INTRODUCTION
Research rationale
Dioxins and dioxin-like compounds are increasingly concerning due to their long-term effects on both human and animal health TCDD and Furans, key representatives of these harmful substances, can adversely affect health even in minimal amounts through bioaccumulation in the food chain The most critical impact of these chemicals is the induction of genetic variation via the activation of the aryl hydrocarbon receptor (AhR), which, upon entering the nucleus of animal cells, can lead to genetic diseases and cancer development.
Diffuse large B lymphoma (DLBCL) is the most common type of B cell non-Hodgkin lymphoma, accounting for 40% of lymphoma cases Although the exact cause of DLBCL remains unknown, numerous studies have identified pro-oncogenes and genetic abnormalities associated with the disease Understanding the biological mechanisms that activate these genes, particularly in relation to dioxins and similar compounds, is crucial Bioinformatics plays a significant role in advancing research across various fields by utilizing techniques such as sequence analysis, gene and protein expression studies, cellular organization analysis, structural bioinformatics, and network and systems biology.
Bioinformatics is gaining traction in biomedical research in developed countries, yet it remains underutilized in Vietnam Research highlights the importance of high-throughput sequencing and DNA microarray technologies in identifying genetic and transcriptomic alterations linked to diffuse large B-cell lymphoma (DLBCL) and in discovering prognostic biomarkers for lymphoma treatment By employing bioinformatics, researchers can elucidate the activation of abnormal genes and the impact of dioxins on health The study titled "Identifying the Effect of Exposure to TCDD and Furans on Human Health Leading to Diffuse Large B Lymphoma Through Gene-Network Construction" aims to enhance lymphoma diagnosis and promote biomedical advancements.
Engineering and Environmental Science faculty of National Tsing Hua University in Taiwan.
Research objectives
The objectives of this research are:
- To investigate respectively the differentially expressed genes for diffuse large
B lymphoma (DLBCL) tissues and dioxin exposure of human cell lines;
- To construct the gene-network for exploring number whether exposure to dioxin can induce DLBCL;
- To identify the potential pathway exposure to dioxin corresponding to DLBCL.
LITERATURE REVIEW
Persistent Organic Compounds (POPs)
Persistent organic pollutants (POPs) are lipophilic compounds that contribute significantly to environmental degradation Among them, organochlorine (OC) pesticides and industrial by-products containing chlorine are particularly harmful, leading to their ban and strict regulation in many countries Despite these measures, exposure to POPs persists in the general population, primarily through the consumption of animal-derived fatty acids Notably, the concentration of POPs tends to increase within food webs due to biomagnification, resulting in higher levels of these pollutants accumulating in human bodies compared to the surrounding environment.
Persistent Organic Pollutants (POPs) accumulate in adipose tissue throughout life, leading to chronic exposure These harmful substances are continuously released from fat tissues into the bloodstream and vital organs, which have a high lipid content.
Persistent Organic Pollutants (POPs) are characterized by their lipophilic nature, allowing them to accumulate primarily in lipid-rich tissues such as adipose tissue, where they are transported throughout the body bound to lipids.
Persistent Organic Pollutants (POPs) often exist as chemical mixtures in the environment due to their integration into the food web and their long-term accumulation in fatty tissues This results in distinct classifications for various groups of organic contaminants, including OC pesticides, polychlorinated biphenyls (PCBs), and dioxins, which are categorized based on the specific chemical mixtures associated with each subclass of POPs.
Dioxins and dioxin – liked compounds
Polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs) are classified as persistent organic pollutants (POPs) and are recognized as two of the three subclasses of halogenated aromatic hydrocarbons These compounds include dioxins and dioxin-like compounds, which are significant environmental contaminants.
Figure 2.1: General molecular structure of polychlorinated dibenzo-p-dioxins
Dioxins and dioxin-like compounds are pervasive in the environment, including remote areas, due to their persistence and lipophilicity, which allows for bioaccumulation in food chains and potential adverse effects on both human health and wildlife Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) are subclasses of halogenated aromatic hydrocarbons characterized by a benzene ring structure The distinction between dioxins and dioxin-like compounds lies in the number of oxygen rings in their structure, with dioxins having two rings and dioxin-like compounds having one The biological effects of PCDDs/Fs are mediated through their binding to the Aryl Hydrocarbon Receptor (AHR), with their molecular planar shape facilitating this interaction; their potency is influenced by their persistence and compatibility with the receptor PCDDs/Fs, particularly tetrachlorodibenzo-p-dioxins (TCDD), exhibit a strong affinity for AHR, leading to significant toxic effects These compounds originate from four primary sources: combustion, metal smelting, refining, and processing.
