Category: Cancer

Abstract

The mortality rate due to lung cancer is increasing rapidly day by day. The major reason behind this increasing mortality rate is not being able to detect the lung cancer at an early stage. Even due to advancement in technology, the number of radiologists is limited and they are being overworked. Various methods which are based on technologies like deep learning and CNN (Convolutional Neural Network) have been developed to automatically detect lung cancer through medical images. This paper presents a CNN system which is used for analyzing the patient imagery captured by the CT (Computed Tomography) scans, using the knowledge from both nuclear medicine and neural network. In this paper, the implementation of the CNN system to detect lung cancer is provided. Also, the layers which helps CNN in identifying the lung cancer are explained with the reasons for its suitability in medical image analysis. Along with that, a brief description of medical image dataset used, as well as the working environment required for managing lung nodule analysis using CNN, is specified. Due to advancement in the technology of CNN, it has become possible to diagnose the possibility of lung cancer and hence, begin with the medications earlier, thus helping to reduce the mortality rate.

INTRODUCTION

Cancer can be defined as the growth of abnormal cells which divide uncontrollably and destroy the body tissues. Major types of death causing cancers includes lung cancer, breast cancer, brain cancer, mouth cancer, blood cancer, etc. Lung cancer basically begins in the lungs and may either spread to the lymph nodes or to other organs in the body. It is caused due to major reasons like smoking, exposure to toxin and sometimes even due to family hereditary. Two broadly classified types of lung cancer are:

• A. Small Cell Lung Cancer (SCLC): For every count of 100 lung cancers diagnosed, 12 are of this type. It is usually caused due to smoking. This type of cancer tends to spread early, affecting the other organs.
• B. Non-small Cell Lung Cancer (NSCLC): For every count of 100 lung cancers diagnosed, 87 are of this type.

The rapid advancement in CT (Computed Tomography) and PET scan techniques have been remarkable. But ultimately it has led to the production of image data in huge numbers. This increases the workload on the radiologists which can be prone to erroneous diagnosis. This ultimately affects the end result. Recently, Convolutional Neural Network has been utilized remarkably in the field of medical, for diagnosis and analysis of medical image datasets. Appreciable reviews have been published on the working of CNN in applications like analysis of lungs, brain, prostate and breast cancers [1][2][3].

CT SCAN

CT stands for Computed Tomography. It is a medical imaging procedure that makes use of x-ray measurements from different angles to observe the object by producing the cross-sectional images of it. It helps the viewer to see inside the object without actually cutting it. CT scans are mostly used for diagnosis and therapy purpose. Computed tomography is better than x-rays in the sense that x-rays do not show the acute and chronic changes in the tissues of the lungs whereas CT scans can be used for detecting both acute and chronic changes.

An important advantage of CT is that it eliminates the region of disinterest. CT is better than barium enema for detection of tumors and also, it uses a lower radiation dose. CT scans can diagnose life-threatening conditions such as hemorrhage, blood clots, or cancer. It helps in diagnosing lung cancer at earlier stages. It is highly accurate in determining the cancerous mass, if present. Early changes in cell detection is remarkable than Magnetic Resonance Imaging (MRI).

DATASETS

As the implementation of CNNs require a huge setup of parameters consisting of specific hardware and software requirements. The datasets used for training and testing of the proposed system are:

A. Datasets of lung cancer CT images:

LIDC/IDRI Lung CT dataset:

To identify, address, and resolve challenging organizational, technical, and clinical issues, eight medical imaging companies and seven academic centers collaborated to provide a robust database. In total 1018 cases are there in LIDC/IDRI Database, each of which includes CT scan images and an associated XML file that records the results of the process performed by four experienced radiologists. In other words, XML file contains the labelled data of the corresponding CT images. In the initial blinded-read phase, each CT scan were reviewed by each radiologist independently and were marked the lesions belonging to one of three categories (‘nodule > or =3 mm,’ ‘nodule or =3 mm’). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to get a final opinion. In the database, there are 7371 lesions marked ‘nodule’ by at least one radiologist. 2669 of these lesions were marked ‘nodule > or =3 mm’ by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to train, validate, and test the model [4].

B. Datasets used for the proposed system:

The described dataset is used for training the system for identifying the presence of cancer. The identified nodule is classified into three classes:

i. Benign:

A benign tumor is a tumor that does not invade its surrounding tissue or spread around the body. Hence, it can be stated that if the tumor is found to be benign then it is classified as non-cancerous.

ii. Malignant:

A malignant tumor is a tumor that may invade its surrounding tissue or spread around the body. Hence, it can be stated that if the tumor is found to be malignant then it is classified as cancerous.

iii. Malignant and Metastatic:

Metastasis is the process in which the malignant tumor breaks down and invade in the other tissues of the body. Such cancer cells of primary tumor which can travel to other organs such as lungs, bones, liver, brain are called metastatic tumors. These metastatic tumors are called secondary cancers as they are not primary cancer but arising from primary cancer. Some of them are curable, but many of them are not.

The proposed dataset is the actual PET image dataset of the patients of a multispecialty hospital which has been diagnosed for lung cancer. This dataset is used as testing data for the system, hence, classifying the patients’ data into benign and malignant.

In cancer research, viability tests are immensely significant. These tests are used to observe the characteristics of different kinds of cancer. In drug development, viability of hostile cells are tested against chemical substances to evaluate the potential of those substances in pharmaceutical use. Additionally, the adequate dosage for those drugs are also studied in vitro. Established in 1977 from a patient with acute myeloid lymphoma in the National Cancer Institute in the United States, human Caucasian promyelocytic leukemia (HL-60) cells are used in a variety of chemotherapeutic researches (Jackobson, 2020). The cells are characterised by the ability to self differentiate and being manipulated to differentiate continuously in vitro. Recent experiments have also given rise to specialised HL-60 cell lines.

Viability is the survival tendency of organisms. Microorganisms and single cells are studied under cytotoxic conditions to assess viability. In this study, the viability of a HL-60 cell line has been evaluated using MTT assay. The concentration of the cells have been assessed using haemocytometer. After the achievement of desired concentration, the cells have been subjected to cytarabine or cytosine arabinoside (Ara-C). The drug is used to treat several kinds of leukemia and lymphoma (Higginset al. 2018). The assessment of vitality has been done through chromatographic method. Dimethyl sulfoxide (DMSO) has been used for solubilisation of formazan crystals. The final goal of the study is to better understand the properties of HL-60 cells and overall leukemia itself. Moreover, the effects of cytarabine on leukemia have also been explored.

Method

20 ml of the provided suspension of HL-60 was taken into an eppendorf tube. 20 ml trypan blue was added to the tube. The mixture of cell suspension and trypan blue was mixed well and 20ml of it was transferred to one of the chambers of a haemocytometer by carefully touching the cover slip’s edge with a pipette tip, containing the solution. The four corner squares of the haemocytometerwere observed under a microscope. The blue dots representing dead cells and the white dots representing the live cells in each chamber were counted separately. The dots on the inward left boundary and the outward bottom boundary were also taken into account.

The number of live cells and the number of dead cells were added to calculate the total number of cells. The number of viable cells in the four chambers were divided by four to calculate the average number of viable cells in each chamber. Similarly, the average number of dead cells in each chamber was counted. The average number of viable cells present in 1 ml of the suspension was counted using the volume of a single chamber and the ratio of cell suspension and trypan blue in the solution. Cell suspension containing 40000 cells was added to the wells of the MTT assay plate as marked in Figure-2. The volume of cytarabine was measured for four different concentrations with the common volume of 5ml each. Using sterile pipettes, the drug solutions were transferred to the cell containing wells on the plate. The culture was incubated overnight (24 hours) at 37° C. After incubation, 200ul MTT reagent was added to each cell containing well. The plate was covered in aluminium foil and incubated for 4-6 hours in humid air with 5% CO2 at 37° C. After 2 hours, the plate was taken out of the incubator. 100ul DMSO was added to the wells with cells. The cells were resuspended manually,

Results

Each chamber of the haemocytometer is 1 mm in length and 1 mm in width. Therefore, the surface area is 1 square mm. The height of each chamber with cover glass is 0.1 mm. Therefore, the volume of each chamber is 1 mm2 x 0.1 mm = 0.1mm3. Therefore, each chamber contained 0.1mm3 or 10-4ml trypan blue-cell suspension solution. The number of viable cells in the four corner chambers was 23, 23, 17, and 12. Thus, the average number of viable cells in each chamber is (25+23+17+12)/4 = 19.25 cell/ml.

