Genetic Insights into Coronary Heart Disease: A Multi-Omic Approach

Genetic Insights into Coronary Heart Disease: A Multi-Omic 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 Genetic Insights into Coronary Heart Disease: A Multi-Omic Approach Usha Adiga, Sampara Vasishta, T Amulya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6281414/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background: Coronary heart disease (CHD) is a leading cause of mortality worldwide, with a complex interplay of genetic and environmental factors influencing its development. Genome-wide association studies (GWAS) have identified multiple genetic loci associated with CHD, providing crucial insights into its pathophysiology. However, the full spectrum of genetic contributors and their biological mechanisms remains to be elucidated. Methods: This study integrates GWAS data with functional enrichment, transcriptomic, and metabolomic analyses to identify key genetic determinants of CHD. Variants associated with CHD were retrieved from publicly available datasets and analyzed using bioinformatics tools to explore their biological significance. Pathway enrichment, protein-protein interaction (PPI) networks, and clustering algorithms were employed to delineate functional relationships among candidate genes. Additionally, microRNA (miRNA) interactions were assessed to understand post-transcriptional regulatory mechanisms. Results: Our analysis confirms the strong association of CHD with loci such as 9p21.3 (CDKN2B-AS1), COL4A2, and PHACTR1, emphasizing their roles in vascular remodeling, inflammation, and lipid metabolism. Functional enrichment revealed significant involvement in TGF-beta signaling, extracellular matrix organization, and AGE-RAGE signaling pathways. Furthermore, miRNA analysis highlighted potential regulatory targets such as hsa-miR-147b and hsa-miR-4790-5p, which may modulate disease progression. Conclusion: This study advances our understanding of CHD genetics by integrating multi-omic data to identify key genetic determinants and biological pathways. The findings underscore the potential of precision medicine approaches for early detection and targeted intervention in high-risk individuals. Coronary heart disease genome-wide association studies genetic risk factors precision medicine functional enrichment analysis Introduction Coronary heart disease (CHD) remains a leading cause of morbidity and mortality worldwide, with significant contributions from genetic, lifestyle, and environmental factors [ 1 ]. Advances in genome-wide association studies (GWAS) have identified numerous genetic variants associated with CHD, providing deeper insights into the pathophysiology of the disease and potential therapeutic targets [ 2 ]. Despite these findings, the complexity of CHD genetics necessitates further research to understand the interplay between these variants and their biological mechanisms [ 3 ]. CHD is a multifactorial disease influenced by both common and rare genetic variants. Studies have demonstrated that a large proportion of CHD heritability is attributable to polygenic risk, with loci such as 9p21.3 consistently implicated across diverse populations [ 4 ]. The NHGRI-EBI GWAS catalog has curated a growing list of CHD-associated loci, many of which are linked to biological pathways such as lipid metabolism, inflammation, vascular remodeling, and coagulation [ 5 ]. These findings underscore the critical role of genetic susceptibility in CHD development and highlight the need for precision medicine approaches in risk stratification and management [ 6 ]. A pivotal discovery in CHD genetics was the identification of the 9p21.3 locus, which remains one of the most robustly associated genetic regions across multiple studies [ 7 ]. This locus, encompassing the CDKN2B-AS1 gene, is implicated in vascular smooth muscle cell proliferation and atherosclerotic plaque stability. Additionally, COL4A2, a gene involved in extracellular matrix integrity, has been associated with CHD susceptibility, emphasizing the importance of vascular remodeling in disease progression [ 8 ]. Recent GWAS meta-analyses have further expanded the list of CHD risk loci, with studies identifying 15 novel loci associated with arterial-wall-specific mechanisms [ 9 ]. These findings reinforce the role of endothelial function and vascular homeostasis in CHD pathogenesis. Notably, transethnic meta-analyses have revealed population-specific risk variants, highlighting the genetic diversity underlying CHD susceptibility [ 10 ]. For instance, studies in South Asian populations have identified distinct risk alleles that are not observed in European cohorts, indicating the necessity for ethnicity-specific genetic research [ 11 ]. In addition to common risk loci, rare variants and copy number variations (CNVs) have emerged as contributors to CHD heritability [ 12 ]. Whole-exome and whole-genome sequencing studies have identified rare deleterious mutations in genes such as LDLR, PCSK9, and APOB, which play key roles in lipid metabolism [ 13 ]. These findings have direct clinical implications, as individuals harboring pathogenic variants in these genes may benefit from early interventions such as statin therapy or PCSK9 inhibitors [ 14 ]. Wellcome Trust Case Control Consortium) was a large-scale GWAS covering seven common diseases, including coronary artery disease (CAD) [ 15 ]. This study involved 14,000 cases across seven diseases and 3,000 shared controls, using the Affymetrix GeneChip 500K Mapping Array. The study identified 24 independent association signals (P < 5 × 10⁻⁷) across different diseases. Specifically for CAD, chromosome 9p21.3 was confirmed as the strongest associated region, consistent with previous findings. The study demonstrated that shared genetic susceptibility exists across different complex diseases, with several loci being implicated in multiple conditions. Furthermore, the research highlighted the importance of large-scale data aggregation and the use of shared controls to maximize statistical power while addressing concerns of population stratification. The study provided a robust dataset for future genetic research and emphasized the role of GWAS in understanding the genetic architecture of common diseases​. The study (Samani et al.) conducted a genome-wide association study (GWAS) of coronary artery disease (CAD) using two datasets: the Wellcome Trust Case Control Consortium (WTCCC) and the German Myocardial Infarction (MI) Family Study​ 16 . The study aimed to identify genetic loci associated with CAD by analyzing a large sample of cases and controls using the GeneChip Human Mapping 500K Array Set (Affymetrix). The study identified chromosome 9p21.3 (SNP rs1333049) as the strongest locus associated with CAD in both datasets, with a combined P-value of 2.91 × 10⁻¹⁹, significantly increasing the risk of CAD. Other replicated loci included 6q25.1 (rs6922269, MTHFD1L gene) and 2q36.3 (rs2943634). Additionally, four more loci were identified: 1p13.3 (PSRC1), 1q41 (MIA3), 10q11.21 (CXCL12), and 15q22.33 (SMAD3). The study concluded that multiple genetic loci influence CAD risk, emphasizing the polygenic nature of the disease and the importance of genetic screening for CAD predisposition. These two datasets which are publicly available were analyzed further. Methodology This study utilized a comprehensive approach to investigate the genetic determinants of coronary heart disease (CHD) by integrating genome-wide association study (GWAS) data [ 15 , 16 ] with functional enrichment, pathway, and clustering analyses. The methodology encompassed several key steps: data acquisition, quality control, variant annotation, functional enrichment analysis, microRNA interaction studies, protein-protein interaction (PPI) network construction, and clustering techniques to categorize functionally related genes. GWAS Data Acquisition and Processing Publicly available GWAS datasets related to coronary heart disease (CHD) were retrieved and analyzed to identify significant single nucleotide polymorphisms (SNPs) associated with CHD [ 15 , 16 ]. The datasets were pre-processed to ensure consistency across different study populations, and quality control measures were applied to remove low-quality variants and samples. Minor allele frequency (MAF) thresholds, Hardy-Weinberg equilibrium (HWE) filtering, and genotype call rate assessments were performed to retain only high-confidence variants. Genome-wide significance was determined using a threshold of P < 5 × 10⁻⁸ to identify robust genetic associations. Functional Enrichment Analysis Identified GWAS loci were further analyzed through Gene Ontology (GO) biological processes, molecular function, and cellular component enrichment analyses. The goal was to determine the biological relevance of associated genes in the context of CHD. GO enrichment analysis was performed using online bioinformatics tools such as Enrichr and DAVID (Database for Annotation, Visualization, and Integrated Discovery). Pathway enrichment was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, highlighting significant signaling pathways, including TGF-beta signaling, extracellular matrix remodeling, and lipid metabolism pathways. MicroRNA (miRNA) Interaction Studies To identify potential post-transcriptional regulatory mechanisms, we analyzed miRNA interactions with CHD-associated genes. miRNA target prediction databases, including miRTarBase and TargetScan, were used to assess miRNA-gene interactions. miRNAs with strong binding affinities to CHD-associated genes were identified, providing insights into regulatory networks that may influence disease progression. Key miRNAs, such as hsa-miR-4790-5p and hsa-miR-147b, were found to target genes like CDKN2B and COL4A2, suggesting a role in endothelial function and extracellular matrix remodeling. Protein-Protein Interaction (PPI) Network Construction To understand the molecular interactions among CHD-associated proteins, a PPI network was constructed using STRING (Search Tool for the Retrieval of Interacting Genes/Proteins). The network was analyzed to identify hub proteins with high centrality, indicating key regulatory roles in CHD. CDKN2B, SMAD3, and COL4A2 emerged as central nodes, suggesting their significance in vascular remodeling, fibrosis, and cellular proliferation. Interactions between these proteins and known cardiovascular disease-related molecules were mapped to uncover functional associations. Clustering Analyses for Functional Grouping of Genes To categorize CHD-associated genes into functionally relevant groups, three clustering techniques were applied: K-means clustering, Markov clustering (MCL), and density-based spatial clustering (DBSCAN). K-means Clustering: This method grouped genes based on shared biological functions, revealing three major clusters. Notably, COL4A2 was placed in a distinct cluster, emphasizing its unique role in extracellular matrix integrity, while genes involved in cell adhesion and mitotic regulation (CELSR2, PSRC1, and MIA3) clustered together. Markov Clustering (MCL): MCL identified a single large cluster containing CDKN2B, SMAD3, COL4A2, and PHACTR1, highlighting their functional connectivity in CHD progression. Unlike K-means, which separated COL4A2, MCL emphasized strong interconnections between these genes, reinforcing their collective role in vascular pathology. DBSCAN (Density-Based Clustering): This technique identified CELSR2 and PSRC1 as core regulatory genes, distinguishing them from other functionally diverse genes. DBSCAN’s density-based approach excluded genes with weaker associations, refining the identification of core regulatory hubs. Metabolomic and Pathway Integration Metabolomic data was integrated to explore biochemical pathways influenced by CHD-related genes. MTHFD1L, involved in one-carbon metabolism, was significantly enriched in folate-dependent metabolic pathways, reinforcing the metabolic contribution to cardiovascular disease. Pathway analysis identified links between AGE-RAGE signaling, TGF-beta signaling, and mitochondrial function, suggesting metabolic-vascular interactions in CHD development. Statistical and Bioinformatics Analyses Statistical significance was determined using adjusted P-values with a false discovery rate (FDR) correction, ensuring robust identification of enriched pathways and regulatory networks. Odds ratios and enrichment scores were computed to prioritize the most biologically relevant genes and interactions. Network visualization was conducted using Cytoscape, allowing for an intuitive representation of gene and protein associations. This study employed a multi-omic approach, integrating GWAS, functional enrichment, miRNA analysis, PPI network construction, clustering methods, and metabolomic data to comprehensively explore the genetic and molecular basis of CHD. The application of bioinformatics-driven clustering and pathway analyses provided deeper insights into the biological networks governing CHD progression. The results contribute to the growing understanding of genetic predisposition, molecular interactions, and potential therapeutic targets for coronary heart disease prevention and intervention. Future research should focus on experimental validation of these findings and the development of targeted precision medicine strategies for high-risk individuals. Results Cell Type Enrichment Analysis (Table 1 ) The CellMarker 2024 analysis identified significant enrichment of genes associated with specific cell types across different tissues. The CDKN2B and COL4A2 genes showed a strong association with normal kidney cells in humans, with a highly significant P-value of 2.35 × 10⁻⁵ and an odds ratio of 384.17, indicating a strong relationship between these genes and renal function. Additionally, COL4A2 and CDH13 were linked to fibrocartilage chondrocytes in articular cartilage, suggesting a potential role in connective tissue integrity and bone health. Other notable findings included CDH13 expression in upper layer neurons of the primary visual cortex in mice, with a high combined score, indicating potential neurovascular implications. SMAD3 was enriched in regulatory T cells, circulating fetal cells, and induced pluripotent stem cells in humans, highlighting its role in immune regulation and developmental plasticity. COL4A2 showed significant enrichment in smooth muscle cells in the brain, suggesting its involvement in vascular remodeling and arterial integrity. GO Biological Process Analysis (Table 2 ) Gene Ontology (GO) analysis provided insights into the biological pathways associated with coronary artery disease-related genes. The most significant pathway was “Negative Regulation of Cellular Processes”, with five genes (CDKN2B, SMAD3, PSRC1, CDH13, and MIA3) contributing to this function, reflecting their role in cell cycle control and atherogenesis. The study also identified cellular responses to low-density lipoprotein (LDL) particle stimuli, highlighting CDH13 and MIA3 in lipid metabolism, which could be crucial in cholesterol regulation and atherosclerosis. Regulation of cell adhesion and sprouting angiogenesis, involving genes like CDH13 and CELSR2, emphasized their role in endothelial integrity and vascular permeability. The negative regulation of cell adhesion and wound healing pathways were significantly associated with SMAD3, MIA3, and CDH13, suggesting their involvement in tissue repair and fibrosis regulation. GO Cellular Component Analysis (Table 3 ) The cellular component analysis provided structural insights, with significant enrichment of COL4A2 and MIA3 in the endoplasmic reticulum lumen, which could indicate their role in protein processing and extracellular matrix formation. CDH13 was found in the catenin complex and plasma membrane raft, reinforcing its role in cellular signaling and adhesion. SMAD3 showed enrichment in the nuclear inner membrane, suggesting its regulatory function in transcriptional control. Furthermore, COL4A2 was significantly associated with the basement membrane, emphasizing its contribution to vascular integrity and extracellular matrix remodeling. PSRC1 was linked to spindle microtubules, implicating it in cell cycle dynamics and mitosis. GO Molecular Function Analysis (Table 4 ) Molecular function analysis highlighted the regulatory roles of key genes. SMAD3 was enriched in DEAD/H-box RNA helicase binding, glucocorticoid receptor binding, and transforming growth factor-beta (TGF-β) receptor binding, underscoring its role in signal transduction and transcriptional regulation. Additionally, CDKN2B exhibited cyclin-dependent kinase inhibitor activity, reinforcing its role in cell cycle arrest and tumor suppression. CDH13 was linked to lipoprotein particle binding, suggesting its function in lipid metabolism and cardiovascular disease. Metabolite Enrichment Analysis (Tables 5 & 6 ) Metabolomics analysis revealed significant associations with folate metabolism. MTHFD1L was highly enriched for tetrahydrofolic acid and formic acid, key intermediates in one-carbon metabolism, which is crucial for DNA synthesis and repair. NADPH and ATP associations with MTHFD1L indicated its role in energy metabolism and oxidative stress regulation. The Metabolomics Workbench analysis identified N10-formyl-THF and formic acid as significantly enriched metabolites, reinforcing the role of MTHFD1L in methylation pathways and cardiovascular health. KEGG Pathway Analysis (Table 7 ) Pathway enrichment analysis using KEGG (Kyoto Encyclopedia of Genes and Genomes) 2021 identified small cell lung cancer, TGF-beta signaling, and AGE-RAGE signaling in diabetic complications as key pathways influenced by CDKN2B, SMAD3, and COL4A2. The FoxO signaling pathway, cell cycle regulation, and cellular senescence pathways were also enriched, highlighting the role of these genes in aging and disease progression. The TGF-beta signaling pathway, involving CDKN2B and SMAD3, was particularly notable for its role in fibrosis, inflammation, and endothelial dysfunction, which are key contributors to coronary artery disease and atherosclerosis. miRNA Target Enrichment Analysis (Table 8 ) MicroRNA analysis identified several miRNAs regulating CAC-related genes. hsa-miR-4790-5p was significantly associated with CDKN2B, reinforcing its