Deciphering crucial genes in triple negative breast cancer and non-alcoholic fatty liver disease pathogenesis and drug repurposing: A systems biology approach

Deciphering crucial genes in triple negative breast cancer and non-alcoholic fatty liver disease pathogenesis and drug repurposing: A systems biology approach | 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 Deciphering crucial genes in triple negative breast cancer and non-alcoholic fatty liver disease pathogenesis and drug repurposing: A systems biology approach Mohammad-Mahdi Bastan, Amir Hossein Behnoush, Amirmohammad Khalaji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6185733/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: It has been widely recognized that triple negative breast cancer (TNBC) and non-alcoholic fatty liver disease (NAFLD) are related, but underlying mechanisms remain unclear. This study investigated the possible co-pathogenesis and prognostic connections between TNBC and NAFLD and relevant hub genes associated with them. Aim: Using a systems biology approach, we identify crucial genes that contribute to TNBC and NAFLD to investigate new biomarkers and propose new medicines. Methods: Cytoscape was used to construct protein-protein interaction (PPI) networks and functional enrichment analysis to determine which molecules were crucial. Disease genes from the DisGeNET and STRING databases were used to construct disease networks. A network of gene-drug interactions and gene-disease associations was also created for the purpose of suggesting drugs and mapping diseases. Results: Using the STRING database, 343 common genes between TNBC and NAFLD were used to construct a PPI network. This network has 182 nodes and 2591 edges and 3 clusters along with 26 hub-bottleneck genes. Enrichment of these gens lead to recognition of locomotion, membrane-enclosed lumen, molecular function regulator, and pathways in cancer as top biological process, cellular component, molecular function, and pathway, respectively. Drug-gene analysis revealed that Cisplatin, carboplatin, sorafenib, cetuximab, paclitaxel, gemcitabine, and etoposide have the highest degree of interaction with key genes. Conclusion: In silico data analysis approaches indicated that TNBC and NAFLD share common genes and signaling pathways. Additionally, we identified key drugs that target both TNBC and NAFLD genes. Systems biology bioinformatics analysis hub genes drug biomarkers protein-protein interaction (PPI) triple-negative breast cancer (TNBC) non-alcoholic fatty liver disease (NAFLD) Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Globally, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related deaths in women ( 1 ). Breast cancer risk is influenced by a variety of factors, including hereditary factors like mutations, gender, race, and age. In addition, there are non-hereditary factors like obesity, nutritional patterns, and unhealthy lifestyles ( 2 ). Globally, the high prevalence of obesity has been correlated with a surge in patients with metabolic syndrome and certain cancers ( 3 ). Furthermore, a positive association between breast cancer particularly triple-negative breast cancer (TNBC), and metabolic syndrome was found ( 4 ). Components of metabolic syndrome (i.e., abdominal obesity, hyperlipidemia, insulin resistance, and hypertension) have been closely connected to non-alcoholic fatty liver disease (NAFLD) ( 5 ). The influence of these two diseases on breast cancer may not be entirely separated. However, liver malfunction resulting from NAFLD leads to systemic metabolic instability, including sexual hormone exposure and nutrition rewiring ( 6 , 7 ). NAFLD often develops in patients with breast cancer during their illness and its association with breast cancer exists regardless of established risk factors ( 8 ). Particularly, hormone replacement therapy among patients with breast cancer may cause higher rates of developing NAFLD ( 9 ). Histologically, patients with fatty liver and breast cancer have a significantly lower risk of liver metastasis than those with normal livers ( 10 ). Conversely, breast cancer is one of the most common extrahepatic complications of NAFLD ( 11 ). It should be noted that these studies have largely been observational and that there remains considerable uncertainty regarding the mechanism linking NAFLD and TNBC. Thus, it is clinically important to investigate molecular mechanisms enabling earlier diagnosis and treatment. Advances in sequencing technology and bioinformatics provide comprehensive therapeutic and diagnostic tools to investigate the interaction between different diseases. We can also study the specific pathogenesis of each disease at the gene and protein levels. To investigate the underlying mechanisms for both NAFLD and TNBC, we examined the common genes of both diseases. We identified candidate hub genes that may play a role in causing both diseases. Eventually, the signaling pathways were enriched to uncover the common mechanisms for both diseases. 2. Methods 2.1. Overview DisGeNET is an open-source discovery platform containing thousands of genes and variants associated with human diseases ( 12 ). The DisGeNET database was used to export NAFLD and TNBC genes to construct PPI networks. Based on many databases, like Mint, BioGrid, and others, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) predicts PPI ( 13 ). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations are also available for the most accurate annotation of protein interactions. Through submission of gene lists to the STRING database, we constructed NAFLD and TNBC networks and performed network analysis using Cytoscape ( 14 ). In a network, nodes (such as proteins or genes) interact with links/edges (such as physical interactions or co-expression relationships). A network's topology is determined by two centrality parameters: degree and betweenness. High-degree nodes are termed hubs, and those with a high degree of betweenness are called bottlenecks. Hub-bottleneck nodes are nodes that are simultaneously hubs and bottlenecks ( 15 ). 2.2. MCODE analysis Molecular complex detection (MCODE) was applied to identify significant modules in the PPI network ( 16 ). It is an algorithm that determines closely connected proteins in large PPI through graph-theoretic clustering. Based on MCODE parameters, dense regions are isolated according to specified structures. MCODE uses protein interaction data to predict molecular complexes, resulting in reliable and accurate functional annotations. Vertex weighting is calculated based on the density of local neighborhoods. It is followed by a traversal in an outward direction within a seed protein with a dense local neighborhood. 2.3. Functional analysis Database for Annotation, Visualization, and Integrated Discovery (DAVID) was utilized to enrich the hub bottleneck genes as a gene set to produce functional annotation ( 17 ). The DAVID gene clustering method is a single-linkage method that uses a combination of gene sequences from multiple genomic libraries. Furthermore, it incorporates data visualization and enrichment analysis in addition to retrieving, displaying, and analyzing gene sets. Gene ontology (GO) analyses were executed for pathway analysis (using KEGG), cellular component, molecular function, and biological process. We reported top the ten annotations based on their P value for hub-bottleneck genes and cluster 1 genes. Gene multiple association network integration algorithm (GeneMANIA) was utilized to provide additional functional analyses. Using this tool, genomic and proteomic data is examined based on a query list to identify functionally similar genes ( 18 ). 2.4. Genetic Association of Diseases Analysis In an effort to acquire a better perspective of underlying mechanisms and determine connections with other diseases, we assessed the association between TNBC and NAFLD and other diseases by employing the Genetic Association Database (GAD), which is a disease tool of the DAVID database ( 19 ). 2.5. Drug-gene network Based on currently FDA-approved drugs registered in DrugBank, the study's hub-bottleneck genes were utilized to construct the drug-gene network. With the Drug-Gene Interaction Database, the designated hub-bottleneck genes were chosen as targets for the repurposing of novel drug candidates ( 20 ). An analysis of drug-gene interactions was conducted using Cytoscape. A network analysis technique was used, and node degree scores were estimated for each node within the network. 3. Result 3.1. Gene expression analysis The number of genes extracted from DisGeNET for TNBC and NAFLD was 1674 and 1058, respectively (Figure. 1). A total of 343 genes were shared between the two lists, and they were referred to as common genes. 3.2. Topological network analysis Utilizing the STRING database, the common genes network was constructed with 303 nodes and 7690 edges (Figure. 2). To detect hub and bottleneck genes, the common genes network was analyzed. The network contained hub-bottleneck genes with high affinity and association. As a result, AKT1, TP53, TNF, ACTB, IL1B, IL6, STAT3, EGFR, MYC, ALB, BCL2, HIF1A, CTNNB1, JUN, PPARG, MMP9, CASP3, TGFB1, MAPK3, ESR1, EGF, STAT1, CCL2, PTEN, HSP90AA1, and SIRT1 were identified as hub-bottlenecks (Table. 1). 3.3. Functional analysis By using the DAVID database, hub-bottleneck genes were analyzed for gene ontology. Different biological processes, including locomotion, multi-organism process, immune system process, and developmental process are involved (Table. 2). In addition, membrane-enclosed lumen, macromolecular complex, organelle part, and extracellular region part are critical parts of cellular components (Table. 3). Pathway analysis revealed pathways in cancer, AGE-RAGE signaling pathway in diabetic complications, proteoglycans in cancer, and lipid and atherosclerosis as key pathways (Table. 4). Moreover, molecular function regulator, nucleic acid binding transcription factor activity are main participants in molecular functions (Table. 5 ) According to GeneMANIA's categorized biological functions, JUN, PPARG, and TNF appear to be the most significant hub-bottleneck genes. Relevance detection algorithms in GeneMANIA calculate the weight of the link based on a gene's score and the size of its nodes. As defined by GeneMANIA, nodes are colored in accordance with their biological function, with hub genes colored in accordance with their significant biological functions (Figure. 3). 3.4. MCODE analysis and GO MCODE and GO analysis was conducted on the network of common genes and the top three clusters based on scores were extracted (Figure S1-3). DAVID database was used to enrich genes derived from Cluster 1 genes which were extracted from MCODE modules. In Cluster 1, the leading biological processes detected are the immune system, multi-organism processes, developmental processes, and locomotion processes (Table. S1). Furthermore, key cellular components have been determined, including membrane-enclosed lumen, extracellular region, extracellular region part, and macromolecular complexes (Table. S2). Additionally, KEGG pathway reports indicated that cluster 1 genes play a significant role in Pathways in cancer, Lipids and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and IL-17 signaling pathway (Table. S3). Several molecular functions were identified as significant, such as molecular function regulator, signal transducer activity, binding, and nucleic acid binding transcription factor activity (Table. S4). 