The ratio of cell suspension and trypan blue in the solution was 1:1. Therefore, the dilution factor for cell suspension in the solution is, (1+1=)2. The average concentration of viable cells in each chamber is, (the average number of viable cells in each chamber/the volume of trypan blue-cell suspension in each chamber) or (19.25/10-4ml). Therefore the concentration of viable HL-60 cells in the pure cell suspension was, (19.25 x 2 x 104 ml) cells/ml or 385000 cells/ml or 385 cells/µl.

Therefore, the volume of cell suspension containing 40000 viable cells is, (40000/ number of viable cells in one µl cell suspension) µl or (40000/385) µl or 103.89 µl. Therefore, 103.89 µl cell suspension containing 40000 cells was added to each well.

The total stock solution of cytarabine consisted of 1000uM. The required volume of cytarabine to be added to each well is 5ml. The required 4 concentrations of cytarabine are, 0.25µM, 1µM, 5µM and 10µM. The respective amounts of diluent (DMSO) required for creating each concentration of cytarabine are calculated using the following formula. C1V1=C2V2 or V2 = C1V1/C2, where C1 = Initial concentration of cytarabine, V1 = Initial volume of the solution, C2 = Final concentration of cytarabine and V2 = Final volume of the solution. The volume of each necessary cytarabine solution of concentrations mentioned earlier is 5ml.

The volume of diluent (DMSO) to be added to each cytarabine solution was calculated by subtracting the V2 value for each solution from 5000ul. Table-1 depicts the volume of the cytarabine (stock) and diluents (DMSO) required to prepare cytarabine solutions of 0.25µM, 1uM, 5uM and 10µM respectively.

After the addition of cytarabine to the wells containing viable HL-60, and incubation for 6-8 hours, the MTT assay was performed. The recorded OD for each well containing different concentrations of cytarabine at 570nm are listed in Table-2

Discussion

Trypan blue is a low cost vital stain, and is excluded from live cells. Live cells possess intact cell membranes, which prohibit the entry of trypan blue into the cytoplasm (Lebeauet al. 2019). On the other hand, the membrane integrity of dead cells is not intact. Therefore, trypan blue can easily enter dead cells cytoplasm. When the stain is applied to a cell suspension, the alive cells do not accumulate trypan blue whereas dead cells do. When the suspension is subjected to microscopic observation, the dead cells appear deep blue, but the viable cells appear clear. On the haemocytometer, only one chamber is filled with the solution of cell suspension and stain. The solution was filled into the other chambers by capillary action. The transfer was carefully controlled so as to not overfill or underfill the chamber. Average number of viable cells present in one chamber of the haemocytometer indicates the number of live cells present in 0.1mm3 of the provided HL-60 cell suspension. The calculation of average number of cells present in 1ml sample helped distribute more or less equal number of cells in each well in MTT assay.

Cytarabine or cytosine arabinoside (arc-c) is an antineoplastic chemotherapeutic compound used to treat different forms of leukemia through cytotoxicity (Zhang et al. 2018). Cytarabine is generally administered through intravenous or intra tracheal infusion (Islam, 2017). Intrathecal infusion is generally done to transfer the drug to cerebrospinal fluid (CSF). Due to its antimetabolite property, cytarabine seizes the multiplication of cells upon incorporation. This occurs due to termination of the cell cycle. Upon entering a cell, Ara-C is rapidly converted into ara-CTP and ara-U. The former of these two substances inhibits DNA polymerase. It renders the cell’s DNA unable to replicate. A portion of Ara-C also incorporates into the DNA in the cell. The drug is cell cycle specific. The application of cytarabine involves several drawbacks. The side effects of the drug include headache, nausea and temporary decrease in blood RBC count. With precise administration, cytarabine offers promising results in cancer treatment. However, it is very important to accurately assess the quantity of the drug to be administered. Although the amount depends upon the patient’s characteristics, it should be sufficient to draw significant results. Ara-C is an antimetabolite. The drug has been used in this experiment to evaluate the viability of HL-60 cells. The cell line has been administered with different concentrations of the drug and the results have been tested using the MTT assay.

Thiazolyl blue tetrazolium bromide (MTT) reduction assay is one of the most useful colorimetric assays for cytotoxicity measurement (Tobólskaet al. 2018, p.216). The method is based on the conversion of (3-[4,5- dimethylthiazol-2-yl]-2,5 diphenyltetrazolium bromide or MTT into insoluble crystals of formazan. Upon entering a cell, MTT is subjected to mitochondrial enzymes. These enzymes convert MTT into formazan (Adan et al. 2016). On the other hand, dead cells do not form formazen. Therefore, the OD of dead cells and live cells subjected to MTT show different OD during colorimetry. When the formazan crystals are subjected to dimethyl sulfoxide (DMSO), the crystals dissolve. As an organosulfur dipolar aprotic solvent (Nguyen et al. 2019), DMSO dissolves both polar and non polar compounds. The formazan concentration is measurable by employing wavelengths between 540nm and 720nm. The cytotoxicity of a drug is generally evaluated based on the IC50 value. The half maximum concentration or IC50 is the potency of a substance to decrease 50% of the original viability of a cellular sample in vitro. The IC50 standard is common practice in drug evaluation.

In this experiment, the IC50 value of cytarabine has been measured. The IC50 value is greatly dependent on the characteristics of the drug being used and the sample. The results show that the change in viability of HL-60 cells does not change consistently with change in drug concentration. In the graph depicted in Figure-3, cell viability is a logarithmic function of the concentration of Ara-C. Between the drug concentration of 0.25uM and 1uM, the viability of the cells changed at a high rate. Between 1uM and 10uM, this rate is slower. Despite the increase in rational difference between two concentrations. Such phenomenon indicates that a third entity or property may be influencing the viability of the HL-60 cells. However, Chen et al.(2017) in their study on YAP inhibition effects on HL-60 cells observed no additional effects of DMSO on the viability of the leukemia cells. Kao et al. (2019) in their study also observed no effect of DMSO on HL-60 cells. Yet, the study conducted by Kao et al. was conducted in an Ara-C free medium. Many other researchers have closely studied the combined effect of the two substances (Papież M. and Krzyściak, 2018). Although the researchers have not observed any additional effects of DMSO on Ara-C led apoptosis on HL-60 cells. On the contrary, DMSO has been monitored to decrease cell proliferation in peripheral blood lymphocytes (Henrique et al. 2017). The deviation of decrease in viability can be caused by the intrinsic properties of the drug itself. However, the study itself can also be affected by various limitation. The nutrophile property of the cell line makes it vulnerable to cryopreservation related drawbacks (Al-Otaibiet al. 2019). The pre preservation and post preservation molecular states of the cells are also different. Also, minute mistakes in measurements can also cause the deviation. Besides, potential defects in the MTT assay technique must also be considered. Such problematic results have been observed in the past. In a 2016 research, the standard curve OD of the MTT assay was altered due to the presence of trisilanol phenyl and trisilanolisooctyl polyhedral oligomericsilsesquioxane particles (Almutary and Sanderson, 2016). Therefore more research is needed in the field to evaluate the cause of such deviation and inconsistency in viability of HL-60.

Normally people use to intake food for being active and to make the bodywork properly. People used to take hygienic and natural types of food for getting a cure for diseases. Some disease is getting a cure by taking of health food items. The food habit creates immunity power to fight against the virus or bacteria which affect the body cells. Every day the body cells need the energy to do their regular work. One of the dangerous and deadly diseases is cancer. Cancer is the cell which used to multiples and frequently affects everybody’s cell. Every year the death rate increases due to cancer, their cancer treatment but you need to find the cancer cell initial stage which can be completely cured.

Going beyond the initial stage of cancer will leads the life to a critical stage. To avoid cancer cell formation on your body also getting cancer treatment is need to eat more to get better. Eating much will be an effective way to reduce the risk of cancer cell formation. To stay healthy enough you need to eat regular and hygienic food for a better condition lifestyle on it. The food is needed to be taken on time and gets sufficient energy on it.