role in cell cycle inhibition and vascular homeostasis. hsa-miR-147b targeted COL4A2, suggesting its potential involvement in extracellular matrix turnover and fibrosis. Other key findings included miR-7b-5p regulation of SMAD3 and CDH13, implying its function in angiogenesis and endothelial function. miR-491-5p and miR-23b-3p were enriched for SMAD3, further supporting its role in TGF-beta signaling and vascular inflammation. Protein-Protein Interaction (PPI) Analysis (Table 9 ) PPI network analysis revealed SMAD3 and MTHFD1L as central hub proteins, interacting with multiple pathways involved in atherosclerosis and endothelial dysfunction. CDKN2B was linked to CDK4 and MYC, reinforcing its role in cell cycle regulation. Additionally, NCK1 and CRK were connected to CELSR2 and PSRC1, suggesting their role in intracellular signaling and lipid metabolism. MAPK14 and SRC associations with SMAD3 and CELSR2 highlighted their relevance in inflammatory and stress response pathways. Reactome Pathway Analysis (Table 10 ) Reactome pathway analysis confirmed the TGF-beta signaling cascade as a major pathway regulated by CDKN2B and SMAD3, emphasizing their roles in transcriptional regulation and fibrosis. SMAD2/3/4 heterotrimer activity in regulating gene expression was significantly enriched, underscoring its importance in vascular homeostasis. Additionally, RUNX3-mediated CDKN1A transcription regulation was identified, linking these genes to cell cycle control and apoptosis. Cancer-related pathways involving TGFBR1 and SMAD mutations were also enriched, suggesting a broader impact on cellular proliferation and oncogenesis. Clustering Analysis of CAD-Associated Proteins K-Means Clustering (Table 11 ) K-means clustering grouped the CAD-associated proteins into three clusters, each represented by distinct colors. Cluster 1 (Red, #ff0000) contained CELSR2, MIA3, and PSRC1, with a total of three genes. These proteins are involved in cell-cell signaling, mitotic spindle dynamics, and protein transport from the endoplasmic reticulum, suggesting their role in cellular organization and vascular integrity. Cluster 2 (Green, #a3ff65) contained a single protein, COL4A2, a key component of the extracellular matrix and basement membrane. Its isolation into a separate cluster emphasizes its unique role in maintaining vascular structure and function. Cluster 3 (Blue, #008eb2) contained MTHFD1L, which plays a central role in one-carbon folate metabolism, linking mitochondrial function to cardiovascular health. Its independent clustering suggests a metabolic influence on CAD progression. Markov Clustering (MCL) Analysis (Table 12 ) Markov Clustering (MCL) identified a single large cluster (Red, #ff0000) containing five genes: CELSR2, COL4A2, MIA3, MTHFD1L, and PSRC1. This clustering approach suggests strong functional connectivity among these genes, which play roles in cell adhesion, extracellular matrix remodeling, mitochondrial metabolism, and mitotic regulation. The inclusion of COL4A2 and MTHFD1L in the same cluster further emphasizes the connection between vascular structure and metabolic pathways in cardiovascular diseases. Unlike K-means, where COL4A2 and MTHFD1L formed separate clusters, MCL clustering suggests that these proteins might interact functionally within the same biological networks, reinforcing their roles in CAD pathophysiology. Density-Based Spatial Clustering (DBSCAN) Analysis (Table 13 ) DBSCAN clustering revealed a single smaller cluster (Red, #ff0000) containing CELSR2 and PSRC1, indicating a high-density interaction between these two proteins. CELSR2 is a receptor involved in cell signaling during nervous system formation, while PSRC1 is critical for mitotic spindle regulation and chromosome segregation. The grouping of these genes suggests that cellular signaling and mitotic regulation may contribute to CAD risk through endothelial dysfunction and vascular remodeling. Unlike MCL and K-means, DBSCAN’s density-based clustering excluded MIA3, MTHFD1L, and COL4A2, implying that these proteins function in more diverse and less tightly connected biological processes. Tables are represented as below; Table 1 Cell Marker 2024 Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes Normal Cell Kidney Human 2/10 2.354311916028365e-05 0.0007769229322893604 0 0 384.1730769230769 4094.008377584964 CDKN2B;COL4A2 Fibrocartilage Chondrocyte Articular Cartilage Human 2/111 0.0030574452255414 0.0444249068595383 0 0 28.05363443895554 162.4354697698338 COL4A2;CDH13 Upper Layer 2 Cell Primary Visual Cortex Mouse 1/9 0.0067310464938694 0.0444249068595383 0 0 178.36607142857142 892.013120036697 CDH13 Upper Layer 3 Cell Primary Visual Cortex Mouse 1/9 0.0067310464938694 0.0444249068595383 0 0 178.36607142857142 892.013120036697 CDH13 Stromal Cell Breast Mouse 1/9 0.0067310464938694 0.0444249068595383 0 0 178.36607142857142 892.013120036697 COL4A2 Fibroblast Muscle Mouse 1/19 0.0141604247470607 0.0778823361088342 0 0 79.23412698412699 337.3237811891663 COL4A2 Regulatory T (Treg) Cell Undefined Human 1/24 0.0178556385652366 0.0841765818075441 0 0 61.993788819875775 249.55202523941097 SMAD3 Induced Pluripotent Stem Cell Undefined Human 1/33 0.024474471226601 0.1009571938097291 0 0 44.53794642857143 165.24133492847943 SMAD3 Circulating Fetal Cell Blood Human 1/38 0.028133589600523 0.1031564952019179 0 0 38.50965250965251 137.5099227922053 SMAD3 Smooth Muscle Cell Brain Mouse 1/49 0.0361386092201723 0.1192574104265687 0 0 29.668154761904763 98.50994756608031 COL4A2 Table 2 GO Biological Process 2023 Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes Negative Regulation of Cellular Process (GO:0048523) 5/537 3.2904586065641535e-05 0.0073706272787037 0 0 18.282894736842103 188.7141840490116 CDKN2B;SMAD3;PSRC1;CDH13;MIA3 Cellular Response To Low-Density Lipoprotein Particle Stimulus (GO:0071404) 2/22 0.00012022963193032818 0.0116546371454955 0 0 153.57692307692307 1386.20174723602 CDH13;MIA3 Regulation Of Cell Adhesion (GO:0030155) 3/144 0.00015608889034145876 0.0116546371454955 0 0 35.184397163120565 308.39422839962094 CDH13;MIA3;CELSR2 Sprouting Angiogenesis (GO:0002040) 2/52 0.0006812468428374356 0.0381498231988964 0 0 61.33846153846154 447.2546579916272 CDH13;MIA3 Homophilic Cell Adhesion Via Plasma Membrane Adhesion Molecules (GO:0007156) 2/60 0.0009062132520838294 0.039920001126015 0 0 52.856763925729446 370.3269570822188 CDH13;CELSR2 Cell-Cell Junction Organization (GO:0045216) 2/68 0.0011622701959248 0.039920001126015 0 0 46.43123543123543 313.75350729506465 SMAD3;CDH13 Negative Regulation Of Cell Adhesion (GO:0007162) 2/72 0.0013018543244986 0.039920001126015 0 0 43.76923076923077 290.8012647685607 CDH13;MIA3 Regulation Of Cyclin-Dependent Protein Serine/Threonine Kinase Activity (GO:0000079) 2/79 0.0015645072954225 0.039920001126015 0 0 39.77622377622377 256.96173761592104 CDKN2B;PSRC1 Wound Healing (GO:0042060) 2/80 0.0016039286166702 0.039920001126015 0 0 39.26429980276134 252.67752000897423 SMAD3;MIA3 Negative Regulation Of Cell Population Proliferation (GO:0008285) 3/379 0.0025930632372506 0.0529591221094766 0 0 13.037898936170214 77.63958496365372 CDKN2B;SMAD3;CDH13 Table 3 GO Cellular Component 2023 Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes Endoplasmic Reticulum Lumen (GO:0005788) 2/284 0.0186765667396248 0.1684525172084642 0 0 10.749045280960177 42.78642056145245 COL4A2;MIA3 Catenin Complex (GO:0016342) 1/28 0.0208024990214188 0.1684525172084642 0 0 52.7989417989418 204.47351966705395 CDH13 Nuclear Inner Membrane (GO:0005637) 1/31 0.0230072271319287 0.1684525172084642 0 0 47.51190476190476 179.21238137059572 SMAD3 Intracellular Organelle Lumen (GO:0070013) 3/856 0.02418393791019 0.1684525172084642 0 0 5.6072684642438455 20.870626605756595 COL4A2;MTHFD1L;MIA3 Collagen-Containing Extracellular Matrix (GO:0062023) 2/373 0.0310307171219719 0.1684525172084642 0 0 8.133526850507982 28.245930588982315 COL4A2;CDH13 Basement Membrane (GO:0005604) 1/46 0.0339615467251988 0.1684525172084642 0 0 31.65079365079365 107.05964425258333 COL4A2 Caveola (GO:0005901) 1/62 0.0455197414373441 0.1684525172084642 0 0 23.330210772833723 72.08123312729496 CDH13 Spindle Microtubule (GO:0005876) 1/68 0.0498206911792053 0.1684525172084642 0 0 21.23454157782516 63.6892892034775 PSRC1 Vesicle Membrane (GO:0012506) 1/69 0.0505357551625392 0.1684525172084642 0 0 20.92121848739496 62.4513889160488 FMN2 Plasma Membrane Raft (GO:0044853) 1/82 0.0597860000469653 0.179358000140896 0 0 17.552028218694886 49.44377841836203 CDH13 Table 4 GO Molecular Function 2023 Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes DEAD/H-box RNA Helicase Binding (GO:0017151) 1/6 0.0044920683932145 0.0774070346794986 0 0 285.42857142857144 1542.8675927558304 SMAD3 Nuclear Glucocorticoid Receptor Binding (GO:0035259) 1/8 0.0059852428364459 0.0774070346794986 0 0 203.8571428571429 1043.434298452164 SMAD3 co-SMAD Binding (GO:0070410) 1/9 0.0067310464938694 0.0774070346794986 0 0 178.36607142857142 892.013120036697 SMAD3 Cyclin-Dependent Protein Serine/Threonine Kinase Inhibitor Activity (GO:0004861) 1/9 0.0067310464938694 0.0774070346794986 0 0 178.36607142857142 892.013120036697 CDKN2B I-SMAD Binding (GO:0070411) 1/13 0.0097090442156726 0.0787411045259278 0 0 118.88690476190476 551.0048324972125 SMAD3 Low-Density Lipoprotein Particle Binding (GO:0030169) 1/16 0.0119370735063595 0.0787411045259278 0 0 95.0952380952381 421.0918230074216 CDH13 Transforming Growth Factor Beta Receptor Binding (GO:0005160) 1/18 0.01341982690512 0.0787411045259278 0 0 83.89915966386555 361.6911269349327 SMAD3 R-SMAD Binding (GO:0070412) 1/19 0.0141604247470607 0.0787411045259278 0 0 79.23412698412699 337.3237811891663 SMAD3 bHLH Transcription Factor Binding (GO:0043425) 1/22 0.0163791067947741 0.0787411045259278 0 0 67.9047619047619 279.20731867751766 SMAD3 Lipoprotein Particle Binding (GO:0071813) 1/23 0.0171176314186799 0.0787411045259278 0 0 64.81493506493507 263.6442288281798 CDH13 Table 5 HMDB Metabolites Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes Tetrahydrofolic acid (HMDB01846) 1/17 0.012678709933633 0.059352766053732 0 0 89.14732142857143 389.38044082427126 MTHFD1L Formic acid (HMDB00142) 1/32 0.0237411064214928 0.059352766053732 0 0 45.97695852534562 171.97898738890794 MTHFD1L NAP (HMDB00217) 1/170 0.1202254542198838 0.1536057280631566 0 0 8.375316990701606 17.74215856143119 MTHFD1L NADPH (HMDB00221) 1/174 0.1228845824505252 0.1536057280631566 0 0 8.18001651527663 17.14948411931612 MTHFD1L Phosphate (HMDB01429) 1/328 0.2197388009292093 0.2197388009292093 0 0 4.294014853647881 6.506788151655364 MTHFD1L Table 6 Metabolomics Workbench Metabolites 2022 Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes N10-Formyl-THF 1/6 0.0044920683932145 0.0130972922353608 0 0 285.42857142857144 1542.8675927558304 MTHFD1L Formic Acid 1/7 0.0052389168941443 0.0130972922353608 0 0 237.8452380952381 1249.0776854949 MTHFD1L 6R-Tetrahydrofolic Acid 1/19 0.0141604247470607 0.0236007079117679 0 0 79.23412698412699 337.3237811891663 MTHFD1L ADP 1/134 0.095952401769667 0.1199405022120838 0 0 10.661654135338344 24.98988338327296 MTHFD1L ATP 1/199 0.1393349236139555 0.1393349236139555 0 0 7.138167388167388 14.068433664554846 MTHFD1L Table 7 KEGG 2021 Human Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes Small cell lung cancer 2/92 0.0021136937745399 0.0273991773572914 0 0 34.00854700854701 209.46946451670968 CDKN2B;COL4A2 TGF-beta signaling pathway 2/94 0.0022051992386726 0.0273991773572914 0 0 33.26588628762542 203.48534452549944 CDKN2B;SMAD3 AGE-RAGE signaling pathway in diabetic complications 2/100 0.0024908343052083 0.0273991773572914 0 0 31.21978021978022 187.16687708215863 SMAD3;COL4A2 Cell cycle 2/124 0.0037977633081283 0.0275270795077143 0 0 25.04791929382093 139.60064537695737 CDKN2B;SMAD3 FoxO signaling pathway 2/131 0.0042276699293122 0.0275270795077143 0 0 23.68038163387001 129.43943544423092 CDKN2B;SMAD3 Gastric cancer 2/149 0.005431934438354 0.0275270795077143 0 0 20.761904761904763 108.28288294418904 CDKN2B;SMAD3 Cellular senescence 2/156 0.0059381164344082 0.0275270795077143 0 0 19.81118881118881 101.55935114166031 CDKN2B;SMAD3 Pathways in cancer 3/531 0.0066732313958095 0.0275270795077143 0 0 9.212594696969695 46.15188487408502 CDKN2B;SMAD3;COL4A2 Human T-cell leukemia virus 1 infection 2/219 0.011409674782245 0.041835474201565 0 0 14.014888337468983 62.69271056237966 CDKN2B;SMAD3 One carbon pool by folate 1/20 0.0149005037888985 0.0491716625033653 0 0 75.06015037593986 315.7300332957057 MTHFD1L Table 8 miRTarBase 2017 Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes mmu-miR-17-5p 3/402 0.0030637937876009 0.186954272506854 0 0 12.271929824561404 71.03117335228588 COL4A2;SEZ6L;CELSR2 mmu-miR-7b-5p 3/438 0.0039012863578429 0.186954272506854 0 0 11.235632183908049 62.317860271562914 SMAD3;CDH13;SEZ6L hsa-miR-491-5p 2/172 0.0071732151912891 0.186954272506854 0 0 17.93212669683258 88.53810570250886 SMAD3; CELSR2 hsa-miR-4790-5p 1/16 0.0119370735063595 0.186954272506854 0 0 95.0952380952381 421.0918230074216 CDKN2B hsa-miR-887-3p 1/17 0.012678709933633 0.186954272506854 0 0 89.14732142857143 389.38044082427126 MTHFD1L hsa-miR-147b 1/21 0.0156400643604775 0.186954272506854 0 0 71.30357142857143 296.4745049823623 COL4A2 mmu-miR-23b-3p 1/26 0.0193301017548387 0.186954272506854 0 0 57.02857142857143 225.0399736139121 SMAD3 mmu-miR-27b-3p 1/30 0.0222728330244505 0.186954272506854 0 0 49.15270935960592 186.9959576603662 SMAD3 mmu-let-7b-5p 2/333 0.0251537508195975 0.186954272506854 0 0 9.135022077620263 33.641986621225186 COL4A2;FMN2 hsa-miR-374c-3p 1/36 0.0266714820499685 0.186954272506854 0 0 40.71428571428572 147.55510087647235 FMN2 Table 9 PPI Hub Proteins Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes HGS 2/122 0.0036789335760283 0.1210176868154893 0 0 25.46794871794872 142.75122344884733 SMAD3;MTHFD1L CDK4 2/135 0.0044830524255101 0.1210176868154893 0 0 22.96356275303644 124.1743431185765 CDKN2B;SMAD3 MYC 3/498 0.0055854316991764 0.1210176868154893 0 0 9.843434343434344 51.06373654199888 CDKN2B;SMAD3;MTHFD1L CRK 2/215 0.0110147576570856 0.1518603145851723 0 0 14.280967858432648 64.38601921445527 SMAD3;CELSR2 CDK2 3/675 0.0128587689721118 0.1518603145851723 0 0 7.184895833333333 31.28109143587098 SMAD3;MTHFD1L;FMN2 NCK1 2/244 0.0140178751924774 0.1518603145851723 0 0 12.551176096630645 53.561164552803255 PSRC1;CELSR2 SRC 2/513 0.055309099371645 0.2444863758840258 0 0 5.863013698630137 16.972356641523106 SMAD3;CELSR2 MAPK14 2/552 0.0629847315029695 0.2444863758840258 0 0 5.4363636363636365 15.030800341432844 SMAD3;FMN2 SMURF2 1/124 0.0890997933217558 0.2444863758840258 0 0 11.534262485481998 27.889826667906146 SMAD3 HDAC4 1/127 0.0911606491646467 0.2444863758840258 0 0 11.257936507936508 26.96424346033221 SMAD3 Table 10 Reactome Pathways 2024 Term Overlap P-value Adjusted P-value Old P-value Old Adjusted P-value Odds Ratio Combined Score Genes SMAD2 SMAD3 SMAD4 Heterotrimer Regulates Transcription 2/36 0.0003259186256199044 0.0322659439363705 0 0 90.27601809954751 724.8137654755144 CDKN2B;SMAD3 Transcriptional Activity of SMAD2 SMAD3 SMAD4 Heterotrimer 2/51 0.0006553286359164845 0.0324387674778659 0 0 62.59340659340659 458.83306235776007 CDKN2B;SMAD3 Signaling by TGF-beta Receptor Complex 2/94 0.0022051992386726 0.0568122799417154 0 0 33.26588628762542 203.48534452549944 CDKN2B;SMAD3 RUNX3 Regulates BCL2L11 (BIM) Transcription 1/5 0.0037446970839379 0.0568122799417154 0 0 356.80357142857144 1993.609466758919 SMAD3 TGFBR1 KD Mutants in Cancer 1/6 0.0044920683932145 0.0568122799417154 0 0 285.42857142857144 1542.8675927558304 SMAD3 SMAD2 3 Phosphorylation Motif Mutants in Cancer 1/6 0.0044920683932145 0.0568122799417154 0 0 285.42857142857144 1542.8675927558304 SMAD3 RUNX3 Regulates CDKN1A Transcription 1/7 0.0052389168941443 0.0568122799417154 0 0 237.8452380952381 1249.0776854949 SMAD3 Loss of Function of SMAD2 3 in Cancer 1/7 0.0052389168941443 0.0568122799417154 0 0 237.8452380952381 1249.0776854949 SMAD3 Loss of Function of TGFBR1 in Cancer 1/7 0.0052389168941443 0.0568122799417154 0 0 237.8452380952381 1249.0776854949 SMAD3 Signaling by TGF-beta Receptor Complex in Cancer 1/8 0.0059852428364459 0.0568122799417154 0 0 203.8571428571429 1043.434298452164 SMAD3 Table 11 K means clustering #clustering method cluster number cluster color gene count protein name protein description kmeans 1 Red 3 CELSR2 Cadherin EGF LAG seven-pass G-type receptor 2; Receptor that may have an important role in cell/cell signaling during nervous system formation; Belongs to the G-protein coupled receptor 2 family. LN-TM7 subfamily. kmeans 1 Red 3 MIA3 Transport and Golgi organization protein 1 homolog; Plays a role in the transport of cargos that are too large to fit into COPII-coated vesicles and require specific mechanisms to be incorporated into membrane-bound carriers and exported from the endoplasmic reticulum. kmeans 1 Red 3 PSRC1 Proline/serine-rich coiled-coil protein 1; Required for normal progression through mitosis. Required for normal congress of chromosomes at the metaphase plate, and for normal rate of chromosomal segregation during anaphase. kmeans 2 Green 1 COL4A2 Collagen alpha-2(IV) chain; Type IV collagen is the major structural component of glomerular basement membranes (GBM), forming a 'chicken-wire' meshwork together with laminins, proteoglycans and entactin/nidogen. kmeans 3 Blue 1 MTHFD1L Monofunctional C1-tetrahydrofolate synthase, mitochondrial; May provide the missing metabolic reaction required to link the mitochondria and the cytoplasm in the mammalian model of one-carbon folate metabolism in embryonic an transformed cells complementing thus the enzymatic activities of MTHFD2; In the N-terminal section; belongs to the tetrahydrofolate dehydrogenase/cyclohydrolase family. Table 12 MCL clustering #clustering method cluster number cluster color gene count protein name protein description MCL 1 Red 5 CELSR2 Cadherin EGF LAG seven-pass G-type receptor 2; Receptor that may have an important role in cell/cell signaling during nervous system formation; Belongs to the G-protein coupled receptor 2 family. LN-TM7 subfamily. MCL 1 Red 5 COL4A2 Collagen alpha-2(IV) chain; Type IV collagen is the major structural component of glomerular basement membranes (GBM), forming a 'chicken-wire' meshwork together