(3) biological and photochemical process (US National Research Council, 2006)
PCDD/Fs are linked to serious health risks, including cancer, birth defects, reproductive issues, and immunotoxicity Additionally, exposure to these compounds can lead to liver diseases, thyroid dysfunction, lipid disorders, neurotoxicity, cardiovascular diseases, and metabolic disorders like diabetes, as highlighted by the US National Research Council in 2006.
According Pereira (2004) 2,3,7,8-tetrachhlorodibenzo-p-dioxins (TCDD) is structured as below (see Figure 2.2)
Figure 2.2: Representative structure of 2,3,7,8-tetrachhlorodibenzo-p-dioxins
2,3,7,8 tetrachlorodibenzo-p-dioxin (TCDD) is a highly toxic environmental contaminant and a member of the polychlorinated dibenzodioxins (PCDDs) family It is primarily produced as a byproduct in the manufacturing of chlorophenols and chlorophenoxy herbicides, and can also form during burning processes, waste incineration, metal production, and fossil fuel or wood combustion Due to its long biological half-life and low water solubility, TCDD is prone to bioaccumulation in the food chain, with even minimal amounts leading to significant concentrations Its toxic effects are mediated through the binding of TCDD to the aryl hydrocarbon receptor (AhR) in various mammalian species.
The aryl hydrocarbon receptor (AhR) is a basic-loop-helix/PAS transcription factor located in the cytoplasm, where it forms complexes with various proteins and lipophilic compounds In the cytoplasm, AhR associates with pp60, which binds to the epidermal growth factor receptor (EGFR) and activates mitogen-activated protein signaling Once in the nucleus, AhR partners with the aryl hydrocarbon receptor nuclear translocator (ARNT) to create the AhR-ARNT complex, promoting transcription of xenobiotic response elements (XRE) and interacting with key pathways such as Wnt-beta-catenin, estrogen receptors, and NF-kB AhR plays a crucial role in various physiological processes, including cell cycle regulation and proliferation Notably, studies have shown a link between prolonged exposure to TCDD and an increased risk of Non-Hodgkin lymphoma, particularly among workers in Sweden and the US exposed to phenoxyl herbicides.
From 1962 to 1971, approximately 45 million liters of Agent Orange, containing the toxic compound TCDD, were sprayed across South Vietnam and Cambodia to eliminate vegetation This widespread use has been linked to a persistent increase in cancer incidences in the affected regions (Stellman et al., 2003).
This study aims to explore the potential gene networks and pathways involved in how the highly toxic compounds TCDD and furans, which belong to the dioxin-like group, can induce diffuse large B-cell lymphoma, a common form of Non-Hodgkin lymphoma.
Figure 2.3: A schematic representation of signal transduction after TCDD/AHR interaction
Lymphoma and non – Hodgkin lymphoma
Lymphoma, a term for neoplasms originating from lymphoid precursor cells, was first identified by Thomas Hodgkin in 1832, leading to the designation of Hodgkin’s lymphoma Since then, various types of lymphoma have been recognized, primarily categorized into two subclasses: Hodgkin lymphoma and non-Hodgkin lymphoma Non-Hodgkin lymphoma predominantly consists of B-cell lymphoma, alongside T-cell and NK-cell lymphoma These lymphoid neoplasms represent a diverse group of diseases that mirror the complexity of the immune system In Vietnam, the incidence of non-Hodgkin lymphoma has notably risen over the past decade, with approximately 2,700 cases reported annually.
Diffuse large B lymphoma (DLBCL) is the most common type of B cell non-Hodgkin lymphoma in adults, accounting for 40% of cases It has three main subclasses based on molecular characteristics: germinal center B-cell like DLBCL (GCB DLBCL), activated B-cell like DLBCL (ABC DLBCL), and primary mediastinal B-cell lymphoma GCB DLBCL originates from germinal center B cells and expresses genes typical of these lymphocytes, while ABC DLBCL expresses genes associated with plasma cells, indicating differentiation from activated B-cells Primary mediastinal B-cell lymphoma is believed to arise from rare B-cell populations in the thymus, exhibiting distinct gene expression profiles compared to GCB and ABC DLBCL.
