{"paper_id":"3d08ac86-0e43-4ea7-b1d9-f38d8d7ca4e9","body_text":"Network Analysis of Genes and Identification of Candidate Drug Compound for Associated Deadly Diseases of Female | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Network Analysis of Genes and Identification of Candidate Drug Compound for Associated Deadly Diseases of Female Siam Ahmed, Sayed Asaduzzaman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7340632/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background & objective Bladder cancer (BLC), breast cancer (BRC), endometrial cancer (ENC), cervical cancer (CEC), thyroid cancer (TYC), brain cancer (BRC), and prostate cancer (PRC) are all prevalent diseases in women. The fatality rate from these cancers is extremely high. When a woman suffers from one of these disorders, her chances of contracting the others rise. It is also discovered that many common genetic variables are interconnected among various disorders, as demonstrated in the works. The primary goal is to eliminate the most prevalent gene targets among BLC, BRC, ENC, CEC, TYC, BRC, and PRC illnesses. Method The preprocessing and filtering procedure results in a reduction in generation. Protein-protein interaction (PPI) networks for seven seed genes are divided into two categories: generic PPI and tissue-specific PPI. Finally, topological analysis identifies eight common genes required for the examination of pathways, gene regulatory networks (GRN), co-expression, and physical interaction networks. Gene ontology analysis provides a better knowledge of biological processes, cellular components, and molecular functions. Results We can see from the research that the Interaction of proteins with drug molecules plays a role in the design of effective medicine. These drugs can then be considered in real life by further research and verification through various chemical experiments. Conclusions Future studies will analyse the structure of those genes to help take precautionary measures. The outcomes of the study will help in the development of future medications for cancer diseases. Gene Analysis PPI Network Drug Design Bioinformatics Female Disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Analysis of various web portals and data shows that Cancer is a major public health problem globally. Cancer is the second leading cause of death in the United States. In 2020, the diagnosis and treatment of cancer were badly exaggerated due to the outbreak of the coronavirus and its spread (COVID-19) [ 1 ]. Cancer treatment is a long-term process that can put patients at risk for negative psychological outcomes, including distress, mental anguish, fatigue, anxiety, sleep problems, and impaired quality of life. Diagnosing cancer and later treating it is a frustrating experience for many [ 2 ]. Bladder cancer is common anemia in women and starts with nonaggressive and usually non-invasive tumors. Bladder cancer has a 0.27% lifetime risk among women and is responsible for an estimated 5,00,000 new cases and 2,00,000 deaths worldwide, with 80,000 new cases being reported each year in the United States and 17,000 deaths [ 3 ]. Increasing age, chronic bladder inflammation, Exposure to certain chemicals, family history of cancer, Smoking, and previous cancer treatment are the risk factors for BLC. A cystoscopy is the main procedure to detect and diagnose bladder cancer. Breast cancer is diagnosed in 12% of all women in their entire lifetime in the United States, and in 2017 more than 250,000 breast cancer patients were found in the United States. Invasive ductal carcinoma (50%-75% of patients) is the most common histology of breast cancer. It is also said that breast cancer is the most common cancer among women worldwide except for nonmelanoma skin cancer [ 4 ]. Physical idleness, increased alcohol consumption, extraneous hormones, and certain female generative factors are the main reasons for the increase in breast cancer. Older age at first full-term pregnancy, younger age at menarche, and parity can have long-term effects on various biological processes, or variation in hormone levels may affect the risk of breast cancer. The causes of this risk have been well-established by epidemiological studies, including society, race, family history of breast cancer, and genetic traits [ 5 ]. The main causes of Endometrial cancer and the risk of developing it are various conditions associated with metabolic syndrome, including diabetes, obesity, and polycystic ovary syndrome [ 6 ]. Worldwide, 319,605 new cases of Endometrial cancer were reported in 2012, and 76,160 of them died. Endometrial cancer is considered the second most common cancer in women after Breast cancer in the established world and is the most common gynecological cancer [ 7 ]. Cervical cancer is the fourth most common cancer among the growing trend of cancer in women worldwide [ 8 ]. The main causes of Cervical cancer are the high-risk subtypes of papillomavirus (HPV). The disease has so far killed more than 300,000 people and infected more than half a million women each year [ 9 ]. A group of related viruses is named HPV. They can feast through other warm, skin-to-skin interactions. Of all cancers, Thyroid cancer accounts for 3.4% of all cancers diagnosed annually, indicating the most common endocrine hostility [ 10 ]. Thyroid cancer is now the 11th most common cancer in the world and the 5th most common cancer among women, affecting people ≥ 65 years because Thyroid cancer tests are known to have the highest rates in the elderly and adult population. Treatment for Thyroid cancer is the riskiest because it reduces the risk of overtreatment with dangerous iodine, leading to renal clearance, risk of bone loss, and thyroid hormone replacement, leading to severe arrhythmias. In addition, thyroid surgery causes the most inconsistencies in the body and increases the risk of various diseases and deaths. The incidence of thyroid cancer in the United States has almost folded since [ 11 ]. Brain cancer is a growth or mass of abnormal cells in and around the brain. Brain cancer shortens life expectancy by an average of 20 years and is the highest of any cancer. These cancers increase mortality in young adults, which is the third-highest among compact cancers. Analysis of brain cancer in adults shows that only 19% of patients survive 5years. Giving to the American Cancer Society in 2018, about 23,880 persons were analyzed with a malignant brain or backbone tumor and about 70% of those with a malignant tumor are not endured as a result of their analysis [ 12 ]. Known as the most destructive invasive and malignant neoplasm of brain cancer, it is the most common type of glioblastoma [ 13 ]. Prostate cancer is the second most frequent enmity in men globally. Female prostate cancer is enormously rare. The skene’s glands are often stated to as the female prostate since they harvest the enzymes as the male prostate. Counting 1,276,106 new cases and causing 358,989 deaths (3.8% of all deaths caused by cancer in men) in 2018 [ 14 ]. Tasnimul A.T., Md Kawsar, Bikash K.P., Kawsar A. works for some devastating cancer for women which is based on computational analysis. Breast cancer (BC), Endometrial cancer (EC), and Ovarian cancer are the three most deadly cancers in women because they have the highest mortality rate. The researcher researched these three types of cancer & find out the Interaction of proteins with drug particles to come up with an efficient drug design for this research. [ 15 ]. Chronic Kidney Disease (CKD), High Blood Pressure (HBP), and Thyroid Disorder (TD) diseases, when people are infected with one of these diseases, the chances of getting infected with the other two are many times higher and this shows that these diseases are interrelated. In this analysis, three dangerous diseases were selected for analysis. The reason for the assortment of the three different disorder diseases was to regulate their relations. In this paper Analysis of the gene network of Thyroid Disorder and associated diseases for Md Kawsar, Tasnimul A. T., Bikash K. P., S. Mahmud, Md M. Islam, Touhid B., Kawsar A. [ 16 ]. Mining and predicting protein-drug interaction network of breast cancer risk genes is another good work for Muhammad N. I., S.S. Shaolin, Bikash K.P., Manowarul I., Touhid B., Kawsar A. They find the relations of Breast cancer (BC) and its two significant risk factors the Atypical Hyperplasia (AH) and Lobular Carcinoma in SITU (LCIS). Patients have the risk of carrying BC more than others. So they have encountered the 12 common interrelated genes of these diseases & they have got a drug signature suggestion. [ 17 ]. This study attempted to identify potential therapeutic candidates for the common genes identified for BLC, BRC, ENC, CEC TYC, BRC, and PRC. The networks and routes for the seven malignancies were developed using high-performance protein-protein interaction and biological network data. 2. Proposed methodology Previous research has led to infrequent usage of exact gene finding, enactment of protein-protein interactions, various forms of regulatory networks, pathway analysis, and medication design for BLC, BRC, ENC, CEC TYC, BRC, and PRC. We discuss the research methods in depth, starting with the collection of linked genes from the NCBI gene database ( https://www.ncbi.nlm.nih.gov/gene/ ). The retrieved genes are then assigned to Network Analyst ( https://www.networkanalyst.ca/ ) to provide a view of drug particles. Study for relevant genes, create a PPI network, assess topological features to find target proteins, followed by gene regulatory networks, medication design for these disorders, and experiment with data mining methods. Figure 1 depicts the proposed study process as it progresses step by step. Our plan is shown in Fig. 1 to gain a better empathy of what we have tried to reach in our following study. The early measure of the goal movement procedure of this research is pictured in Fig. 1 . 2.1 Gene collection Genes were obtained from the National Center for Biotechnology Information (NCBI) gene database. The NCBI database is one of several websites that collect and update gene and protein information. It is essentially a bioinformatics project that adds one billion bytes of information to each genetic database item (Lomax & McCray 2004). Furthermore, NCBI is efficiently annotated and recognized since many databases use a mix of PubMed, Gene Bank, and Epigenomics databases to determine NCBI, and biological data is also decided using this high-throughput database. Then, we download the separate genes of BLC, BRC, ENC, CEC TYC, BRC & PRC diseases for Homo sapiens rendering the weight of the genes because this study is only for human drug design. Mainly CSV files are imported from NCBI because then common genes are extracted from them in different ways and the main goal is to study them and find out the cause of diseases. 2.2 Common gene finding After collecting genes for various diseases from NCBI, the main objective is to extract the interconnected genes (BLC, BRC, ENC, CEC TYC, BRC & PRC), from the genes of these diseases which are mainly responsible for causing diseases. So, I used Rstudio (available at https://rstudio.com/ ) to do this job perfectly which results in the genes that intersect between the seven diseases. This task is so important that later research reduces these genes simplifying the process of designing drugs for human diseases. So, the common gene finding process is the most important part of this research. 2.3Protein-protein interaction (PPI) network design After collecting genes for various diseases from NCBI, the major goal is to extract the structure of protein activities and compounds, which is the most important component of the protein [ 18 ]. To initially identify a protein's biophysical mechanisms, we must interact with it. To accomplish this, we will use NetworkAnalyst ( https://www.networkanalyst.ca/ ), a web-based bioinformatics program. We next utilize Cytoscape ( https://cytoscape.org/ ) to evaluate and present this data. In Cytoscape, we build a PPI network for common genes. The most widely used uses of Cytoscape include evaluating biological information, building PPI networks, controlling the number of secret biological processes in cells through PPI, providing results of molecular processes, and fulfilling protein functions. Essentially, in this step, we may separate and identify the most important hub proteins in the PPI network. 2.4 Gene co‑expression network (GCN) A co-expression system is used to find the process-level efficiency of genes. The function of genes at the structure level, the identification of genes on different nodes, and the connection of nodes with nodes are all reflected in the structure of these Gene co-expression networks. GCNs are of biological concern. We used the online tool genemania ( www.genemania.org ) to research some of these. 2.5 Gene regulatory network (GRN) A gene regulatory network (GRN) is a collection of genes or parts of genes that are linked together and control certain cells to aid a gene's biological function. Important gene regulatory network tasks include a wide range of development, control over body plan organization, cellular processes, differentiation, and response to environmental cues. Finally, it is difficult to uncover gene regulatory networks through study, because the findings are required to build effective medications in real life. We used the web-based program NetworkAnalyst (available at https://www.networkanalyst.ca/ ) to explore the gene regulatory network; this tool designed the most significant sort of gene-miRNA interaction, TF-gene interaction, which has accelerated my research. 2.6 Protein–drug interactions Our main goal in protein-drug interactions is to design drugs, the results of drug research with proteins are therefore essentially a part of drug design. Exploiting drug proficiency and reducing the harmfulness of drugs are measured by the activity of drug design. Therapeutic drug monitoring (TDM) is a process that includes medications for thin therapeutic directories. In this study, we used the NetworkAnalyst (available at https://www.networkanalyst.ca/ ), website and found out the interaction of targeted genes with drugs. 2.7 Protein–chemical interactions Protein-chemical interaction is a difficult task because to do this we need to find out the similarities and differences between the targeted genes with the chemical set of bioinformatics. This process has evolved into a developing field of modern computational biology. The secrecy of the chemical location of the compound is found using chemoinformatic which is being used in the current drug design. This information will help to make the design of medicine more effective for the next generation. We used the NetworkAnalyst website for protein-chemical research. 2.8 GO and pathway finding in terms of enrichment analysis A computational method that describes the chromosomal position of gene sets and the common biological functions of genes is called gene set enrichment analysis [ 18 ]. Gene Ontology is the design of computational representations of the biological systems of genes and the formulation of structures to describe the functions of all gene products. We have studied three sections of GO which are biological process, molecular function, and cellular component [ 19 ]. Genes use a variety of methods to get their work done, and GO is used to determine that. Kyoto Encyclopedia of Genes and Genomes (KEGG) is used in bioinformatics research, modeling in biology including genomics, and drug development data research. It is a pathway for which significant metabolic pathway function and gene annotation are of significant use [ 20 ]. Although the analysis of KEGG pathway has played a significant role, in addition to this we have worked with For WikiPathways, Reactome, and Bio-Carta databases and in doing so we have to use our web-based tool Enrichr( https://amp.pharm.mssm.edu/Enrichr/ ). This tool plays a role in enriching our gene set. In this tool, we can see how significant a pathway is through a database. 