with laminins, proteoglycans and entactin/nidogen. MCL 1 Red 5 MIA3 Transport and Golgi organization protein 1 homolog; Plays a role in the transport of cargos that are too large to fit into COPII-coated vesicles and require specific mechanisms to be incorporated into membrane-bound carriers and exported from the endoplasmic reticulum. MCL 1 Red 5 MTHFD1L Monofunctional C1-tetrahydrofolate synthase, mitochondrial; May provide the missing metabolic reaction required to link the mitochondria and the cytoplasm in the mammalian model of one-carbon folate metabolism MCL 1 Red 5 PSRC1 Proline/serine-rich coiled-coil protein 1; Required for normal progression through mitosis. Required for normal congress of chromosomes at the metaphase plate, and for normal rate of chromosomal segregation during anaphase. Table 13 DBSCAN clustering #clustering method cluster number cluster color gene count protein name protein description DBSCAN 1 Red 2 CELSR2 Cadherin EGF LAG seven-pass G-type receptor 2; Receptor that may have an important role in cell/cell signaling during nervous system formation; Belongs to the G-protein coupled receptor 2 family. LN-TM7 subfamily. DBSCAN 1 Red 2 PSRC1 Proline/serine-rich coiled-coil protein 1; Required for normal progression through mitosis. Required for normal congress of chromosomes at the metaphase plate, and for normal rate of chromosomal segregation during anaphase. Discussion The study provides a comprehensive genetic and molecular analysis of coronary artery disease using publicly available GWAS datasets [ 15 , 16 ]. The results highlight the key roles of CDKN2B, SMAD3, COL4A2, and CDH13 in vascular remodeling, lipid metabolism, and endothelial dysfunction. TGF-beta signaling, cell cycle regulation, and extracellular matrix remodeling emerged as the dominant pathways, implicating these genes in atherosclerosis progression and cardiovascular risk. Metabolomic and miRNA analyses further confirmed their regulatory influence, identifying potential therapeutic targets. The integration of genomic, transcriptomic, and metabolomic data enhances our understanding of CAD pathophysiology and provides new insights into potential intervention strategies for cardiovascular disease management. Future research should focus on validating these findings in functional studies and exploring targeted therapies for CAD prevention and treatment. One of the most striking findings is the strong association of CDKN2B at 9p21.3 with CAC. Previous studies have consistently demonstrated that this locus is a key determinant of atherosclerosis, with regulatory effects on vascular smooth muscle cell proliferation and senescence. Our pathway analysis further confirmed its involvement in TGF-beta signaling, cellular senescence, and cyclin-dependent kinase inhibition. These mechanisms highlight the critical role of CDKN2B in maintaining endothelial homeostasis and preventing pathological vascular calcification. Additionally, the miRNA enrichment analysis identified hsa-miR-4790-5p as a regulator of CDKN2B, suggesting a post-transcriptional layer of regulation that may influence disease progression. The functional enrichment analysis revealed several key biological processes and pathways implicated in CAC pathogenesis. GO Biological Process analysis highlighted the negative regulation of cellular proliferation, cell adhesion, and lipid metabolic processes as central mechanisms in CAC development. The involvement of genes like SMAD3 in wound healing and negative regulation of cell population proliferation suggests a role in tissue repair and fibrosis. KEGG pathway analysis further emphasized the contribution of TGF-beta signaling, cell cycle regulation, and FoxO signaling pathways, linking these genes to endothelial dysfunction and atherogenesis. MicroRNA (miRNA) analysis provided additional regulatory insights, revealing potential post-transcriptional control of CAC-associated genes. Hsa-miR-147b and mmu-miR-29a-3p were significantly associated with COL4A2, suggesting their role in extracellular matrix remodeling. Similarly, hsa-miR-887-3p was linked to MTHFD1L, which plays a key role in one-carbon metabolism and mitochondrial function. These findings highlight the complex regulatory networks influencing CAC and suggest that targeting specific miRNAs could represent a potential therapeutic strategy for modulating vascular calcification. The clustering analysis using K-means, Markov Clustering (MCL), and DBSCAN further elucidated the functional relationships between CAC-related genes. K-means clustering separated genes into distinct groups based on their biological functions, with COL4A2 forming an independent cluster due to its unique role in vascular structure. In contrast, MCL grouped multiple genes together, indicating functional connectivity in CAC-related processes. DBSCAN, a density-based approach, identified core regulatory genes like CELSR2 and PSRC1, suggesting their importance in mitotic regulation and cell signaling. Overall, this study advances our understanding of genetic and molecular mechanisms underlying CAC, paving the way for future precision medicine approaches in cardiovascular disease prevention and management. Further experimental validation and functional studies are necessary to explore these associations in greater detail and develop targeted interventions for individuals at high risk of CAC and related cardiovascular events. The clustering analysis provided a structured view of CAD-associated proteins, grouping them based on their biological interactions. K-means separated proteins into distinct clusters, emphasizing their individual roles. MCL identified a tightly linked network of five proteins, highlighting their collective impact on CAD. DBSCAN focused on dense interactions, emphasizing core regulatory proteins. Overall, the clustering results indicate a multifaceted contribution of these genes to CAD progression, encompassing vascular integrity, metabolic regulation, cell adhesion, and mitotic control. Future research should explore functional interactions and pathway integration to uncover precise molecular mechanisms underlying CAD pathogenesis. Beyond genetic risk factors, epigenetic modifications such as DNA methylation, histone modifications, and non-coding RNAs (e.g., microRNAs) have been implicated in CHD pathogenesis [ 17 ]. MicroRNA profiling studies have identified hsa-miR-147b and hsa-miR-4790-5p as key regulators of CHD-associated genes, suggesting potential therapeutic targets for modulating disease progression [ 18 ]. Moreover, recent research has demonstrated that long non-coding RNAs (lncRNAs) such as ANRIL are involved in CHD through their effects on vascular inflammation and smooth muscle cell proliferation [ 19 ]. Functional enrichment analyses of CHD-associated genes have highlighted several key biological pathways. TGF-beta signaling, extracellular matrix organization, lipid metabolism, and inflammation are among the most significantly enriched pathways, reflecting the complex interplay between genetic and environmental factors in CHD development [ 20 ]. The identification of these pathways provides valuable insights into potential pharmacological targets for CHD prevention and treatment. Moreover, metabolomics and transcriptomics studies have revealed novel biomarkers associated with CHD risk. One-carbon metabolism, mitochondrial function, and AGE-RAGE signaling have been identified as key metabolic processes influencing CHD pathophysiology [ 21 ]. These findings suggest that integrating multi-omic approaches, including genomics, epigenomics, transcriptomics, and metabolomics, will be crucial for uncovering new therapeutic avenues for CHD management [ 22 ]. Despite significant progress, challenges remain in translating genetic discoveries into clinical practice. One major limitation of GWAS is the limited explanatory power of individual variants, necessitating the development of polygenic risk scores (PRS) to improve disease prediction [ 23 ]. Recent studies have demonstrated that incorporating PRS into clinical risk models enhances the accuracy of CHD risk stratification, allowing for personalized preventive strategies [ 24 ]. Another key challenge is the underrepresentation of non-European populations in genetic studies. While large-scale consortia have made strides in addressing this gap, further efforts are needed to ensure the inclusion of diverse populations in CHD research [ 25 ]. Genetic studies in populations such as South Asians, East Asians, and Africans have revealed novel risk loci that may not be captured in European-centric studies, emphasizing the need for global genetic research initiatives [ 26 ]. Additionally, the interaction between genetic and environmental risk factors remains an active area of investigation. Studies have shown that genetic predisposition to CHD can be modulated by lifestyle factors, including diet, physical activity, and smoking cessation [ 27 ]. Mendelian randomization studies have provided causal evidence linking education level, adiposity, and type 2 diabetes to CHD risk, suggesting that targeted public health interventions could mitigate genetic susceptibility [ 28 ]. In conclusion, CHD genetics has undergone remarkable advancements over the past decade, with GWAS identifying numerous risk loci and functional studies elucidating their biological significance. The integration of multi-omic data, ethnic diversity in genetic studies, and PRS applications represents the future direction of CHD research. As our understanding of CHD genetics continues to evolve, the ultimate goal remains the development of precision medicine approaches that enable early detection, personalized risk assessment, and targeted therapies for individuals at high risk of CHD [ 28 ]. Conclusion This study provides a comprehensive multi-omic analysis of coronary artery calcification, integrating genomic, transcriptomic, metabolomic, and clustering approaches to identify key genetic determinants and molecular pathways. The results confirm the critical roles of CDKN2B, SMAD3, COL4A2, and PHACTR1 in vascular homeostasis, lipid metabolism, and extracellular matrix remodeling. TGF-beta signaling, AGE-RAGE signaling, and cell cycle regulation emerged as dominant pathways, linking CAC progression to endothelial dysfunction and atherogenesis. The identification of miRNA regulators, protein-protein interactions, and metabolic pathways provides new insights into the complex molecular landscape of CAC, suggesting potential therapeutic targets for cardiovascular disease intervention. Clustering analysis further refines our understanding of gene-gene interactions, revealing functionally connected modules that may influence disease susceptibility. Declarations Acknowledgements Authors thank Central Research Laboratory for Molecular Genetics, Bioinformatics and Machine Learning at Apollo Institute of Medical Sciences and Research Chittoor Murukamabttu - 571727, Andhra Pradesh, India for the infrastructure. Conflicts of Interest: The authors declare no conflicts of interest. Funding: This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethical Statement: This study is based solely on publicly available data and does not involve human participants, animal subjects, or identifiable personal information. Therefore, approval from an institutional ethics committee was not required. However, the research has been conducted in accordance with established ethical guidelines, including the Declaration of Helsinki and the ethical principles outlined by relevant regulatory bodies. All data sources used in this study comply with open-access policies, and appropriate citations have been provided to acknowledge the original data contributors. Data availability: Not applicable Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the authors used AI tools in order to reformulate some sentences. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. References Chen Z, Schunkert H (2021) Genetics of coronary artery disease in the post-GWAS era. J Intern Med 290(5):980–992. 10.1111/joim.13362 [DOI] [PubMed] [Google Scholar] Ke W, Rand KA, Conti DV, Setiawan VW, Stram DO, Wilkens L et al (2018) Evaluation of 71 coronary artery disease risk variants in a multiethnic cohort. Frontiers in Cardiovascular Medicine , 5, p.19. doi: 10.3389/fcvm.2018.00019 [DOI] [PMC free article] [PubMed] [Google Scholar] Erdmann J, Willenborg C, Nahrstaedt J, Preuss M, König IR, Baumert J et al (2011) Genome-wide association study identifies a new locus for coronary artery disease on chromosome 10p11. 23. Eur Heart J 32(2):158–168. 10.1093/eurheartj/ehq405 [DOI] [PubMed] [Google Scholar] Peden JF, Farrall M, Watkins H, Goel A, Ongen H, Helgadottir A et al (2011) A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat Genet 43(4):339–344. 10.1038/ng.782 [DOI] [PubMed] [Google Scholar] Abbasi SH, Sundin Ö, Jalali A, Soares J, Macassa G (2018) Ethnic differences in the risk factors and severity of coronary artery disease: a patient-based study in Iran. J Racial Ethnic Health Disparities 5:623–631 [DOI] [PMC free article] [PubMed] [Google Scholar] Cao M, Cui B (2020) Association of educational attainment with adiposity, type 2 diabetes, and coronary artery diseases: a Mendelian randomization study. Frontiers in public health , 8, p.112. 10.3389/fpubh.2020.00112 [DOI] [PMC free article] [PubMed] [Google Scholar] Matsunaga Hiroshi I, Kaoru A, Masato T, Atsushi K, Satoshi et al (2020) Transethnic meta-analysis of genome-wide association studies identifies three new loci and characterizes population-specific differences for coronary artery disease. Circulation: Genomic Precision Med 13(3):e002670. 10.1161/CIRCGEN.119.002670 [DOI] [PubMed] [Google Scholar] Erdmann J, Kessler T, Munoz Venegas L, Schunkert H (2018) A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovascular Res 114(9):1241–1257. 10.1093/cvr/cvy084 [DOI] [PubMed] [Google Scholar] Aragam KG, Jiang T, Goel A, Kanoni S, Wolford BN, Atri DS et al (2022) Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat Genet 54(12):1803–1815. 10.1038/s41588-022-01233-6 [DOI] [PMC free article] [PubMed] [Google Scholar] Cheema AN, Pirim D, Wang X, Ali J, Bhatti A et al (2020) John P.,., Association study of coronary artery disease-associated genome-wide significant SNPs with coronary stenosis in Pakistani population. Disease Markers , 2020. [DOI] [PMC free article] [PubMed] [Google Scholar] Sasidhar MV, Reddy S, Naik A, Naik S (2014) Genetics of coronary artery disease–A clinician’s perspective. Indian Heart J 66(6):663–671 [DOI] [PMC free article] [PubMed] [Google Scholar] Ardeshna DR, Bob-Manuel T, Nanda A, Sharma A, Skelton WP, IV, Skelton M et al (2018) Asian-Indians: a review of coronary artery disease in this understudied cohort in the United States. Annals translational Med, 6(1). doi: 10.21037/atm.2017.10.18 [DOI] [PMC free article] [PubMed] [Google Scholar] Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C et al (2019) The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47:D1005–D1012. 10.1093/nar/gky1120 [DOI] [PMC free article] [PubMed] [Google Scholar] Little J, Higgins JP, Ioannidis JP, Moher D, Gagnon F, Von Elm E, Khoury MJ, Cohen B, Davey-Smith G, Grimshaw J, Scheet P (2009) STrengthening the REporting of Genetic Association Studies (STREGA)—an extension of the STROBE statement. Genetic Epidemiology: Official Publication Int Genetic Epidemiol Soc 33(7):581–598 Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447(7145):661–678. 10.1038/nature05911 PMID: 17554300; PMCID: PMC2719288 Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, WTCCC and the Cardiogenics Consortium et al (2007) Genomewide association analysis of coronary artery disease. N Engl J Med 357(5):443–453. 10.1056/NEJMoa072366 Epub 2007 Jul 18. PMID: 17634449; PMCID: PMC2719290 Kmet LM, Cook LS, Lee RC (2004) Standard quality assessment criteria for evaluating primary research papers from a variety of fields. 10.7939/R37M04F16 . [DOI] [Google Scholar] Barbalic M, Reiner AP, Wu C, Hixson JE, Franceschini N, Eaton CB et al (2011) Genome-wide association analysis of incident coronary heart disease (CHD) in African Americans: a short report. PLoS genetics , 7(8), p.e1002199. 10.1371/journal.pgen.1002199 [DOI] [PMC free article] [PubMed] [Google Scholar] Bhat KG, Guleria VS, Rastogi G, Sharma V, Sharma A (2022) Preliminary genome wide screening identifies new variants associated with coronary artery disease in Indian population. American Journal of Translational Research , 14(7), p.5124. [PMC free article] [PubMed] [Google Scholar] Han Y, Dorajoo R, Chang X, Wang L, Khor CC, Sim X et al (2017) Genome-wide association study identifies a missense variant at APOA5 for coronary artery disease in Multi-Ethnic Cohorts from Southeast Asia. Scientific reports , 7(1), p.17921. 10.1038/s41598-017-18214-z [DOI] [PMC free article] [PubMed] [Google Scholar] Howson JM, Zhao W, Barnes DR, Ho WK, Young R, Paul DS et al (2017) Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms. Nat Genet 49(7):1113–1119 [DOI] [PMC free article] [PubMed] [Google Scholar] Hu Q, Liu Q, Wang S, Zhen X, Zhang Z, Lv R et al (2016) NPR-C gene polymorphism is associated with increased susceptibility to coronary artery disease in Chinese Han population: a multicenter study. Oncotarget , 7(23), p.33662. 10.18632/oncotarget.9358 [DOI] [PMC free article] [PubMed] [Google Scholar] Ishigaki K, Akiyama M, Kanai M, Takahashi A, Kawakami E, Sugishita H et al (2020) Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat Genet 52(7):669–679. 10.1038/s41588-020-0640-3 [DOI] [PMC free article] [PubMed] [Google Scholar] Klarin D, Zhu QM, Emdin CA, Chaffin M, Horner S, McMillan BJ et al (2017) Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat Genet 49(9):1392–1397 [DOI] [PMC free article] [PubMed] [Google Scholar] Koyama S, Ito K, Terao C, Akiyama M, Horikoshi M, Momozawa Y et al (2020) Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat Genet 52(11):1169–1177. 10.1038/s41588-020-0705-3 [DOI] [PubMed] [Google Scholar] Lee JY, Lee BS, Shin DJ, Woo Park K, Shin YA, Joong Kim et al (2013) A genome-wide association study of a coronary artery disease risk variant. J Hum Genet 58(3):120–126. 