Talking of food items the question raises everyone’s mind is which foods kill cancer cells. There are many types of foods that can prevent your come from cancer cell formation. Some of them are the apples, berries, carrot, fish, and many more which are rich in vitamin and protein which will give more immunity power and other strength for body cells to grow on your body of it. Even with nuts like walnut, legumes, and much more which are rich in fiber and proteins over it. These food are the more essential ones that can be effort one and it will better functionality.

The proto oncogenes of genes are made of sequences of DNA that contain more information that is necessary for your cell to function to grow properly. The gene contains a set of instructions that tells a cell to make a specific type of protein also every protein has some specialized function in everyone’s body. These are the normal gene founded in every cell of the human body. With several proto-oncogenes, they are responsible for making a portion to involved in cell growth, division, and another process in the body cell. When the cell grows in normal foam they are much effective and do many things over it.

When it starts to grow more and uncontrollably will lead to cancer in their body The DNA platform is extremely accurate, time-efficient plus they are cost-effective mechanisms to analyze your sequence of interest include high-quality synthesized sequences. They offer competitive prices on gene synthesis for a wide range of gene lengths up. It clearly shows that gene synthesis is a more effective mechanism to utilize when conducting a certain type of genetic research on it. Your cells contain many important genes that regulate cell growth including division. The normal forms of these genes are described as proto-oncogenes. The mutated forms are named oncogenes.

Popeye domain-containing (POPDC) proteins are effector proteins that bind to cAMP to create a second messenger response that can influence the behaviour of cancer cells (Amunjela & Tucker, 2016). There are three different genes POPDC1, POPDC2 and POPDC3 that encode these proteins, however only POPDC1 and POPDC3 have been related to cancer cell behaviour. POPDC1 and POPDC3 are organised in tandem on chromosome 6q21 and POPDC2 can be found on chromosome 3 (Andree et al., 2000). All three of these proteins are found within embryonic epithelium, the heart and skeletal muscle (Kim et al., 2010). POPDC proteins are transmembrane proteins that consist of an extracellular amino terminus, three transmembrane domains and a cytoplasmic Popeye domain (Amunjela & Tucker, 2016).The Popeye domain is where cAMP binding occurs. cAMP is a key second messenger that regulates many physiologically crucial processes such as cell growth, gene transcription and expression (Yan et al., 2016). This signal transduction cascade begins when a specific ligand binds to a G-protein-coupled receptor (GPCR). This causes a conformational change in the protein and a Gαs stimulatory subunit is released and activates the enzyme adenylyl cyclase which converts ATP into cAMP (Amunjela & Tucker, 2016). There are three key effector molecules that bind to cAMP and signal in cancer cells known as protein kinase A (PKA), Epac proteins and POPDC proteins (Amunjela & Tucker, 2016).

These signalling cascades have substantial roles in modulating the proliferation and migration of cancer cells and are therefore, especially POPDC proteins, targeted as potential therapies to treat cancer as their domains can be targeted specifically to prevent growth and spreading of these cancer cells throughout the body (Amunjela & Tucker, 2016). Here, the role of POPDC proteins in cancer progression, their interaction with other genes such as GEFT to regulate signalling cascades occurring in cancer cells, and how this makes them targets for cancer therapies is discussed. Several studies have demonstrated that POPDC1 and POPDC1 expression is downregulated in cancer cells. One of these studies by Kim et al. (2010) confirms the downregulation of POPDC proteins in gastric cancer cells. A real-time quantitative PCR (qRT-PCR) was used to analyse 96-paired gastric tumours and their nearby normal tissues. The expression of POPDC1 was downregulated in 69% (66 of 96) of the gastric tumours and POPDC3 expression was downregulated in 87% (83 of 96) of the gastric tumours. However, POPDC2 expression was only downregulated in 24% (23 of 96) of the gastric cell tumours. This determines that POPDC2 is not associated with cancer development as significantly as POPDC1 and POPDC3 proteins.

Deng et al., (2012) conducted an immunohistochemical analysis to discover the amount of POPDC3 expression in 306 gastric cancer tissues and 84 normal tissues and related this to prognosis of stage 1 and stage 2 cancer patients. Elevated levels of POPDC3 expression were found in 72 of 84 normal gastric tissues whereas low expression of POPDC3 was found in 228 of 306 gastric cancer tissues. This suggests that POPDC3 expression has a role in downregulating cancer progression. This is confirmed by Deng et al., (2010) as the prognosis of cancer patients that present with higher POPDC3 expression in their cancerous cells is significantly better than in patients with lower POPDC3 expression.

To determine whether POPDC3 downregulation is involved in migration in gastric cancer cells, Kim et al., (2010) studied how shRNA-mediated deletion of POPDC3 expression in SNU-216 cells affected the levels of migration. POPDC3 expression was downregulated by POPDC3-sh#1 and -sh#2. POPDC3 was then subjected to a cell migration assay. The cells treated with EGF showed an increase in cell migration in comparison to the controls. In addition, POPDC3-sh#1 or -sh#2 containing cells showed a substantial increase in cell migration in comparison to control cells, suggesting that the downregulation of POPDC proteins in gastric cancer cells induces cell migration and invasion in gastric cancer cells. DNA methylation is frequently used to silence gene transcription by adding methyl groups to DNA and modifying histones by adding a histone deacetylase to the DNA (Nan et al., 1998). DNA methylation and histone deacetylation play a role in POPDC1 and POPDC3 silencing in gastric cancer cells (Kim et al., 2010).

A study by Kim et al., (2010) confirms DNA methylation in POPDC1 and POPDC3 genes by using pyrosequencing to discover the amount of methylation in POPDC1 and POPDC3 genes in 76-paired gastric tumour and normal tissues. The mean methylation for POPDC1 was higher in tumour tissues (18.0 ± 9.6%) in comparison to normal tissues (9.4 ± 7.9%). The mean methylation for POPDC3 was also higher in tumour tissues (26.9 ± 21.6%) in comparison to normal tissues (13.2 ± 8.9%), suggesting that DNA methylation plays a role in silencing POPDC1 and POPDC3 proteins in gastric cancer cells. Kim et al., (2010) then set out to determine whether the treatment of DNA methylation inhibitor 5-aza-dC or histone deacetylase inhibitor TSA would induce the expression of POPDC1 and POPDC3 in gastric cancer cells. As seen in Figure 6, a combination of both inhibitors increased expression of POPDC1 and POPDC3, suggesting that these inhibitors are key to initiating POPDC protein expression and therefore, preventing cancer progression.An important discovery that has given insight into how POPDC proteins regulate cancer cell behaviour is through the interaction with guanine exchange factor T (GEFT). This was experimented using a yeast two-hybrid assay in embryonic mouse heart cells (Guo et al., 2003).

GEFT is a guanine exchange factor that activates Rho GTPases such as Ras-related C3 botulinum toxin substrate (Rac1) and cell division control protein 42 (Cdc42). Rac1 and Cdc42 are known to control cellular behaviours such as cell proliferation, migration, cell-cell adhesion, and gene expression (Bishop & Hall, 2000). In relation to cancer, these proteins are excessively synthesised in many carcinogenic tumours, suggesting that they play a role in cancer progression.

To determine whether POPD1 expression changes the activity levels of Rac1 and Cdc42 GTPases, a methodology known as a PAK-21 pulldown was used to determine GTPase activity upon binding to POPDC1 (Smith et al., 2008). The carboxyl terminus of the POPDC1 protein in mouse cells was transfected with pEGFP-mBvesCT or pEGFP-C3 (control). Transfection of mBves-CT negatively regulates the activity of Rac1 and Cdc42 when it interacts with GEFT, resulting in less active levels of Rac1 and Cdc42 and therefore, a reduction in cancer development (Smith et al., 2008). It is evident that the downregulation or deletion of POPDC proteins play a significant role in cancer progression, specifically in gastric cancer cells. Among the three POPDC genes, POPDC1 and POPDC3 expression are substantially reduced in gastric cancer cells whilst POPDC2 expression remains unchanged. A study by Kim et al., (2010) confirmed that the downregulation or silencing of POPDC3 and POPDC1 induced the spread and growth of gastric cancer cells, ultimately confirming that the deletion or suppression of these proteins are associated with a poor prognosis for cancer patients as it can lead to proliferation, migration, and invasion of cancer cells (Amunjela & Tucker, 2016).