2.9 Topological properties Property defined or preserved under homomorphism is called topological property. For a flat domain, connections, compactness, degree of genes, mean centrality, proximity-concentration, clustering coefficients, and biological information can be determined by studying topological properties. It also plays a role in the analysis of drug functions, graph-based expression of biological networks, and identification of drug-target proteins. The degree of protein-protein interaction (PPI) is calculated to find out which genes are most responsible for cancer. And to complete this process, the study of topological properties is required. We initially downloaded the SIF file, including the PPI network design, using the NetworkAnalyst web tool. Next, we will check whether clustering is possible through this network in Cytoscape ( https://cytoscape.org/ ), import the SIF file, and extract the topological property results using the NetworkAnalyzer plugin. 2.10 Drug design Currently, drugs are a blessing for humanity since the brutality of diseases increases daily. Drugs are substances that are used to prevent or cure the symptoms of abnormal conditions, diagnosis, treatment, pain relief. Medicines play a major role in controlling the nutrition, vitamins, calcium, and other elements in our body and in controlling the activity of various cells within the body. Scientists are constantly researching and discovering new drugs based on data from continuous mutations in genes and drug-target interactions. So, it is a significant part of this research. In this part, the information obtained from our protein-drug interactions, protein–chemical interactions, works. Drug molecules are designed using the DSigDB database in the Enrichr ( https://amp.pharm.mssm.edu/Enrichr/ ) web tool. Enrichr is a platform where initially the common genes are taken as input and the cooperative functions on it indicate some specific features [ 21 ]. 2.11 Data Mining approach The data mining method is particularly suitable and important for bioinformatics research, where it lacks a complete theory at the molecular level of the organization of life. First, data is the science of integrating complete knowledge of what I’m researching, then mining is the science of finding new stimulus patterns and discovering this huge amount of data or knowledge. In a word, acquiring knowledge from a large amount of data is called data mining. Mining actually helps to gather the necessary knowledge from this huge biological data related to biology and science. Some data of string interaction has been taken for data mining and in this case, Orange software, K-means clustering algorithm, and Ranked algorithm have been used. 3. Results and discussion The detailed result section has been described in this section 3.1 Gene collection We began this work by collecting the genes of all species from NCBI using the illness names in the search box. We identified genes for Homo sapiens. In this analysis, we consider 1515, 5093, 730, 1546, 820, 39, and 3302 genes of Homo sapiens, namely BLC, BRC, ENC, CEC TYC, BRC, and PRC. This bar table has been created by excluding the gene numbers of other animals by conducting research only to find out the species Homo sapiens (Fig. 2 ). 3.2 Common gene finding We use Rstudio to identify intersecting genes in seven diseases and this process has to be done immediately after the total gene is collected. During this study, from intersecting BLC, BRC, ENC, CEC TYC, BRC & PRC we determined 8 common genes using R Programming. These are as follows, TERT, MYC, GSTM1, GSTT1, GSTP1, CXCR4, CXCL12, and SLC2A1. 3.3 PPI network design In bioinformatics research, researchers must use a web interface for simple and complex meta-testing of gene expression data, which is essentially an online resource. And the NetworkAnalyst ( https://www.networkanalyst.ca/ ) tool is the best used in this work (Xia et al. 2015). The main function of protein-protein interaction (PPI) is to form in a specific way in a cellular process with each other. In this study, we used the NetworkAnalyst program to generate a simple interaction format (SIF) file. The SIF file is then used to create a consistent PPI network in Cytoscape. The results of this analysis are shown in Fig. 2 (a), (b). Here Generic PPI network has a total of 829 nodes, 884 edges, and 7 seed nodes. The red & orange color nodes indicate the most significant hub gene. JUN is found as a common protein in the linkage of MYC, TERT, CXCR4, and GSTP1 genes. The highest linkage of generic PPI is between MYC, TERT, CXCR4, GSTP1, GSTM1, and SLC2A1. A protein called UBC is found in the linkage of genes. Tissue-Specific PPI network has a total of 826 nodes, 837 edges, and 7 seed nodes. Twice the maximum linkage of tissue-specific PPI is when 3 genes come together. MYC, GSTP1, GSTM1 genes are found in MAP3K5, and MYC, CXCL12, GSTP1 are found in FN1. 3.4 Gene Co‑expression Network GeneMANIA (available at https://genemania.org/ ) is an online-based tool that uses a network of equivalent genes here used to diagnose potential gene function. GeneMANIA is mainly used to create gene co-expression and physical interactions [ 19 ]. In order to support various biological studies, GeneMANIA is performed by estimating genes, publishing gene lists, and performing significant gene functions [ 19 ]. The graphic depicts the co-expression and physical interaction networks of eight hub genes. 3.5 Gene regulatory network (GRN) This work describes the genome base for genetic expression, which was previously known as adenine, thymine, guanine, and cytosine, and it happens during the early stages of the Gene Regulatory Network (GRN) [ 20 ]. A web tool that can control and analyze genes NetworkAnalyst. Using this, the Gene regulatory network of TERT, MYC, GSTM1, GSTT1, GSTP1, CXCR4, CXCL12, and SLC2A1 genes has been analyzed. In Fig. 5 (a, b) gene regulatory networks of gene–miRNA interaction, and TF–gene interaction are shown similarly. Gene mi-RNA interaction network comprises 234 nodes and 245 edges. Edges show the interaction between genes and nodes show the genes. The TF-gene interaction network consists of 174 nodes, 227 edges, and seven seed genes. The highest linkage in gene-miRNA interaction is between the MYC, CXCR4, and TERT genes. Has-mir-335-5p is available in their linkage. 3.6 String Analysis Direct relationships of eight common genes have been shown using gene optimization. The direct relationship between the eight common genes is shown in Fig. 6. This figure is attained by using STRING, which is a web-based tool that signifies gene interaction. 3.7 Clustering Clustering is the process of separating groups with similar characteristics and dividing them into different clusters. It is also an unsupervised machine learning method for identifying and grouping similar data points across large datasets. Data clustering is one of the common tasks and is important in classifying patterns between different regions. Clustering is used to separate data and there are different types of this application. Such as Model-based clustering, Fuzzy clustering, Segmentation method, Hierarchical clustering, etc. We use two types of clustering tools on gene interaction using the Cytoscape application in research, one is MCL clustering and the other is MCODE. These two tools have been used in seven selected diseases and for 8 common genes that are evident in Fig. 7 . 3.8 g:Profiler Analysis As the number of genes continues to increase and traits increase, the list of these evolved genes comes out through various studies. Working with these genes is called biological data analysis. The g:Profiler is used for gene identification and conversion between identifiers, enriched in gene lists, and mapping gene orthologs. g:Profiler is an online web tool that displays an Enrichment map of our entire cluster when genes are named. From this, we can see that GO: BP has the highest clustering and the highest value is found in KEGG clustering. Figure 8 (a) shows the Enrichment map of the clustering of gene sets. The x-axis displays the functional terms, while the y-axis displays the equivalent enrichment P-values on the negative log10 scale. The circles on the plot represent single functional terms. The circles are color-coded by data source and size-scaled based on the number of genes indicated for that phrase. 3.9 Protein–drug interactions A protein-drug interaction network has been designed using input genes using the NetworkAnalyst tool. This protein-drug analysis plays an important role in drug discovery or lab research. Basically, the common genes we input here detect the interaction between molecules and see how effective the proteins are. Also formulates the effectiveness of proteins from the interaction of different ligands and proteins. We have analyzed the functions of the protein-drug by specifying the TERT, MYC, GSTM1, GSTT1, GSTP1, CXCR4, CXCL12, and SLC2A1 genes as input in the NetworkAnalyst tool for our work, as shown in the figure below. Figures 9 show the protein-drug interaction of three subnetworks generated from NetworkAnalyst. Protein–drug interaction network (Subnetwork 1) contains 21 nodes, 21 edges, and 2 seed nodes. (Subnetwork 2) contains 4 nodes, 3 edges, and 1 seed gene. (Subnetwork 3) contains 4 nodes, 3 edges, and 1 seed gene. 3.10 Protein–Chemical interactions We represent biological systems as a network with a variety of tools aimed at facilitating research, and these biological networks help us in analyzing human behavioral changes and diagnosing diseases [ 21 ]. We use the NetworkAnalyst tool to determine the interaction of proteins and chemicals and find out the protein-chemical relationship between the eight genes. At the present time, protein-chemical interaction in the process of gene analysis is an epoch-making study that will give far-reaching results to the new generation. Protein and chemical relationships are signified by edges. Using R programming we get 8 common genes and through topological analysis, we find similarities with the top 8 genes from the PPI network. These eight genes are carried as input for analyzing any interaction of the NetworkAnalyst. 3.11 GO and pathway finding in terms of enrichment analysis Enrichr is a web tool that studies the gene set enrichment process, so to further enrich our research we study more genes at enrichr. Gene set enrichment is an important part of analyzing the different conditions of genes and how genes use pathway networks with each other (Cha et al. 2010). The present study evaluates GO terms and KEGG pathways for 8(TERT, MYC, GSTM1, GSTT1, GSTP1, CXCR4, CXCL12, SLC2A1) common DEGs. Biological process, molecular functions, and cellular components are the three most eminent GO terms. The enduring study demonstrates the top 10 GO terms for each of the subcategories which are presented in Table 1 (a), (b), (c). The data in Table 1 (a), (b), (c) validate that the common DEGs are highly heightened in protein-containing complex assemblage for the biological process subsection. Molecular function subsection data indicate glutathione transferase activity in frequent DEGs. A cellular component analysis reveals a considerable role of the nucleolus and nuclear lumen in common DEGs. Tables 2 (a), (b), (c), and (d) show results from KEGG, WikiPathways, Reactome, and BioCarta pathway analyses. The table shows the pathways in cancer and hepatocellular carcinoma that have the most genes added to the KEGG pathway database. Figures 11 and 12 represent a collection of GO keywords and pathways that contribute to the combined score. The Enrichr web tool generates a combined score based on the log of the P-value and z-scores. Figures 11 and 12 illustrate GO keywords and pathway analysis results from several pathway databases, respectively. Table 1 (a) GO Category, GO terms and their corresponding P -values and genes for common differentially uttered genes Category Term P-value Adjusted P-value Genes GO Biological Process glutathione derivative biosynthetic process (GO:1901687) 6.45E-08 9.80E-06 GSTM1;GSTP1;GSTT1 glutathione derivative metabolic process (GO:1901685) 6.45E-08 9.80E-06 GSTM1;GSTP1;GSTT1 glutathione metabolic process (GO:0006749) 5.14E-07 5.21E-05 GSTM1;GSTP1;GSTT1 chemokine (C-X-C motif) ligand 12 signaling pathway (GO:0038146) 1.40E-06 1.06E-04 CXCL12;CXCR4 protein-containing complex assembly (GO:0065003) 2.08E-06 1.27E-04 CXCL12;TERT;MYC;SLC2A1 negative regulation of stress-activated protein kinase signaling cascade (GO:0070303) 2.94E-06 1.49E-04 GSTP1;MYC peptide metabolic process (GO:0006518) 3.80E-06 1.65E-04 GSTM1;GSTP1;GSTT1 hepoxilin biosynthetic process (GO:0051122) 5.03E-06 1.70E-04 GSTM1;GSTP1 hepoxilin metabolic process (GO:0051121) 5.03E-06 1.70E-04 GSTM1;GSTP1 sulfur compound biosynthetic process (GO:0044272) 9.63E-06 2.93E-04 GSTM1;GSTP1;GSTT1 Table 1 (b) GO Category, GO terms and their corresponding P -values and genes for common differentially uttered genes Category Term P-value Adjusted P-value Genes GO Molecular Function glutathione transferase activity (GO:0004364) 1.22E-07 5.63E-06 GSTM1;GSTP1;GSTT1 C-X-C chemokine receptor activity (GO:0016494) 0.001998563 0.021443782 CXCR4 myosin light chain binding (GO:0032027) 0.002397859 0.021443782 CXCR4 RNA-directed DNA polymerase activity (GO:0003964) 0.002397859 0.021443782 TERT telomerase activity (GO:0003720) 0.002397859 0.021443782 TERT JUN kinase binding (GO:0008432) 0.002797015 0.021443782 GSTP1 hexose transmembrane transporter activity (GO:0015149) 0.005985243 0.029321451 SLC2A1 CXCR chemokine receptor binding (GO:0045236) 0.006780906 0.029321451 CXCL12 nucleotidyltransferase activity (GO:0016779) 0.007576012 0.029321451 TERT transcription coactivator binding (GO:0001223) 0.007973357 0.029321451 TERT Table 1 (c) GO Category, GO terms and their corresponding P -values and genes for common differentially uttered genes Category Term P-value Adjusted P-value Genes GO Cellular Component transferase complex, transferring phosphorus-containing groups (GO:0061695) 0.003594909 0.089872717 TERT cortical actin cytoskeleton (GO:0030864) 0.016679798 0.139252189 SLC2A1 sarcolemma (GO:0042383) 0.020615122 0.139252189 SLC2A1 cortical cytoskeleton (GO:0030863) 0.022969697 0.139252189 SLC2A1 nucleolus (GO:0005730) 0.03243026 0.139252189 TERT;MYC nuclear lumen (GO:0031981) 0.033420525 0.139252189 TERT;MYC cytoplasmic vesicle lumen (GO:0060205) 0.045092327 0.150505972 GSTP1 ficolin-1-rich granule lumen (GO:1904813) 0.048161911 0.150505972 GSTP1 basolateral plasma membrane (GO:0016323) 0.058837576 0.15481925 SLC2A1 ficolin-1-rich granule (GO:0101002) 0.071284988 0.15481925 GSTP1 Table 2 (a) Top Ten pathways from KEGG databases and their corresponding p-values , Adjusted p-values , and genes for common differentially uttered genes Databases Pathways P-value Adjusted P-value Genes KEGG Pathways in cancer 2.34E-13 1.41E-11 GSTM1;CXCL12;TERT; MYC;GSTP1;SLC2A1;CXCR4;GSTT1 Hepatocellular carcinoma 2.16E-09 6.48E-08 GSTM1;TERT;MYC;GSTP1;GSTT1 Glutathione metabolism 1.22E-06 2.01E-05 GSTM1;GSTP1;GSTT1 Chemical carcinogenesis 1.34E-06 2.01E-05 GSTM1;MYC;GSTP1; GSTT1 Metabolism of xenobiotics by cytochrome P450 2.91E-06 3.50E-05 GSTM1;GSTP1;GSTT1 Drug metabolism 8.41E-06 8.41E-05 GSTM1;GSTP1;GSTT1 Fluid shear stress and atherosclerosis 1.79E-05 1.54E-04 GSTM1;GSTP1;GSTT1 Human T-cell leukemia virus 1 infection 6.96E-05 5.03E-04 TERT;MYC;SLC2A1 Human cytomegalovirus infection 7.55E-05 5.03E-04 CXCL12;MYC;CXCR4 Intestinal immune network for IgA production 1.56E-04 9.39E-04 CXCL12;CXCR4 Table 2 (b) Top Ten pathways from Reactome databases and their corresponding p-values , Adjusted p-values , and genes for common differentially uttered genes Databases Pathways P-value Adjusted P-value Genes Reactome Glutathione conjugation Homo sapiens R-HSA-156590 3.52E-07 2.29E-05 GSTM1;GSTP1;GSTT1 Phase II conjugation Homo sapiens R-HSA-156580 6.67E-06 2.17E-04 GSTM1;GSTP1;GSTT1 Biological oxidations Homo sapiens R-HSA-211859 5.24E-05 0.001134782 GSTM1;GSTP1;GSTT1 Chemokine receptors bind chemokines Homo sapiens R-HSA-380108 2.13E-04 0.002974983 CXCL12;CXCR4 Formation of the beta-catenin:TCF transactivating complex Homo sapiens R-HSA-201722 2.29E-04 0.002974983 TERT;MYC Telomere Extension By Telomerase Homo sapiens R-HSA-171319 0.002397859 0.018885641 TERT Peptide ligand-binding receptors Homo sapiens R-HSA-375276 0.00249665 0.018885641 CXCL12;CXCR4 TCF dependent signaling in response to WNT Homo sapiens R-HSA-201681 0.002651521 0.018885641 TERT;MYC Binding of TCF/LEF:CTNNB1 to target gene promoters Homo sapiens R-HSA-4411364 0.002797015 0.018885641 MYC Vitamin C (ascorbate) metabolism Homo sapiens R-HSA-196836 0.003196032 0.018885641 SLC2A1 Table 2 (c) Top Ten pathways from Wikipathways databases and their corresponding p-values , Adjusted p-values , and genes for common differentially uttered genes Databases Pathways P-value Adjusted P-value Genes Wikipathways Nuclear Receptors