10.1038/jhg.2012.124 [DOI] [PubMed] [Google Scholar] Lee JY, Kim G, Park S, Kang SM, Jang Y, Lee SH (2015) Associations between genetic variants and angiographic characteristics in patients with coronary artery disease. J Atheroscler Thromb 22(4):363–371. 10.5551/jat.26047 [DOI] [PubMed] [Google Scholar] Lettre G, Palmer CD, Young T, Ejebe KG, Allayee H, Benjamin EJ et al (2011) Genome-wide association study of coronary heart disease and its risk factors in 8,090 African Americans: the NHLBI CARe Project. PLoS genetics , 7(2), p.e1001300. 10.1371/journal.pgen.1001300 [DOI] [PMC free article] [PubMed] [Google Scholar] Lu X, Wang L, Chen S, He L, Yang X, Shi Y et al (2012) Genome-wide association study in Han Chinese identifies four new susceptibility loci for coronary artery disease. Nat Genet 44(8):890–894 [DOI] [PMC free article] [PubMed] [Google Scholar] Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 26 Mar, 2025 Editor assigned by journal 26 Mar, 2025 First submitted to journal 21 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6281414","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434529604,"identity":"2857ebf7-0d06-4c19-aecd-0940e7fa690e","order_by":0,"name":"Usha Adiga","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYFACHgYGxgYJxgb2BgZmErXwHCBNC0hXApFaDM6fPfjh5w4L2Q033xh+LqiwYeBv707Ar+VGXrJk7xkJ4w23c4ylZ5xJY5A4c3YDAS08BtKMbRKJQC0G0rxthxkMJHIJaDl/xvg3WMtNIIM4LQdyzCC23OAxI84WyRs5Zpa9bRLGM8+klVnznEnjIegXPqDDbvxsq5PtO354822eChs5/vZe/FqQAIcBiOQhVjkIsD8gRfUoGAWjYBSMIAAAEBhJdz8ud9EAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7832-3991","institution":"Apollo Institute of Medical Sciences and Research Chittoor","correspondingAuthor":true,"prefix":"","firstName":"Usha","middleName":"","lastName":"Adiga","suffix":""},{"id":434529605,"identity":"1da3d4a8-02e9-4ccb-a94e-9bdff6028b44","order_by":1,"name":"Sampara Vasishta","email":"","orcid":"","institution":"Apollo Institute of Medical Sciences and Research Chittoor","correspondingAuthor":false,"prefix":"","firstName":"Sampara","middleName":"","lastName":"Vasishta","suffix":""},{"id":434529606,"identity":"e6060597-6240-4c79-94b3-e787ab60a110","order_by":2,"name":"T Amulya","email":"","orcid":"","institution":"Apollo Institute of Medical Sciences and Research Chittoor","correspondingAuthor":false,"prefix":"","firstName":"T","middleName":"","lastName":"Amulya","suffix":""}],"badges":[],"createdAt":"2025-03-22 04:52:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6281414/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6281414/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80551959,"identity":"440d8e43-b382-46c1-8c9a-394645183aa9","added_by":"auto","created_at":"2025-04-14 15:00:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1844605,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6281414/v1/a4442734-c325-4036-bd9d-61ef778c102f.pdf"}],"financialInterests":"","formattedTitle":"Genetic Insights into Coronary Heart Disease: A Multi-Omic Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary heart disease (CHD) remains a leading cause of morbidity and mortality worldwide, with significant contributions from genetic, lifestyle, and environmental factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Advances in genome-wide association studies (GWAS) have identified numerous genetic variants associated with CHD, providing deeper insights into the pathophysiology of the disease and potential therapeutic targets [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite these findings, the complexity of CHD genetics necessitates further research to understand the interplay between these variants and their biological mechanisms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCHD is a multifactorial disease influenced by both common and rare genetic variants. Studies have demonstrated that a large proportion of CHD heritability is attributable to polygenic risk, with loci such as 9p21.3 consistently implicated across diverse populations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The NHGRI-EBI GWAS catalog has curated a growing list of CHD-associated loci, many of which are linked to biological pathways such as lipid metabolism, inflammation, vascular remodeling, and coagulation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These findings underscore the critical role of genetic susceptibility in CHD development and highlight the need for precision medicine approaches in risk stratification and management [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA pivotal discovery in CHD genetics was the identification of the 9p21.3 locus, which remains one of the most robustly associated genetic regions across multiple studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This locus, encompassing the CDKN2B-AS1 gene, is implicated in vascular smooth muscle cell proliferation and atherosclerotic plaque stability. Additionally, COL4A2, a gene involved in extracellular matrix integrity, has been associated with CHD susceptibility, emphasizing the importance of vascular remodeling in disease progression [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent GWAS meta-analyses have further expanded the list of CHD risk loci, with studies identifying 15 novel loci associated with arterial-wall-specific mechanisms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These findings reinforce the role of endothelial function and vascular homeostasis in CHD pathogenesis. Notably, transethnic meta-analyses have revealed population-specific risk variants, highlighting the genetic diversity underlying CHD susceptibility [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For instance, studies in South Asian populations have identified distinct risk alleles that are not observed in European cohorts, indicating the necessity for ethnicity-specific genetic research [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to common risk loci, rare variants and copy number variations (CNVs) have emerged as contributors to CHD heritability [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Whole-exome and whole-genome sequencing studies have identified rare deleterious mutations in genes such as LDLR, PCSK9, and APOB, which play key roles in lipid metabolism [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings have direct clinical implications, as individuals harboring pathogenic variants in these genes may benefit from early interventions such as statin therapy or PCSK9 inhibitors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWellcome Trust Case Control Consortium) was a large-scale GWAS covering seven common diseases, including coronary artery disease (CAD) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This study involved 14,000 cases across seven diseases and 3,000 shared controls, using the Affymetrix GeneChip 500K Mapping Array. The study identified 24 independent association signals (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁷) across different diseases. Specifically for CAD, chromosome 9p21.3 was confirmed as the strongest associated region, consistent with previous findings. The study demonstrated that shared genetic susceptibility exists across different complex diseases, with several loci being implicated in multiple conditions. Furthermore, the research highlighted the importance of large-scale data aggregation and the use of shared controls to maximize statistical power while addressing concerns of population stratification. The study provided a robust dataset for future genetic research and emphasized the role of GWAS in understanding the genetic architecture of common diseases​.\u003c/p\u003e \u003cp\u003eThe study (Samani et al.) conducted a genome-wide association study (GWAS) of coronary artery disease (CAD) using two datasets: the Wellcome Trust Case Control Consortium (WTCCC) and the German Myocardial Infarction (MI) Family Study​\u003csup\u003e16\u003c/sup\u003e. The study aimed to identify genetic loci associated with CAD by analyzing a large sample of cases and controls using the GeneChip Human Mapping 500K Array Set (Affymetrix). The study identified chromosome 9p21.3 (SNP rs1333049) as the strongest locus associated with CAD in both datasets, with a combined P-value of 2.91 \u0026times; 10⁻\u0026sup1;⁹, significantly increasing the risk of CAD. Other replicated loci included 6q25.1 (rs6922269, MTHFD1L gene) and 2q36.3 (rs2943634). Additionally, four more loci were identified: 1p13.3 (PSRC1), 1q41 (MIA3), 10q11.21 (CXCL12), and 15q22.33 (SMAD3). The study concluded that multiple genetic loci influence CAD risk, emphasizing the polygenic nature of the disease and the importance of genetic screening for CAD predisposition.\u003c/p\u003e \u003cp\u003eThese two datasets which are publicly available were analyzed further.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study utilized a comprehensive approach to investigate the genetic determinants of coronary heart disease (CHD) by integrating genome-wide association study (GWAS) data [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] with functional enrichment, pathway, and clustering analyses. The methodology encompassed several key steps: data acquisition, quality control, variant annotation, functional enrichment analysis, microRNA interaction studies, protein-protein interaction (PPI) network construction, and clustering techniques to categorize functionally related genes.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGWAS Data Acquisition and Processing\u003c/h2\u003e \u003cp\u003ePublicly available GWAS datasets related to coronary heart disease (CHD) were retrieved and analyzed to identify significant single nucleotide polymorphisms (SNPs) associated with CHD [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The datasets were pre-processed to ensure consistency across different study populations, and quality control measures were applied to remove low-quality variants and samples. Minor allele frequency (MAF) thresholds, Hardy-Weinberg equilibrium (HWE) filtering, and genotype call rate assessments were performed to retain only high-confidence variants. Genome-wide significance was determined using a threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸ to identify robust genetic associations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eIdentified GWAS loci were further analyzed through Gene Ontology (GO) biological processes, molecular function, and cellular component enrichment analyses. The goal was to determine the biological relevance of associated genes in the context of CHD. GO enrichment analysis was performed using online bioinformatics tools such as Enrichr and DAVID (Database for Annotation, Visualization, and Integrated Discovery). Pathway enrichment was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, highlighting significant signaling pathways, including TGF-beta signaling, extracellular matrix remodeling, and lipid metabolism pathways.\u003c/p\u003e\n\u003ch3\u003eMicroRNA (miRNA) Interaction Studies\u003c/h3\u003e\n\u003cp\u003eTo identify potential post-transcriptional regulatory mechanisms, we analyzed miRNA interactions with CHD-associated genes. miRNA target prediction databases, including miRTarBase and TargetScan, were used to assess miRNA-gene interactions. miRNAs with strong binding affinities to CHD-associated genes were identified, providing insights into regulatory networks that may influence disease progression. Key miRNAs, such as hsa-miR-4790-5p and hsa-miR-147b, were found to target genes like CDKN2B and COL4A2, suggesting a role in endothelial function and extracellular matrix remodeling.\u003c/p\u003e\n\u003ch3\u003eProtein-Protein Interaction (PPI) Network Construction\u003c/h3\u003e\n\u003cp\u003eTo understand the molecular interactions among CHD-associated proteins, a PPI network was constructed using STRING (Search Tool for the Retrieval of Interacting Genes/Proteins). The network was analyzed to identify hub proteins with high centrality, indicating key regulatory roles in CHD. CDKN2B, SMAD3, and COL4A2 emerged as central nodes, suggesting their significance in vascular remodeling, fibrosis, and cellular proliferation. Interactions between these proteins and known cardiovascular disease-related molecules were mapped to uncover functional associations.\u003c/p\u003e\n\u003ch3\u003eClustering Analyses for Functional Grouping of Genes\u003c/h3\u003e\n\u003cp\u003eTo categorize CHD-associated genes into functionally relevant groups, three clustering techniques were applied: K-means clustering, Markov clustering (MCL), and density-based spatial clustering (DBSCAN).\u003c/p\u003e \u003cp\u003eK-means Clustering: This method grouped genes based on shared biological functions, revealing three major clusters. Notably, COL4A2 was placed in a distinct cluster, emphasizing its unique role in extracellular matrix integrity, while genes involved in cell adhesion and mitotic regulation (CELSR2, PSRC1, and MIA3) clustered together.\u003c/p\u003e \u003cp\u003eMarkov Clustering (MCL): MCL identified a single large cluster containing CDKN2B, SMAD3, COL4A2, and PHACTR1, highlighting their functional connectivity in CHD progression. Unlike K-means, which separated COL4A2, MCL emphasized strong interconnections between these genes, reinforcing their collective role in vascular pathology.\u003c/p\u003e \u003cp\u003eDBSCAN (Density-Based Clustering): This technique identified CELSR2 and PSRC1 as core regulatory genes, distinguishing them from other functionally diverse genes. DBSCAN\u0026rsquo;s density-based approach excluded genes with weaker associations, refining the identification of core regulatory hubs.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomic and Pathway Integration\u003c/h2\u003e \u003cp\u003eMetabolomic data was integrated to explore biochemical pathways influenced by CHD-related genes. MTHFD1L, involved in one-carbon metabolism, was significantly enriched in folate-dependent metabolic pathways, reinforcing the metabolic contribution to cardiovascular disease. Pathway analysis identified links between AGE-RAGE signaling, TGF-beta signaling, and mitochondrial function, suggesting metabolic-vascular interactions in CHD development.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical and Bioinformatics Analyses\u003c/h3\u003e\n\u003cp\u003eStatistical significance was determined using adjusted P-values with a false discovery rate (FDR) correction, ensuring robust identification of enriched pathways and regulatory networks. Odds ratios and enrichment scores were computed to prioritize the most biologically relevant genes and interactions. Network visualization was conducted using Cytoscape, allowing for an intuitive representation of gene and protein associations.\u003c/p\u003e \u003cp\u003eThis study employed a multi-omic approach, integrating GWAS, functional enrichment, miRNA analysis, PPI network construction, clustering methods, and metabolomic data to comprehensively explore the genetic and molecular basis of CHD. The application of bioinformatics-driven clustering and pathway analyses provided deeper insights into the biological networks governing CHD progression. The results contribute to the growing understanding of genetic predisposition, molecular interactions, and potential therapeutic targets for coronary heart disease prevention and intervention. Future research should focus on experimental validation of these findings and the development of targeted precision medicine strategies for high-risk individuals.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell Type Enrichment Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eThe CellMarker 2024 analysis identified significant enrichment of genes associated with specific cell types across different tissues. The CDKN2B and COL4A2 genes showed a strong association with normal kidney cells in humans, with a highly significant P-value of 2.35 \u0026times; 10⁻⁵ and an odds ratio of 384.17, indicating a strong relationship between these genes and renal function. Additionally, COL4A2 and CDH13 were linked to fibrocartilage chondrocytes in articular cartilage, suggesting a potential role in connective tissue integrity and bone health.\u003c/p\u003e \u003cp\u003eOther notable findings included CDH13 expression in upper layer neurons of the primary visual cortex in mice, with a high combined score, indicating potential neurovascular implications. SMAD3 was enriched in regulatory T cells, circulating fetal cells, and induced pluripotent stem cells in humans, highlighting its role in immune regulation and developmental plasticity. COL4A2 showed significant enrichment in smooth muscle cells in the brain, suggesting its involvement in vascular remodeling and arterial integrity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGO Biological Process Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) analysis provided insights into the biological pathways associated with coronary artery disease-related genes. The most significant pathway was \u0026ldquo;Negative Regulation of Cellular Processes\u0026rdquo;, with five genes (CDKN2B, SMAD3, PSRC1, CDH13, and MIA3) contributing to this function, reflecting their role in cell cycle control and atherogenesis.\u003c/p\u003e \u003cp\u003eThe study also identified cellular responses to low-density lipoprotein (LDL) particle stimuli, highlighting CDH13 and MIA3 in lipid metabolism, which could be crucial in cholesterol regulation and atherosclerosis. Regulation of cell adhesion and sprouting angiogenesis, involving genes like CDH13 and CELSR2, emphasized their role in endothelial integrity and vascular permeability. The negative regulation of cell adhesion and wound healing pathways were significantly associated with SMAD3, MIA3, and CDH13, suggesting their involvement in tissue repair and fibrosis regulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGO Cellular Component Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eThe cellular component analysis provided structural insights, with significant enrichment of COL4A2 and MIA3 in the endoplasmic reticulum lumen, which could indicate their role in protein processing and extracellular matrix formation. CDH13 was found in the catenin complex and plasma membrane raft, reinforcing its role in cellular signaling and adhesion. SMAD3 showed enrichment in the nuclear inner membrane, suggesting its regulatory function in transcriptional control.