2.3.2 SNPs of Diffuse Large B lymphoma
Gene expression and genome sequencing are essential for enhancing our understanding of DLBCL subclasses and the molecular mechanisms behind chemotherapy resistance This research aids in identifying novel molecular subsets of DLBCL and potential drug targets, ultimately contributing to improved prevention and treatment strategies for the disease (Lossos et al., 2006).
Diffuse large B-cell lymphoma (DLBCL) typically originates from normal antigen-exposed B cells at various differentiation stages, undergoing clonal expansion in the germinal centers of peripheral lymphoid organs The progression of DLBCL can occur through a complex multistep transformation process, which may happen slowly or rapidly, influenced by clonal evolution and extensive DNA rearrangements in subclones This malignancy exhibits significant clinical and genetic heterogeneity, characterized by various genetic abnormalities such as aberrant somatic hypermutation, nonrandom chromosomal deletions, and balanced reciprocal translocations These changes often lead to the deregulation of proto-oncogenes, including BCL6, BCL2, REL, and c-MYC, as well as impaired apoptosis due to defective DNA repair mechanisms.
Research has identified multiple gene mutations linked to diffuse large B-cell lymphoma (DLBCL), with key early oncogenic events primarily involving chromosomal translocations of oncogenes like BCL6, BCL2, REL, and c-MYC Additionally, mutations in genes such as BCL2, PRDM1, CARD11, MyD88, TNFAIP3, CREBBP, TP53, EZH2, and MLL2 contribute to the complexity of this disease.
In the context of secondary or late oncogenic events, genes such as MYOM2, PIM1, LYN, CD36, B2M, CD79B, MEF2B, ANKLE2, KDM2B, HNF1B, NOTCH1/2, DTX1, and MYCCD58 frequently emerge as recurrent mutations or gene alterations, as highlighted by Morin et al.
In DLBCL tumors, alterations in DNA repair and signaling genes have been linked to the development of intermediate cancer driver events, contributing to lymphomagenesis Key mutations or translocations in genes such as BCL6, BCL2, REL, and c-MYC lead to the overexpression of proto-oncogenes, while genetic lesions in TNFAIP3, CARD11, CD79A/B, MYD88, and TRAF2 activate both canonical and non-canonical NF-kB pathways Additionally, frequent cancer driver events in DLBCL involve epigenetic reprogramming due to mutations in genes like TET1, MLL2, EZH2, MEF2B, EP300, and CREBBP Consequently, these genetic alterations result in tumor cells exhibiting gene expression plasticity, evading apoptosis, and enhanced growth through persistent survival and proliferative signals.
Gene - network components
DNA microarrays are essential tools for analyzing the expression levels of numerous genes, utilizing both single-color and two-color systems Among these, Affymetrix Gene Chip arrays stand out as a popular single-platform option, featuring probes that specifically target regions of mRNA transcripts, typically at their 3’ ends Each probe set comprises 11 to 20 perfect match (PM) probes, each 25 nucleotides long, alongside an equal number of mismatch (MM) probes that differ from the PM probes by a single nucleotide substitution in the center.
DNA microarray techniques play a crucial role in predicting the treatment success of Diffuse Large B-Cell Lymphoma (DLBCL) and understanding its disease heterogeneity through five clinical features: age, tumor stage, serum lactate dehydrogenase concentration, performance status, and the number of extranodal disease sites (Gohlmanm and Talloen, 2009) This technology is predominantly utilized for comprehensive gene expression profiling on a genomic scale, facilitating a series of microarray-based studies aimed at refining prognosis with molecular-level insights (Segal, 2005) Additionally, DNA microarrays have been employed to investigate the alterations in human B-cell gene expression caused by dioxins (Kovalova et al., 2017).
This study utilized DNA microarray techniques to analyze gene expression profiling related to Diffuse Large B-Cell Lymphoma (DLBCL) and dioxins, specifically TCDD and Furans The gene expression datasets were sourced from two primary databases: Gene Expression Omnibus (GEO) and Array Express, which will be explored in greater detail in the subsequent sections.
2.4.2 Gene network database: Array Express and GEO
This study utilized datasets from the Array Express and Gene Expression Omnibus (GEO) databases Array Express is a public repository for high-throughput functional genomics data, comprising the Array Express Repository, which archives MIAME-compliant microarray data, and the Array Express Data Warehouse, which organizes and re-annotates gene expression profiles from the repository Users can locate specific samples or experiments using various attributes such as keywords, species, array platforms, authors, journals, or accession numbers Additionally, gene names, properties, and ontology terms aid in visualizing gene expression profiles The Array Express database is continually expanding, currently encompassing over 50,000 hybridizations and 1.5 million individual expression profiles It supports community standards including MIAME, MAGE-ML, and MAGE-TAB (Parkinson et al., 2007).