Meta-Pathway WP2882 4.23E-06 4.48E-04 GSTM1;MYC; GSTP1;SLC2A1 Mammary gland development pathway - Embryonic development (Stage 1 of 4) WP2813 1.47E-05 7.34E-04 TERT;MYC NRF2 pathway WP2884 2.08E-05 7.34E-04 GSTM1;GSTP1; SLC2A1 Neovascularisation processes WP4331 9.26E-05 0.002453638 CXCL12;CXCR4 Genes controlling nephrogenesis WP4823 1.25E-04 0.002658237 CXCL12;CXCR4 TGF-beta Signaling Pathway WP366 0.001179376 0.020835647 TERT;MYC Chemokine signaling pathway WP3929 0.001811568 0.021173709 CXCL12;CXCR4 Metapathway biotransformation Phase I and II WP702 0.002248496 0.021173709 GSTM1;GSTP1 let-7 inhibition of ES cell reprogramming WP3299 0.002397859 0.021173709 MYC Benzene metabolism WP3891 0.002397859 0.021173709 GSTM1 Table 2 (d) Top Ten pathways from BioCarta databases and their corresponding p-values , Adjusted p-values , and genes for common differentially uttered genes Databases Pathways P-value Adjusted P-value Genes BioCarta Pertussis toxin-insensitive CCR5 Signaling in Macrophage Homo sapiens h Ccr5Pathway 5.03E-06 7.17E-05 CXCL12;CXCR4 Overview of telomerase protein component gene hTert Transcriptional Regulation Homo sapiens h tertpathway 6.29E-06 7.17E-05 TERT;MYC CXCR4 Signaling Pathway Homo sapiens h cxcr4Pathway 7.69E-06 7.17E-05 CXCL12;CXCR4 Telomeres, Telomerase, Cellular Aging, and Immortality Homo sapiens h telPathway 1.47E-05 1.03E-04 TERT;MYC Erk1/Erk2 Mapk Signaling pathway Homo sapiens h erkPathway 3.22E-05 1.80E-04 TERT;MYC Beta-arrestins in GPCR Desensitization Homo sapiens h bArrestinPathway 5.26E-05 2.12E-04 CXCL12;CXCR4 Activation of cAMP-dependent protein kinase, PKA Homo sapiens h gsPathway 5.65E-05 2.12E-04 CXCL12;CXCR4 Role of Beta-arrestins in the activation and targeting of MAP kinases Homo sapiens h barr-mapkPathway 6.06E-05 2.12E-04 CXCL12;CXCR4 Roles of Beta-arrestin-dependent Recruitment of Src Kinases in GPCR Signaling Homo sapiens h bArrestin-srcPathway 7.80E-05 2.43E-04 CXCL12;CXCR4 ChREBP regulation by carbohydrates and cAMP Homo sapiens h chrebpPathway 1.08E-04 3.03E-04 CXCL12;CXCR4 3.12 Topological properties Topological features serve as key neutralizations in the study of biological networks in drug-target protein, protein-chemical interaction, and monitoring of drug interactions. NetworkAnalyzer tools are used to create topological tables or graphs by plugging in the Cytoscape application. The PPI network represents a protein's clustering coefficient, which includes degree, topological coefficient, stress, closeness, and betweenness centrality. Table 3 Seven genes selected from PPIs network using Cytoscape for topological properties name Betweenness Centrality Closeness Centrality Clustering Coefficient Degree Stress Topological Coefficient MYC 0.0582768 0.794871795 0.349836031 138 26742 0.29351722 TERT 0.005383064 0.588607595 0.565789474 57 3436 0.366808914 CXCR4 0.002085671 0.555223881 0.645625692 43 2078 0.379328165 CXCL12 0.002255694 0.531428571 0.620168067 35 1770 0.366969447 SLC2A1 1.19E-04 0.52991453 0.866666667 25 130 0.470769231 GSTP1 2.28E-04 0.513812155 0.758169935 18 154 0.434144819 GSTM1 2.15E-06 0.4781491 0.933333333 6 2 0.542944785 3.13 Identification of candidate drugs Drug molecules are identified with 8 common DEGs and require the Enrichr tool. Enrichr web tool has different functions which are used for different purposes. In this case, drug data is collected from the DSigDB database. The results of the P -value and adjusted P -value have been taken for candidate drug design. The study reveals that the two medication compounds with the most significant gene interactions are styrene oxide CTD 00000717 and troglitazone CTD 00002415. Because these signature medicines were discovered for common DEGs, they define common pharmaceuticals for detecting cancer. Table 4 lists candidate medications from the DSigDB database for frequent DEGs. Table 4 Suggested top 10 drug compounds for the Seven most cancer diseases Name of drugs P -value Adjusted P -value Odds Ratio Genes Styrene oxide CTD 00000717 6.27E-11 *** 4.94E-08 1175 GSTM1;MYC;GSTP1;GSTT1 troglitazone CTD 00002415 2.92E-10 ** 8.64E-08 210.3043 GSTM1;TERT;MYC; GSTP1;SLC2A1;CXCR4;GSTT1 Microcystin LR CTD 00002422 3.29E-10 ** 8.64E-08 739.4444 GSTM1;MYC;GSTP1;GSTT1 busulfan CTD 00005543 1.17E-09 * 2.30E-07 525.1053 GSTM1;MYC;GSTP1;GSTT1 paclitaxel CTD 00007144 5.84E-09 * 9.21E-07 120.154 GSTM1;TERT;MYC; GSTP1;CXCR4;GSTT1 Chloroethylene CTD 00006592 4.06E-08 5.33E-06 749.1 GSTM1;GSTP1;GSTT1 tamoxifen CTD 00006827 1.07E-07 1.20E-05 72.34673 CXCL12;TERT;MYC;GSTP1;SLC2A1; CXCR4 glutathione CTD 00006035 1.40E-07 1.38E-05 150.4545 GSTM1;MYC;GSTP1;GSTT1 OZONE CTD 00006460 3.52E-07 3.00E-05 342.12 GSTM1;GSTP1;GSTT1 chlorambucil CTD 00005626 3.81E-07 3.00E-05 332.6 GSTM1;MYC;GSTP1 ***Most Significant **Significant 3.14 Data Mining approach String interaction analysis using data mining To do string interaction analysis, I first uploaded the string data to software called orange. In this case, I use the K-means clustering algorithm for data mining with the value of k as 3. In the image, C1, C2, C3 are the values of this k which are separated by clustering. The AverageShortestPathLength along the X-axis and the NeighborhoodConnectivity along the Y-axis are taken. C1 clustering has the most data and C3 has the least. 2.50–2.75 more data is available in this range. Data numbers are highest between 1.5 to 2.1 on the X-axis and 50–80 on the Y-axis. Pearson Correlation among string interaction analysis The Pearson correlation technique is the most commonly used method for analyzing numerical variables. It returns a value ranging from − 1 to 1, where 0 indicates no correlation, 1 indicates total positive correlation, and − 1 indicates whole negative correlation. Below is the Pearson correlation of other features with eccentricity, and we can observe that the Pearson correlation of topological coefficient with positive eccentricity is positive. A positive correlation means that if variable A rises, so will variable B, whereas a negative correlation says that if A increases, B declines. In this table, A represents eccentricity, and B represents other traits. In addition, I utilize a ranker method to rank the desired eccentricity. Table 5 Pearson correlation and evaluation of various features with Eccentricity Serial number Pearson Correlation Value 1 Eccentricity NumberOfUndirectedEdges -0.622 2 Eccentricity Radiality -0.603 3 Eccentricity Stress -0.527 4 Eccentricity TopologicalCoefficient + 0.073 Table 6 Submission of ranked algorithm for targeted Eccentricity Serial number Features name RReliefF 1 IsSingleNode Null 2 TopologicalCoefficient 0.04302504089531061 3 Stress 0.08476774591738742 4 Radiality 0.058541136558620685 5 NumberOfUndirectedEdges 0.08347787055064551 6 NeighborhoodConnectivity 0.05194071094998019 7 Degree 0.08347787055064551 8 ClusteringCoefficient 0.07019668593188903 9 ClosenessCentrality 0.06752249124196281 10 BetweennessCentrality 0.06309275104642066 11 AverageShortestPathLength 0.05854114014530909 4. Discussion From the very beginning, the main goal of cancer research was to design possible drugs for BLC, BRC, ENC, CEC, TYC, BRC, and PRC. To achieve this goal, I have to go through different stages and use different methods of research to get the expected results. When I first collected the genes for selected cancers, the number of genes from the NCBI gene database was much higher. We then collect genes after using R programming to pre-process the genes, filtering and mining the genes. The PPI network analysis identified 829 nodes, 884 edges, and 7 seed nodes. To carry out the research, work is started on the top eight (TERT, MYC, GSTM1, GSTT1, GSTP1, CXCR4, CXCL12, and SLC2A1) genes that are common and they are highlighted so that knowledge about them can be acquired more effectively. Pathway analysis, gene regulatory networks, and co-expression networks are important contributors to research. The protein-drug and protein-chemical interaction network is used to identify the effect of drugs and chemical compounds interacting with a certain gene. By studying these genes, we determine multiple relationships, including how cells form a network with other genes, how biological properties, and how various biological functions work secretly. Finally, after completing all the research, all the information is used in the final stage to complete the main purpose of this study is to design the drug. 5. Conclusion We use BLC, BRC, ENC, CEC, TYC, BRC, and PRC to study the various bioinformatics methods for sorting common genes in the main research subjects. We identify eight common genes from seven different cancers and require bioinformatics analysis. Similarly, we continue to design this drug identify potential drug targets for the discovery process, and keep this analysis ongoing. Our ultimate goal is to research and analyze effective drugs and find common genes for them and each common gene plays a different role in researching effective drugs. These procedures use gene intersection and filtering to determine the reactive common gene among specified disorders. Next, the PPI network for the shared genes is presented. Gene Ontology (GO) has been used to compute biological systems that provide complete models and again reduce the complexity of expression studies of biological processes. Enhances protein-drug interactions as a result of analysis of PPI networks. Topological results have been needed to unravel co-expression and path analysis in less complex ways. This study illustrates common genes and their network profiles, identifies how common genes interact with other genes, and designs candidate drugs. Abbreviations BLC = Bladder Cancer BRC = Breast Cancer ENC = Endometrial Cancer CEC = Cervical Cancer TYC = Thyroid Cancer BRC = Brain Cancer Declarations Authors’ contributions Siam Ahmed= Analysis, Data collection, Writing. Sayed Asaduzzaman: Problem Formulation, Verification of Analysis , Supervision, Writing. Ethical statement This work did not receive any grant from funding agencies. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors are grateful to NCBI (National Center for Biotechnology Informatics) to provide Genetic information. References Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2022). Cancer statistics, 2022. CA: a cancer journal for clinicians . Seiler, A., & Jenewein, J. (2019). Resilience in cancer patients. Frontiers in psychiatry , 10 , 208. Lenis, A. T., Lec, P. M., & Chamie, K. (2020). Bladder cancer: a review. Jama , 324 (19), 1980-1991. Waks, A. G., & Winer, E. P. (2019). Breast cancer treatment: a review. Jama , 321 (3), 288-300. Coughlin, S. S. (2019). Epidemiology of breast cancer in women. Breast cancer metastasis and drug resistance , 9-29. Lu, K. H., & Broaddus, R. R. (2020). Endometrial cancer. New England Journal of Medicine , 383 (21), 2053-2064. Raglan, O., Kalliala, I., Markozannes, G., Cividini, S., Gunter, M. J., Nautiyal, J., ... & Kyrgiou, M. (2019). Risk factors for endometrial cancer: An umbrella review of the literature. International journal of cancer , 145 (7), 1719-1730. Vu, M., Yu, J., Awolude, O. A., & Chuang, L. (2018). Cervical cancer worldwide. Current problems in cancer , 42 (5), 457-465. Cohen, P. A., Jhingran, A., Oaknin, A., & Denny, L. (2019). Cervical cancer. The Lancet , 393 (10167), 169-182. Prete, A., Borges de Souza, P., Censi, S., Muzza, M., Nucci, N., & Sponziello, M. (2020). Update on fundamental mechanisms of thyroid cancer. Frontiers in Endocrinology , 11 , 102. Haymart, M. R., Banerjee, M., Reyes-Gastelum, D., Caoili, E., & Norton, E. C. (2019). Thyroid ultrasound and the increase in diagnosis of low-risk thyroid cancer. The Journal of Clinical Endocrinology & Metabolism , 104 (3), 785-792. Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020). An ensemble learning approach for brain cancer detection exploiting radiomic features. Computer methods and programs in biomedicine , 185 , 105134. Tang, W., Fan, W., Lau, J., Deng, L., Shen, Z., & Chen, X. (2019). Emerging blood–brain-barrier-crossing nanotechnology for brain cancer theranostics. Chemical Society Reviews , 48 (11), 2967-3014. Rawla, P. (2019). Epidemiology of prostate cancer. World journal of oncology , 10 (2), 63. Taz, T. A., Kawsar, M., Paul, B. K., & Ahmed, K. (2020). Computational analysis of regulatory genes network pathways among devastating cancer diseases. Journal of Proteins and Proteomics , 11 (1), 63-76. Kawsar, M., Taz, T. A., Paul, B. K., Mahmud, S., Islam, M. M., Bhuyian, T., & Ahmed, K. (2020). Analysis of gene network model of Thyroid Disorder and associated diseases: A bioinformatics approach. Informatics in Medicine Unlocked , 20 , 100381. Islam, M. N., Shaolin, S. S., Paul, B. K., Islam, M. M., Bhuyian, T., & Ahmed, K. (2020). Mining and predicting protein-drug interaction network of breast cancer risk genes. Gene Reports , 20 , 100753. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 2005; 102 (43):15545–50. Doms A, Schroeder M. GoPubMed: exploring PubMed with the gene ontology. Nucleic Acids Res 2005; 33 (suppl_2): W783–6. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28 (1):27–30. Chen EY, Tan CM, Kou Y, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 2013; 14 (1):128. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7340632\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":499719426,\"identity\":\"119deab9-6378-4034-9205-effead40c16c\",\"order_by\":0,\"name\":\"Siam Ahmed\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Bangladesh University of Engineering and Technology\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Siam\",\"middleName\":\"\",\"lastName\":\"Ahmed\",\"suffix\":\"\"},{\"id\":499719428,\"identity\":\"a83b7322-b845-4fa4-bd68-539d4e8bd88f\",\"order_by\":1,\"name\":\"Sayed Asaduzzaman\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Rangamati Science and Technology University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sayed\",\"middleName\":\"\",\"lastName\":\"Asaduzzaman\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-08-10 19:23:24\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7340632/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7340632/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":89361682,\"identity\":\"08902167-3655-43ca-9f23-94b9d9967165\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:31:37\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":88692,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFlowchart of the Proposed Methodology\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/9041eede74c6e2946be61210.png\"},{\"id\":89362870,\"identity\":\"ffd7a0c0-ecff-47e6-8317-05ab7370686e\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:39:37\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":53110,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eHomo\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003esapiens \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003egene collection phase excluded from total genes\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/d86a88629ce43968f4f352f0.png\"},{\"id\":89363204,\"identity\":\"e6d5b702-10de-48d9-96eb-bab61c5e49d7\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:47:37\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":269462,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003e(a) The protein-protein interaction (PPI) network is designed for The Generic PPI. (b) The protein-protein interaction (PPI) network is designed for Tissue-Specific PPI network\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/89839f3c23455bf882f8755f.png\"},{\"id\":89361684,\"identity\":\"fd5776c0-f28f-48cf-b210-f064a1f1f935\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:31:37\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":340080,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCo-Expression, Physical interaction, and pathway analysis network between the 8 Hub genes (MYC, GSTT1, GSTP1, GSTM1, TERT, CXCR4, SLC2A1, CXCL12).\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/aae39fe17557267c3b5a18e8.png\"},{\"id\":89364178,\"identity\":\"2959a44f-085c-41e3-ba51-a735f3dca1d7\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:55:37\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":251750,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003e(a). Gene mi-RNA interaction network. The 6 seed nodes are in an orange circle. (b). TF–gene interaction network. The seven seed nodes are in a red circle and these are the most significant genes.