3.5. Gene-disease assessment According to a DAVID analysis that was carried out with the GAD database, hub-bottleneck genes are associated with breast cancer, hepatocellular carcinoma, ovarian cancer, and colorectal cancer (Table. 6). 3.6. The drug-gene analysis The Network of interaction Analysis was performed using Cytoscape software as a method of analyzing the possible interactions between the hub-bottleneck genes identified by this study and potential drugs that have been approved based on their interaction network. The analysis resulted in 22 nodes linked with 43 edges. Cisplatin, carboplatin, sorafenib, cetuximab, paclitaxel, gemcitabine, and etoposide were drugs with high degrees (Figure. 4). 4. Discussion In this study, a systems biology approach was used to examine common genes associated with TNBC and NAFLD. In order to Achieve an understanding of cells' system-level functioning, we analyzed PPI, hub-bottleneck genes, and modules in the network. It has been shown that measures such as betweenness centrality and degree are crucial in identifying potential targets for drugs and understanding how genes are interconnected ( 19 ). Comfortability is defined as the co-occurrence of two or more disorders in one person. Thus, characterizing genes and pathways underlying related diseases would aid in identifying patterns of comfortability ( 21 ). In numerous studies, the potential connection between TNBC and NAFLD has been extensively explored ( 8 – 11 ). In this paper, we discuss molecules that are crucial to understanding TNBC and NAFLD pathogenesis based on their high value in various analyses. The top explored hub-bottleneck genes with the highest combined degree and betweenness centrality were AKT1, TP53, EGFR, and ACTB. TP53 codes P53 protein. P53 regulates metabolic homeostasis by directing catabolic over anabolic processes, and by reducing NADPH levels by inhibiting G6PD in the pentose phosphate pathway. Mutant P53 proteins confer new oncogenic metabolic functions ( 22 ). Mutant P53 promotes anabolism by inducing SREBPs and lipogenic enzymes, and suppresses catabolism by inhibiting AMPK, thereby promoting lipid synthesis, glycolysis, and nucleotide production ( 23 – 25 ). This is a key mechanism by which mutant P53 facilitates metabolic reprogramming during tumorigenesis. The tumor suppressor P53 regulates apoptosis and cell cycle arrest and is involved in molecular mechanisms underlying hepatocellular injury and lipid metabolism ( 26 , 27 ). Under normal conditions, P53 regulates genes involved in lipid metabolism, such as ceramide synthesis, fatty acid metabolism, lipid absorption, and lipoprotein metabolism, and enhances lipid catabolism while inhibiting anabolism ( 28 , 29 ). It also reduces PPARG activity, adipocyte differentiation and lipid accumulation ( 30 – 32 ). But under stress, P53 upregulation can trigger insulin resistance, inflammation, and oxidative stress ( 28 ). Regulatory roles of P53 by modulating target genes may introduce P53 as a valuable therapeutic indicator for predicting and diagnosing NAFLD. AKT promotes fat accumulation by activating SREBP-1c, a major lipid metabolism regulator ( 33 ). AKT might thus aid proliferation by increasing the raw materials required for membrane synthesis, cholesterol, and fatty acids ( 34 ). Lipids might augment cancer development in several ways. For instance, the role of lipids in prostate cancer development is executed by stiffening the mitochondrial membrane, disrupting oxidative phosphorylation, and preferring glycolysis over oxidative phosphorylation for energy ( 33 ). AKT1 regulates growth, AKT2 regulates metabolism, and AKT3 regulates brain development. It is conceivable that AKT1 may regulate NAFLD susceptibility ( 35 ). The metabolic syndrome components are associated with AKT1 polymorphisms in genotyping experiments ( 36 ). Furthermore, AKT1's role in breast cancer is stage-dependent. Regarding tumor growth, AKT1 is involved in the initiation and progression of breast cancer. It has been shown to play a crucial role in the In vitro and in vivo proliferative and tumor-initiating effects. In contrast, breast cancer AKT1 is an anti-metastatic protein, and loss of it in the primary tumor could result in metastasis ( 37 ). This dichotomy was previously confirmed by observing a Breast cancer mouse model with suppressed AKT1 exhibits an increase in metastasis but a decrease in tumor growth ( 38 ). EGFRs are expressed in many malignancies, including head and neck, esophageal, lung, and breast cancer ( 39 ). EGFR is a member of the ErbB family of receptors that induces the formation of homodimers and heterodimers and activates a signaling pathway that controls cell proliferation, survival, and migration at the downstream level ( 40 ). Growth factor receptors such as EGFR and IGF1R can cause a lack of response to tamoxifen by activating the MAPK and PI3K signaling pathways ( 41 , 42 ). In response to receptor dimerization triggered by ligands, EGFR signaling activates several signaling pathways, such as AKT, STAT, PI3K, and PLCG1. Proliferation, angiogenesis, migration, survival, and adhesion are all affected by these pathways ( 43 , 44 ). In the context of partial hepatectomy, EGFR provides a major contribution to regulating liver regeneration ( 45 , 46 ). Mice fed a fast-food diet showed remarkable reductions in steatosis, liver injury, and fibrosis when EGFR inhibition was administered ( 47 ). When NAFLD occurs, EGFR modulates key transcription factors involved in lipid metabolism, including PPARG, SREBF1, ChREBP, and HNF4α ( 47 ). There is no clear understanding of the signaling pathways downstream of EGFR. However, one study indicates that AKT signaling appears to be critical for the regulation of lipogenic transcription factors during NAFLD development ( 48 ). ACTB is a housekeeping gene that has been found to be related to many cancers ( 49 ). ACTB encodes the β-actin protein, which is predominantly expressed in non-muscle cells and aids in building and moving the cytoskeleton. As a regulatory protein, ACTB has many functions, including regulating cell growth, regulating cell migration, controlling differentiation, and transmitting signals ( 50 ). ACTB is abnormally expressed and polymerized in tumors, and this results in an increased rate of tumor formation, invasion, and metastasis ( 51 – 54 ). For instance, hepatocellular carcinoma has also been associated with high ACTB mRNA levels. This may be related to the overexpression of ACTB in tumor cells ( 51 , 52 ). As ACTB mRNA delocalized from the leading edges of cancer cells, cell polarity, and directional motion were lost as a result of accumulating ACTB mRNA. Moreover, significant upregulation of ACTB expression was observed in highly invasive tumor cell lines, confirming that ACTB takes part in the metastatic process ( 52 – 54 ). In the liver, actin expression level increased with the progression of fibrosis in the liver ( 55 ). Hepatocellular carcinoma and breast cancer are among the most relevant diseases associated with TNBC and NAFLD. There is less than a three-year median survival rate after metastatic breast cancer occurs in the liver in approximately 50–70% of cases ( 56 , 57 ). Metastases in the liver can lead to sudden hepatocellular failure, refractory ascites, and portal vein thrombosis, as well as nutritional deficiencies ( 58 ). On the other hand, hyperexpression of hepatic FGF21 causes breast cancer progression by impairing the anti-apoptotic properties of breast cancer cells in the presence of high-fat diet-induced NAFLD ( 59 ). Also, NAFLD predisposes patients to hepatocellular carcinoma both with and without cirrhosis ( 60 ). The DAVID database lists locomotion as the top biological process and Pathways in cancer as the top pathway. NAFLD promotes liver metastasis by promoting tumor-induced lipid deposits by activating reciprocal lipolysis of triglycerides between juxtaposed hepatocytes. During mitochondrial oxidation, lipolytic products are metabolized by cancer cells via FATP1, promoting tumor growth ( 61 ). The proliferation of cells in the mammary glands during pregnancy is facilitated by IL-4 and IL-13, leading to the expansion of acini with incompletely filled lumens. Moreover, the tyrosine phosphorylation of insulin receptor substrate-1 by IL-4 and IL-13 enhances cell proliferation ( 62 ). Two known chain alpha-receptors, IL13R1 and IL13R2, bind to IL-13, and each activates a different signaling cascade ( 63 ). The IL13R1 pathway promotes liver fibrosis by activating the Janus kinase signal transducer ( 64 ). Macrophages can be differentiated into two primary subtypes, namely classically activated (M1) and alternatively activated (M2) macrophages. IL-4 and IL-13 can activate M2 macrophages. Angiogenesis, immune regulation, tissue remodeling, parasitic infection prevention, and tumorigenesis are all processes controlled by M2 macrophages, which produce anti-inflammatory cytokines such as IL-10 and TGF- β ( 65 , 66 ). In NAFLD-associated fibrosis, simultaneous inhibition of IL-13 and TGF-β signaling attenuates the fibrotic machinery more effectively than isolated inhibition of TGF-β ( 67 ). The M1 macrophage induces pro-inflammatory T helper type 1 responses through its inflammatory responses and secretion of cytokines such as IL-1β, TNF-a, and IL-12 ( 68 ). Also, Type 2 immunity is Distinguished by enhanced production of IL-13, which is important in liver fibrogenesis of different etiologies ( 69 , 70 ). Furthermore, overexpression of IL-13 in activated hepatic stellate cells has a role in progressive NASH fibrosis ( 63 ). This is confirmed by an elevation in serum levels of IL-13 and a rise in IL-13RA2 expression. It has also been suggested that IL-13 upregulation may contribute to hepatocellular carcinoma development in NAFLD ( 71 ). Inflammation and fibrosis of the liver are caused by the activation of liver macrophages. Depending on the microenvironment, macrophages exhibit remarkable plasticity and polarize into different phenotypes ( 72 ). A variety of macrophage functions are modulated by the PI3K/AKT pathway, including survival, migration, proliferation, metabolic responses, and inflammatory responses ( 73 ). An activated PI3K/AKT pathway promotes the production of anti-inflammatory cytokines, thereby facilitating the healing of injured tissues and the resolution of inflammation. The opposite could also occur if PI3K/AKT signaling is inhibited, which would add to liver damage by enhancing M1-like phenotypes ( 73 ). Chronic liver diseases can be treated by targeting the PI3K/AKT pathway for controlling macrophage polarization and activity ( 74 ). Interestingly, PI3K/AKT pathway signal transduction may be impaired by fat accumulation in the liver, resulting in activation of the apoptosis pathway in the mitochondrial membrane ( 75 ). In breast cancer cells, the PI3K/AKT/mTOR signaling pathway plays a key role in growth, survival, and motility ( 76 ). Different mechanisms cause the PI3K/AKT/mTOR pathway to be dysregulated in breast cancer, resulting in excessive PI3K activity and/or impairment of PI3K inhibitory functions along with mutations in tumor suppressor genes like PTEN and INPP4B ( 77 ). Among the PI3K genes, PIK3CA and PIK3CB are frequently altered ( 77 , 78 ). More than one-third