The GEO database, sourced from the National Center for Biotechnology Information (NCBI), offers a vast collection of gene expression data produced by DNA microarray technology It is designed to accommodate both unprocessed and processed data in compliance with MIAME standards With approximately a billion gene expression data points derived from over 100 organisms and 1,500 laboratories, GEO captures a wide array of biological phenomena To enhance usability, several user-friendly web applications have been developed, facilitating effective exploration, querying, and visualization of these data for individual and comprehensive studies (Barrett, 2004).
Meta-analysis is a statistical technique used to combine results from multiple studies, enhancing the reliability of findings, particularly in the context of microarray studies for Differentially Expressed Genes (DEGs) This method involves seven key steps: identifying relevant microarray studies, extracting and preparing data, annotating datasets, resolving probe-gene relationships, combining study estimates, and analyzing results In this study, meta-analysis aims to identify DEGs in DLBCL tissues and dioxin-exposed groups compared to normal tissues, providing valuable insights for further research.
The false discovery rate (FDR) represents the anticipated proportion of incorrect rejections among hypotheses that have been rejected This approach is utilized in microanalysis to assess the ratio of false positive findings among genes identified as differentially expressed (Gohlmann and Talloen).
In this study, the widely recognized Benjamini and Hochberg method is employed to control the false discovery rate (FDR) This method is calculated using the formula p = p m order(pi), where p represents the adjusted p-value, p i denotes the p-value of gene i, and m indicates the total number of genes in the dataset.
Microarray analysis is a vital tool for identifying differentially expressed genes, which can reveal mutations that lead to abnormal gene expression and diseases like cancer, particularly involving the p53 tumor suppressor gene By comparing gene expression in diseased cells versus normal cells, researchers can pinpoint multiple target genes whose regulation is affected by the disease This information is crucial for developing targeted therapies aimed at specific mutated genes to mitigate adverse effects Furthermore, understanding different gene expression enhances our knowledge of gene function and protein interactions, contributing to the reconstruction of gene networks, metabolic pathways, and gene annotation.
2006) In this study, DEGs are the main components for gene-network construction to figure out whether dioxins can induce DLBCL
A gene-network is consisted of various nodes, which are connected by edges
In molecular biology, genes and proteins are represented as nodes within a gene network, while molecular interactions are depicted as edges, illustrating the interplay that drives various biological processes Nodes in these networks are categorized into two types: highly-connected hub-proteins and poorly-connected non-hub proteins Hub-proteins play a crucial role in maintaining the integrity of the network, as they are often more essential than non-hubs This significance is underscored by the centrality-lethality rule, which posits that a node's functional importance increases with its structural prominence in the network Consequently, hub-proteins are linked to critical biological pathways that can trigger significant reactions in the human body (He and Zhang, 2006).
In this study hub-proteins play an important role in order to observe the potential pathway exposure to dioxins leading to DLBCL
The Gene Ontology (GO) is a hierarchical framework that categorizes gene products into three main groups: molecular functions, biological processes, and cellular components, reflecting their biological roles (Balakrishnan, 2013) This information is derived from published research and is made accessible through the GO database, allowing researchers to annotate and analyze high-throughput datasets such as transcriptomic and proteomic studies Additionally, the GO database serves as a key resource for identifying functions, pathways, and cellular components represented in these datasets (Pavlidis, 2004).
The GO database serves as a pathway-driven analysis tool for identifying risks associated with single nucleotide polymorphisms (SNPs), which play a crucial role in biomarker identification studies (Holmans, 2009).
Gene Network construction tools
The Network Analyst website is a user-friendly tool that streamlines the analysis of protein-protein interaction networks, offering high-quality visualizations of the results Accessible to all users, it efficiently processes data derived from numerous gene expression experiments across various species, primarily focusing on human and mouse studies.
The Network Analyst website is designed around three key steps in network analysis: significant gene identification through data processing, network construction for mapping and refining, and network analysis and visualization Each of these main steps offers multiple options (Xia et al., 2014) However, this study finds Network Analyst to be insufficient for identifying the most significant differentially expressed genes (DEGs) in diffuse large B-cell lymphoma (DLBCL), dioxins, and hub proteins for gene-network construction and potential pathway identification.