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/cb06da4f0bc714058c64aa98.png\"},{\"id\":89361704,\"identity\":\"16d31efa-8be5-4890-89c2-62366556b036\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:31:38\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":82311,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eConnection of 8 common genes using string.GSTT1 has no protein.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/e7419aaf5dd463fe387bc4c9.png\"},{\"id\":89362872,\"identity\":\"25a30e4f-a894-4c44-9ed3-8e9fb8ed4710\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:39:37\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":193147,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003e(a) Common genes using the MCL algorithm are connected to the connection of 8 (TERT, MYC, SLC2A1, CXCL12, CXCR4, GSTP1, GSTM1, GSTT1). (b)Gene interaction using MCODE in Cytoscape.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/765996dcde279b0567a42c54.png\"},{\"id\":89361690,\"identity\":\"757f95e9-b556-4f70-a246-cd92c08cb1c6\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:31:37\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":543358,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003e(a) g;Profiler Enrichment map; (b) KEGG pathways analysis in g:Profiler; (c) gene ontology: biological process analysis\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/f82a767745a008467056b3b5.png\"},{\"id\":89362880,\"identity\":\"93057255-5334-4f71-93a5-62d3b7225182\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:39:38\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":294282,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003e(a). Protein–drug interaction network (Subnetwork 1). The node in green color is tinted as the targeted gene and the gene is GSTP1 and GSTM1. (b) (Subnetwork 2) for TERT gene. (c) (Subnetwork 3) for CXCR4 gene. The nodes show protein and edges show interaction with the protein.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/d0b6369a090cca80d039ba07.png\"},{\"id\":89362877,\"identity\":\"ba06294a-bd35-4578-bb33-0c90e919e142\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:39:37\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":180905,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eProtein–chemical interaction network. The network contains of 863 nodes, 1396 edges, and 8 seed nodes. The seed nodes are tinted.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/49e9627e9ad784469e485bb1.png\"},{\"id\":89363206,\"identity\":\"50343375-dc60-4fa6-9409-1329b801eb77\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:47:37\",\"extension\":\"png\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":108700,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAccording to combined score, biological process, molecular function, and cellular component related GO terms identification result. The higher number of genes and the enrichment score are involved in a certain ontology.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/49c698102482a34832a5b977.png\"},{\"id\":89361702,\"identity\":\"6d13a5dd-e57e-40ba-9bf5-780265643762\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:31:38\",\"extension\":\"png\",\"order_by\":12,\"title\":\"Figure 12\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":161341,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eKEGG, WikiPathways, Reactome, and BioCarta are the pathway analysis identification result. The results of the pathway terms were identified through the combined score.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"12.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/2768344257b4b121ed3852a3.png\"},{\"id\":89361706,\"identity\":\"55dd788b-2686-45e2-98d9-ed97c44a9470\",\"added_by\":\"auto\",\"created_at\":\"2025-08-19 08:31:38\",\"extension\":\"png\",\"order_by\":13,\"title\":\"Figure 13\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":156545,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eString interaction data analysis using K-means clustering.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"13.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/d75ac0455c503ed8a4668c66.png\"},{\"id\":89742254,\"identity\":\"c5dcb14b-7dbc-4a3d-8724-90a0379d9a2f\",\"added_by\":\"auto\",\"created_at\":\"2025-08-23 21:46:28\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4527761,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7340632/v1/8c7a1508-b0f5-4503-a228-8ec693bfe980.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Network Analysis of Genes and Identification of Candidate Drug Compound for Associated Deadly Diseases of Female\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eAnalysis of various web portals and data shows that Cancer is a major public health problem globally. Cancer is the second leading cause of death in the United States. In 2020, the diagnosis and treatment of cancer were badly exaggerated due to the outbreak of the coronavirus and its spread (COVID-19) [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Cancer treatment is a long-term process that can put patients at risk for negative psychological outcomes, including distress, mental anguish, fatigue, anxiety, sleep problems, and impaired quality of life. Diagnosing cancer and later treating it is a frustrating experience for many [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eBladder cancer is common anemia in women and starts with nonaggressive and usually non-invasive tumors. Bladder cancer has a 0.27% lifetime risk among women and is responsible for an estimated 5,00,000 new cases and 2,00,000 deaths worldwide, with 80,000 new cases being reported each year in the United States and 17,000 deaths [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Increasing age, chronic bladder inflammation, Exposure to certain chemicals, family history of cancer, Smoking, and previous cancer treatment are the risk factors for BLC. A cystoscopy is the main procedure to detect and diagnose bladder cancer. Breast cancer is diagnosed in 12% of all women in their entire lifetime in the United States, and in 2017 more than 250,000 breast cancer patients were found in the United States. Invasive ductal carcinoma (50%-75% of patients) is the most common histology of breast cancer. It is also said that breast cancer is the most common cancer among women worldwide except for nonmelanoma skin cancer [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Physical idleness, increased alcohol consumption, extraneous hormones, and certain female generative factors are the main reasons for the increase in breast cancer. Older age at first full-term pregnancy, younger age at menarche, and parity can have long-term effects on various biological processes, or variation in hormone levels may affect the risk of breast cancer. The causes of this risk have been well-established by epidemiological studies, including society, race, family history of breast cancer, and genetic traits [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. The main causes of Endometrial cancer and the risk of developing it are various conditions associated with metabolic syndrome, including diabetes, obesity, and polycystic ovary syndrome [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Worldwide, 319,605 new cases of Endometrial cancer were reported in 2012, and 76,160 of them died. Endometrial cancer is considered the second most common cancer in women after Breast cancer in the established world and is the most common gynecological cancer [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Cervical cancer is the fourth most common cancer among the growing trend of cancer in women worldwide [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. The main causes of Cervical cancer are the high-risk subtypes of papillomavirus (HPV). The disease has so far killed more than 300,000 people and infected more than half a million women each year [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. A group of related viruses is named HPV. They can feast through other warm, skin-to-skin interactions. Of all cancers, Thyroid cancer accounts for 3.4% of all cancers diagnosed annually, indicating the most common endocrine hostility [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Thyroid cancer is now the 11th most common cancer in the world and the 5th most common cancer among women, affecting people\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 years because Thyroid cancer tests are known to have the highest rates in the elderly and adult population. Treatment for Thyroid cancer is the riskiest because it reduces the risk of overtreatment with dangerous iodine, leading to renal clearance, risk of bone loss, and thyroid hormone replacement, leading to severe arrhythmias. In addition, thyroid surgery causes the most inconsistencies in the body and increases the risk of various diseases and deaths. The incidence of thyroid cancer in the United States has almost folded since [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Brain cancer is a growth or mass of abnormal cells in and around the brain. Brain cancer shortens life expectancy by an average of 20 years and is the highest of any cancer. These cancers increase mortality in young adults, which is the third-highest among compact cancers. Analysis of brain cancer in adults shows that only 19% of patients survive 5years. Giving to the American Cancer Society in 2018, about 23,880 persons were analyzed with a malignant brain or backbone tumor and about 70% of those with a malignant tumor are not endured as a result of their analysis [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Known as the most destructive invasive and malignant neoplasm of brain cancer, it is the most common type of glioblastoma [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Prostate cancer is the second most frequent enmity in men globally. Female prostate cancer is enormously rare. The skene\\u0026rsquo;s glands are often stated to as the female prostate since they harvest the enzymes as the male prostate. Counting 1,276,106 new cases and causing 358,989 deaths (3.8% of all deaths caused by cancer in men) in 2018 [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eTasnimul A.T., Md Kawsar, Bikash K.P., Kawsar A. works for some devastating cancer for women which is based on computational analysis. Breast cancer (BC), Endometrial cancer (EC), and Ovarian cancer are the three most deadly cancers in women because they have the highest mortality rate. The researcher researched these three types of cancer \\u0026amp; find out the Interaction of proteins with drug particles to come up with an efficient drug design for this research. [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Chronic Kidney Disease (CKD), High Blood Pressure (HBP), and Thyroid Disorder (TD) diseases, when people are infected with one of these diseases, the chances of getting infected with the other two are many times higher and this shows that these diseases are interrelated. In this analysis, three dangerous diseases were selected for analysis. The reason for the assortment of the three different disorder diseases was to regulate their relations. In this paper Analysis of the gene network of Thyroid Disorder and associated diseases for Md Kawsar, Tasnimul A. T., Bikash K. P., S. Mahmud, Md M. Islam, Touhid B., Kawsar A. [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Mining and predicting protein-drug interaction network of breast cancer risk genes is another good work for Muhammad N. I., S.S. Shaolin, Bikash K.P., Manowarul I., Touhid B., Kawsar A. They find the relations of Breast cancer (BC) and its two significant risk factors the \\u003cem\\u003eAtypical Hyperplasia\\u003c/em\\u003e (AH) and \\u003cem\\u003eLobular Carcinoma in SITU\\u003c/em\\u003e (LCIS). Patients have the risk of carrying BC more than others. So they have encountered the 12 common interrelated genes of these diseases \\u0026amp; they have got a drug signature suggestion. [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThis study attempted to identify potential therapeutic candidates for the common genes identified for BLC, BRC, ENC, CEC TYC, BRC, and PRC. The networks and routes for the seven malignancies were developed using high-performance protein-protein interaction and biological network data.\\u003c/p\\u003e\"},{\"header\":\"2. Proposed methodology\",\"content\":\"\\u003cp\\u003ePrevious research has led to infrequent usage of exact gene finding, enactment of protein-protein interactions, various forms of regulatory networks, pathway analysis, and medication design for BLC, BRC, ENC, CEC TYC, BRC, and PRC. We discuss the research methods in depth, starting with the collection of linked genes from the NCBI gene database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ncbi.nlm.nih.gov/gene/\\u003c/span\\u003e\\u003c/span\\u003e). The retrieved genes are then assigned to Network Analyst (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.networkanalyst.ca/\\u003c/span\\u003e\\u003c/span\\u003e) to provide a view of drug particles. Study for relevant genes, create a PPI network, assess topological features to find target proteins, followed by gene regulatory networks, medication design for these disorders, and experiment with data mining methods. Figure \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e depicts the proposed study process as it progresses step by step. Our plan is shown in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e to gain a better empathy of what we have tried to reach in our following study. The early measure of the goal movement procedure of this research is pictured in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.1 Gene collection\\u003c/h2\\u003e\\n \\u003cp\\u003eGenes were obtained from the National Center for Biotechnology Information (NCBI) gene database. The NCBI database is one of several websites that collect and update gene and protein information. It is essentially a bioinformatics project that adds one billion bytes of information to each genetic database item (Lomax \\u0026amp; McCray 2004). Furthermore, NCBI is efficiently annotated and recognized since many databases use a mix of PubMed, Gene Bank, and Epigenomics databases to determine NCBI, and biological data is also decided using this high-throughput database. Then, we download the separate genes of BLC, BRC, ENC, CEC TYC, BRC \\u0026amp; PRC diseases for Homo sapiens rendering the weight of the genes because this study is only for human drug design. Mainly CSV files are imported from NCBI because then common genes are extracted from them in different ways and the main goal is to study them and find out the cause of diseases.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.2 Common gene finding\\u003c/h2\\u003e\\n \\u003cp\\u003eAfter collecting genes for various diseases from NCBI, the main objective is to extract the interconnected genes (BLC, BRC, ENC, CEC TYC, BRC \\u0026amp; PRC), from the genes of these diseases which are mainly responsible for causing diseases. So, I used Rstudio (available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://rstudio.com/\\u003c/span\\u003e\\u003c/span\\u003e) to do this job perfectly which results in the genes that intersect between the seven diseases. This task is so important that later research reduces these genes simplifying the process of designing drugs for human diseases. So, the common gene finding process is the most important part of this research.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.3Protein-protein interaction (PPI) network design\\u003c/h2\\u003e\\n \\u003cp\\u003eAfter collecting genes for various diseases from NCBI, the major goal is to extract the structure of protein activities and compounds, which is the most important component of the protein [\\u003cspan class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. To initially identify a protein\\u0026apos;s biophysical mechanisms, we must interact with it. To accomplish this, we will use NetworkAnalyst (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.networkanalyst.ca/\\u003c/span\\u003e\\u003c/span\\u003e), a web-based bioinformatics program. We next utilize Cytoscape (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://cytoscape.org/\\u003c/span\\u003e\\u003c/span\\u003e) to evaluate and present this data. In Cytoscape, we build a PPI network for common genes. The most widely used uses of Cytoscape include evaluating biological information, building PPI networks, controlling the number of secret biological processes in cells through PPI, providing results of molecular processes, and fulfilling protein functions. Essentially, in this step, we may separate and identify the most important hub proteins in the PPI network.