\u003c/p\u003e \u003cp\u003eFurthermore, COL4A2 was significantly associated with the basement membrane, emphasizing its contribution to vascular integrity and extracellular matrix remodeling. PSRC1 was linked to spindle microtubules, implicating it in cell cycle dynamics and mitosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGO Molecular Function Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eMolecular function analysis highlighted the regulatory roles of key genes. SMAD3 was enriched in DEAD/H-box RNA helicase binding, glucocorticoid receptor binding, and transforming growth factor-beta (TGF-β) receptor binding, underscoring its role in signal transduction and transcriptional regulation.\u003c/p\u003e \u003cp\u003eAdditionally, CDKN2B exhibited cyclin-dependent kinase inhibitor activity, reinforcing its role in cell cycle arrest and tumor suppression. CDH13 was linked to lipoprotein particle binding, suggesting its function in lipid metabolism and cardiovascular disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMetabolite Enrichment Analysis (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eMetabolomics analysis revealed significant associations with folate metabolism. MTHFD1L was highly enriched for tetrahydrofolic acid and formic acid, key intermediates in one-carbon metabolism, which is crucial for DNA synthesis and repair. NADPH and ATP associations with MTHFD1L indicated its role in energy metabolism and oxidative stress regulation.\u003c/p\u003e \u003cp\u003eThe Metabolomics Workbench analysis identified N10-formyl-THF and formic acid as significantly enriched metabolites, reinforcing the role of MTHFD1L in methylation pathways and cardiovascular health.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eKEGG Pathway Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003ePathway enrichment analysis using KEGG (Kyoto Encyclopedia of Genes and Genomes) 2021 identified small cell lung cancer, TGF-beta signaling, and AGE-RAGE signaling in diabetic complications as key pathways influenced by CDKN2B, SMAD3, and COL4A2. The FoxO signaling pathway, cell cycle regulation, and cellular senescence pathways were also enriched, highlighting the role of these genes in aging and disease progression.\u003c/p\u003e \u003cp\u003eThe TGF-beta signaling pathway, involving CDKN2B and SMAD3, was particularly notable for its role in fibrosis, inflammation, and endothelial dysfunction, which are key contributors to coronary artery disease and atherosclerosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003emiRNA Target Enrichment Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eMicroRNA analysis identified several miRNAs regulating CAC-related genes. hsa-miR-4790-5p was significantly associated with CDKN2B, reinforcing its role in cell cycle inhibition and vascular homeostasis. hsa-miR-147b targeted COL4A2, suggesting its potential involvement in extracellular matrix turnover and fibrosis.\u003c/p\u003e \u003cp\u003eOther key findings included miR-7b-5p regulation of SMAD3 and CDH13, implying its function in angiogenesis and endothelial function. miR-491-5p and miR-23b-3p were enriched for SMAD3, further supporting its role in TGF-beta signaling and vascular inflammation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eProtein-Protein Interaction (PPI) Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003ePPI network analysis revealed SMAD3 and MTHFD1L as central hub proteins, interacting with multiple pathways involved in atherosclerosis and endothelial dysfunction. CDKN2B was linked to CDK4 and MYC, reinforcing its role in cell cycle regulation.\u003c/p\u003e \u003cp\u003eAdditionally, NCK1 and CRK were connected to CELSR2 and PSRC1, suggesting their role in intracellular signaling and lipid metabolism. MAPK14 and SRC associations with SMAD3 and CELSR2 highlighted their relevance in inflammatory and stress response pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eReactome Pathway Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eReactome pathway analysis confirmed the TGF-beta signaling cascade as a major pathway regulated by CDKN2B and SMAD3, emphasizing their roles in transcriptional regulation and fibrosis. SMAD2/3/4 heterotrimer activity in regulating gene expression was significantly enriched, underscoring its importance in vascular homeostasis.\u003c/p\u003e \u003cp\u003eAdditionally, RUNX3-mediated CDKN1A transcription regulation was identified, linking these genes to cell cycle control and apoptosis. Cancer-related pathways involving TGFBR1 and SMAD mutations were also enriched, suggesting a broader impact on cellular proliferation and oncogenesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eClustering Analysis of CAD-Associated Proteins\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003eK-Means Clustering (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eK-means clustering grouped the CAD-associated proteins into three clusters, each represented by distinct colors. Cluster 1 (Red, #ff0000) contained CELSR2, MIA3, and PSRC1, with a total of three genes. These proteins are involved in cell-cell signaling, mitotic spindle dynamics, and protein transport from the endoplasmic reticulum, suggesting their role in cellular organization and vascular integrity.\u003c/p\u003e \u003cp\u003eCluster 2 (Green, #a3ff65) contained a single protein, COL4A2, a key component of the extracellular matrix and basement membrane. Its isolation into a separate cluster emphasizes its unique role in maintaining vascular structure and function.\u003c/p\u003e \u003cp\u003eCluster 3 (Blue, #008eb2) contained MTHFD1L, which plays a central role in one-carbon folate metabolism, linking mitochondrial function to cardiovascular health. Its independent clustering suggests a metabolic influence on CAD progression.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMarkov Clustering (MCL) Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eMarkov Clustering (MCL) identified a single large cluster (Red, #ff0000) containing five genes: CELSR2, COL4A2, MIA3, MTHFD1L, and PSRC1. This clustering approach suggests strong functional connectivity among these genes, which play roles in cell adhesion, extracellular matrix remodeling, mitochondrial metabolism, and mitotic regulation. The inclusion of COL4A2 and MTHFD1L in the same cluster further emphasizes the connection between vascular structure and metabolic pathways in cardiovascular diseases.\u003c/p\u003e \u003cp\u003eUnlike K-means, where COL4A2 and MTHFD1L formed separate clusters, MCL clustering suggests that these proteins might interact functionally within the same biological networks, reinforcing their roles in CAD pathophysiology.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eDensity-Based Spatial Clustering (DBSCAN) Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eDBSCAN clustering revealed a single smaller cluster (Red, #ff0000) containing CELSR2 and PSRC1, indicating a high-density interaction between these two proteins. CELSR2 is a receptor involved in cell signaling during nervous system formation, while PSRC1 is critical for mitotic spindle regulation and chromosome segregation. The grouping of these genes suggests that cellular signaling and mitotic regulation may contribute to CAD risk through endothelial dysfunction and vascular remodeling.\u003c/p\u003e \u003cp\u003eUnlike MCL and K-means, DBSCAN\u0026rsquo;s density-based clustering excluded MIA3, MTHFD1L, and COL4A2, implying that these proteins function in more diverse and less tightly connected biological processes.\u003c/p\u003e \u003cp\u003eTables are represented as below;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCell Marker 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOld P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld Adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal Cell Kidney Human\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.354311916028365e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0007769229322893604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e384.1730769230769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4094.008377584964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;COL4A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrocartilage Chondrocyte Articular Cartilage Human\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0030574452255414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0444249068595383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.05363443895554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e162.4354697698338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2;CDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Layer 2 Cell Primary Visual Cortex Mouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0067310464938694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0444249068595383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e178.36607142857142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e892.013120036697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Layer 3 Cell Primary Visual Cortex Mouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0067310464938694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0444249068595383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e178.36607142857142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e892.013120036697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStromal Cell Breast Mouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0067310464938694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0444249068595383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e178.36607142857142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e892.013120036697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibroblast Muscle Mouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0141604247470607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0778823361088342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79.23412698412699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e337.3237811891663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulatory T (Treg) Cell Undefined Human\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0178556385652366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0841765818075441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.993788819875775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e249.55202523941097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInduced Pluripotent Stem Cell Undefined Human\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024474471226601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1009571938097291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.53794642857143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e165.24133492847943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirculating Fetal Cell Blood Human\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028133589600523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1031564952019179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.50965250965251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e137.5099227922053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmooth Muscle Cell Brain Mouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0361386092201723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1192574104265687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.668154761904763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e98.50994756608031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGO Biological Process 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOld P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld Adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative Regulation of Cellular Process (GO:0048523)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2904586065641535e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0073706272787037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.282894736842103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e188.7141840490116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3;PSRC1;CDH13;MIA3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellular Response To Low-Density Lipoprotein Particle Stimulus (GO:0071404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00012022963193032818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0116546371454955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e153.57692307692307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1386.20174723602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13;MIA3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulation Of Cell Adhesion (GO:0030155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00015608889034145876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0116546371454955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.184397163120565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e308.39422839962094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13;MIA3;CELSR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprouting Angiogenesis (GO:0002040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006812468428374356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0381498231988964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.33846153846154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e447.2546579916272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13;MIA3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomophilic Cell Adhesion Via Plasma Membrane Adhesion Molecules (GO:0007156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0009062132520838294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039920001126015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52.856763925729446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e370.3269570822188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13;CELSR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell-Cell Junction Organization (GO:0045216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0011622701959248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039920001126015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.43123543123543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e313.75350729506465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3;CDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative Regulation Of Cell Adhesion (GO:0007162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0013018543244986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039920001126015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.76923076923077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e290.8012647685607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13;MIA3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulation Of Cyclin-Dependent Protein Serine/Threonine Kinase Activity (GO:0000079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0015645072954225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039920001126015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.77622377622377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e256.96173761592104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;PSRC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWound Healing (GO:0042060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0016039286166702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039920001126015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.26429980276134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e252.67752000897423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3;MIA3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative Regulation Of Cell Population Proliferation (GO:0008285)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0025930632372506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0529591221094766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.037898936170214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e77.63958496365372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3;CDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGO Cellular Component 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOld P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld Adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoplasmic Reticulum Lumen (GO:0005788)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0186765667396248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1684525172084642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.749045280960177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e42.78642056145245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2;MIA3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatenin Complex (GO:0016342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0208024990214188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1684525172084642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52.7989417989418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e204.47351966705395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuclear Inner Membrane (GO:0005637)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0230072271319287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1684525172084642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.51190476190476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e179.21238137059572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracellular Organelle Lumen (GO:0070013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02418393791019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1684525172084642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.6072684642438455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.870626605756595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2;MTHFD1L;MIA3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollagen-Containing Extracellular Matrix (GO:0062023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0310307171219719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1684525172084642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.133526850507982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.245930588982315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2;CDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasement Membrane (GO:0005604)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0339615467251988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1684525172084642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.65079365079365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e107.05964425258333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaveola (GO:0005901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0455197414373441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1684525172084642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.330210772833723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72.08123312729496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpindle Microtubule (GO:0005876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0498206911792053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1684525172084642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.23454157782516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63.6892892034775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePSRC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVesicle Membrane (GO:0012506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0505357551625392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1684525172084642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.92121848739496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.4513889160488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFMN2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma Membrane Raft (GO:0044853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0597860000469653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.179358000140896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.552028218694886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e49.44377841836203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGO Molecular Function 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOld P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld Adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEAD/H-box RNA Helicase Binding (GO:0017151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0044920683932145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0774070346794986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e285.42857142857144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1542.8675927558304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuclear Glucocorticoid Receptor Binding (GO:0035259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0059852428364459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0774070346794986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e203.8571428571429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1043.434298452164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eco-SMAD Binding (GO:0070410)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0067310464938694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0774070346794986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e178.36607142857142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e892.013120036697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclin-Dependent Protein Serine/Threonine Kinase Inhibitor Activity (GO:0004861)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0067310464938694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0774070346794986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e178.36607142857142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e892.013120036697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI-SMAD Binding (GO:0070411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0097090442156726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0787411045259278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e118.88690476190476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e551.0048324972125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-Density Lipoprotein Particle Binding (GO:0030169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0119370735063595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0787411045259278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e95.0952380952381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e421.0918230074216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransforming Growth Factor Beta Receptor Binding (GO:0005160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01341982690512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0787411045259278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83.89915966386555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e361.6911269349327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-SMAD Binding (GO:0070412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0141604247470607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0787411045259278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79.23412698412699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e337.3237811891663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebHLH Transcription Factor Binding (GO:0043425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0163791067947741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0787411045259278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.9047619047619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e279.20731867751766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoprotein Particle Binding (GO:0071813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0171176314186799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0787411045259278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.81493506493507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e263.6442288281798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHMDB Metabolites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOld P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld Adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTetrahydrofolic acid (HMDB01846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012678709933633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059352766053732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89.14732142857143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e389.38044082427126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormic acid (HMDB00142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0237411064214928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059352766053732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.97695852534562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e171.97898738890794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAP (HMDB00217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1202254542198838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1536057280631566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.375316990701606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.74215856143119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNADPH (HMDB00221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1228845824505252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1536057280631566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.18001651527663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.14948411931612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphate (HMDB01429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2197388009292093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2197388009292093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.294014853647881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.506788151655364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetabolomics Workbench Metabolites 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOld P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld Adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN10-Formyl-THF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0044920683932145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0130972922353608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e285.42857142857144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1542.8675927558304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0052389168941443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0130972922353608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e237.8452380952381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1249.0776854949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6R-Tetrahydrofolic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0141604247470607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0236007079117679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79.23412698412699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e337.3237811891663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095952401769667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1199405022120838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.661654135338344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.98988338327296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1393349236139555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1393349236139555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.138167388167388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.068433664554846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKEGG 2021 Human\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOld P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld Adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall cell lung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0021136937745399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0273991773572914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.00854700854701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e209.46946451670968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;COL4A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGF-beta signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0022051992386726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0273991773572914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.26588628762542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e203.48534452549944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGE-RAGE signaling pathway in diabetic complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0024908343052083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0273991773572914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.21978021978022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e187.16687708215863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3;COL4A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0037977633081283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0275270795077143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.04791929382093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e139.60064537695737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoxO signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0042276699293122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0275270795077143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.68038163387001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e129.43943544423092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005431934438354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0275270795077143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.761904761904763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e108.28288294418904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellular senescence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0059381164344082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0275270795077143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.81118881118881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e101.55935114166031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0066732313958095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0275270795077143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.212594696969695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.15188487408502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3;COL4A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman T-cell leukemia virus 1 infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011409674782245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041835474201565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.014888337468983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.69271056237966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne carbon pool by folate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0149005037888985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0491716625033653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.06015037593986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e315.7300332957057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emiRTarBase 2017\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOld P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld Adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emmu-miR-17-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0030637937876009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186954272506854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.271929824561404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e71.03117335228588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2;SEZ6L;CELSR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emmu-miR-7b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0039012863578429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186954272506854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.235632183908049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.317860271562914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3;CDH13;SEZ6L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-491-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0071732151912891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186954272506854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.93212669683258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.53810570250886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3; CELSR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-4790-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0119370735063595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186954272506854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e95.0952380952381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e421.0918230074216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-887-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012678709933633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186954272506854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89.14732142857143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e389.38044082427126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-147b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0156400643604775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186954272506854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.30357142857143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e296.4745049823623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emmu-miR-23b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0193301017548387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186954272506854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.02857142857143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e225.0399736139121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emmu-miR-27b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0222728330244505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186954272506854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.15270935960592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e186.9959576603662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emmu-let-7b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0251537508195975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186954272506854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.135022077620263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33.641986621225186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCOL4A2;FMN2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-374c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0266714820499685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186954272506854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.71428571428572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e147.55510087647235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFMN2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePPI Hub Proteins\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOld P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld Adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0036789335760283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1210176868154893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.46794871794872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e142.75122344884733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3;MTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDK4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0044830524255101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1210176868154893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.96356275303644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e124.1743431185765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0055854316991764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1210176868154893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.843434343434344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51.06373654199888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3;MTHFD1L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0110147576570856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1518603145851723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.280967858432648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e64.38601921445527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3;CELSR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0128587689721118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1518603145851723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.184895833333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31.28109143587098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3;MTHFD1L;FMN2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0140178751924774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1518603145851723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.551176096630645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.561164552803255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePSRC1;CELSR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055309099371645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2444863758840258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.863013698630137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.972356641523106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3;CELSR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAPK14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0629847315029695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2444863758840258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.4363636363636365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.030800341432844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3;FMN2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMURF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0890997933217558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2444863758840258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.534262485481998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.889826667906146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDAC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0911606491646467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2444863758840258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.257936507936508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.96424346033221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReactome Pathways 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOld P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOld Adjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMAD2 SMAD3 SMAD4 Heterotrimer Regulates Transcription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0003259186256199044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0322659439363705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90.27601809954751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e724.8137654755144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranscriptional Activity of SMAD2 SMAD3 SMAD4 Heterotrimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0006553286359164845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0324387674778659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e62.59340659340659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e458.83306235776007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignaling by TGF-beta Receptor Complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0022051992386726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0568122799417154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.26588628762542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e203.48534452549944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDKN2B;SMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRUNX3 Regulates BCL2L11 (BIM) Transcription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0037446970839379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0568122799417154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e356.80357142857144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1993.609466758919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGFBR1 KD Mutants in Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0044920683932145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0568122799417154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e285.42857142857144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1542.8675927558304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMAD2 3 Phosphorylation Motif Mutants in Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0044920683932145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0568122799417154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e285.42857142857144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1542.8675927558304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRUNX3 Regulates CDKN1A Transcription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0052389168941443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0568122799417154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e237.8452380952381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1249.0776854949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of Function of SMAD2 3 in Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0052389168941443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0568122799417154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e237.8452380952381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1249.0776854949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of Function of TGFBR1 in Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0052389168941443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0568122799417154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e237.8452380952381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1249.0776854949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignaling by TGF-beta Receptor Complex in Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0059852428364459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0568122799417154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e203.8571428571429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1043.434298452164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eK means clustering\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#clustering method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecluster number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecluster color\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003egene count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eprotein description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ekmeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCELSR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCadherin EGF LAG seven-pass G-type receptor 2; Receptor that may have an important role in cell/cell signaling during nervous system formation; Belongs to the G-protein coupled receptor 2 family. LN-TM7 subfamily.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ekmeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMIA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTransport and Golgi organization protein 1 homolog; Plays a role in the transport of cargos that are too large to fit into COPII-coated vesicles and require specific mechanisms to be incorporated into membrane-bound carriers and exported from the endoplasmic reticulum.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ekmeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePSRC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProline/serine-rich coiled-coil protein 1; Required for normal progression through mitosis. Required for normal congress of chromosomes at the metaphase plate, and for normal rate of chromosomal segregation during anaphase.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ekmeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOL4A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCollagen alpha-2(IV) chain; Type IV collagen is the major structural component of glomerular basement membranes (GBM), forming a 'chicken-wire' meshwork together with laminins, proteoglycans and entactin/nidogen.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ekmeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMonofunctional C1-tetrahydrofolate synthase, mitochondrial; May provide the missing metabolic reaction required to link the mitochondria and the cytoplasm in the mammalian model of one-carbon folate metabolism in embryonic an transformed cells complementing thus the enzymatic activities of MTHFD2; In the N-terminal section; belongs to the tetrahydrofolate dehydrogenase/cyclohydrolase family.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMCL clustering\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#clustering method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecluster number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecluster color\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003egene count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eprotein description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCELSR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCadherin EGF LAG seven-pass G-type receptor 2; Receptor that may have an important role in cell/cell signaling during nervous system formation; Belongs to the G-protein coupled receptor 2 family. LN-TM7 subfamily.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOL4A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCollagen alpha-2(IV) chain; Type IV collagen is the major structural component of glomerular basement membranes (GBM), forming a 'chicken-wire' meshwork together with laminins, proteoglycans and entactin/nidogen.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMIA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTransport and Golgi organization protein 1 homolog; Plays a role in the transport of cargos that are too large to fit into COPII-coated vesicles and require specific mechanisms to be incorporated into membrane-bound carriers and exported from the endoplasmic reticulum.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMTHFD1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMonofunctional C1-tetrahydrofolate synthase, mitochondrial; May provide the missing metabolic reaction required to link the mitochondria and the cytoplasm in the mammalian model of one-carbon folate metabolism\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePSRC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProline/serine-rich coiled-coil protein 1; Required for normal progression through mitosis. Required for normal congress of chromosomes at the metaphase plate, and for normal rate of chromosomal segregation during anaphase.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDBSCAN clustering\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#clustering method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecluster number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecluster color\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003egene count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eprotein description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBSCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCELSR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCadherin EGF LAG seven-pass G-type receptor 2; Receptor that may have an important role in cell/cell signaling during nervous system formation; Belongs to the G-protein coupled receptor 2 family. LN-TM7 subfamily.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBSCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePSRC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProline/serine-rich coiled-coil protein 1; Required for normal progression through mitosis. Required for normal congress of chromosomes at the metaphase plate, and for normal rate of chromosomal segregation during anaphase.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study provides a comprehensive genetic and molecular analysis of coronary artery disease using publicly available GWAS datasets [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The results highlight the key roles of CDKN2B, SMAD3, COL4A2, and CDH13 in vascular remodeling, lipid metabolism, and endothelial dysfunction.\u003c/p\u003e \u003cp\u003eTGF-beta signaling, cell cycle regulation, and extracellular matrix remodeling emerged as the dominant pathways, implicating these genes in atherosclerosis progression and cardiovascular risk. Metabolomic and miRNA analyses further confirmed their regulatory influence, identifying potential therapeutic targets.\u003c/p\u003e \u003cp\u003eThe integration of genomic, transcriptomic, and metabolomic data enhances our understanding of CAD pathophysiology and provides new insights into potential intervention strategies for cardiovascular disease management. Future research should focus on validating these findings in functional studies and exploring targeted therapies for CAD prevention and treatment.\u003c/p\u003e \u003cp\u003eOne of the most striking findings is the strong association of CDKN2B at 9p21.3 with CAC. Previous studies have consistently demonstrated that this locus is a key determinant of atherosclerosis, with regulatory effects on vascular smooth muscle cell proliferation and senescence. Our pathway analysis further confirmed its involvement in TGF-beta signaling, cellular senescence, and cyclin-dependent kinase inhibition. These mechanisms highlight the critical role of CDKN2B in maintaining endothelial homeostasis and preventing pathological vascular calcification. Additionally, the miRNA enrichment analysis identified hsa-miR-4790-5p as a regulator of CDKN2B, suggesting a post-transcriptional layer of regulation that may influence disease progression.\u003c/p\u003e \u003cp\u003eThe functional enrichment analysis revealed several key biological processes and pathways implicated in CAC pathogenesis. GO Biological Process analysis highlighted the negative regulation of cellular proliferation, cell adhesion, and lipid metabolic processes as central mechanisms in CAC development. The involvement of genes like SMAD3 in wound healing and negative regulation of cell population proliferation suggests a role in tissue repair and fibrosis. KEGG pathway analysis further emphasized the contribution of TGF-beta signaling, cell cycle regulation, and FoxO signaling pathways, linking these genes to endothelial dysfunction and atherogenesis.\u003c/p\u003e \u003cp\u003eMicroRNA (miRNA) analysis provided additional regulatory insights, revealing potential post-transcriptional control of CAC-associated genes. Hsa-miR-147b and mmu-miR-29a-3p were significantly associated with COL4A2, suggesting their role in extracellular matrix remodeling. Similarly, hsa-miR-887-3p was linked to MTHFD1L, which plays a key role in one-carbon metabolism and mitochondrial function. These findings highlight the complex regulatory networks influencing CAC and suggest that targeting specific miRNAs could represent a potential therapeutic strategy for modulating vascular calcification.\u003c/p\u003e \u003cp\u003eThe clustering analysis using K-means, Markov Clustering (MCL), and DBSCAN further elucidated the functional relationships between CAC-related genes. K-means clustering separated genes into distinct groups based on their biological functions, with COL4A2 forming an independent cluster due to its unique role in vascular structure. In contrast, MCL grouped multiple genes together, indicating functional connectivity in CAC-related processes. DBSCAN, a density-based approach, identified core regulatory genes like CELSR2 and PSRC1, suggesting their importance in mitotic regulation and cell signaling.\u003c/p\u003e \u003cp\u003eOverall, this study advances our understanding of genetic and molecular mechanisms underlying CAC, paving the way for future precision medicine approaches in cardiovascular disease prevention and management. Further experimental validation and functional studies are necessary to explore these associations in greater detail and develop targeted interventions for individuals at high risk of CAC and related cardiovascular events.\u003c/p\u003e \u003cp\u003eThe clustering analysis provided a structured view of CAD-associated proteins, grouping them based on their biological interactions. K-means separated proteins into distinct clusters, emphasizing their individual roles. MCL identified a tightly linked network of five proteins, highlighting their collective impact on CAD. DBSCAN focused on dense interactions, emphasizing core regulatory proteins.\u003c/p\u003e \u003cp\u003eOverall, the clustering results indicate a multifaceted contribution of these genes to CAD progression, encompassing vascular integrity, metabolic regulation, cell adhesion, and mitotic control. Future research should explore functional interactions and pathway integration to uncover precise molecular mechanisms underlying CAD pathogenesis.\u003c/p\u003e \u003cp\u003eBeyond genetic risk factors, epigenetic modifications such as DNA methylation, histone modifications, and non-coding RNAs (e.g., microRNAs) have been implicated in CHD pathogenesis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. MicroRNA profiling studies have identified hsa-miR-147b and hsa-miR-4790-5p as key regulators of CHD-associated genes, suggesting potential therapeutic targets for modulating disease progression [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, recent research has demonstrated that long non-coding RNAs (lncRNAs) such as ANRIL are involved in CHD through their effects on vascular inflammation and smooth muscle cell proliferation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFunctional enrichment analyses of CHD-associated genes have highlighted several key biological pathways. TGF-beta signaling, extracellular matrix organization, lipid metabolism, and inflammation are among the most significantly enriched pathways, reflecting the complex interplay between genetic and environmental factors in CHD development [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The identification of these pathways provides valuable insights into potential pharmacological targets for CHD prevention and treatment.