2.5.2 Cytoscape software and plugins: ClueGO and CluePedia Apps
Cytoscape is an open-source software tool designed to integrate high-throughput expression data and various molecular states into a cohesive conceptual framework It plays a crucial role in analyzing databases related to protein-protein interactions, protein-DNA interactions, and genetic interactions across humans and other organisms (Shannon, 2003).
ClueGO is a Cytoscape plugin designed for effective biological interpretation and visualization of functional group terms through networks and charts It utilizes the Kappa statistic to establish connections between terms, thereby functionally organizing Gene Ontology (GO) terms and pathways As a valuable tool for analyzing relationships and functional groups within biological networks, ClueGO enhances data interpretation in the field of bioinformatics (Bindea et al., 2009).
CluePedia is a Cytoscape plugin utilized in this study for identifying new markers associated with biological pathways This tool enables the integration of various genes, proteins, and miRNAs based on experimental data before connecting to the ClueGO network Additionally, it provides insights into new pathway associations through gene, protein, and miRNA enrichments With its user-friendly interface and robust visualization capabilities, CluePedia effectively presents the connections between genes, miRNAs, and proteins, making it a valuable resource for researchers (Bindea et al., 2013).
Cytoscape software and ClueGO/CluePedia plugins are applied to perform gene-network reconstruction and identify potential pathway corresponding to the second and the third objectives of this study.
METHODOLOGY
Data collection
The initial step involved gathering essential microarray datasets from two primary platforms: the Gene Expression Omnibus and Array Express These websites serve as significant public resources for gene expression data, offering users versatile tools for data mining.
This study investigates the gene expression patterns in diffuse large B-cell lymphoma (DLBCL) and their correlation with chemical exposure, utilizing datasets sourced from various platforms Key search terms included "Homo sapiens," "DLBCL," "TCDD," and "Furans," focusing on untreated samples from both normal and DLBCL tissues The research exclusively utilized Affymetrix array platforms, with data collected from September 2015 onward Ultimately, ten relevant microarray datasets were identified, including E-GEOD-12195, E-GEOD-83632, GSE47355, GSE56313, E-GEOD-69844, and E-GEOD-69845, which align with the study's objectives.
Data processing
Data analysis was conducted using Network Analyst, a web-based tool for network analysis and interactive exploration The datasets were categorized into three distinct groups based on sample sources from text files: (1) DLBCL and normal tissues, (2) control and TCDD, and (3) control and Furans Initially, text files were uploaded to identify the organism type—Homo sapiens—and the Official Gene Symbol ID type was selected Following this, ID conversion steps were implemented to determine the number of matched or unmatched genes corresponding to the chosen ID type The files were then submitted for gene annotation to ensure consistent labeling across all datasets Subsequently, a data normalization procedure was performed; however, no normalization was set for the DLBCL and normal tissues data In contrast, log2 normalization was applied to the control group and dioxin treatment data to enhance variance at low intensities.
The normalized data underwent transformation for various gene expression analyses across individual datasets, enabling the detection of differentially expressed genes (DEGs) between DLBCL and normal tissues, as well as between control and chemical groups An analysis of variance (ANOVA) was performed on each dataset, with the cut-off p-value adjusted using Benjamini-Hochberg’s false discovery rate (FDR), set at 0.05 for the differential expression analysis on the Network Analyst website Following data summarization, four datasets for DLBCL, five for TCDD, and one for Furans were utilized for further analysis (refer to Tables 4.1.1 and 4.1.2).
“directed merge” method in meta-analysis step in order to merge all datasets into a single data to analyze
Finally, three distinct result tables containing top-ranking DEGs and relevant statistics (CombineLogFC, adjust P value) for DLBCL, TCDD and Furans were separately exported (Appendix 1,2,3).
Network construction
In this study, differentially expressed genes (DEGs) of DLBCL, TCDD, and Furans were identified using a |fold-change| ratio threshold of 2.0, 1.2, and 1.2 (|Combine LogFC| ≥ 0.26) to filter the most significant up-regulated and down-regulated genes for further analysis Gene ontology analysis and gene network reconstruction were performed using the Cytoscape plug-in ClueGO, which facilitated the identification of biological pathways associated with these DEGs The resulting gene networks illustrated the biological pathways involved, and the DEGs were integrated into ClueGO within Cytoscape to reconstruct the gene networks for DLBCL, TCDD, and Furans A Kappa score threshold of 0.4 was set to customize network connectivity and establish functional gene groups Ultimately, the three subnetworks for DLBCL, TCDD, and Furans were merged into a single network, highlighting the potential pathways through which TCDD and Furans may impact human health and contribute to DLBCL development.