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.4 Gene co‑expression network (GCN)\\u003c/h2\\u003e\\n \\u003cp\\u003eA co-expression system is used to find the process-level efficiency of genes. The function of genes at the structure level, the identification of genes on different nodes, and the connection of nodes with nodes are all reflected in the structure of these Gene co-expression networks. GCNs are of biological concern. We used the online tool genemania (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ewww.genemania.org\\u003c/span\\u003e\\u003c/span\\u003e) to research some of these.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.5 Gene regulatory network (GRN)\\u003c/h2\\u003e\\n \\u003cp\\u003eA gene regulatory network (GRN) is a collection of genes or parts of genes that are linked together and control certain cells to aid a gene\\u0026apos;s biological function. Important gene regulatory network tasks include a wide range of development, control over body plan organization, cellular processes, differentiation, and response to environmental cues. Finally, it is difficult to uncover gene regulatory networks through study, because the findings are required to build effective medications in real life. We used the web-based program NetworkAnalyst (available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.networkanalyst.ca/\\u003c/span\\u003e\\u003c/span\\u003e) to explore the gene regulatory network; this tool designed the most significant sort of gene-miRNA interaction, TF-gene interaction, which has accelerated my research.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.6 Protein\\u0026ndash;drug interactions\\u003c/h2\\u003e\\n \\u003cp\\u003eOur main goal in protein-drug interactions is to design drugs, the results of drug research with proteins are therefore essentially a part of drug design. Exploiting drug proficiency and reducing the harmfulness of drugs are measured by the activity of drug design. Therapeutic drug monitoring (TDM) is a process that includes medications for thin therapeutic directories. In this study, we used the NetworkAnalyst (available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.networkanalyst.ca/\\u003c/span\\u003e\\u003c/span\\u003e), website and found out the interaction of targeted genes with drugs.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.7 Protein\\u0026ndash;chemical interactions\\u003c/h2\\u003e\\n \\u003cp\\u003eProtein-chemical interaction is a difficult task because to do this we need to find out the similarities and differences between the targeted genes with the chemical set of bioinformatics. This process has evolved into a developing field of modern computational biology. The secrecy of the chemical location of the compound is found using chemoinformatic which is being used in the current drug design. This information will help to make the design of medicine more effective for the next generation. We used the NetworkAnalyst website for protein-chemical research.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.8 GO and pathway finding in terms of enrichment analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eA computational method that describes the chromosomal position of gene sets and the common biological functions of genes is called gene set enrichment analysis [\\u003cspan class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Gene Ontology is the design of computational representations of the biological systems of genes and the formulation of structures to describe the functions of all gene products. We have studied three sections of GO which are biological process, molecular function, and cellular component [\\u003cspan class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Genes use a variety of methods to get their work done, and GO is used to determine that. Kyoto Encyclopedia of Genes and Genomes (KEGG) is used in bioinformatics research, modeling in biology including genomics, and drug development data research. It is a pathway for which significant metabolic pathway function and gene annotation are of significant use [\\u003cspan class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Although the analysis of KEGG pathway has played a significant role, in addition to this we have worked with For WikiPathways, Reactome, and Bio-Carta databases and in doing so we have to use our web-based tool Enrichr(\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://amp.pharm.mssm.edu/Enrichr/\\u003c/span\\u003e\\u003c/span\\u003e). This tool plays a role in enriching our gene set. In this tool, we can see how significant a pathway is through a database.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.9 Topological properties\\u003c/h2\\u003e\\n \\u003cp\\u003eProperty defined or preserved under homomorphism is called topological property. For a flat domain, connections, compactness, degree of genes, mean centrality, proximity-concentration, clustering coefficients, and biological information can be determined by studying topological properties. It also plays a role in the analysis of drug functions, graph-based expression of biological networks, and identification of drug-target proteins. The degree of protein-protein interaction (PPI) is calculated to find out which genes are most responsible for cancer. And to complete this process, the study of topological properties is required. We initially downloaded the SIF file, including the PPI network design, using the NetworkAnalyst web tool. Next, we will check whether clustering is possible through this network in Cytoscape (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://cytoscape.org/\\u003c/span\\u003e\\u003c/span\\u003e), import the SIF file, and extract the topological property results using the NetworkAnalyzer plugin.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.10 Drug design\\u003c/h2\\u003e\\n \\u003cp\\u003eCurrently, drugs are a blessing for humanity since the brutality of diseases increases daily. Drugs are substances that are used to prevent or cure the symptoms of abnormal conditions, diagnosis, treatment, pain relief. Medicines play a major role in controlling the nutrition, vitamins, calcium, and other elements in our body and in controlling the activity of various cells within the body. Scientists are constantly researching and discovering new drugs based on data from continuous mutations in genes and drug-target interactions. So, it is a significant part of this research. In this part, the information obtained from our protein-drug interactions, protein\\u0026ndash;chemical interactions, works. Drug molecules are designed using the DSigDB database in the Enrichr (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://amp.pharm.mssm.edu/Enrichr/\\u003c/span\\u003e\\u003c/span\\u003e) web tool. Enrichr is a platform where initially the common genes are taken as input and the cooperative functions on it indicate some specific features [\\u003cspan class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.11 Data Mining approach\\u003c/h2\\u003e\\n \\u003cp\\u003eThe data mining method is particularly suitable and important for bioinformatics research, where it lacks a complete theory at the molecular level of the organization of life. First, data is the science of integrating complete knowledge of what I\\u0026rsquo;m researching, then mining is the science of finding new stimulus patterns and discovering this huge amount of data or knowledge. In a word, acquiring knowledge from a large amount of data is called data mining. Mining actually helps to gather the necessary knowledge from this huge biological data related to biology and science. Some data of string interaction has been taken for data mining and in this case, Orange software, K-means clustering algorithm, and Ranked algorithm have been used.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"3. Results and discussion\",\"content\":\"\\u003cp\\u003eThe detailed result section has been described in this section\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.1 Gene collection\\u003c/h2\\u003e\\n \\u003cp\\u003eWe began this work by collecting the genes of all species from NCBI using the illness names in the search box. We identified genes for Homo sapiens. In this analysis, we consider 1515, 5093, 730, 1546, 820, 39, and 3302 genes of Homo sapiens, namely BLC, BRC, ENC, CEC TYC, BRC, and PRC. This bar table has been created by excluding the gene numbers of other animals by conducting research only to find out the species \\u003cem\\u003eHomo sapiens\\u003c/em\\u003e (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.2 Common gene finding\\u003c/h2\\u003e\\n \\u003cp\\u003eWe use Rstudio to identify intersecting genes in seven diseases and this process has to be done immediately after the total gene is collected. During this study, from intersecting BLC, BRC, ENC, CEC TYC, BRC \\u0026amp; PRC we determined 8 common genes using R Programming. These are as follows, TERT, MYC, GSTM1, GSTT1, GSTP1, CXCR4, CXCL12, and SLC2A1.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.3 PPI network design\\u003c/h2\\u003e\\n \\u003cp\\u003eIn bioinformatics research, researchers must use a web interface for simple and complex meta-testing of gene expression data, which is essentially an online resource. And the NetworkAnalyst (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.networkanalyst.ca/\\u003c/span\\u003e\\u003c/span\\u003e) tool is the best used in this work (Xia et al. 2015). The main function of protein-protein interaction (PPI) is to form in a specific way in a cellular process with each other. In this study, we used the NetworkAnalyst program to generate a simple interaction format (SIF) file. The SIF file is then used to create a consistent PPI network in Cytoscape. The results of this analysis are shown in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e(a), (b). Here Generic PPI network has a total of 829 nodes, 884 edges, and 7 seed nodes. The red \\u0026amp; orange color nodes indicate the most significant hub gene. JUN is found as a common protein in the linkage of MYC, TERT, CXCR4, and GSTP1 genes. The highest linkage of generic PPI is between MYC, TERT, CXCR4, GSTP1, GSTM1, and SLC2A1. A protein called UBC is found in the linkage of genes. Tissue-Specific PPI network has a total of 826 nodes, 837 edges, and 7 seed nodes. Twice the maximum linkage of tissue-specific PPI is when 3 genes come together. MYC, GSTP1, GSTM1 genes are found in MAP3K5, and MYC, CXCL12, GSTP1 are found in FN1.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.4 Gene Co‑expression Network\\u003c/h2\\u003e\\n \\u003cp\\u003eGeneMANIA (available at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://genemania.org/\\u003c/span\\u003e\\u003c/span\\u003e) is an online-based tool that uses a network of equivalent genes here used to diagnose potential gene function. GeneMANIA is mainly used to create gene co-expression and physical interactions [\\u003cspan class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. In order to support various biological studies, GeneMANIA is performed by estimating genes, publishing gene lists, and performing significant gene functions [\\u003cspan class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. The graphic depicts the co-expression and physical interaction networks of eight hub genes.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.5 Gene regulatory network (GRN)\\u003c/h2\\u003e\\n \\u003cp\\u003eThis work describes the genome base for genetic expression, which was previously known as adenine, thymine, guanine, and cytosine, and it happens during the early stages of the Gene Regulatory Network (GRN) [\\u003cspan class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. A web tool that can control and analyze genes NetworkAnalyst. Using this, the Gene regulatory network of TERT, MYC, GSTM1, GSTT1, GSTP1, CXCR4, CXCL12, and SLC2A1 genes has been analyzed. In Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e (a, b) gene regulatory networks of gene\\u0026ndash;miRNA interaction, and TF\\u0026ndash;gene interaction are shown similarly. Gene mi-RNA interaction network comprises 234 nodes and 245 edges. Edges show the interaction between genes and nodes show the genes. The TF-gene interaction network consists of 174 nodes, 227 edges, and seven seed genes. The highest linkage in gene-miRNA interaction is between the MYC, CXCR4, and TERT genes. Has-mir-335-5p is available in their linkage.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.6 String Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eDirect relationships of eight common genes have been shown using gene optimization. The direct relationship between the eight common genes is shown in Fig. 6. This figure is attained by using STRING, which is a web-based tool that signifies gene interaction.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.7 Clustering\\u003c/h2\\u003e\\n \\u003cp\\u003eClustering is the process of separating groups with similar characteristics and dividing them into different clusters. It is also an unsupervised machine learning method for identifying and grouping similar data points across large datasets. Data clustering is one of the common tasks and is important in classifying patterns between different regions. Clustering is used to separate data and there are different types of this application. Such as Model-based clustering, Fuzzy clustering, Segmentation method, Hierarchical clustering, etc. We use two types of clustering tools on gene interaction using the Cytoscape application in research, one is MCL clustering and the other is MCODE. These two tools have been used in seven selected diseases and for 8 common genes that are evident in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.8 g:Profiler Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eAs the number of genes continues to increase and traits increase, the list of these evolved genes comes out through various studies. Working with these genes is called biological data analysis. The g:Profiler is used for gene identification and conversion between identifiers, enriched in gene lists, and mapping gene orthologs. g:Profiler is an online web tool that displays an Enrichment map of our entire cluster when genes are named. From this, we can see that GO: BP has the highest clustering and the highest value is found in KEGG clustering. Figure \\u003cspan class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e (a) shows the Enrichment map of the clustering of gene sets. The x-axis displays the functional terms, while the y-axis displays the equivalent enrichment P-values on the negative log10 scale. The circles on the plot represent single functional terms. The circles are color-coded by data source and size-scaled based on the number of genes indicated for that phrase.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec23\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.9 Protein\\u0026ndash;drug interactions\\u003c/h2\\u003e\\n \\u003cp\\u003eA protein-drug interaction network has been designed using input genes using the NetworkAnalyst tool. This protein-drug analysis plays an important role in drug discovery or lab research. Basically, the common genes we input here detect the interaction between molecules and see how effective the proteins are. Also formulates the effectiveness of proteins from the interaction of different ligands and proteins. We have analyzed the functions of the protein-drug by specifying the TERT, MYC, GSTM1, GSTT1, GSTP1, CXCR4, CXCL12, and SLC2A1 genes as input in the NetworkAnalyst tool for our work, as shown in the figure below. Figures \\u003cspan class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e show the protein-drug interaction of three subnetworks generated from NetworkAnalyst. Protein\\u0026ndash;drug interaction network (Subnetwork 1) contains 21 nodes, 21 edges, and 2 seed nodes. (Subnetwork 2) contains 4 nodes, 3 edges, and 1 seed gene. (Subnetwork 3) contains 4 nodes, 3 edges, and 1 seed gene.