\u003c/p\u003e \u003cp\u003eMoreover, metabolomics and transcriptomics studies have revealed novel biomarkers associated with CHD risk. One-carbon metabolism, mitochondrial function, and AGE-RAGE signaling have been identified as key metabolic processes influencing CHD pathophysiology [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These findings suggest that integrating multi-omic approaches, including genomics, epigenomics, transcriptomics, and metabolomics, will be crucial for uncovering new therapeutic avenues for CHD management [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite significant progress, challenges remain in translating genetic discoveries into clinical practice. One major limitation of GWAS is the limited explanatory power of individual variants, necessitating the development of polygenic risk scores (PRS) to improve disease prediction [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Recent studies have demonstrated that incorporating PRS into clinical risk models enhances the accuracy of CHD risk stratification, allowing for personalized preventive strategies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother key challenge is the underrepresentation of non-European populations in genetic studies. While large-scale consortia have made strides in addressing this gap, further efforts are needed to ensure the inclusion of diverse populations in CHD research [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Genetic studies in populations such as South Asians, East Asians, and Africans have revealed novel risk loci that may not be captured in European-centric studies, emphasizing the need for global genetic research initiatives [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, the interaction between genetic and environmental risk factors remains an active area of investigation. Studies have shown that genetic predisposition to CHD can be modulated by lifestyle factors, including diet, physical activity, and smoking cessation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Mendelian randomization studies have provided causal evidence linking education level, adiposity, and type 2 diabetes to CHD risk, suggesting that targeted public health interventions could mitigate genetic susceptibility [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, CHD genetics has undergone remarkable advancements over the past decade, with GWAS identifying numerous risk loci and functional studies elucidating their biological significance. The integration of multi-omic data, ethnic diversity in genetic studies, and PRS applications represents the future direction of CHD research. As our understanding of CHD genetics continues to evolve, the ultimate goal remains the development of precision medicine approaches that enable early detection, personalized risk assessment, and targeted therapies for individuals at high risk of CHD [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a comprehensive multi-omic analysis of coronary artery calcification, integrating genomic, transcriptomic, metabolomic, and clustering approaches to identify key genetic determinants and molecular pathways. The results confirm the critical roles of CDKN2B, SMAD3, COL4A2, and PHACTR1 in vascular homeostasis, lipid metabolism, and extracellular matrix remodeling. TGF-beta signaling, AGE-RAGE signaling, and cell cycle regulation emerged as dominant pathways, linking CAC progression to endothelial dysfunction and atherogenesis.\u003c/p\u003e \u003cp\u003eThe identification of miRNA regulators, protein-protein interactions, and metabolic pathways provides new insights into the complex molecular landscape of CAC, suggesting potential therapeutic targets for cardiovascular disease intervention. Clustering analysis further refines our understanding of gene-gene interactions, revealing functionally connected modules that may influence disease susceptibility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors thank Central Research Laboratory for Molecular Genetics, Bioinformatics and Machine Learning at Apollo Institute of Medical Sciences and Research Chittoor Murukamabttu - 571727, Andhra Pradesh, India for the infrastructure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement:\u0026nbsp;\u003c/strong\u003eThis study is based solely on publicly available data and does not involve human participants, animal subjects, or identifiable personal information. Therefore, approval from an institutional ethics committee was not required. However, the research has been conducted in accordance with established ethical guidelines, including the Declaration of Helsinki and the ethical principles outlined by relevant regulatory bodies. All data sources used in this study comply with open-access policies, and appropriate citations have been provided to acknowledge the original data contributors.\u003c/p\u003e\n\u003cp\u003eData availability: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used AI tools in order to reformulate some sentences. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen Z, Schunkert H (2021) Genetics of coronary artery disease in the post-GWAS era. J Intern Med 290(5):980\u0026ndash;992. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/joim.13362\u003c/span\u003e\u003cspan address=\"10.1111/joim.13362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKe W, Rand KA, Conti DV, Setiawan VW, Stram DO, Wilkens L et al (2018) Evaluation of 71 coronary artery disease risk variants in a multiethnic cohort. \u003cem\u003eFrontiers in Cardiovascular Medicine\u003c/em\u003e, 5, p.19. doi: 10.3389/fcvm.2018.00019 [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErdmann J, Willenborg C, Nahrstaedt J, Preuss M, K\u0026ouml;nig IR, Baumert J et al (2011) Genome-wide association study identifies a new locus for coronary artery disease on chromosome 10p11. 23. Eur Heart J 32(2):158\u0026ndash;168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehq405\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehq405\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeden JF, Farrall M, Watkins H, Goel A, Ongen H, Helgadottir A et al (2011) A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat Genet 43(4):339\u0026ndash;344. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.782\u003c/span\u003e\u003cspan address=\"10.1038/ng.782\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbasi SH, Sundin \u0026Ouml;, Jalali A, Soares J, Macassa G (2018) Ethnic differences in the risk factors and severity of coronary artery disease: a patient-based study in Iran. J Racial Ethnic Health Disparities 5:623\u0026ndash;631 [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao M, Cui B (2020) Association of educational attainment with adiposity, type 2 diabetes, and coronary artery diseases: a Mendelian randomization study. \u003cem\u003eFrontiers in public health\u003c/em\u003e, 8, p.112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2020.00112\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2020.00112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsunaga Hiroshi I, Kaoru A, Masato T, Atsushi K, Satoshi et al (2020) Transethnic meta-analysis of genome-wide association studies identifies three new loci and characterizes population-specific differences for coronary artery disease. Circulation: Genomic Precision Med 13(3):e002670. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCGEN.119.002670\u003c/span\u003e\u003cspan address=\"10.1161/CIRCGEN.119.002670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErdmann J, Kessler T, Munoz Venegas L, Schunkert H (2018) A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovascular Res 114(9):1241\u0026ndash;1257. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cvr/cvy084\u003c/span\u003e\u003cspan address=\"10.1093/cvr/cvy084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAragam KG, Jiang T, Goel A, Kanoni S, Wolford BN, Atri DS et al (2022) Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat Genet 54(12):1803\u0026ndash;1815. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-022-01233-6\u003c/span\u003e\u003cspan address=\"10.1038/s41588-022-01233-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheema AN, Pirim D, Wang X, Ali J, Bhatti A et al (2020) John P.,., Association study of coronary artery disease-associated genome-wide significant SNPs with coronary stenosis in Pakistani population. \u003cem\u003eDisease Markers\u003c/em\u003e, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasidhar MV, Reddy S, Naik A, Naik S (2014) Genetics of coronary artery disease\u0026ndash;A clinician\u0026rsquo;s perspective. Indian Heart J 66(6):663\u0026ndash;671 [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArdeshna DR, Bob-Manuel T, Nanda A, Sharma A, Skelton WP, IV, Skelton M et al (2018) Asian-Indians: a review of coronary artery disease in this understudied cohort in the United States. Annals translational Med, 6(1). doi: 10.21037/atm.2017.10.18 [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C et al (2019) The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47:D1005\u0026ndash;D1012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gky1120\u003c/span\u003e\u003cspan address=\"10.1093/nar/gky1120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLittle J, Higgins JP, Ioannidis JP, Moher D, Gagnon F, Von Elm E, Khoury MJ, Cohen B, Davey-Smith G, Grimshaw J, Scheet P (2009) STrengthening the REporting of Genetic Association Studies (STREGA)\u0026mdash;an extension of the STROBE statement. Genetic Epidemiology: Official Publication Int Genetic Epidemiol Soc 33(7):581\u0026ndash;598\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447(7145):661\u0026ndash;678. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature05911\u003c/span\u003e\u003cspan address=\"10.1038/nature05911\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 17554300; PMCID: PMC2719288\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, WTCCC and the Cardiogenics Consortium et al (2007) Genomewide association analysis of coronary artery disease. N Engl J Med 357(5):443\u0026ndash;453. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa072366\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa072366\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2007 Jul 18. PMID: 17634449; PMCID: PMC2719290\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKmet LM, Cook LS, Lee RC (2004) Standard quality assessment criteria for evaluating primary research papers from a variety of fields. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7939/R37M04F16\u003c/span\u003e\u003cspan address=\"10.7939/R37M04F16\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [DOI] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbalic M, Reiner AP, Wu C, Hixson JE, Franceschini N, Eaton CB et al (2011) Genome-wide association analysis of incident coronary heart disease (CHD) in African Americans: a short report. \u003cem\u003ePLoS genetics\u003c/em\u003e, 7(8), p.e1002199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pgen.1002199\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1002199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhat KG, Guleria VS, Rastogi G, Sharma V, Sharma A (2022) Preliminary genome wide screening identifies new variants associated with coronary artery disease in Indian population. \u003cem\u003eAmerican Journal of Translational Research\u003c/em\u003e, 14(7), p.5124. [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Dorajoo R, Chang X, Wang L, Khor CC, Sim X et al (2017) Genome-wide association study identifies a missense variant at APOA5 for coronary artery disease in Multi-Ethnic Cohorts from Southeast Asia. \u003cem\u003eScientific reports\u003c/em\u003e, 7(1), p.17921. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-017-18214-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-017-18214-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowson JM, Zhao W, Barnes DR, Ho WK, Young R, Paul DS et al (2017) Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms. Nat Genet 49(7):1113\u0026ndash;1119 [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Q, Liu Q, Wang S, Zhen X, Zhang Z, Lv R et al (2016) NPR-C gene polymorphism is associated with increased susceptibility to coronary artery disease in Chinese Han population: a multicenter study. \u003cem\u003eOncotarget\u003c/em\u003e, 7(23), p.33662. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/oncotarget.9358\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.9358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshigaki K, Akiyama M, Kanai M, Takahashi A, Kawakami E, Sugishita H et al (2020) Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat Genet 52(7):669\u0026ndash;679. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-020-0640-3\u003c/span\u003e\u003cspan address=\"10.1038/s41588-020-0640-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlarin D, Zhu QM, Emdin CA, Chaffin M, Horner S, McMillan BJ et al (2017) Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat Genet 49(9):1392\u0026ndash;1397 [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoyama S, Ito K, Terao C, Akiyama M, Horikoshi M, Momozawa Y et al (2020) Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat Genet 52(11):1169\u0026ndash;1177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-020-0705-3\u003c/span\u003e\u003cspan address=\"10.1038/s41588-020-0705-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JY, Lee BS, Shin DJ, Woo Park K, Shin YA, Joong Kim et al (2013) A genome-wide association study of a coronary artery disease risk variant. J Hum Genet 58(3):120\u0026ndash;126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/jhg.2012.124\u003c/span\u003e\u003cspan address=\"10.1038/jhg.2012.124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JY, Kim G, Park S, Kang SM, Jang Y, Lee SH (2015) Associations between genetic variants and angiographic characteristics in patients with coronary artery disease. J Atheroscler Thromb 22(4):363\u0026ndash;371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5551/jat.26047\u003c/span\u003e\u003cspan address=\"10.5551/jat.26047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e[DOI] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLettre G, Palmer CD, Young T, Ejebe KG, Allayee H, Benjamin EJ et al (2011) Genome-wide association study of coronary heart disease and its risk factors in 8,090 African Americans: the NHLBI CARe Project. \u003cem\u003ePLoS genetics\u003c/em\u003e, 7(2), p.e1001300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pgen.1001300\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1001300\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu X, Wang L, Chen S, He L, Yang X, Shi Y et al (2012) Genome-wide association study in Han Chinese identifies four new susceptibility loci for coronary artery disease. Nat Genet 44(8):890\u0026ndash;894 [DOI] [PMC free article] [PubMed] [Google Scholar]\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Coronary heart disease, genome-wide association studies, genetic risk factors, precision medicine, functional enrichment analysis","lastPublishedDoi":"10.21203/rs.3.rs-6281414/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6281414/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Coronary heart disease (CHD) is a leading cause of mortality worldwide, with a complex interplay of genetic and environmental factors influencing its development. Genome-wide association studies (GWAS) have identified multiple genetic loci associated with CHD, providing crucial insights into its pathophysiology. However, the full spectrum of genetic contributors and their biological mechanisms remains to be elucidated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study integrates GWAS data with functional enrichment, transcriptomic, and metabolomic analyses to identify key genetic determinants of CHD. Variants associated with CHD were retrieved from publicly available datasets and analyzed using bioinformatics tools to explore their biological significance. Pathway enrichment, protein-protein interaction (PPI) networks, and clustering algorithms were employed to delineate functional relationships among candidate genes. Additionally, microRNA (miRNA) interactions were assessed to understand post-transcriptional regulatory mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our analysis confirms the strong association of CHD with loci such as 9p21.3 (CDKN2B-AS1), COL4A2, and PHACTR1, emphasizing their roles in vascular remodeling, inflammation, and lipid metabolism. Functional enrichment revealed significant involvement in TGF-beta signaling, extracellular matrix organization, and AGE-RAGE signaling pathways. Furthermore, miRNA analysis highlighted potential regulatory targets such as hsa-miR-147b and hsa-miR-4790-5p, which may modulate disease progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study advances our understanding of CHD genetics by integrating multi-omic data to identify key genetic determinants and biological pathways. The findings underscore the potential of precision medicine approaches for early detection and targeted intervention in high-risk individuals.\u003c/p\u003e","manuscriptTitle":"Genetic Insights into Coronary Heart Disease: A Multi-Omic Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 14:52:05","doi":"10.21203/rs.3.rs-6281414/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-03-27T00:11:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-26T06:44:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Applied Genetics","date":"2025-03-22T00:52:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joag","sideBox":"Learn more about [Journal of Applied Genetics](https://www.springer.com/journal/13353)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/joag/default.aspx","title":"Journal of Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"39b906f2-28a2-454f-aca9-75ce2e2a1cf1","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T14:52:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-14 14:52:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6281414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6281414","identity":"rs-6281414","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}