Downregulation of these proteins can occur from hypermethylation and histone deacetylation, which epigenetically suppresses POPDC gene expression (Kim et al., 2010). In a study by Kim et al., (2010) POPDC1 and POPDC3 genes had significantly higher amounts of hypermethylation in gastric tumour tissues in comparison to normal gastric tissues, indicating that hypermethylation of both genes is an important event in gastric carcinogenesis. However, treating these hypermethylated genes with DNA methylation inhibitor 5-aza-dC and a histone acetylase inhibitor TSA induced POPDC gene expression (Kim et al., 2010). This provides a promising theory that genes pharmacologically treated with these inhibitors could be an effective way of inhibiting cancer progression through increasing their POPDC gene production.

The discovery of the interaction between POPDC1 and guanine exchange factor T (GEFT) has given insight into the effects of how POPDC proteins can reduce cancer progression from growing and spreading throughout the body. GEFT activates Rho GTPases Rac1 and Cdc42 which are drastically overexpressed in human tumours as they are responsible for regulating cell-cell bonding, proliferation, and migration (Bishop & Hall, 2000). When POPDC1 encounters GEFT in cancer cells, Smith et al., (2008) discovered that it negatively regulates the active levels of Rac1 and Cdc42 in cancer cells. This confirms that POPDC proteins play a significant role in inhibiting cancer progression throughout the body by preventing cancer cells from multiplying and spreading to different tissues through upregulation of Rac1 and Cdc42.

In summary, it is clear that the relationship discovered between cancer development through proliferation and migration of cancer cells in humans and the downregulation and deletion of POPDC proteins and genes demonstrates that POPDC proteins could be a promising and effective pharmacological treatment in combating cancer progression and it is therefore crucial to investigate the structure and functions of POPDC proteins further to improve the survival rate of many cancer patients.

Abstract

Plant tissue culture is a collection of techniques used to grow plant cells, tissue, organs and their components under defined physical and chemical conditions in vitro. Plant tissue culture used to create large number of clones from a single explant and it is easy to select the desirable traits. Plant tissue culture technique is very helpful in genetically modified. In addition, we can do this technique at a very short time and small spaces. Therefore, the plant tissue culture technique has many of advantages. In addition, it increases the agriculture production. The plant tissue culture divided into the cell, tissue culture and organ culture. Root culture and shoot culture are types of organ culture. One of the most important applications for the root culture is the production of secondary metabolites. They are chemical compounds have a defense mechanism to protect the plant. However, these compounds are not important for survival of the plant. There are compounds called primary metabolites these compounds are important for survival and growth. The human uses the secondary metabolites in many of application such as agriculture applications, medicinal applications and industrial applications…etc. To increase the production of secondary metabolites the bioreactors technique has been used and instead of using the root itself, you can use the hairy roots. Because they are advantageous over fast growth, low doubling time, more genetic stability and ability to self-division on hormone free medium.

Introduction

Plant culture divided into the cell, tissue culture, and organ culture. As it is known that root tip culture is a type of organ culture and it has several types of applications. The most important one is the secondary metabolites. The metabolites are the sum of all biochemical reactions.(-Galvan et al., 2016) Secondary metabolites seem to be a very important topic. Secondary metabolites are chemical compounds that produced in the plants and used in several industrial products. Certainly, as we said, secondary metabolite there is also a primary metabolite. What is the difference between them?! It is thought that secondary metabolites are derived from primary metabolites so the actual difference will be illustrated in this review .In addition, during the review, I will discuss the method of increasing these compounds which known secondary metabolites. So let us start from here.

Secondary metabolites

Definition and importance:

Plants produce an enormous variety of chemical compounds, which called secondary metabolites. These compounds distinct from the products of primary metabolites secondary metabolites vary according to family and species so we can use these compounds as a taxonomic marker. Secondary metabolites are considered, as byproducts of cell metabolites are not required for normal growth and development. Many secondary metabolites found in plants have a role in defense against herbivores, pests, and pathogens. This means that it is used as a defense mechanism for the plant. Some of secondary metabolites play an important role in reproductive such as smell and color. They are produced in small quantities and it is very hard to be extracted.(BENNETT & WALLSGROVE, 1994).

Examples of secondary metabolites:

Phenolic, Steroids, essential oils, alkaloids

Primary metabolites

Definition and importance

Unlike Secondary metabolites, which are not important for growth and development the primary metabolites, considered as essential nutrient. Primary metabolites are compounds, which produced during growth phase. They are very important to perform the physiological function and support in overall development of cells. They are produced in very large quantities and they are same in every species so we cannot use them as a taxonomic marker. They are play an important role in cell growth, reproductive and development. (C, 2017)

Examples of primary metabolites:

Vitamins, Carbohydrates, Proteins and lipids are some of examples

]Secondary metabolites as industrial products

Medicinal applications

Many of medicines, which used in medicine, are derived from secondary metabolites. Secondary metabolites are used as antitumor because they have the ability to kill the cancer cell or inhibit their activity. Secondary metabolites with cancer activity include (Flavonoids, quinones, alkaloids and terpenoids)

Secondary metabolites can also function to reduce inflammatory response and have the function to inhibit pathogen progression. (lin, 2017)

Agriculture applications

Secondary metabolites are used for anti-herbivore and anti-fungal effects. They have been developed for agriculture usage as insectides. For example, neonicotinoids are a group of insecticides, which are derived from alkaloid. The nicotine is derived from Tabaco. Another example is Juglone (a phenolic lactone) which secreted from walnut plant and inhibit the growth of neighboring plant species. (lin, 2017)

Industrial applications

Secondary metabolites are responsible for flavors of many spices. Their flavors are given through accumulation of monoterpene. So they used in cooking and food industry. (lin, 2017)

Types of culture producing secondary metabolites

• Root tip culture.
• Shoot tip culture.

Mass production of secondary metabolites is conducted using bioreactors. Bioreactors are vessels made of glass or steel. They are in vitro culture, which ensure cell survival through delivery of essential nutrients for the cell. This means that the bioreactors are devices designed to grow cells or tissue in them. (Blose et al., 2014)

The bioreactors are one of the most effective ways for increasing of secondary metabolites. It is the application for large-scale cultivation for production of secondary metabolites. The cells are suspended in liquid under unique physical and chemical conditions. However, submersion of plantlets into culture has been found, cause another problem. It is causing change in morphology and physiological abnormality. It is also causing a problem with acclimatization and survival of resulting plants. Therefore, a new type of bioreactors known as temporary immersion has been developed and adapted specially for the needs of plant. (Yancheva et al., 2019)

Hairy root culture and its steps

Hairy roots are system based on inoculation with agrobacterium rhizogenes for increasing the production of secondary metabolites from the hairy roots instead of the root itself. Hairy roots are advantageous over fast growth, low doubling time, more genetic stability and ability to self-division on hormone free medium.

During the infection, process rhizogenes transfer a part of DNA (T-DNA) which located on the RI plasmid to the plant. Hairy roots appear within one to four weeks of infection. After growing more than one cm, transfer the excised roots to solid medium with antibiotic to kill the bacteria. Then transfer the hairy roots in liquid medium into bioreactor. (Hussain et al., 2012)Figure 3 steps of hairy root culture

Hairy roots cultures in bioreactors are critical step toward commercial exploitation of this culture system. However, conventional bioreactors without any modifications are usually inefficient to hairy roots culture.so it is necessary to have bioreactors that can maintain low hydrodynamic stress and high volumetric oxygen.(Hood et al., 2007)

References

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6. Yancheva, S., Georgieva, L., Badjakov, I., Dincheva, I., Georgieva, M., Georgiev, V., & Kondakova, V. (2019). Application of bioreactor technology in plant propagation and secondary metabolite production. Journal of Central European Agriculture, 20(1), 321–340. https://doi.org/10.5513/JCEA01/20.1.2224
7. Lin, Yun. Industrial Applications of Plant Secondary Metabolites. The Ohio State University, 2017. etd.ohiolink.edu, https://etd.ohiolink.edu/pg_10?0::NO:10:P10_ACCESSION_NUM:osu1492554952029414.
8. C, R. (2017, july 20). bio differance. Retrieved from https://biodifferences.com/difference-between-primary-metabolites-and-secondary-metabolites.html

Abstract:

Lung cancer is one of the most increasing diseases in the rapidly changing world. This disease can be cured in the initial stage. It should be identified at the early stage for diagnosis purposes. The prediction of lung disease stages can be done using image processing techniques. The proposed algorithm consists of a segmentation process using Dual-Tree Complex Wavelet Transform (DT-CWT). The segmented lungs are subjected to the feature extraction process which includes the Gray Level Co-occurrence (GLCM) algorithm. The main prediction can be done using the classifier process. The classifier included in the proposed work is the Backpropagation neural network classifier. The classes of the tumor level can be predicted using the neural network. This prediction would indicate the early stage of cancer and it will be helpful for the treatment of the cancerous unit in the human body. Once the tumor is detected, the tumor-affected part can be identified using morphological operations which include various algorithms. A morphological operation consists of filling, dilation, open and closing. This technique will give the total area affected and the region where the lung are affected. Thus, the achieved accuracy level of the proposed work is 90%.