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.10 Protein\\u0026ndash;Chemical interactions\\u003c/h2\\u003e\\n \\u003cp\\u003eWe represent biological systems as a network with a variety of tools aimed at facilitating research, and these biological networks help us in analyzing human behavioral changes and diagnosing diseases [\\u003cspan class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. We use the NetworkAnalyst tool to determine the interaction of proteins and chemicals and find out the protein-chemical relationship between the eight genes. At the present time, protein-chemical interaction in the process of gene analysis is an epoch-making study that will give far-reaching results to the new generation. Protein and chemical relationships are signified by edges. Using R programming we get 8 common genes and through topological analysis, we find similarities with the top 8 genes from the PPI network. These eight genes are carried as input for analyzing any interaction of the NetworkAnalyst.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec25\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.11 GO and pathway finding in terms of enrichment analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eEnrichr is a web tool that studies the gene set enrichment process, so to further enrich our research we study more genes at enrichr. Gene set enrichment is an important part of analyzing the different conditions of genes and how genes use pathway networks with each other (Cha et al. 2010). The present study evaluates GO terms and KEGG pathways for 8(TERT, MYC, GSTM1, GSTT1, GSTP1, CXCR4, CXCL12, SLC2A1) common DEGs. Biological process, molecular functions, and cellular components are the three most eminent GO terms. The enduring study demonstrates the top 10 GO terms for each of the subcategories which are presented in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e(a), (b), (c). The data in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e(a), (b), (c) validate that the common DEGs are highly heightened in protein-containing complex assemblage for the biological process subsection. Molecular function subsection data indicate glutathione transferase activity in frequent DEGs. A cellular component analysis reveals a considerable role of the nucleolus and nuclear lumen in common DEGs. Tables \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e(a), (b), (c), and (d) show results from KEGG, WikiPathways, Reactome, and BioCarta pathway analyses. The table shows the pathways in cancer and hepatocellular carcinoma that have the most genes added to the KEGG pathway database. Figures \\u003cspan class=\\\"InternalRef\\\"\\u003e11\\u003c/span\\u003e and \\u003cspan class=\\\"InternalRef\\\"\\u003e12\\u003c/span\\u003e represent a collection of GO keywords and pathways that contribute to the combined score. The Enrichr web tool generates a combined score based on the log of the P-value and z-scores. Figures \\u003cspan class=\\\"InternalRef\\\"\\u003e11\\u003c/span\\u003e and \\u003cspan class=\\\"InternalRef\\\"\\u003e12\\u003c/span\\u003e illustrate GO keywords and pathway analysis results from several pathway databases, respectively.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e(a) GO Category, GO terms and their corresponding \\u003cem\\u003eP\\u003c/em\\u003e-values and genes for common differentially uttered genes\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCategory\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTerm\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdjusted P-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGenes\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003eGO Biological Process\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eglutathione derivative biosynthetic process (GO:1901687)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.45E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.80E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eglutathione derivative metabolic process (GO:1901685)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.45E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.80E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eglutathione metabolic process (GO:0006749)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.14E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.21E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003echemokine (C-X-C motif) ligand 12 signaling pathway (GO:0038146)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.40E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.06E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eprotein-containing complex assembly (GO:0065003)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.08E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.27E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;TERT;MYC;SLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003enegative regulation of stress-activated protein kinase signaling cascade (GO:0070303)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.94E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.49E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTP1;MYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003epeptide metabolic process (GO:0006518)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.80E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.65E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ehepoxilin biosynthetic process (GO:0051122)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.03E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.70E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ehepoxilin metabolic process (GO:0051121)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.03E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.70E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003esulfur compound biosynthetic process (GO:0044272)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.63E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.93E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e(b) GO Category, GO terms and their corresponding \\u003cem\\u003eP\\u003c/em\\u003e-values and genes for common differentially uttered genes\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCategory\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTerm\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdjusted\\u003c/p\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGenes\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003eGO Molecular Function\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eglutathione transferase activity (GO:0004364)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.22E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.63E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eC-X-C chemokine receptor activity (GO:0016494)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.001998563\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.021443782\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emyosin light chain binding (GO:0032027)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002397859\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.021443782\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRNA-directed DNA polymerase activity (GO:0003964)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002397859\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.021443782\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003etelomerase activity (GO:0003720)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002397859\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.021443782\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eJUN kinase binding (GO:0008432)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002797015\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.021443782\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTP1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ehexose transmembrane transporter activity (GO:0015149)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.005985243\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.029321451\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCR chemokine receptor binding (GO:0045236)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.006780906\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.029321451\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003enucleotidyltransferase activity (GO:0016779)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.007576012\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.029321451\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003etranscription coactivator binding (GO:0001223)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.007973357\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.029321451\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e(c) GO Category, GO terms and their corresponding \\u003cem\\u003eP\\u003c/em\\u003e-values and genes for common differentially uttered genes\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCategory\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTerm\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdjusted\\u003c/p\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGenes\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003eGO Cellular Component\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003etransferase complex, transferring phosphorus-containing groups (GO:0061695)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.003594909\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.089872717\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ecortical actin cytoskeleton (GO:0030864)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.016679798\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.139252189\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003esarcolemma (GO:0042383)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.020615122\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.139252189\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ecortical cytoskeleton (GO:0030863)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.022969697\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.139252189\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003enucleolus (GO:0005730)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.03243026\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.139252189\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT;MYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003enuclear lumen (GO:0031981)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.033420525\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.139252189\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT;MYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ecytoplasmic vesicle lumen (GO:0060205)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.045092327\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.150505972\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTP1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eficolin-1-rich granule lumen (GO:1904813)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.048161911\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.150505972\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTP1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ebasolateral plasma membrane (GO:0016323)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.058837576\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.15481925\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eficolin-1-rich granule (GO:0101002)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.071284988\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.15481925\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTP1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e(a) Top Ten pathways from KEGG databases and their corresponding \\u003cem\\u003ep-values\\u003c/em\\u003e, Adjusted \\u003cem\\u003ep-values\\u003c/em\\u003e, and genes for common differentially uttered genes\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDatabases\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathways\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdjusted\\u003c/p\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGenes\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003eKEGG\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathways in cancer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.34E-13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.41E-11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;CXCL12;TERT;\\u003c/p\\u003e\\n \\u003cp\\u003eMYC;GSTP1;SLC2A1;CXCR4;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHepatocellular carcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.16E-09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.48E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;TERT;MYC;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGlutathione metabolism\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.22E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.01E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChemical carcinogenesis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.34E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.01E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;MYC;GSTP1;\\u003c/p\\u003e\\n \\u003cp\\u003eGSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMetabolism of xenobiotics by cytochrome P450\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.91E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.50E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDrug metabolism\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.41E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.41E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFluid shear stress and atherosclerosis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.79E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.54E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHuman T-cell leukemia virus 1 infection\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.96E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.03E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT;MYC;SLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHuman cytomegalovirus infection\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.55E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.03E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;MYC;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIntestinal immune network for IgA production\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.56E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.39E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e(b) Top Ten pathways from Reactome databases and their corresponding \\u003cem\\u003ep-values\\u003c/em\\u003e, Adjusted \\u003cem\\u003ep-values\\u003c/em\\u003e, and genes for common differentially uttered genes\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDatabases\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathways\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdjusted\\u003c/p\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGenes\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003eReactome\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGlutathione conjugation Homo sapiens R-HSA-156590\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.52E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.29E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePhase II conjugation Homo sapiens R-HSA-156580\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.67E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.17E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBiological oxidations Homo sapiens R-HSA-211859\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.24E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.001134782\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChemokine receptors bind chemokines Homo sapiens R-HSA-380108\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.13E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002974983\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFormation of the beta-catenin:TCF transactivating complex Homo sapiens R-HSA-201722\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.29E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002974983\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT;MYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTelomere Extension By Telomerase Homo sapiens R-HSA-171319\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002397859\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.018885641\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePeptide ligand-binding receptors Homo sapiens R-HSA-375276\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00249665\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.018885641\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTCF dependent signaling in response to WNT Homo sapiens R-HSA-201681\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002651521\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.018885641\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT;MYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBinding of TCF/LEF:CTNNB1 to target gene promoters Homo sapiens R-HSA-4411364\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002797015\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.018885641\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVitamin C (ascorbate) metabolism Homo sapiens R-HSA-196836\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.003196032\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.018885641\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e(c) Top Ten pathways from Wikipathways databases and their corresponding \\u003cem\\u003ep-values\\u003c/em\\u003e, Adjusted \\u003cem\\u003ep-values\\u003c/em\\u003e, and genes for common differentially uttered genes\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDatabases\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathways\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdjusted\\u003c/p\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGenes\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003eWikipathways\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNuclear Receptors Meta-Pathway WP2882\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.23E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.48E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;MYC;\\u003c/p\\u003e\\n \\u003cp\\u003eGSTP1;SLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMammary gland development pathway - Embryonic development (Stage 1 of 4) WP2813\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.47E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.34E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT;MYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNRF2 pathway WP2884\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.08E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.34E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;\\u003c/p\\u003e\\n \\u003cp\\u003eSLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNeovascularisation processes WP4331\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.26E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002453638\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGenes controlling nephrogenesis WP4823\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.25E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002658237\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTGF-beta Signaling Pathway WP366\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.001179376\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.020835647\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT;MYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChemokine signaling pathway WP3929\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.001811568\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.021173709\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMetapathway biotransformation Phase I and II WP702\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002248496\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.021173709\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elet-7 inhibition of ES cell reprogramming WP3299\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002397859\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.021173709\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBenzene metabolism WP3891\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002397859\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.021173709\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e(d) Top Ten pathways from BioCarta databases and their corresponding \\u003cem\\u003ep-values\\u003c/em\\u003e, \\u003cem\\u003eAdjusted p-values\\u003c/em\\u003e, and genes for common differentially uttered genes\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDatabases\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathways\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdjusted\\u003c/p\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGenes\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003eBioCarta\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePertussis toxin-insensitive CCR5 Signaling in Macrophage Homo sapiens h Ccr5Pathway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.03E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.17E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOverview of telomerase protein component gene hTert Transcriptional Regulation Homo sapiens h tertpathway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.29E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.17E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT;MYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCR4 Signaling Pathway Homo sapiens h cxcr4Pathway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.69E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.17E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTelomeres, Telomerase, Cellular Aging, and Immortality Homo sapiens h telPathway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.47E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.03E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT;MYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eErk1/Erk2 Mapk Signaling pathway Homo sapiens h erkPathway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.22E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.80E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT;MYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBeta-arrestins in GPCR Desensitization Homo sapiens h bArrestinPathway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.26E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.12E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eActivation of cAMP-dependent protein kinase, PKA Homo sapiens h gsPathway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.65E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.12E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRole of Beta-arrestins in the activation and targeting of MAP kinases Homo sapiens h barr-mapkPathway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.06E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.12E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRoles of Beta-arrestin-dependent Recruitment of Src Kinases in GPCR Signaling Homo sapiens h bArrestin-srcPathway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.80E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.43E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChREBP regulation by carbohydrates and cAMP Homo sapiens h chrebpPathway\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.08E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.03E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;CXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec26\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.12 Topological properties\\u003c/h2\\u003e\\n \\u003cp\\u003eTopological features serve as key neutralizations in the study of biological networks in drug-target protein, protein-chemical interaction, and monitoring of drug interactions. NetworkAnalyzer tools are used to create topological tables or graphs by plugging in the Cytoscape application. The PPI network represents a protein\\u0026apos;s clustering coefficient, which includes degree, topological coefficient, stress, closeness, and betweenness centrality.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab8\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eSeven genes selected from PPIs network using Cytoscape for topological properties\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ename\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBetweenness Centrality\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCloseness\\u003c/p\\u003e\\n \\u003cp\\u003eCentrality\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClustering\\u003c/p\\u003e\\n \\u003cp\\u003eCoefficient\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDegree\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStress\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTopological\\u003c/p\\u003e\\n \\u003cp\\u003eCoefficient\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMYC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0582768\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.794871795\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.349836031\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e138\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e26742\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.29351722\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTERT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.005383064\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.588607595\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.565789474\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3436\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.366808914\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002085671\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.555223881\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.645625692\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2078\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.379328165\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.002255694\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.531428571\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.620168067\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1770\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.366969447\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSLC2A1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.19E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.52991453\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.866666667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e130\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.470769231\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTP1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.28E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.513812155\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.758169935\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e154\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.434144819\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.15E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.4781491\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.933333333\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.542944785\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec27\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.13 Identification of candidate drugs\\u003c/h2\\u003e\\n \\u003cp\\u003eDrug molecules are identified with 8 common DEGs and require the Enrichr tool. Enrichr web tool has different functions which are used for different purposes. In this case, drug data is collected from the DSigDB database. The results of the \\u003cem\\u003eP\\u003c/em\\u003e-value and adjusted \\u003cem\\u003eP\\u003c/em\\u003e-value have been taken for candidate drug design. The study reveals that the two medication compounds with the most significant gene interactions are styrene oxide CTD 00000717 and troglitazone CTD 00002415. Because these signature medicines were discovered for common DEGs, they define common pharmaceuticals for detecting cancer. Table \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e lists candidate medications from the DSigDB database for frequent DEGs.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab9\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eSuggested top 10 drug compounds for the Seven most cancer diseases\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eName of drugs\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdjusted \\u003cem\\u003eP\\u003c/em\\u003e-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOdds Ratio\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGenes\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStyrene oxide CTD 00000717\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.27E-11\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.94E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1175\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;MYC;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003etroglitazone CTD 00002415\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.92E-10\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.64E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e210.3043\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;TERT;MYC;\\u003c/p\\u003e\\n \\u003cp\\u003eGSTP1;SLC2A1;CXCR4;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMicrocystin LR CTD 00002422\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.29E-10\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.64E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e739.4444\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;MYC;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ebusulfan CTD 00005543\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.17E-09\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.30E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e525.1053\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;MYC;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003epaclitaxel CTD 00007144\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.84E-09\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.21E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e120.154\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;TERT;MYC;\\u003c/p\\u003e\\n \\u003cp\\u003eGSTP1;CXCR4;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChloroethylene CTD 00006592\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.06E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.33E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e749.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003etamoxifen CTD 00006827\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.07E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.20E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e72.34673\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCXCL12;TERT;MYC;GSTP1;SLC2A1;\\u003c/p\\u003e\\n \\u003cp\\u003eCXCR4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eglutathione CTD 00006035\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.40E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.38E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e150.4545\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;MYC;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOZONE CTD 00006460\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.52E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.00E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e342.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;GSTP1;GSTT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003echlorambucil CTD 00005626\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.81E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.00E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e332.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGSTM1;MYC;GSTP1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\u003cstrong\\u003e***Most Significant\\u003c/strong\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\u003cstrong\\u003e**Significant\\u003c/strong\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec28\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.14 Data Mining approach\\u003c/h2\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eString interaction analysis using data mining\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eTo do string interaction analysis, I first uploaded the string data to software called orange. In this case, I use the K-means clustering algorithm for data mining with the value of k as 3. In the image, C1, C2, C3 are the values of this k which are separated by clustering. The AverageShortestPathLength along the X-axis and the NeighborhoodConnectivity along the Y-axis are taken. C1 clustering has the most data and C3 has the least. 