of early breast cancer tumors have mutations in the catalytic domain of PIK3CA, and PIK3CB stimulates breast cancer cell proliferation, invasion, and tumorigenesis ( 79 ). When PI3KCA and PI3KCB are inhibited simultaneously, greater antitumor efficacy is obtained in breast cancers with PIK3CA mutants, because PI3KCB accumulates PIP3 and reactivates AKT in breast cancers treated with PI3KCA-specific inhibitors ( 80 ). We found cisplatin, carboplatin, sorafenib, cetuximab, paclitaxel, gemcitabine, and etoposide in our drug gene association study. NAFLD is linked to hepatitis and chemotherapy-associated steatohepatitis because of mitochondrial function changes. Some treatments induce steatosis by reducing fat oxidation, resulting in a rise in reactive oxygen species in the liver. As a result of mitochondrial dysfunction, or a carnitine shuttle blockage from transporting medium and long-chain fatty acids from the fat matrix into the mitochondrial matrix, hepatic steatosis or liver dysfunction results ( 81 ). Cisplatin hepatotoxicity appears to be caused by inflammatory responses in the liver and oxidative stress. It is possible that the gut microbiota and their metabolites contribute to oxidative stress and inflammation. It was determined that antibiotic treatment prevented cisplatin hepatotoxicity and improved liver function. Thus, Hepatotoxicity may be caused by altered gut microbiota due to cisplatin and dysbiosis of metabolites derived from the microbiota ( 82 ). Furthermore, by upregulating genes responsible for lipid oxidation and hydrolysis, sorafenib reduced the viability of fatty spheroids. Sorafenib also inhibited steatosis-induced fibrogenesis by reducing the contents of TNF-α, TGF-β1, and IL-6 in fatty spheroids. Based on in vitro evidence demonstrating sorafenib's antifibrotic properties, it has the potential for therapeutic use in NAFLD/NASH patients ( 83 ). Additionally, as a result of paclitaxel treatment in breast cancer, 26.7% developed hepatic steatosis. The change in liver function was mostly evident on cholestatic liver tests ( 84 ). Moreover, etoposide-containing regimens have been linked to faster progression to steatosis, despite similar baseline risk factors. This suggests that etoposide treatment may increase the likelihood and speed of developing hepatic steatosis compared to other chemotherapy drugs ( 85 ). 5. Strength and limitations Through the use of advanced bioinformatics technology, we investigated the genetic relationship between NAFLD and TNBC. A functional annotation of genes was conducted along with the detection of common genes and their associated pathways. For the first time, drugs associated with common TNBC and NAFLD genes were identified in this study. Despite these findings, several limitations exist. Although multiple studies have explained the role of these genes, there have been no experiments to validate the key pathways and hub genes identified. Eventually, prospective clinical studies are imperative to confirm the causality between hub genes and prognosis in patients with TNBC or NAFLD. 6. Conclusions Using a network-based approach, the current study determined the underlying factors and mechanisms contributing to the development, diagnosis, and treatment of TNBC and NAFLD based on an analysis of various data. According to our findings, TNBC and NAFLD may share similar genetic mechanisms, as evidenced by a strong gene expression correlation that indicates that these diseases may share some genetic characteristics. The results of our study also suggest that there is a link between treatment approaches for TNBC and the progression of NAFLD. 7. Abbreviation AMPK AMP-activated Protein Kinase ChREBP Carbohydrate-responsive element binding protein DAVID Database for Annotation, Visualization, and Integrated Discovery EGFR Epidermal Growth Factor Receptor ErbB Erythroblastic Leukemia Viral Oncogene FATP1 Fatty Acid Transporter Protein 1 GAD Genetic Association Database GeneMANIA Gene multiple association network integration algorithm HNF4α Hepatocyte nuclear factor 4 alpha IGF1R Insulin-like Growth Factor 1 IL Interleukin INPP4B Inositol Polyphosphate 4-phosphatase Type II KEGG Kyoto Encyclopedia of Genes and Genomes MAPK Mitogen-activated Protein Kinase MCODE Molecular complex detection NAFLD Non-alcoholic Fatty Liver Disease PI3K Phosphatidylinositol 3-kinases PIK3CA Phosphatidylinositol-4,5-bisphosphate 3-kinase Catalytic Subunit Alpha PIK3CB Phosphatidylinositol-4,5-bisphosphate 3-kinase Catalytic Subunit Beta PLCG1 phospholipase C gamma protein PPARG Peroxisome Proliferator-activated Receptor Gamma PPI Protein-protein interactions PPI Protein-protein Interaction SREBF1 Sterol Regulatory Element Binding Transcription Factor 1 SREBP Sterol regulatory element binding protein STAT Signal Transducer and Activator of Transcription STRING Search Tool for the Retrieval of Interacting Genes/Proteins TNBC Triple Negative Breast Cancer Declarations Ethical Approval and Consent to participate No individual data were used in this paper and the information is based on aggregated pre-existing online secondary data. 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Journal of translational medicine. 2021;19:1-13. Romualdo GR, Da Silva TC, de Albuquerque Landi MF, Morais JÁ, Barbisan LF, Vinken M, et al. Sorafenib reduces steatosis‐induced fibrogenesis in a human 3D co‐culture model of non‐alcoholic fatty liver disease. Environmental toxicology. 2021;36(2):168-76. Inci F, Karatas F. Paclitaxel-induced hepatic steatosis in patients with breast cancer. J BUON. 2019;24:2355-60. Ben‐Yakov G, Alao H, Haydek JP, Fryzek N, Cho MH, Hemmati M, et al. Development of Hepatic Steatosis After Chemotherapy for Non‐Hodgkin Lymphoma. Hepatology communications. 2019;3(2):220-6. Tables Table 1 . The hub-bottleneck genes with significant centrality values based on Degree and Betweenness. Hub-bottleneck Genes Degree Betweenness Centrality AKT1 205 0.050921633 TP53 198 0.04871762 TNF 190 0.027017316 ACTB 187 0.028816976 IL1B 184 0.023760252 IL6 184 0.024107663 STAT3 178 0.021918688 EGFR 171 0.030249858 MYC 167 0.023875871 ALB 166 0.027669886 BCL2 158 0.013471527 HIF1A 158 0.018912477 CTNNB1 155 0.032878814 JUN 154 0.012653694 PPARG 149 0.019648152 MMP9 148 0.021660477 CASP3 147 0.012383543 TGFB1 146 0.012133513 MAPK3 140 0.012019791 ESR1 131 0.012099128 EGF 131 0.013441009 STAT1 131 0.008932601 CCL2 129 0.009575273 PTEN 129 0.011264034 HSP90AA1 124 0.018739192 SIRT1 123 0.010167564 Table 2 . Gene ontology annotation of biological process for the hub-bottleneck genes Term Count P Value Genes FDR GO:0040011 locomotion 1.14E-15 JUN, HSP90AA1, TGFB1, EGF, STAT3, PTEN, HIF1A, SIRT1, TNF, MMP9, EGFR, ACTB, IL6, MYC, IL1B, BCL2, CCL2, AKT1, CTNNB1, PPARG, MAPK3 2.40E-14 GO:0051704 multi-organism process 5.28E-15 JUN, HSP90AA1, TGFB1, STAT1, STAT3, PTEN, HIF1A, ESR1, SIRT1, TNF, MMP9, EGFR, IL6, MYC, IL1B, CASP3, BCL2, CCL2, AKT1, CTNNB1, TP53, MAPK3 5.55E-14 GO:0002376 immune system process 1.27E-13 JUN, HSP90AA1, TGFB1, STAT1, STAT3, HIF1A, ESR1, SIRT1, TNF, MMP9, ACTB, IL6, MYC, IL1B, CASP3, BCL2, CCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3 8.87E-13 GO:0032502 developmental process 1.63E-10 PTEN, HIF1A, TNF, EGFR, ACTB, MYC, CASP3, CCL2, AKT1, MAPK3, JUN, HSP90AA1, TGFB1, EGF, STAT1, STAT3, MMP9, ESR1, SIRT1, IL6, IL1B, BCL2, CTNNB1, PPARG, TP53 8.57E-10 GO:0032501 multicellular organismal process 3.18E-10 PTEN, HIF1A, TNF, EGFR, ACTB, MYC, CASP3, CCL2, AKT1, MAPK3, JUN, HSP90AA1, TGFB1, EGF, STAT1, STAT3, MMP9, ESR1, SIRT1, IL6, IL1B, ALB, BCL2, CTNNB1, PPARG, TP53 1.34E-09 GO:0040007 growth 5.93E-10 HSP90AA1, TGFB1, STAT3, PTEN, HIF1A, ESR1, SIRT1, EGFR, BCL2, AKT1, CTNNB1, PPARG, TP53 2.07E-09 GO:0022414 reproductive process 1.33E-09 TGFB1, STAT3, PTEN, HIF1A, ESR1, SIRT1, MMP9, EGFR, MYC, IL1B, CASP3, BCL2, AKT1, CTNNB1, PPARG 3.59E-09 GO:0000003 reproduction 1.37E-09 TGFB1, STAT3, PTEN, HIF1A, ESR1, SIRT1, MMP9, EGFR, MYC, IL1B, CASP3, BCL2, AKT1, CTNNB1, PPARG 3.59E-09 GO:0051179 localization 3.83E-09 JUN, HSP90AA1, TGFB1, EGF, STAT3, PTEN, HIF1A, ESR1, SIRT1, TNF, MMP9, EGFR, ACTB, IL6, MYC, IL1B, ALB, BCL2, CCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3 8.38E-09 GO:0023052 signaling 3.99E-09 JUN, HSP90AA1, TGFB1, STAT1, EGF, STAT3, PTEN, HIF1A, ESR1, SIRT1, TNF, MMP9, EGFR, IL6, MYC, IL1B, CASP3, BCL2, CCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3 8.38E-09 Table 3 . Gene ontology annotation of cellular component for the hub-bottleneck genes Term Count P Value Genes FDR GO:0031974 membrane-enclosed lumen 4.51E-09 JUN, HSP90AA1, TGFB1, STAT1, EGF, STAT3, PTEN, HIF1A, ESR1, SIRT1, MMP9, EGFR, ACTB, IL6, MYC, CASP3, ALB, BCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3 2.71E-08 GO:0032991 macromolecular complex 9.37E-06 JUN, HSP90AA1, TGFB1, STAT1, STAT3, HIF1A, ESR1, SIRT1, TNF, EGFR, ACTB, IL6, MYC, CASP3, ALB, BCL2, AKT1, CTNNB1, PPARG, TP53 2.81E-05 GO:0044422 organelle part 5.04E-04 JUN, HSP90AA1, TGFB1, STAT1, EGF, STAT3, PTEN, HIF1A, ESR1, SIRT1, MMP9, EGFR, ACTB, IL6, MYC, CASP3, ALB, BCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3 0.001009 GO:0044421 extracellular region part 0.004057 IL6, HSP90AA1, TGFB1, EGF, IL1B, ALB, CCL2, CTNNB1, TNF, MMP9, EGFR, ACTB 0.005808 GO:0043226 organelle 0.005741 PTEN, HIF1A, TNF, EGFR, ACTB, MYC, CASP3, AKT1, MAPK3, JUN, HSP90AA1, TGFB1, EGF, STAT1, STAT3, MMP9, ESR1, SIRT1, IL6, IL1B, ALB, BCL2, CTNNB1, PPARG, TP53 0.005808 GO:0005576 extracellular region 0.005808 HSP90AA1, TGFB1, EGF, PTEN, TNF, MMP9, EGFR, ACTB, IL6, IL1B, ALB, CCL2, CTNNB1 0.005808 GO:0016020 membrane 0.024063 JUN, HSP90AA1, TGFB1, EGF, STAT3, PTEN, ESR1, SIRT1, TNF, EGFR, ACTB, IL6, MYC, CASP3, BCL2, AKT1, CTNNB1, TP53, MAPK3 0.024063 GO:0030054 cell junction 0.033045 CTNNB1, AKT1, EGFR, ACTB, MAPK3 0.033045 GO:0044425 membrane part 0.044679 HSP90AA1, TGFB1, EGF, PTEN, ESR1, TNF, EGFR, IL6, MYC, CASP3, BCL2, AKT1, CTNNB1, TP53, MAPK3 0.044679 Table 4 . KEGG pathways analysis for shared pathways of the hub-bottleneck genes Term Count P Value Genes FDR hsa05200 Pathways in cancer 1.27E-18 JUN, HSP90AA1, TGFB1, STAT1, EGF, STAT3, PTEN, HIF1A, ESR1, MMP9, EGFR, IL6, MYC, CASP3, BCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3 9.03E-17 hsa04933 AGE-RAGE signaling pathway in diabetic complications 1.37E-15 IL6, JUN, TGFB1, STAT1, IL1B, CASP3, STAT3, BCL2, CCL2, AKT1, TNF, MAPK3 4.85E-14 hsa05205 Proteoglycans in cancer 2.66E-15 TGFB1, STAT3, HIF1A, ESR1, TNF, MMP9, EGFR, ACTB, MYC, CASP3, AKT1, CTNNB1, TP53, MAPK3 6.30E-14 hsa05417 Lipid and atherosclerosis 5.07E-15 JUN, HSP90AA1, STAT3, TNF, MMP9, IL6, IL1B, CASP3, BCL2, CCL2, AKT1, PPARG, TP53, MAPK3 9.01E-14 hsa05161 Hepatitis B 6.71E-15 JUN, TGFB1, STAT1, STAT3, TNF, MMP9, IL6, MYC, CASP3, BCL2, AKT1, TP53, MAPK3 9.53E-14 hsa05210 Colorectal cancer 1.97E-14 JUN, TGFB1, EGF, MYC, CASP3, BCL2, CTNNB1, AKT1, TP53, EGFR, MAPK3 2.34E-13 hsa05418 Fluid shear stress and atherosclerosis 2.73E-12 HSP90AA1, JUN, IL1B, BCL2, CCL2, CTNNB1, AKT1, TNF, TP53, MMP9, ACTB 2.77E-11 hsa05215 Prostate cancer 4.10E-12 HSP90AA1, EGF, PTEN, BCL2, CTNNB1, AKT1, TP53, MMP9, EGFR, MAPK3 3.64E-11 hsa05160 Hepatitis C 9.30E-12 STAT1, EGF, MYC, CASP3, STAT3, CTNNB1, AKT1, TNF, TP53, EGFR, MAPK3 7.34E-11 hsa05163 Human cytomegalovirus infection 1.18E-11 IL6, MYC, IL1B, CASP3, STAT3, CCL2, CTNNB1, AKT1, TNF, TP53, EGFR, MAPK3 8.37E-11 Table 5 . Gene ontology annotation of molecular function for the hub-bottleneck genes Term Count P Value Genes FDR GO:0098772 molecular function regulator 3.22E-06 HSP90AA1, JUN, TGFB1, EGF, CASP3, BCL2, AKT1, ESR1, SIRT1, TNF, TP53, EGFR 2.90E-05 GO:0001071 nucleic acid binding transcription factor activity 0.002274 JUN, STAT1, MYC, STAT3, PPARG, HIF1A, ESR1, TP53 0.010231 GO:0004871 signal transducer activity 0.006472 EGF, CASP3, STAT3, PPARG, ESR1, TNF, EGFR, MAPK3 0.019416 GO:0005488 binding 0.081217 PTEN, HIF1A, TNF, EGFR, ACTB, MYC, CASP3, CCL2, AKT1, MAPK3, JUN, HSP90AA1, TGFB1, EGF, STAT1, STAT3, MMP9, ESR1, SIRT1, IL6, IL1B, ALB, BCL2, CTNNB1, PPARG, TP53 0.182737 Table 6 . disease associated with hub-bottleneck genes using Genetic Association Database Term P Value Genes FDR Breast cancer, somatic 2.80E-04 AKT1, ESR1, TP53 0.020177 Hepatocellular carcinoma, somatic 0.019463 CTNNB1, TP53 0.54413 Ovarian cancer, somatic 0.022672 CTNNB1, AKT1 0.54413 Colorectal cancer, somatic 0.054217 CTNNB1, AKT1 0.975914 Additional Declarations The authors declare no competing interests. 