KEYWORDS: Dual-Tree Complex Wavelet Transform (DT-CWT), Gray Level Co-occurrence Matrix (GLCM), Back Propagation Neural Network.

Introduction

Image processing is the trending technique which is used to predict the disease within the living things. One of the common diseases in the world after the heart disease is cancer. Lung cancer is the single most inimical cause of cancer-related deaths [1]. The symptoms of lung cancer come into light at the final stage. So it is very tough to identify in its beginning stage. For this reason, the death percentage is very high for lung cancer in comparison with all other types of cancer. The two kinds of lung disease which develop and spread in an unexpected way, are little cell lung malignancies (SCLC) and non-little cell lung tumors (NSCLC) [2]. Prediction and diagnosis of lung cancer is mainly done using computed tomography (CT)images. Disease in the patient as soon as possible, especially in tumors [3].In our proposed work we have collected database images from NCBI database. It consists of various angles of CT scan images of different patients. Lung cancer occurs in all type of living things including any type of gender. According to World Health Organization (WHO), lung cancer stood at first position among another type of cancers such as liver cancer, gastric cancer, colorectal cancer, breast cancer and esophagus cancer. According to National Cancer Institute (NCI), in US, 159260 cancerous deaths among 224210 are found due to lung cancer only. In India, 87% of male and 85% of females are suffered from lung cancer due to

smoking. The main cause of lung cancer is the addiction of smoking cigarettes, carcinogenic environment such as radioactive gas and also air pollution. Based on its histopathy, lung cancers are of two types: Small Cell Lung cancer (SCLC) and Non-Small Cell Lung cancer (NSCLC). Around 80 % of deaths are due to lung cancer of NSCLC type [4]. The main aim of the proposed work is to detect the lung cancer with the CT scan images. This would increase the accuracy of the prediction process. The paper is ordered as follows: In section II, the existing approaches for lung cancer detection is reviewed with its result and future scope. Section III, describes the methodology of proposed system. Section IV, results of the proposed approach are discussed and finally section V, concludes the paper with, the analysis and findings from the approach.

Section IILiterature SurveyLITERATURE SURVEY

Various works had been undergone in lung cancer detection and prediction. A detailed survey of lung cancer classification using Support Vector Machine (SVM) is represented in [6].From the represented technique, lung cancer is classified as normal, benign and malignant Tumors. The segmentation techniques for lung cancer detection in CT scan images are presented in [7]. The classification process includes an Artificial Neural network, Back propagation technique and multilayered perceptron techniques which is included in[8]. The major classification of lung cancer is carried out using Artificial Neural Network and Fuzzy Clustering Methods investigated by [9]. The major research has been invented by Almas Pathan, Bairu Saptalkar to predict the lung cancer using Neural Network by taking the dataset as an X-ray format[10]. Many research had been by Zagreb, Croatia based on the classification of asthma and chronic obstructive pulmonary disease (COPD) in the form of fuzzy rules and they have training phases.[11].The main aim of the proposed work is based on the segmentation using Dual-Tree Complex Wavelet Transform (DT-CWT) and classification using Back Propagation Neural Networks.

Section III

Methodology

A.Database

Input database images are collected from the Cancer Archive database. The Cancer Archive database which consists of large number of lung cancer patients records at the different locations in the lungs. As we have trained around 100 images from the collected database and the testing phase is carried out using 50 database images. The CT scan images which consist of normal lung images as well as abnormal lung images are as shown in figure 1(a) and 1(b).

(a) (b)

Figure 1(a)Normal lung 1(b)Abnormal lung

B.Methods

The detection process is carried out using initial pre-processing stage. Pre-processing method consists of gray-scale conversion and filtering process. Input CT scan images was converted into grayscale image where the pixel ranges from 0 to 255.Filtering process was carried out using gabor filter to remove the noise in the input image.FFT algorithm is used work on Fourier transform in the image. It is used in filter in the proposed work. Segmentation process is carried out using DT-CWT technique. Dual Tree Complex Wavelet Transform (DT-CWT)is used for the segmentation process. Segmentation is carried out to find the region of interest. Extracted Region feature are calculated using GLCM (Gray Level Co-occurrence Matrix).Various features are extracted from the GLCM which is included as homogeneity, energy, contrast, correlation, and variance. The Result of GLCM feature extraction can be trained in the training phase using Neural Networks. The Neural network which is used for training the given dataset is the Back Propagation Neural Network. By using the Back Propogation Neural Network the accuracy level of the proposed algorithm can be increased and the training phase time will be lesser as compared with the other neural networks. The above process can be carried using step by step method as shown in the figure.2

Figure 2 Proposed work

C.Results And Discussion

Image pre-processing methods includes four major processing blocks such as image processing, image segmentation, feature extraction and classification.

1. Image-Preprocessing

The processing stage consists of getting the input CT scan image from the collected database is shown in figure 4(a).The CT scan image may have the regularize pixels range of 207×207.The pre-processing stage consists of Gray scale conversion which converts the input Ct scan image into gray scale range as shown in the figure 4(b).

Figure4(a)originalimage,4(b)gray-scale image.

The color conversion process is carried out using filtering process.This filtering process consists of gabor filtering and an FFT filtering algorithm.Gabor filter is used to remove the noise for the processed Gray scale image as shown in figure 4(c) [image: ]

Figure 4(c) Gabor filtered image

FFT filtering algorithm is carried out to undergo noise removal using Fast Fourier Transform technique. Thus the Pre-Processing stage is followed using the image segmentation process.

2. Image Segmentation

Image segmentation is used to find out the region of the cancer-affected part. The area of the cancer affected can be calculated using DT-CWT (Dual Tree Complex Wavelet Transform).The DTCWT computed as a compound transform[12]used to separate the two Discrete Wavelet Transform of the tree of the Decompositions. DT-CWT removes the fissures and detect the cancer-affected region using the Discrete Wavelet Transform. The segmented part of the grayscale image undergoes the feature extraction process.the tumor segmented image is shown in figure 5

Figure 5 segmented tumor image

3. Feature Extraction

The proposed method feature extraction process consists of GLCM algorithm. The GLCM consists of following equations.

Contrast = (1)

Energy= (2)

Entropy = (3)

Usually the angles used are 0°,45 °, 90 °, and 135 0 [13]. GLCM features extraction used in the proposed work can be energy, contrast, entropy.

D. Classification

The extracted features undergo the training stage using the classifiers. The Classifiers included in the proposed work is Back Propogation Neural Network classifier. The classification process consists of training as well as testing phase. Training in the proposed system undergoes using Back Propogation Neural Network which includes different target value and they have a different classification. The classification process which is included as normal lung cancer images as the value of 1’s and the abnormal or defected lung as the value of 0’s.Testing can be allocated with the initial parameters and the weight of the Neural network(learning rate 0.3, hidden layer value 20; and epoch 1000). In the proposed work, considering learning rate 0.3, hidden layer value 20, and epoch 1000.This would increase the accuracy and specificity level of the proposed work. The percentage of the training and validation done can be 90% in the confusion matrix. The classification can be shown in figure6. [image: ]The Performance measure can be carried out by calculating the accuracy, sensitivity and specificity of the proposed algorithm. Performance measure can shown in figure 7 [image: ]IV. CONCLUSION Various image processing stage has been carried out to detect the lung cancer and the area affected by cancer. The testing phase is carried out with 50 Database CT images and the training phase is carried out using 50% database CT images. Thus, the accuracy of the proposed work can be 90% done. In order to obtain higher accuracy, further research is needed by improving the preprocessing process, image segmentation, feature extraction, and learning process.

