the progesterone receptor. This research shows that the absence of the progesterone receptor node causes many changes at the cellular level using in-silico methods. Results showed that the absence of the progesterone receptor in the cellular network simulated in the PRAE024 model causes significant changes in MAPK1 and MAPK3 genes activity. The findings of this project can be u ...
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