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20:03:55","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6185733/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6185733/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78799754,"identity":"3c0a33ea-fc5e-4991-98ac-a3e097ab3a5f","added_by":"auto","created_at":"2025-03-19 06:21:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1980182,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of intersection between triple negative breast cancer (TNBC) and non-alcoholic fatty liver disease (NAFLD) related genes retrieved from DisGeNET server\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6185733/v1/489f364d358215235f5f66cc.png"},{"id":78800369,"identity":"2ed128ce-c357-46f1-8e93-61f1bcde8c9b","added_by":"auto","created_at":"2025-03-19 06:29:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3610046,"visible":true,"origin":"","legend":"\u003cp\u003eCommon genes network between triple negative breast cancer and non-alcoholic fatty liver disease\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6185733/v1/081c044f1d9f4c5c15d4d326.png"},{"id":78799762,"identity":"03e14b6e-61d0-4d6b-b0ee-e1825cd64ff5","added_by":"auto","created_at":"2025-03-19 06:21:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6493047,"visible":true,"origin":"","legend":"\u003cp\u003eGeneMANIA Functional analysis for the selected hub-bottleneck genes.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6185733/v1/103b99f8535daea35bc9a8e2.png"},{"id":78800368,"identity":"defe9b9b-9f00-4f42-a26d-f8f00666ff0e","added_by":"auto","created_at":"2025-03-19 06:29:40","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":169860,"visible":true,"origin":"","legend":"\u003cp\u003edrug-gene interaction network of triple negative breast cancer and non-alcoholic fatty liver disease hub-bottleneck genes\u003c/p\u003e","description":"","filename":"Fig4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6185733/v1/6e1b4b0bcc034b7711397c9b.jpeg"},{"id":78800714,"identity":"ae956bf0-2d18-4eea-8562-599535c6eaf2","added_by":"auto","created_at":"2025-03-19 06:37:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13185231,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6185733/v1/76111acf-9ef3-4102-9650-f51307f3a575.pdf"},{"id":78799757,"identity":"2c74c6a7-8bc4-4ad3-a89f-d93cae1267d2","added_by":"auto","created_at":"2025-03-19 06:21:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2074781,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6185733/v1/47b59b1ba11af930a0781915.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDeciphering crucial genes in triple negative breast cancer and non-alcoholic fatty liver disease pathogenesis and drug repurposing: A systems biology approach\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related deaths in women (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Breast cancer risk is influenced by a variety of factors, including hereditary factors like mutations, gender, race, and age. In addition, there are non-hereditary factors like obesity, nutritional patterns, and unhealthy lifestyles (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Globally, the high prevalence of obesity has been correlated with a surge in patients with metabolic syndrome and certain cancers (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Furthermore, a positive association between breast cancer particularly triple-negative breast cancer (TNBC), and metabolic syndrome was found (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComponents of metabolic syndrome (i.e., abdominal obesity, hyperlipidemia, insulin resistance, and hypertension) have been closely connected to non-alcoholic fatty liver disease (NAFLD) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The influence of these two diseases on breast cancer may not be entirely separated. However, liver malfunction resulting from NAFLD leads to systemic metabolic instability, including sexual hormone exposure and nutrition rewiring (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNAFLD often develops in patients with breast cancer during their illness and its association with breast cancer exists regardless of established risk factors (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Particularly, hormone replacement therapy among patients with breast cancer may cause higher rates of developing NAFLD (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Histologically, patients with fatty liver and breast cancer have a significantly lower risk of liver metastasis than those with normal livers (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Conversely, breast cancer is one of the most common extrahepatic complications of NAFLD (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt should be noted that these studies have largely been observational and that there remains considerable uncertainty regarding the mechanism linking NAFLD and TNBC. Thus, it is clinically important to investigate molecular mechanisms enabling earlier diagnosis and treatment. Advances in sequencing technology and bioinformatics provide comprehensive therapeutic and diagnostic tools to investigate the interaction between different diseases. We can also study the specific pathogenesis of each disease at the gene and protein levels. To investigate the underlying mechanisms for both NAFLD and TNBC, we examined the common genes of both diseases. We identified candidate hub genes that may play a role in causing both diseases. Eventually, the signaling pathways were enriched to uncover the common mechanisms for both diseases.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Overview\u003c/h2\u003e \u003cp\u003eDisGeNET is an open-source discovery platform containing thousands of genes and variants associated with human diseases (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The DisGeNET database was used to export NAFLD and TNBC genes to construct PPI networks. Based on many databases, like Mint, BioGrid, and others, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) predicts PPI (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations are also available for the most accurate annotation of protein interactions. Through submission of gene lists to the STRING database, we constructed NAFLD and TNBC networks and performed network analysis using Cytoscape (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In a network, nodes (such as proteins or genes) interact with links/edges (such as physical interactions or co-expression relationships). A network's topology is determined by two centrality parameters: degree and betweenness. High-degree nodes are termed hubs, and those with a high degree of betweenness are called bottlenecks. Hub-bottleneck nodes are nodes that are simultaneously hubs and bottlenecks (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. MCODE analysis\u003c/h2\u003e \u003cp\u003eMolecular complex detection (MCODE) was applied to identify significant modules in the PPI network (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It is an algorithm that determines closely connected proteins in large PPI through graph-theoretic clustering. Based on MCODE parameters, dense regions are isolated according to specified structures. MCODE uses protein interaction data to predict molecular complexes, resulting in reliable and accurate functional annotations. Vertex weighting is calculated based on the density of local neighborhoods. It is followed by a traversal in an outward direction within a seed protein with a dense local neighborhood.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Functional analysis\u003c/h2\u003e \u003cp\u003eDatabase for Annotation, Visualization, and Integrated Discovery (DAVID) was utilized to enrich the hub bottleneck genes as a gene set to produce functional annotation (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The DAVID gene clustering method is a single-linkage method that uses a combination of gene sequences from multiple genomic libraries. Furthermore, it incorporates data visualization and enrichment analysis in addition to retrieving, displaying, and analyzing gene sets. Gene ontology (GO) analyses were executed for pathway analysis (using KEGG), cellular component, molecular function, and biological process. We reported top the ten annotations based on their P value for hub-bottleneck genes and cluster 1 genes. Gene multiple association network integration algorithm (GeneMANIA) was utilized to provide additional functional analyses. Using this tool, genomic and proteomic data is examined based on a query list to identify functionally similar genes (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Genetic Association of Diseases Analysis\u003c/h2\u003e \u003cp\u003eIn an effort to acquire a better perspective of underlying mechanisms and determine connections with other diseases, we assessed the association between TNBC and NAFLD and other diseases by employing the Genetic Association Database (GAD), which is a disease tool of the DAVID database (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Drug-gene network\u003c/h2\u003e \u003cp\u003eBased on currently FDA-approved drugs registered in DrugBank, the study's hub-bottleneck genes were utilized to construct the drug-gene network. With the Drug-Gene Interaction Database, the designated hub-bottleneck genes were chosen as targets for the repurposing of novel drug candidates (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). An analysis of drug-gene interactions was conducted using Cytoscape. A network analysis technique was used, and node degree scores were estimated for each node within the network.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Gene expression analysis\u003c/h2\u003e \u003cp\u003eThe number of genes extracted from DisGeNET for TNBC and NAFLD was 1674 and 1058, respectively (Figure. 1). A total of 343 genes were shared between the two lists, and they were referred to as common genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Topological network analysis\u003c/h2\u003e \u003cp\u003eUtilizing the STRING database, the common genes network was constructed with 303 nodes and 7690 edges (Figure. 