Abstract

Female non-smokers face a much higher risk of death from lung cancer over the last few years. Many causes explain why lung cancer rates have skyrocketed among the women non-smokers. The increase in mortality risk from lung cancer in female never-smokers can be due to causes including environmental tobacco smoke, radon(a radioactive gas found in nature in soil and rocks)asbestos, outdoor and indoor air pollution. This literature study has shown that in addition to these cases it is likely that genetic factors have the potential to cause lung cancer in the female nonsmoking population. There is also evidence that the infection of HPV virus can increase the risk of lung cancer but this varies significantly depending on geography. This means that even though HPV vaccination can, in theory, reduce the chances of developing lung cancer, future research attention is needed to address whether an HPV vaccine can effectively aim in the prevention of the occurrence of lung cancer.

Introduction

Lung cancer is the predominant cancer-related cause globally. Amongst all different cancer types, it has had the least improvement of long-term survival rate. Historically, men have had the highest prevalence of lung cancer; nevertheless, the rate of lung cancer in women is under rising. Unfortunately, women are starting to smoke, whereas men are giving up smoking. Even though the vast majority of lung cancer cases are related to tobacco, more and more non-smokers are diagnosed with this deadly disease. It is important to note that 25% of the people who are diagnosed with lung cancer are never-smokers. Studies show that lung cancer differs biologically among men and women. The most common cancer in women is adenocarcinoma while in men, it is squamous cell carcinoma, which causes more symptoms and therefore it is easier to diagnose. In the U.S. and Europe, the incidence rates of people who have never smoked is higher in women than in men- 20% approximately of women with lung cancer have never smoked unlike to 2–6% of nonsmoking men. (Helland and Brustugun 2009). Lung cancer has become a concern for both genders due to a recent drastic increase in cigarette smoking. Therefore, for diagnostic evaluation and treatment of lung cancer, it is vital that the risk factors in never smokers are identified. This research will study the etiological factors that bear the responsibility for increasing the incidence of lung cancer in the female never smokers. To answer this question, the genetic predisposition to lung cancer in women will be explored. Next, the association between Human Papilloma Virus haplotypes and lung cancer development in female never-smokers will be looked at.

Lung cancer risk factors in never-smoking women

In general, cancers develop when the normal processes, which keep people healthy and alive by making new cells, go wrong. Carcinogenic chemicals, ultraviolet radiation, and viruses can all harm the DNA in cells causing a cancerous malfunction. However, in many cancers, there is not an identifiable external risk and this may be the case for some of the non-smoking people who get lung cancer. There are numerous factors linked up with the risk of developing lung cancer in never smokers such as second-hand smoke, radon, asbestos, other occupational causes, indoor and outdoor air pollution, and genetic factors.

Genetic profiling in lung cancer

The genetic makeup influences the chance of getting lung cancer in nonsmoking women. Females are more likely to be presented with genetic components connected to lung cancer risk. There is a correlation between p53 mutation risk and tobacco consumption; mutations are thus rarer in non-smoking females (10–47%) than in smoking women (26–71%). Another genetic factor that may appear to play a role in the pathogenesis of lung cancer in for women is the epidermal growth factor (EFGR), a protein found at a greater rate on the surface of lung cancer. EGFR mutations are identified more frequently in the female, especially with Asian ethnicity who have adenocarcinoma histology and particularly in women who have never smoked. (Helland and Brustugun 2009)HER2 is another type of EGFR mutation and it appears to be more frequent in never-smoking women with adenocarcinomas. The EML4-ALK fusion tyrosine kinase is a rearrangement in the chromosomes that leads to the activation of the tyrosine kinase and it is resulting in unopposed cell proliferation found in 3–11% of younger nonsmoking patients with adenocarcinomas. (Helland and Brustugun 2009). Moreover, KRAS mutations are encountered in 20–30% of NSCLC cases, mostly in adenocarcinomas and even though they are more frequently found in female smokers, they may be presented in never smokers a well. (Helland and Brustugun 2009)

The association between Human Papilloma Virus and lung cancer

Besides genetic factors, it has been proposed that the Human Papilloma Virus is probably related to lung neoplasms. Studies comparing female smokers among East Asian patients who had lung cancer have shown dramatic growth in the expression of HPV haplotypes in pulmonary squamous cells and connection in the development of this disease. Human Papillomavirus (HPV) is a carcinogen substance which is known to cause head and neck cancer in never-smoking populations. In one study it was found that women of Taiwanese origin

were more likely to be diagnosed with lung cancer under the condition that they had been exposed to HPV; HPV infections rates were encountered in 43–49% of adenocarcinomas cases in comparison with 24–29% of squamous cell carcinomas. (North and Christiani 2013). There are two proposed explanation for the development of HPV infection in lung tissue. The first proposed mechanism postulates that cervical infection results in the circulation of the virus and subsequently in systemic dissemination including pulmonary tissue. A second hypothesis suggests that the high risk of oral and genital contact results in oral HPV contagions, which leads to lung squamous cell infection. (North and Christiani 2013)

Areas of research

Apart from the above advances in targeted therapies, the selection of lung cancer treatment remains still challenge. Further research to assess the existence of HPV in lung cancer cells needs to be conducted given that it has been established success with the protection of HPV vaccines against cervical cancer. The difference in the expression of specific genetic mutations paves the way for molecularly-oriented therapy, which may help in the decrease of the side effects and as well as improve the overall survival.

Discussion

While tobacco smoke is the leading cancer-related cause, not all cases of lung cancer occur in people who smoke. Even though not every non-smoker who suffers from lung cancer will have a risk factor that has great responsibility for developing the disease, numerous conditions and circumstances have been found that will increase a non-smoker’s incidence rate of getting this cancer. Aside from second-hand smoke, other factors such as radon, asbestos, other occupational exposure, and outdoor air pollution would be considered core contributors to the risk of developing lung cancer. Around 50% of never-smoking women show molecular mutations that might be treated at present or in the future through targeted therapies compared to 10% of smoking female patients. (Helland and Brustugun 2009)

Female never smokers are more susceptible to lung cancer than male. Both genders share the same risk factors, the greatest of which is second-hand tobacco smoke. But among nonsmokers, more women than men have been linked to a high incidence of the disease. Aside from secondhand smoke, there are other factors like radon (a naturally occurring gas) or asbestos and certain other airborne chemical contaminants which can increase a person’s chance of getting lung cancer. Additionally, the pathogenesis of the disease may be clarified by genetic and hormonal causes. For instance, the kind of mutation in p53 or KRAS alters with tobacco smoking status in females. Although there has been detection of HPV virus in lung cancer cells, the importance of finding HPV in lung cancer cells isn’t still known or understood.

Natural resources are simply the naturally occurring of resources where. People use these natural resources like water, woods, coals, lead, and many more in their daily lives for medical and construction purposes. This has benefited in the lives of many people and also has advanced in the evolution of the industries and modernization of the world. However, with the modernization and overexploitation of natural resources, it has affected the environment and human health in the past years. This evolution in the world has introduced the use of chemicals. Chemical helps in the daily lives of the people from using cosmetics to medicine, however, it has also given a negative impact on the health which directly or indirectly affects them. In the early twentieth century, the mass production of technologies has brought rapid growth in the consumption of tobacco allowing people to consume more. The author in the articles describes the effect of the toxic element polonium-210 (210PO) in the tobacco which affects the human health, which I believe is true as later on in the future it has led in the introduction of more diseases and an increase in the number of toxic cases in the world with the consumptions of tobacco.

As per Zagà et al. (2011) consuming tobacco smoke has been considered to be toxic to health since the early fifties. I agree with this claim because most people recognize and realize that tobacco smoke contains numerous poisonous substances including tar and nicotine and this tobacco cigarette has given a negative effect on human wellbeing. With Smoking and Radiation (n.d.) study in 1929, Surgeon General S. Cumming cautioned of the risks of tobacco, stating that the consumption of such a large number of tobacco create anxiety, sleeping disorder, and other sick effects among young people (National Center for Biotechnology Information, n.d.). Moreover, the accumulation of excessive tobacco smoke in our body can disturb the throat causing throat cancer. For example, Terrie Hall, a former cheerleader that has been introduced to smoking tobacco adapted by her surroundings from friends at the age of 13 was diagnosed as oral cancer and later to throat cancer. Despite many tobacco-related deaths and the negative impact on the environment it is still widely used in many parts of the world from a small country like Bhutan to Europe where approximately 650000 people die every year that are related to smoking (Zagà et al., 2011).