2.50\\u0026ndash;2.75 more data is available in this range. Data numbers are highest between 1.5 to 2.1 on the X-axis and 50\\u0026ndash;80 on the Y-axis.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePearson Correlation among string interaction analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eThe Pearson correlation technique is the most commonly used method for analyzing numerical variables. It returns a value ranging from \\u0026minus;\\u0026thinsp;1 to 1, where 0 indicates no correlation, 1 indicates total positive correlation, and \\u0026minus;\\u0026thinsp;1 indicates whole negative correlation. Below is the Pearson correlation of other features with eccentricity, and we can observe that the Pearson correlation of topological coefficient with positive eccentricity is positive. A positive correlation means that if variable A rises, so will variable B, whereas a negative correlation says that if A increases, B declines. In this table, A represents eccentricity, and B represents other traits. In addition, I utilize a ranker method to rank the desired eccentricity.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab10\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003ePearson correlation and evaluation of various features with Eccentricity\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSerial number\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003ePearson Correlation\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eValue\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEccentricity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNumberOfUndirectedEdges\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.622\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEccentricity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRadiality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.603\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEccentricity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStress\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.527\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEccentricity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTopologicalCoefficient\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e+\\u0026thinsp;0.073\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab11\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eSubmission of ranked algorithm for targeted Eccentricity\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSerial number\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFeatures name\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRReliefF\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIsSingleNode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNull\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTopologicalCoefficient\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.04302504089531061\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStress\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.08476774591738742\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRadiality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.058541136558620685\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNumberOfUndirectedEdges\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.08347787055064551\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNeighborhoodConnectivity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.05194071094998019\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDegree\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.08347787055064551\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClusteringCoefficient\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.07019668593188903\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClosenessCentrality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.06752249124196281\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBetweennessCentrality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.06309275104642066\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAverageShortestPathLength\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.05854114014530909\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eFrom the very beginning, the main goal of cancer research was to design possible drugs for BLC, BRC, ENC, CEC, TYC, BRC, and PRC. To achieve this goal, I have to go through different stages and use different methods of research to get the expected results. When I first collected the genes for selected cancers, the number of genes from the NCBI gene database was much higher. We then collect genes after using R programming to pre-process the genes, filtering and mining the genes. The PPI network analysis identified 829 nodes, 884 edges, and 7 seed nodes. To carry out the research, work is started on the top eight (TERT, MYC, GSTM1, GSTT1, GSTP1, CXCR4, CXCL12, and SLC2A1) genes that are common and they are highlighted so that knowledge about them can be acquired more effectively. Pathway analysis, gene regulatory networks, and co-expression networks are important contributors to research. The protein-drug and protein-chemical interaction network is used to identify the effect of drugs and chemical compounds interacting with a certain gene. By studying these genes, we determine multiple relationships, including how cells form a network with other genes, how biological properties, and how various biological functions work secretly. Finally, after completing all the research, all the information is used in the final stage to complete the main purpose of this study is to design the drug.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eWe use BLC, BRC, ENC, CEC, TYC, BRC, and PRC to study the various bioinformatics methods for sorting common genes in the main research subjects. We identify eight common genes from seven different cancers and require bioinformatics analysis. Similarly, we continue to design this drug identify potential drug targets for the discovery process, and keep this analysis ongoing. Our ultimate goal is to research and analyze effective drugs and find common genes for them and each common gene plays a different role in researching effective drugs. These procedures use gene intersection and filtering to determine the reactive common gene among specified disorders. Next, the PPI network for the shared genes is presented. Gene Ontology (GO) has been used to compute biological systems that provide complete models and again reduce the complexity of expression studies of biological processes. Enhances protein-drug interactions as a result of analysis of PPI networks. Topological results have been needed to unravel co-expression and path analysis in less complex ways. This study illustrates common genes and their network profiles, identifies how common genes interact with other genes, and designs candidate drugs.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eBLC = Bladder Cancer\\u003c/p\\u003e\\n\\u003cp\\u003eBRC = Breast Cancer\\u003c/p\\u003e\\n\\u003cp\\u003eENC = Endometrial Cancer\\u003c/p\\u003e\\n\\u003cp\\u003eCEC = Cervical Cancer\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTYC = Thyroid Cancer \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eBRC = Brain Cancer\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eAuthors’ contributions\\u003c/h2\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSiam Ahmed= Analysis, Data collection, Writing.\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSayed Asaduzzaman: Problem Formulation, Verification of Analysis , Supervision, Writing.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work did not receive any grant from funding agencies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of competing interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors are grateful to NCBI (National Center for Biotechnology Informatics) to provide Genetic information.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eSiegel, R. L., Miller, K. D., Fuchs, H. E., \\u0026amp; Jemal, A. (2022). Cancer statistics, 2022. \\u003cem\\u003eCA: a cancer journal for clinicians\\u003c/em\\u003e.\\u003c/li\\u003e\\n \\u003cli\\u003eSeiler, A., \\u0026amp; Jenewein, J. (2019). Resilience in cancer patients. \\u003cem\\u003eFrontiers in psychiatry\\u003c/em\\u003e, \\u003cem\\u003e10\\u003c/em\\u003e, 208.\\u003c/li\\u003e\\n \\u003cli\\u003eLenis, A. T., Lec, P. M., \\u0026amp; Chamie, K. (2020). Bladder cancer: a review. \\u003cem\\u003eJama\\u003c/em\\u003e, \\u003cem\\u003e324\\u003c/em\\u003e(19), 1980-1991.\\u003c/li\\u003e\\n \\u003cli\\u003eWaks, A. G., \\u0026amp; Winer, E. P. (2019). Breast cancer treatment: a review. \\u003cem\\u003eJama\\u003c/em\\u003e, \\u003cem\\u003e321\\u003c/em\\u003e(3), 288-300.\\u003c/li\\u003e\\n \\u003cli\\u003eCoughlin, S. S. (2019). Epidemiology of breast cancer in women. \\u003cem\\u003eBreast cancer metastasis and drug resistance\\u003c/em\\u003e, 9-29.\\u003c/li\\u003e\\n \\u003cli\\u003eLu, K. H., \\u0026amp; Broaddus, R. R. (2020). Endometrial cancer. \\u003cem\\u003eNew England Journal of Medicine\\u003c/em\\u003e, \\u003cem\\u003e383\\u003c/em\\u003e(21), 2053-2064.\\u003c/li\\u003e\\n \\u003cli\\u003eRaglan, O., Kalliala, I., Markozannes, G., Cividini, S., Gunter, M. J., Nautiyal, J., ... \\u0026amp; Kyrgiou, M. (2019). Risk factors for endometrial cancer: An umbrella review of the literature. \\u003cem\\u003eInternational journal of cancer\\u003c/em\\u003e, \\u003cem\\u003e145\\u003c/em\\u003e(7), 1719-1730.\\u003c/li\\u003e\\n \\u003cli\\u003eVu, M., Yu, J., Awolude, O. A., \\u0026amp; Chuang, L. (2018). Cervical cancer worldwide. \\u003cem\\u003eCurrent problems in cancer\\u003c/em\\u003e, \\u003cem\\u003e42\\u003c/em\\u003e(5), 457-465.\\u003c/li\\u003e\\n \\u003cli\\u003eCohen, P. A., Jhingran, A., Oaknin, A., \\u0026amp; Denny, L. (2019). Cervical cancer. \\u003cem\\u003eThe Lancet\\u003c/em\\u003e, \\u003cem\\u003e393\\u003c/em\\u003e(10167), 169-182.\\u003c/li\\u003e\\n \\u003cli\\u003ePrete, A., Borges de Souza, P., Censi, S., Muzza, M., Nucci, N., \\u0026amp; Sponziello, M. (2020). Update on fundamental mechanisms of thyroid cancer. \\u003cem\\u003eFrontiers in Endocrinology\\u003c/em\\u003e, \\u003cem\\u003e11\\u003c/em\\u003e, 102.\\u003c/li\\u003e\\n \\u003cli\\u003eHaymart, M. R., Banerjee, M., Reyes-Gastelum, D., Caoili, E., \\u0026amp; Norton, E. C. (2019). Thyroid ultrasound and the increase in diagnosis of low-risk thyroid cancer. \\u003cem\\u003eThe Journal of Clinical Endocrinology \\u0026amp; Metabolism\\u003c/em\\u003e, \\u003cem\\u003e104\\u003c/em\\u003e(3), 785-792.\\u003c/li\\u003e\\n \\u003cli\\u003eBrunese, L., Mercaldo, F., Reginelli, A., \\u0026amp; Santone, A. (2020). An ensemble learning approach for brain cancer detection exploiting radiomic features. \\u003cem\\u003eComputer methods and programs in biomedicine\\u003c/em\\u003e, \\u003cem\\u003e185\\u003c/em\\u003e, 105134.\\u003c/li\\u003e\\n \\u003cli\\u003eTang, W., Fan, W., Lau, J., Deng, L., Shen, Z., \\u0026amp; Chen, X. (2019). Emerging blood\\u0026ndash;brain-barrier-crossing nanotechnology for brain cancer theranostics. \\u003cem\\u003eChemical Society Reviews\\u003c/em\\u003e, \\u003cem\\u003e48\\u003c/em\\u003e(11), 2967-3014.\\u003c/li\\u003e\\n \\u003cli\\u003eRawla, P. (2019). Epidemiology of prostate cancer. \\u003cem\\u003eWorld journal of oncology\\u003c/em\\u003e, \\u003cem\\u003e10\\u003c/em\\u003e(2), 63.\\u003c/li\\u003e\\n \\u003cli\\u003eTaz, T. A., Kawsar, M., Paul, B. K., \\u0026amp; Ahmed, K. (2020). Computational analysis of regulatory genes network pathways among devastating cancer diseases. \\u003cem\\u003eJournal of Proteins and Proteomics\\u003c/em\\u003e, \\u003cem\\u003e11\\u003c/em\\u003e(1), 63-76.\\u003c/li\\u003e\\n \\u003cli\\u003eKawsar, M., Taz, T. A., Paul, B. K., Mahmud, S., Islam, M. M., Bhuyian, T., \\u0026amp; Ahmed, K. (2020). Analysis of gene network model of Thyroid Disorder and associated diseases: A bioinformatics approach. \\u003cem\\u003eInformatics in Medicine Unlocked\\u003c/em\\u003e, \\u003cem\\u003e20\\u003c/em\\u003e, 100381.\\u003c/li\\u003e\\n \\u003cli\\u003eIslam, M. N., Shaolin, S. S., Paul, B. K., Islam, M. M., Bhuyian, T., \\u0026amp; Ahmed, K. (2020). Mining and predicting protein-drug interaction network of breast cancer risk genes. \\u003cem\\u003eGene Reports\\u003c/em\\u003e, \\u003cem\\u003e20\\u003c/em\\u003e, 100753.\\u003c/li\\u003e\\n \\u003cli\\u003eSubramanian A, Tamayo P, Mootha VK, \\u003cem\\u003eet al.\\u0026nbsp;\\u003c/em\\u003eGene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. \\u003cem\\u003eProc Natl Acad Sci\\u003c/em\\u003e2005;\\u003cstrong\\u003e102\\u003c/strong\\u003e(43):15545\\u0026ndash;50.\\u003c/li\\u003e\\n \\u003cli\\u003eDoms A, Schroeder M. GoPubMed: exploring PubMed with the gene ontology. \\u003cem\\u003eNucleic Acids Res\\u0026nbsp;\\u003c/em\\u003e2005;\\u003cstrong\\u003e33\\u003c/strong\\u003e(suppl_2): W783\\u0026ndash;6.\\u003c/li\\u003e\\n \\u003cli\\u003eKanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. \\u003cem\\u003eNucleic Acids Res\\u0026nbsp;\\u003c/em\\u003e2000;\\u003cstrong\\u003e28\\u003c/strong\\u003e(1):27\\u0026ndash;30.\\u003c/li\\u003e\\n \\u003cli\\u003eChen EY, Tan CM, Kou Y, \\u003cem\\u003eet al.\\u0026nbsp;\\u003c/em\\u003eEnrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.\\u003cem\\u003eBMC Bioinformatics\\u0026nbsp;\\u003c/em\\u003e2013;\\u003cstrong\\u003e14\\u003c/strong\\u003e(1):128.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Gene Analysis, PPI Network, Drug Design, Bioinformatics, Female Disease\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7340632/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7340632/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground \\u0026amp; objective\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBladder cancer (BLC), breast cancer (BRC), endometrial cancer (ENC), cervical cancer (CEC), thyroid cancer (TYC), brain cancer (BRC), and prostate cancer (PRC) are all prevalent diseases in women. The fatality rate from these cancers is extremely high. When a woman suffers from one of these disorders, her chances of contracting the others rise. It is also discovered that many common genetic variables are interconnected among various disorders, as demonstrated in the works. The primary goal is to eliminate the most prevalent gene targets among BLC, BRC, ENC, CEC, TYC, BRC, and PRC illnesses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethod\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe preprocessing and filtering procedure results in a reduction in generation. Protein-protein interaction (PPI) networks for seven seed genes are divided into two categories: generic PPI and tissue-specific PPI. Finally, topological analysis identifies eight common genes required for the examination of pathways, gene regulatory networks (GRN), co-expression, and physical interaction networks. Gene ontology analysis provides a better knowledge of biological processes, cellular components, and molecular functions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe can see from the research that the Interaction of proteins with drug molecules plays a role in the design of effective medicine. These drugs can then be considered in real life by further research and verification through various chemical experiments.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFuture studies will analyse the structure of those genes to help take precautionary measures. The outcomes of the study will help in the development of future medications for cancer diseases.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Network Analysis of Genes and Identification of Candidate Drug Compound for Associated Deadly Diseases of Female\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-08-19 08:31:32\",\"doi\":\"10.21203/rs.3.rs-7340632/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"fa8f0f1b-6237-40cc-8926-67199634db66\",\"owner\":[],\"postedDate\":\"August 19th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-08-23T21:38:19+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-08-19 08:31:32\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7340632\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7340632\",\"identity\":\"rs-7340632\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}