2). To detect hub and bottleneck genes, the common genes network was analyzed. The network contained hub-bottleneck genes with high affinity and association. As a result, AKT1, TP53, TNF, ACTB, IL1B, IL6, STAT3, EGFR, MYC, ALB, BCL2, HIF1A, CTNNB1, JUN, PPARG, MMP9, CASP3, TGFB1, MAPK3, ESR1, EGF, STAT1, CCL2, PTEN, HSP90AA1, and SIRT1 were identified as hub-bottlenecks (Table. 1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Functional analysis\u003c/h2\u003e \u003cp\u003eBy using the DAVID database, hub-bottleneck genes were analyzed for gene ontology. Different biological processes, including locomotion, multi-organism process, immune system process, and developmental process are involved (Table. 2). In addition, membrane-enclosed lumen, macromolecular complex, organelle part, and extracellular region part are critical parts of cellular components (Table. 3). Pathway analysis revealed pathways in cancer, AGE-RAGE signaling pathway in diabetic complications, proteoglycans in cancer, and lipid and atherosclerosis as key pathways (Table. 4). Moreover, molecular function regulator, nucleic acid binding transcription factor activity are main participants in molecular functions (Table. 5 )\u003c/p\u003e \u003cp\u003eAccording to GeneMANIA's categorized biological functions, JUN, PPARG, and TNF appear to be the most significant hub-bottleneck genes. Relevance detection algorithms in GeneMANIA calculate the weight of the link based on a gene's score and the size of its nodes. As defined by GeneMANIA, nodes are colored in accordance with their biological function, with hub genes colored in accordance with their significant biological functions (Figure. 3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. MCODE analysis and GO\u003c/h2\u003e \u003cp\u003eMCODE and GO analysis was conducted on the network of common genes and the top three clusters based on scores were extracted (Figure S1-3). DAVID database was used to enrich genes derived from Cluster 1 genes which were extracted from MCODE modules.\u003c/p\u003e \u003cp\u003eIn Cluster 1, the leading biological processes detected are the immune system, multi-organism processes, developmental processes, and locomotion processes (Table. S1). Furthermore, key cellular components have been determined, including membrane-enclosed lumen, extracellular region, extracellular region part, and macromolecular complexes (Table. S2). Additionally, KEGG pathway reports indicated that cluster 1 genes play a significant role in Pathways in cancer, Lipids and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and IL-17 signaling pathway (Table. S3). Several molecular functions were identified as significant, such as molecular function regulator, signal transducer activity, binding, and nucleic acid binding transcription factor activity (Table. S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Gene-disease assessment\u003c/h2\u003e \u003cp\u003eAccording to a DAVID analysis that was carried out with the GAD database, hub-bottleneck genes are associated with breast cancer, hepatocellular carcinoma, ovarian cancer, and colorectal cancer (Table. 6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6. The drug-gene analysis\u003c/h2\u003e \u003cp\u003eThe Network of interaction Analysis was performed using Cytoscape software as a method of analyzing the possible interactions between the hub-bottleneck genes identified by this study and potential drugs that have been approved based on their interaction network. The analysis resulted in 22 nodes linked with 43 edges. Cisplatin, carboplatin, sorafenib, cetuximab, paclitaxel, gemcitabine, and etoposide were drugs with high degrees (Figure. 4).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, a systems biology approach was used to examine common genes associated with TNBC and NAFLD. In order to Achieve an understanding of cells' system-level functioning, we analyzed PPI, hub-bottleneck genes, and modules in the network. It has been shown that measures such as betweenness centrality and degree are crucial in identifying potential targets for drugs and understanding how genes are interconnected (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Comfortability is defined as the co-occurrence of two or more disorders in one person. Thus, characterizing genes and pathways underlying related diseases would aid in identifying patterns of comfortability (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In numerous studies, the potential connection between TNBC and NAFLD has been extensively explored (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In this paper, we discuss molecules that are crucial to understanding TNBC and NAFLD pathogenesis based on their high value in various analyses. The top explored hub-bottleneck genes with the highest combined degree and betweenness centrality were AKT1, TP53, EGFR, and ACTB.\u003c/p\u003e \u003cp\u003eTP53 codes P53 protein. P53 regulates metabolic homeostasis by directing catabolic over anabolic processes, and by reducing NADPH levels by inhibiting G6PD in the pentose phosphate pathway. Mutant P53 proteins confer new oncogenic metabolic functions (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Mutant P53 promotes anabolism by inducing SREBPs and lipogenic enzymes, and suppresses catabolism by inhibiting AMPK, thereby promoting lipid synthesis, glycolysis, and nucleotide production (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This is a key mechanism by which mutant P53 facilitates metabolic reprogramming during tumorigenesis. The tumor suppressor P53 regulates apoptosis and cell cycle arrest and is involved in molecular mechanisms underlying hepatocellular injury and lipid metabolism (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Under normal conditions, P53 regulates genes involved in lipid metabolism, such as ceramide synthesis, fatty acid metabolism, lipid absorption, and lipoprotein metabolism, and enhances lipid catabolism while inhibiting anabolism (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). It also reduces PPARG activity, adipocyte differentiation and lipid accumulation (\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). But under stress, P53 upregulation can trigger insulin resistance, inflammation, and oxidative stress (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Regulatory roles of P53 by modulating target genes may introduce P53 as a valuable therapeutic indicator for predicting and diagnosing NAFLD.\u003c/p\u003e \u003cp\u003eAKT promotes fat accumulation by activating SREBP-1c, a major lipid metabolism regulator (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). AKT might thus aid proliferation by increasing the raw materials required for membrane synthesis, cholesterol, and fatty acids (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Lipids might augment cancer development in several ways. For instance, the role of lipids in prostate cancer development is executed by stiffening the mitochondrial membrane, disrupting oxidative phosphorylation, and preferring glycolysis over oxidative phosphorylation for energy (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). AKT1 regulates growth, AKT2 regulates metabolism, and AKT3 regulates brain development. It is conceivable that AKT1 may regulate NAFLD susceptibility (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The metabolic syndrome components are associated with AKT1 polymorphisms in genotyping experiments (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Furthermore, AKT1's role in breast cancer is stage-dependent. Regarding tumor growth, AKT1 is involved in the initiation and progression of breast cancer. It has been shown to play a crucial role in the In vitro and in vivo proliferative and tumor-initiating effects. In contrast, breast cancer AKT1 is an anti-metastatic protein, and loss of it in the primary tumor could result in metastasis (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). This dichotomy was previously confirmed by observing a Breast cancer mouse model with suppressed AKT1 exhibits an increase in metastasis but a decrease in tumor growth (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEGFRs are expressed in many malignancies, including head and neck, esophageal, lung, and breast cancer (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). EGFR is a member of the ErbB family of receptors that induces the formation of homodimers and heterodimers and activates a signaling pathway that controls cell proliferation, survival, and migration at the downstream level (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Growth factor receptors such as EGFR and IGF1R can cause a lack of response to tamoxifen by activating the MAPK and PI3K signaling pathways (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). In response to receptor dimerization triggered by ligands, EGFR signaling activates several signaling pathways, such as AKT, STAT, PI3K, and PLCG1. Proliferation, angiogenesis, migration, survival, and adhesion are all affected by these pathways (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). In the context of partial hepatectomy, EGFR provides a major contribution to regulating liver regeneration (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Mice fed a fast-food diet showed remarkable reductions in steatosis, liver injury, and fibrosis when EGFR inhibition was administered (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). When NAFLD occurs, EGFR modulates key transcription factors involved in lipid metabolism, including PPARG, SREBF1, ChREBP, and HNF4α (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). There is no clear understanding of the signaling pathways downstream of EGFR. However, one study indicates that AKT signaling appears to be critical for the regulation of lipogenic transcription factors during NAFLD development (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eACTB is a housekeeping gene that has been found to be related to many cancers (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). ACTB encodes the β-actin protein, which is predominantly expressed in non-muscle cells and aids in building and moving the cytoskeleton. As a regulatory protein, ACTB has many functions, including regulating cell growth, regulating cell migration, controlling differentiation, and transmitting signals (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). ACTB is abnormally expressed and polymerized in tumors, and this results in an increased rate of tumor formation, invasion, and metastasis (\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). For instance, hepatocellular carcinoma has also been associated with high ACTB mRNA levels. This may be related to the overexpression of ACTB in tumor cells (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). As ACTB mRNA delocalized from the leading edges of cancer cells, cell polarity, and directional motion were lost as a result of accumulating ACTB mRNA. Moreover, significant upregulation of ACTB expression was observed in highly invasive tumor cell lines, confirming that ACTB takes part in the metastatic process (\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). In the liver, actin expression level increased with the progression of fibrosis in the liver (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHepatocellular carcinoma and breast cancer are among the most relevant diseases associated with TNBC and NAFLD. There is less than a three-year median survival rate after metastatic breast cancer