Additionally, every year about 11 million people around the world are diagnosed with cancer (Zagà et al., 2011). This is theoretically true since the tobacco epidemic has been announced by the WHO (World Health Organization), revealing the growth of smoking dependency impacting over 1.3 million people globally and bringing over 5.4 million tobacco-related deaths. Moreover, I believe that smoking tobacco is among the primary contributors to deaths globally, with lung cancer being the largest one. As mentioned by the author, tobacco consists of toxic elements containing harmful radioactive substances like polonium-210 (210PO) which can solely produce cancer and can destroy one’s body and health by itself, which is a major public health problem worldwide. Polonium-210 is an exceptionally poisonous component recognized as one of the dangers in human health that can easily kill a cell and can often be called a perfect poison (Recknagel, 2013). In the year 2006, Litvinenko a former Russian soldier was a victim of polonium-210 where he was diagnosed with rare syndromes that caused him to die later, upon investigation it was discovered that polonium-210 was present in the body causing him to die (MD, 2009).

In the early 20th century, lung cancer was an exceedingly rare illness, with almost no cases (Zagà et al., 2011). Similarly, in mid-1899 there were just 140 cases enlisted every year in the United States (Ruegg, 2015) whereas of now there are more than 480,000 death including 41,000 deaths coming about because of second-hand-smoking (“Smoking & Tobacco Use”, n.d.). Likewise, passive smoking is rising in the public health problem. With a large number of smokers, it has been affecting non-smokers in society despite keeping themselves away from the direct contact of such chemicals, they are widely being affected as a result of the smokers around those innocents. So, they directly or indirectly inhale polonium-210 which affects them in the long run.

To conclude, it is seen that tobacco influence humans wellbeing through the poisonous component polonium-210 leading to an expansion of harmful cases with the utilization of tobacco. The high absorption of toxic tobacco smoke in the body had also contributed to many risks, along with lung and throat cancer. The usage of polonium-210 in tobacco has been proven to be one of the dangerous elements to inhale or consume. This not only brings people fast deaths but also is introduced to diseases and destroys the body of the people. However, despite many people being a victim it is still used worldwide.

References

1. Zagà, V., Lygidakis, C., Chaoua, K., & Gattavecchia, G. (2011, June). Polonium and lung Cancer. Journal of Oncology,1-5. Retrieved from https://www.hindawi.com/journals/jo/2011/860103/
2. Recknagel, C. (2013, November 13). Five Things You Should Know About Polonium. Retrieved from https://www.rferl.org/a/polonium-facts/25161473.html?fbclid=IwAR0iixdKSTKL52fZe-_V7o60otuyEB0lWjYu5Lk8TqDD8NnO2WfDQxEMPjw
3. Ruegg, T. A. (2015, May 14). Historical Perspectives of the Causation of Lung Cancer. National Center for Biotechnology Information. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5342645/
4. Smoking & Tobacco Use: Tobacco-Releated Mortality. (n.d.). Centers for Disease Control and Prevention. Retrieved from https://www.cdc.gov/tobacco/data_statistics/fact_sheets/health_effects/tobacco_related_mortality/index.htm
5. Radiation and Your Health: Cigarette Smoking and Radiation. (n.d.). Centers for Disease Control and Prevention. Retrieved from https://www.cdc.gov/nceh/radiation/smoking.htm
6. National Center for Biotechnology Information. (n.d.). The Health Consequences of Smoking. 50 Years of Progress: A Report of the Surgeon General. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK294310/?fbclid=IwAR3zW_k4jfz5JfhMwyXq-zfiOuIA3SiBV8ByttCs35bQaDJqsbODKixt96w
7. MD, J. B. (2009, March). ). Death by polonium-210: Lessons learned from the murder of former Soviet spy Alexander Litvinenko. Research Gate. Retrieved from https://www.researchgate.net/publication/24206298_Death_by_polonium-210_Lessons_learned_from_the_murder_of_former_Soviet_spy_Alexander_Litvinenko

What is lung cancer?

Lung cancer has become the most prevalent and threatening cancer worldwide. Parallel to most malignancies, lung cancer is composed of sub-populations of cells with distinct molecular features, resulting in intra-tumoral heterogeneity (Herbst, et.al, 2018). There are two types of lung cancer; small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) (Molina, et.al, 2008). NSCLC accounts for around 85% of all cancer incidents, whereas SCLC only accounts for 15% (Herbst, et.al, 2008). NSCLC can be further divided into three main sub-types; squamous cell carcinoma, which contributes to 25-30% of all lung cancers, adenocarcinoma, which contributes to 40% and large cell carcinoma, contributing 10-15% (Gilad, et.al, 2012).

As shown in figure 1, lung cancer develop throughout the lung at different areas. The development of the cancer can be defined by stages, all describing the cancers size, position and its metastasis status. When the tumour is localised within the lung and is 75% of SCLCs and about 50% of NSCLS (Fong, et.al, 2003). Again, mutations within p53 are related to smoking. Three key mutations occur at codons 157, 248 and 273. Within these hotspots, G:C to T: A mutations occur (Toyooka, et.al, 2003).

Evading Apoptosis

Apoptosis is the process of programmed cell death that organisms have acquired to eradicate unhealthy or toxic cells within the body, in response to cellular stress. The deregulation of apoptosis is a major component of tumour growth and progression.

Bcl2 family proteins regulate apoptosis along the intrinsic mitochondrial apoptosis pathway, that is activated in the response to several stress stimuli, including growth-factor deprivation, cytokine withdrawal, calcium ion fulgurations or DNA damage (Frenzel, et.al, 2009). The Bcl-2 family is comprised of anti-apoptotic proteins and pro-apoptotic proteins. When the balance of these two sub-groups of proteins is disturbed, apoptosis is deregulated, allowing the accumulation of toxic cells (Wong, 2011). The unbalance can be caused, by either an overexpression of anti-apoptotic Bcl-2 proteins, or an under-expression of pro-apoptotic proteins. Bcl2 proteins are anti-apoptotic proteins located within the outer mitochondrial-membrane. Blc-2 protein forma a heterodimer with pro-apoptotic proteins, like BAX, leading to their inactivation. BAX is a downstream transcription of p53, and therefore, if there are disruptions within p53 expressions BAX can also be affected (Panov, 2005). In lung cancer, BCL-2 overexpression was found in 75-95% of SCLC tumours, 25-30% of the squamous cell carcinomas and in 10% of adenocarcinomas (Kaiser, et.al, 1996) Moreover, high Bcl-2 expression and low BAX expression occurs in most SCLC tumours which also have p53 deficient (Larsen, Minna, 2011).

Limitless Replicative Potential: telomerase

Telomeres are nucleoprotein structures located at the end of chromosomes, consisting of repetitive sequences of TTAGGG and are associated with telomere-binding protein complexes (Albanell, et.al, 1997). The telomeres of somatic cells shorten with every mitotic division, as DNA polymerase is unable to fully replicate the 3’ end of DNA. Telomeres function to cap the ends of chromosomes to prevent the chromosomes form degradation, end-to-end fusion and irregular recombination and if faulty can increase the formation of tumours. Telomerase is an RNA -dependant DNA polymerase that contains template RNA and catalytic reverse transcriptase. The enzyme is responsible for adding telomeric DNA repeats after each cell division, maintaining telomere length and function (Jeon, et.al, 2012). Cells in which telomerase is active become immortal and can divide indefinitely (Dobja-Kubica, et.al, 2016). The consequence of this is that if the telomere is activated after the interaction of an oncogene or other mechanisms of carcinogenesis then the newly formed tumour cell will become immortal. A high telomerase activity was detected in almost 100%of SCLC and 80% of NSCLC (Panov, 2005). With NSCLC, high activity of the enzyme was associated with increased proliferation rates and advanced pathologic stage (Herbst, et.al, 2005).