occurs in the liver in approximately 50\u0026ndash;70% of cases (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Metastases in the liver can lead to sudden hepatocellular failure, refractory ascites, and portal vein thrombosis, as well as nutritional deficiencies (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). On the other hand, hyperexpression of hepatic FGF21 causes breast cancer progression by impairing the anti-apoptotic properties of breast cancer cells in the presence of high-fat diet-induced NAFLD (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Also, NAFLD predisposes patients to hepatocellular carcinoma both with and without cirrhosis (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe DAVID database lists locomotion as the top biological process and Pathways in cancer as the top pathway. NAFLD promotes liver metastasis by promoting tumor-induced lipid deposits by activating reciprocal lipolysis of triglycerides between juxtaposed hepatocytes. During mitochondrial oxidation, lipolytic products are metabolized by cancer cells via FATP1, promoting tumor growth (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). The proliferation of cells in the mammary glands during pregnancy is facilitated by IL-4 and IL-13, leading to the expansion of acini with incompletely filled lumens. Moreover, the tyrosine phosphorylation of insulin receptor substrate-1 by IL-4 and IL-13 enhances cell proliferation (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwo known chain alpha-receptors, IL13R1 and IL13R2, bind to IL-13, and each activates a different signaling cascade (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). The IL13R1 pathway promotes liver fibrosis by activating the Janus kinase signal transducer (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). Macrophages can be differentiated into two primary subtypes, namely classically activated (M1) and alternatively activated (M2) macrophages. IL-4 and IL-13 can activate M2 macrophages. Angiogenesis, immune regulation, tissue remodeling, parasitic infection prevention, and tumorigenesis are all processes controlled by M2 macrophages, which produce anti-inflammatory cytokines such as IL-10 and TGF- β (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). In NAFLD-associated fibrosis, simultaneous inhibition of IL-13 and TGF-β signaling attenuates the fibrotic machinery more effectively than isolated inhibition of TGF-β (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe M1 macrophage induces pro-inflammatory T helper type 1 responses through its inflammatory responses and secretion of cytokines such as IL-1β, TNF-a, and IL-12 (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Also, Type 2 immunity is Distinguished by enhanced production of IL-13, which is important in liver fibrogenesis of different etiologies (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Furthermore, overexpression of IL-13 in activated hepatic stellate cells has a role in progressive NASH fibrosis (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). This is confirmed by an elevation in serum levels of IL-13 and a rise in IL-13RA2 expression. It has also been suggested that IL-13 upregulation may contribute to hepatocellular carcinoma development in NAFLD (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInflammation and fibrosis of the liver are caused by the activation of liver macrophages. Depending on the microenvironment, macrophages exhibit remarkable plasticity and polarize into different phenotypes (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). A variety of macrophage functions are modulated by the PI3K/AKT pathway, including survival, migration, proliferation, metabolic responses, and inflammatory responses (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). An activated PI3K/AKT pathway promotes the production of anti-inflammatory cytokines, thereby facilitating the healing of injured tissues and the resolution of inflammation. The opposite could also occur if PI3K/AKT signaling is inhibited, which would add to liver damage by enhancing M1-like phenotypes (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). Chronic liver diseases can be treated by targeting the PI3K/AKT pathway for controlling macrophage polarization and activity (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, PI3K/AKT pathway signal transduction may be impaired by fat accumulation in the liver, resulting in activation of the apoptosis pathway in the mitochondrial membrane (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). In breast cancer cells, the PI3K/AKT/mTOR signaling pathway plays a key role in growth, survival, and motility (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). Different mechanisms cause the PI3K/AKT/mTOR pathway to be dysregulated in breast cancer, resulting in excessive PI3K activity and/or impairment of PI3K inhibitory functions along with mutations in tumor suppressor genes like PTEN and INPP4B (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the PI3K genes, PIK3CA and PIK3CB are frequently altered (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). More than one-third of early breast cancer tumors have mutations in the catalytic domain of PIK3CA, and PIK3CB stimulates breast cancer cell proliferation, invasion, and tumorigenesis (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). When PI3KCA and PI3KCB are inhibited simultaneously, greater antitumor efficacy is obtained in breast cancers with PIK3CA mutants, because PI3KCB accumulates PIP3 and reactivates AKT in breast cancers treated with PI3KCA-specific inhibitors (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found cisplatin, carboplatin, sorafenib, cetuximab, paclitaxel, gemcitabine, and etoposide in our drug gene association study. NAFLD is linked to hepatitis and chemotherapy-associated steatohepatitis because of mitochondrial function changes. Some treatments induce steatosis by reducing fat oxidation, resulting in a rise in reactive oxygen species in the liver. As a result of mitochondrial dysfunction, or a carnitine shuttle blockage from transporting medium and long-chain fatty acids from the fat matrix into the mitochondrial matrix, hepatic steatosis or liver dysfunction results (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Cisplatin hepatotoxicity appears to be caused by inflammatory responses in the liver and oxidative stress. It is possible that the gut microbiota and their metabolites contribute to oxidative stress and inflammation. It was determined that antibiotic treatment prevented cisplatin hepatotoxicity and improved liver function. Thus, Hepatotoxicity may be caused by altered gut microbiota due to cisplatin and dysbiosis of metabolites derived from the microbiota (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, by upregulating genes responsible for lipid oxidation and hydrolysis, sorafenib reduced the viability of fatty spheroids. Sorafenib also inhibited steatosis-induced fibrogenesis by reducing the contents of TNF-α, TGF-β1, and IL-6 in fatty spheroids. Based on in vitro evidence demonstrating sorafenib's antifibrotic properties, it has the potential for therapeutic use in NAFLD/NASH patients (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). Additionally, as a result of paclitaxel treatment in breast cancer, 26.7% developed hepatic steatosis. The change in liver function was mostly evident on cholestatic liver tests (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e). Moreover, etoposide-containing regimens have been linked to faster progression to steatosis, despite similar baseline risk factors. This suggests that etoposide treatment may increase the likelihood and speed of developing hepatic steatosis compared to other chemotherapy drugs (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Strength and limitations","content":"\u003cp\u003eThrough the use of advanced bioinformatics technology, we investigated the genetic relationship between NAFLD and TNBC. A functional annotation of genes was conducted along with the detection of common genes and their associated pathways. For the first time, drugs associated with common TNBC and NAFLD genes were identified in this study. Despite these findings, several limitations exist. Although multiple studies have explained the role of these genes, there have been no experiments to validate the key pathways and hub genes identified. Eventually, prospective clinical studies are imperative to confirm the causality between hub genes and prognosis in patients with TNBC or NAFLD.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eUsing a network-based approach, the current study determined the underlying factors and mechanisms contributing to the development, diagnosis, and treatment of TNBC and NAFLD based on an analysis of various data. According to our findings, TNBC and NAFLD may share similar genetic mechanisms, as evidenced by a strong gene expression correlation that indicates that these diseases may share some genetic characteristics. The results of our study also suggest that there is a link between treatment approaches for TNBC and the progression of NAFLD.\u003c/p\u003e"},{"header":"7. Abbreviation","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eAMPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eAMP-activated Protein Kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eChREBP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eCarbohydrate-responsive element binding protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eDAVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eDatabase for Annotation, Visualization, and Integrated Discovery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eEpidermal Growth Factor Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eErbB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eErythroblastic Leukemia Viral Oncogene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eFATP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eFatty Acid Transporter Protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eGAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eGenetic Association Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eGeneMANIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eGene multiple association network integration algorithm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eHNF4\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eHepatocyte nuclear factor 4 alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eIGF1R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eInsulin-like Growth Factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eInterleukin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eINPP4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eInositol Polyphosphate 4-phosphatase Type II\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eMAPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eMitogen-activated Protein Kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eMCODE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eMolecular complex detection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eNAFLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eNon-alcoholic Fatty Liver Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePI3K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003ePhosphatidylinositol 3-kinases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePIK3CA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003ePhosphatidylinositol-4,5-bisphosphate 3-kinase Catalytic Subunit Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePIK3CB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003ePhosphatidylinositol-4,5-bisphosphate 3-kinase Catalytic Subunit Beta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePLCG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003ephospholipase C gamma protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003ePeroxisome Proliferator-activated Receptor Gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eProtein-protein interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eProtein-protein Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSREBF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eSterol Regulatory Element Binding Transcription Factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSREBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eSterol regulatory element binding protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSTAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eSignal Transducer and Activator of Transcription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSTRING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eSearch Tool for the Retrieval of Interacting Genes/Proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eTriple Negative Breast Cancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo individual data were used in this paper and the information is based on aggregated pre-existing online secondary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study had no funding support and received no grants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Hepatology communications. 2019;3(2):220-6.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e The hub-bottleneck genes with significant centrality values based on Degree and Betweenness.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"634\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eHub-bottleneck Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003eBetweenness Centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eAKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.050921633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.04871762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eTNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.027017316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eACTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.028816976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eIL1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.023760252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eIL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.024107663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eSTAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.021918688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.030249858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eMYC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.023875871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.027669886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eBCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.013471527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.018912477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eCTNNB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.032878814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eJUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.012653694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003ePPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.019648152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eMMP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.021660477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eCASP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.012383543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eTGFB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.012133513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eMAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.012019791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.012099128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eEGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.013441009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.008932601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eCCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.009575273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003ePTEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.011264034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eHSP90AA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.018739192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eSIRT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 286px;\"\u003e\n \u003cp\u003e0.010167564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Gene ontology annotation of biological process for the hub-bottleneck genes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGO:0040011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003elocomotion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.14E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, TGFB1, EGF, STAT3, PTEN, HIF1A, SIRT1, TNF, MMP9, EGFR, ACTB, IL6, MYC, IL1B, BCL2, CCL2, AKT1, CTNNB1, PPARG, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.40E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGO:0051704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003emulti-organism process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e5.28E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, TGFB1, STAT1, STAT3, PTEN, HIF1A, ESR1, SIRT1, TNF, MMP9, EGFR, IL6, MYC, IL1B, CASP3, BCL2, CCL2, AKT1, CTNNB1, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e5.55E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGO:0002376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eimmune system process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.27E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, TGFB1, STAT1, STAT3, HIF1A, ESR1, SIRT1, TNF, MMP9, ACTB, IL6, MYC, IL1B, CASP3, BCL2, CCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e8.87E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGO:0032502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003edevelopmental process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.63E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003ePTEN, HIF1A, TNF, EGFR, ACTB, MYC, CASP3, CCL2, AKT1, MAPK3, JUN, HSP90AA1, TGFB1, EGF, STAT1, STAT3, MMP9, ESR1, SIRT1, IL6, IL1B, BCL2, CTNNB1, PPARG, TP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e8.57E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGO:0032501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003emulticellular organismal process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.18E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003ePTEN, HIF1A, TNF, EGFR, ACTB, MYC, CASP3, CCL2, AKT1, MAPK3, JUN, HSP90AA1, TGFB1, EGF, STAT1, STAT3, MMP9, ESR1, SIRT1, IL6, IL1B, ALB, BCL2, CTNNB1, PPARG, TP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.34E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGO:0040007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003egrowth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e5.93E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eHSP90AA1, TGFB1, STAT3, PTEN, HIF1A, ESR1, SIRT1, EGFR, BCL2, AKT1, CTNNB1, PPARG, TP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.07E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGO:0022414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003ereproductive process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.33E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eTGFB1, STAT3, PTEN, HIF1A, ESR1, SIRT1, MMP9, EGFR, MYC, IL1B, CASP3, BCL2, AKT1, CTNNB1, PPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3.59E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGO:0000003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003ereproduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.37E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eTGFB1, STAT3, PTEN, HIF1A, ESR1, SIRT1, MMP9, EGFR, MYC, IL1B, CASP3, BCL2, AKT1, CTNNB1, PPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3.59E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGO:0051179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003elocalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.83E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, TGFB1, EGF, STAT3, PTEN, HIF1A, ESR1, SIRT1, TNF, MMP9, EGFR, ACTB, IL6, MYC, IL1B, ALB, BCL2, CCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e8.38E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGO:0023052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003esignaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.99E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 347px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, TGFB1, STAT1, EGF, STAT3, PTEN, HIF1A, ESR1, SIRT1, TNF, MMP9, EGFR, IL6, MYC, IL1B, CASP3, BCL2, CCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e8.38E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Gene ontology annotation of cellular component for the hub-bottleneck genes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0031974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003emembrane-enclosed lumen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e4.51E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, TGFB1, STAT1, EGF, STAT3, PTEN, HIF1A, ESR1, SIRT1, MMP9, EGFR, ACTB, IL6, MYC, CASP3, ALB, BCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.71E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0032991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003emacromolecular complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e9.37E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, TGFB1, STAT1, STAT3, HIF1A, ESR1, SIRT1, TNF, EGFR, ACTB, IL6, MYC, CASP3, ALB, BCL2, AKT1, CTNNB1, PPARG, TP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.81E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0044422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eorganelle part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e5.04E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, TGFB1, STAT1, EGF, STAT3, PTEN, HIF1A, ESR1, SIRT1, MMP9, EGFR, ACTB, IL6, MYC, CASP3, ALB, BCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.001009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0044421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eextracellular region part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.004057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eIL6, HSP90AA1, TGFB1, EGF, IL1B, ALB, CCL2, CTNNB1, TNF, MMP9, EGFR, ACTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.005808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0043226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eorganelle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.005741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePTEN, HIF1A, TNF, EGFR, ACTB, MYC, CASP3, AKT1, MAPK3, JUN, HSP90AA1, TGFB1, EGF, STAT1, STAT3, MMP9, ESR1, SIRT1, IL6, IL1B, ALB, BCL2, CTNNB1, PPARG, TP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.005808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0005576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eextracellular