Sustained Angiogenesis

The growth, progression and metastasis of tumours is critically dependant on a functional vascular supply. Angiogenesis is the formation a new blood vessel, which provided tumours with the blood supply they need (Herbst, et.al, 2005). Under moral conditions, angiogenic is tightly regulated by a balance of pro-angiogenic and anti-angiogenic factors. However, in cancer, angiogenesis increases due to an increase in activation of pro-angiogenic factors and involves both the alteration of existing structures and mobilization of progenitor cells (Alevizakos, et.al, 2013). Vascular endothelial growth factor (VEGF) is the main mediator of angiogenic in lung cancer. VEGF plays a significant role in several processes that result in angiogenesis. Firstly, VEGF losses the connections between endothelial cells and production of nitric oxide. This increases permeability and vasodilation of blood vessels (Dong, Ha, 2010). Furthermore, BEGF induces the expression o f proteases which dissolve the extracellular matrix around the vessels. This results in the release of other pro-angiogenic molecules and creates space for new vessels to form (Alevizakos, et.al, 2013). In addition, VEGF promotes the recruitment of endothelial progenitor cells and other bone marrow-derived cells, providing the newly forming blood vessels with a source of dividing cells and more pro-angiogenic factors (Melero-Martin, Juan, 2011). Both NSCLC and SCLC express VEGF, with adenocarcinomas having the highest level of VEGF expression (Korpanty, et.al, 2010). It has been shown that males with the 634C allele of the VEGF gene are at higher risk of developing adenocarcinomas of the lungs (Jain, et.al, 2009). VEGF can be used as a prognostic indicator in lung cancer. It has been recorded, that VEGF overexpression results in a decreased chance of survival (Zhan, et.al, 2009). Furthermore, high expressions of VEGF189 ratio (an isoform of VEGF) has been associated with a significantly shorter survival rate (Jain, et.al, 2009).

Treatments

How lung cancer is treated depends on a number of factors including; the patients health, the stage of cancer, they type of cancer and the location within the lung and the rest of body. As mentioned above, the best treatment for early stage I cancer, whether it is NSCLC or SCLC is surgical removal of the tumour. Because at stage one, the tumour is small and contained within a specific area, it can be easily removed. Furthermore, to prevent a reoccurrence of the cancer, patient can then undergo adjuvant therapy. This included radiation, chemotherapy and targeted therapy (Zappa, Mousa, 2016). However, 40% of newly diagnosed lung cancer patients are stage IV, and therefore surgical removal is not an option. When surgery is not an option, patients can receive chemotherapy or radiotherapy to try and treat lung cancer (Zappa, Mousa, 2016). Due to the aggressiveness of SCLC, treatments tend to be limited to a combination of chemotherapy treatments and radiotherapy (Cooper, Spiro, 2006)

Because of advances in genetic screening and biomarker testing, targeted therapeutic treatments are being developed and tested worldwide. Biomarkers can be used for personalised treatment and prognostics. Personalised medicine by targeting appropriate molecular targets in tumours has helped improve survival in lung cancer patients. This is a prominent technique used in patients with NCLC. One key biomarker is EGRF mutations. Patients exhibiting EGFR mutations tend to be prescribed Tyrosine Kinase (TK) inhibitors. One specific TK inhibitor which is prescribed to patients which show high levels of EGRF mutation is Gefitinib. Anti-EGRF antibodies have also been used to target and treat EGRF related cancers (Pao, et.al, 2004). However, these treatments aren’t perfect with only 10% of patients responding positively to these treatments alone. Therefore, another biomarker used for targeted therapeutics is KRAS. As KRAS is downstream to EGFR, it is used as a biomarker for patients resistant of EGRF drugs (Riely, Ladanyi, 2008).

Concluding remarks

Even though lung cancer is a prominent disease, diagnosing and treating it is difficult due to its complexities. However, there is light at the end of the tunnel. Now countries are associating lung cancer with smoking, regulations are being put in place, reducing incident numbers. Furthermore, developments within the last decades in diagnostics and treatment are showing promising results. New molecular targets are continuously being found increasing the possibility of new therapeutics and reducing drug resistance within patients.

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Cancer is a pretty broad term, there are so many different types of cancer a person can possess. The type that I decided to talk about today is childhood leukemia. Leukemia is cancer of the blood cells, white blood cells (WBC) are commonly the ones that are cancerous. The cancer attacks the white blood cells which are meant to fight infections off in the body. The type of leukemia a child can possess all depends on certain factors. Some leukemia cancers are considered acute, meaning the cancer moves at a very rapid pace While others are known to be more chronic, which means the cancer spreads at a much slower pace throughout the body. Other types of this cancer begin in other cells. Childhood leukemia is known to be one of the most common types of cancer in children. While cancer is a very horrible sickness to go through, there is still hope for the child that may be diagnosed with leukemia. According to experts, “The good news is that about 90 percent of children diagnosed with leukemia can be cured, thanks to advances in the past several decades” (‘Leukemia in Children’). So much time and effort has been put into understanding childhood leukemia, and while there is a high percentage of surviving this cancer. The pain it puts a child through is unfair.

There are few known risk factors that could potentially increase the chances of childhood leukemia. Some of those include genetic syndromes, inherited immune system problems, or even having a sibling that already has leukemia. Knowing that your child has leukemia can be a hard thing to grasp, some symptoms that a parent might want to look out for include fatigue, high fevers, bone pain, or even bleeding. When it comes to childhood leukemia, it can be very hard for a parent to come to terms with it. Having the pediatric oncologist explain the symptoms to look out for and possible risk factors that could potentially lead to cancer is important. The news of leukemia is a horrifying thing to find out, but with the support of the doctors and nurses the child has an even better chance of making it through. Making sure the parents understand just how serious the situation is important, because treatment needs to be started as soon as possible to ensure the safety and wellbeing of the child at stake. There are several genes that are affected during the journey of leukemia. “In 2008, one of the first genes linked to leukemia – RUNX1 – was identified and became available for genetic testing in 2008. People who inherit changes in the RUNX1 gene can face a higher risk of acute myeloid leukemia (AML)” (MD Anderson Cancer Center and Staff). Knowing one of the genes that a person may need to inherit leukemia is important and could potentially help doctors detect the cancer before it gets too serious.

Cancer can be a very scary and painful thing to endure, but doctors and experts have found ways to help treat patients that have come in contact with childhood leukemia. There are several ways a child can be treated for leukemia. Doctors that focus on childhood cancers are called pediatric oncologists, and they are the ones that will run tests on the child, and then alert the parents of the options they can choose from. While there are several options to choose from, the main treatment for most childhood leukemias is chemotherapy. For some children with higher risk leukemias, high-dose chemotherapy may be given along with a stem cell transplant (‘Treating Childhood Leukemia’). Chemotherapy is a common treatment used throughout all cancer patients, the idea of it is to pump a large amount of drugs into the body to eventually kill the cancer inside. Though there are definitely results from patients going through chemo, it is also very time consuming and brutal on the person receiving it. Receiving chemotherapy has shown many positive results, many children with leukemia that go through chemotherapy have gone into remission. Remission is when cancer is kind of like ‘sleeping’ the cancer is gone from the body, but there is still a chance the cancer could come back sometime in the future. If the leukemia is not treated it could end up very badly, the child could end up in constant pain leading to being admitted into the hospital, or even worse it could lead to death. There is always room for more research and improvement on all types of cancer, finding out more about childhood leukemia could increase the survival rate even more. Experts have been trying to find new ways to treat childhood leukemia and develop an even deeper understanding of what is happening to the body when receiving these treatments. According to the Pediatric Immunotherapy Discovery and Development Network, they are working to discover and characterize new targets for immunotherapies, design experimental models to test the effectiveness of pediatric immunotherapies, develop new immunotherapy treatments, and improve the understanding of tumor immunity in pediatric cancer patient. Experimenting new ways to treat leukemia may not only help children with leukemia but also other types of cancer that may take a toll on the human body.

There was no underlying reason for why I decided to research childhood leukemia. I feel like a part of me chose this cancer because in the future I want to work with children, so understanding types of diseases and disorders that could affect a child’s life is important to me and could potentially help me in my future career. Children need so much more support, they’re young and innocent. They don’t understand what is going on or the facts portrayed to them. Having a good support system is important because children need to know that there is going to be someone there for them when they need it. What I found most interesting about this topic is that there is a high chance that the children diagnosed with leukemia may be cured. Childhood leukemia is such a serious topic, it takes a huge toll on the child with the cancer and the family of the child. Educating myself as much as possible on childhood leukemia could help me in the future, being able to notice signs and symptoms that a child may have leukemia may save a child’s life.