region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.005808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eHSP90AA1, TGFB1, EGF, PTEN, TNF, MMP9, EGFR, ACTB, IL6, IL1B, ALB, CCL2, CTNNB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.005808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0016020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003emembrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.024063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, TGFB1, EGF, STAT3, PTEN, ESR1, SIRT1, TNF, EGFR, ACTB, IL6, MYC, CASP3, BCL2, AKT1, CTNNB1, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.024063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0030054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ecell junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.033045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCTNNB1, AKT1, EGFR, ACTB, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.033045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0044425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003emembrane part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.044679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eHSP90AA1, TGFB1, EGF, PTEN, ESR1, TNF, EGFR, IL6, MYC, CASP3, BCL2, AKT1, CTNNB1, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.044679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e KEGG pathways analysis for shared pathways of \u0026nbsp;the hub-bottleneck genes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ehsa05200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ePathways in cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.27E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, TGFB1, STAT1, EGF, STAT3, PTEN, HIF1A, ESR1, MMP9, EGFR, IL6, MYC, CASP3, BCL2, AKT1, CTNNB1, PPARG, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e9.03E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ehsa04933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eAGE-RAGE signaling pathway in diabetic complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.37E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eIL6, JUN, TGFB1, STAT1, IL1B, CASP3, STAT3, BCL2, CCL2, AKT1, TNF, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4.85E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ehsa05205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eProteoglycans in cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.66E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eTGFB1, STAT3, HIF1A, ESR1, TNF, MMP9, EGFR, ACTB, MYC, CASP3, AKT1, CTNNB1, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6.30E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ehsa05417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eLipid and atherosclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.07E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eJUN, HSP90AA1, STAT3, TNF, MMP9, IL6, IL1B, CASP3, BCL2, CCL2, AKT1, PPARG, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e9.01E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ehsa05161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eHepatitis B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.71E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eJUN, TGFB1, STAT1, STAT3, TNF, MMP9, IL6, MYC, CASP3, BCL2, AKT1, TP53, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e9.53E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ehsa05210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.97E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eJUN, TGFB1, EGF, MYC, CASP3, BCL2, CTNNB1, AKT1, TP53, EGFR, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2.34E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ehsa05418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eFluid shear stress and atherosclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.73E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eHSP90AA1, JUN, IL1B, BCL2, CCL2, CTNNB1, AKT1, TNF, TP53, MMP9, ACTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2.77E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ehsa05215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eProstate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.10E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eHSP90AA1, EGF, PTEN, BCL2, CTNNB1, AKT1, TP53, MMP9, EGFR, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3.64E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ehsa05160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eHepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e9.30E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eSTAT1, EGF, MYC, CASP3, STAT3, CTNNB1, AKT1, TNF, TP53, EGFR, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e7.34E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ehsa05163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eHuman cytomegalovirus infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.18E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eIL6, MYC, IL1B, CASP3, STAT3, CCL2, CTNNB1, AKT1, TNF, TP53, EGFR, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8.37E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Gene ontology annotation of molecular function for the hub-bottleneck genes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0098772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003emolecular function regulator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.22E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eHSP90AA1, JUN, TGFB1, EGF, CASP3, BCL2, AKT1, ESR1, SIRT1, TNF, TP53, EGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2.90E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0001071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003enucleic acid binding transcription factor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.002274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eJUN, STAT1, MYC, STAT3, PPARG, HIF1A, ESR1, TP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.010231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0004871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003esignal transducer activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.006472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eEGF, CASP3, STAT3, PPARG, ESR1, TNF, EGFR, MAPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.019416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGO:0005488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ebinding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.081217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003ePTEN, HIF1A, TNF, EGFR, ACTB, MYC, CASP3, CCL2, AKT1, MAPK3, JUN, HSP90AA1, TGFB1, EGF, STAT1, STAT3, MMP9, ESR1, SIRT1, IL6, IL1B, ALB, BCL2, CTNNB1, PPARG, TP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.182737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e disease associated with hub-bottleneck genes using Genetic Association Database\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 303px;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eBreast cancer, somatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2.80E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAKT1, ESR1, TP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 303px;\"\u003e\n \u003cp\u003e0.020177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eHepatocellular carcinoma, somatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.019463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCTNNB1, TP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 303px;\"\u003e\n \u003cp\u003e0.54413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eOvarian cancer, somatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.022672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCTNNB1, AKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 303px;\"\u003e\n \u003cp\u003e0.54413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eColorectal cancer, somatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.054217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCTNNB1, AKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 303px;\"\u003e\n \u003cp\u003e0.975914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"[email protected]","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":"Systems biology, bioinformatics analysis, hub genes, drug, biomarkers, protein-protein interaction (PPI), triple-negative breast cancer (TNBC), non-alcoholic fatty liver disease (NAFLD)","lastPublishedDoi":"10.21203/rs.3.rs-6185733/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6185733/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e It has been widely recognized that triple negative breast cancer (TNBC) and non-alcoholic fatty liver disease (NAFLD) are related, but underlying mechanisms remain unclear. This study investigated the possible co-pathogenesis and prognostic connections between TNBC and NAFLD and relevant hub genes associated with them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim:\u003c/strong\u003e Using a systems biology approach, we identify crucial genes that contribute to TNBC and NAFLD to investigate new biomarkers and propose new medicines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Cytoscape was used to construct protein-protein interaction (PPI) networks and functional enrichment analysis to determine which molecules were crucial. Disease genes from the DisGeNET and STRING databases were used to construct disease networks. A network of gene-drug interactions and gene-disease associations was also created for the purpose of suggesting drugs and mapping diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eUsing the STRING database, 343 common genes between TNBC and NAFLD were used to construct a PPI network. This network has 182 nodes and 2591 edges and 3 clusters along with 26 hub-bottleneck genes. Enrichment of these gens lead to recognition of locomotion, membrane-enclosed lumen, molecular function regulator, and pathways in cancer as top biological process, cellular component, molecular function, and pathway, respectively. Drug-gene analysis revealed that Cisplatin, carboplatin, sorafenib, cetuximab, paclitaxel, gemcitabine, and etoposide have the highest degree of interaction with key genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn silico data analysis approaches indicated that TNBC and NAFLD share common genes and signaling pathways. Additionally, we identified key drugs that target both TNBC and NAFLD genes.\u003c/p\u003e","manuscriptTitle":"Deciphering crucial genes in triple negative breast cancer and non-alcoholic fatty liver disease pathogenesis and drug repurposing: A systems biology approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-19 06:21:35","doi":"10.21203/rs.3.rs-6185733/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"24a307e0-3761-4a4f-add3-16fa9fabab87","owner":[],"postedDate":"March 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-19T06:21:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-19 06:21:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6185733","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6185733","identity":"rs-6185733","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}