To explore the connection between TCDD/Furans exposure and DLBCL, a protein-protein interaction network was developed to identify hub genes that play crucial roles in various biological processes (Raman et al., 2013) This analysis focused on all filtered differentially expressed genes (DEGs) related to DLBCL, TCDD, and Furans.
Furans were individually analyzed on the Network Analyst website to establish their unique protein-protein interaction networks Key hub proteins associated with DLBCL, along with two dioxin-related compounds exhibiting the highest node degree and betweenness, were identified for subsequent pathway analysis These hub proteins and additional target genes were integrated into the Clue Pedia app within Cytoscape software to visualize the potential pathways through which TCDD and Furans may contribute to DLBCL The pathway network was constructed using directed edges representing gene activation and gene expression, illustrating the mechanisms by which dioxin-related compounds can lead to DLBCL in the human body.
In this research, all necessary steps to be undertaken are assembled in the following flowchart (Figure 3.1) for better illustration
Figure 3.1: The flowchart of methodology
DLBCL and TCDD and Furans and control
RESULTS AND DISCUSSION
Results
This section outlines data mining conducted using the Gene Expression Omnibus and Array Express websites, focusing on 10 relevant datasets: 4 related to DLBCL, 5 concerning TCDD, and 1 involving Furans All datasets utilize the Affymetrix array platform and pertain to Homo sapiens, comprising a total of 809 samples—243 for DLBCL analysis, 376 for TCDD, and 30 for Furans DLBCL samples are categorized into normal tissues and DLBCL, while TCDD and Furans samples are divided into control and chemical exposure groups DLBCL samples were primarily sourced from tissues or nodes, whereas cell lines were predominantly used for experiments involving chemical exposure.
Sample source (type of tissues)
Normal tissues DLBCL Total samples
Fresh frozen tissue, normal tonsil
Lymph node tissues of DLBCL patients
Lymph node tissues of DLBCL patients
Table 4.2: Database of TCDD and Furans
Name Data source Species Sample source
MCF7 Breast Adenocarcinom a Cell Line
Ishikawa Endometrial adenocarcinoma Cell Line
HepG2 Human Hepatocyte Carcinoma Cell Line
HepaRG Hepatocyte Carcinoma Cell Line
Expression Profiles of HepG2 cells treated with furans
Using the Network Analyst and Benjamini-Hochberg’s FDR statistical method, a total of 1,228 differentially expressed genes (DEGs) were identified with a |fold change| ≥ 1.2 (|Combine LogFC| ≥ 0.26), comprising 488 DEGs related to DLBCL, 288 DEGs associated with TCDD, and 512 DEGs linked to Furans The analysis revealed that the number of up-regulated genes was 316 for DLBCL, 268 for TCDD, and 217 for Furans, while the down-regulated genes were counted as 172 for DLBCL, 20 for TCDD, and 295 for Furans (refer to Tables 4.3, 4.4, and 4.5).
Table 4.3: Differentially expressed genes, including up- and down – regulated genes in Diffuse Large B lymphoma compared to normal cells
MMP9, NDUFA1, CREG1 DYNLT1, ENPP2, PFDN5,PLA2G7,C1QC, UBE2E1, AHCY, UQCRQ, CXCL10, SERPINF1, HSPB1, C1QB, MRPL51, DBI,
The article discusses a range of proteins and genes, including COX7A2, CSTA, NDUFA13, and AIM2, highlighting their potential roles in various biological processes Key proteins like STK25, NDUFAB1, and AKR1A1 are mentioned for their involvement in metabolic functions Additional proteins such as GNG5, RARS, and SNRPF are noted for their significance in cellular signaling and protein synthesis The list continues with essential components like HLA-DMB, RBX1, and SEC61G, emphasizing their contributions to immune response and cellular transport mechanisms Other notable mentions include CAPG, PSMA4, and CRIP1, which are linked to cellular structure and function The article also references proteins involved in stress responses and apoptosis, such as PDCD10, GPX1, and ALDH2, underscoring their importance in maintaining cellular health Overall, the discussion encompasses a diverse array of proteins critical for various physiological processes, suggesting their potential implications in health and disease.
The article highlights a comprehensive list of genes and proteins, including COMMD8, NDUFS3, and MFSD1, which play significant roles in various biological processes Key players such as VAMP8, HSD17B10, and PSMD14 are involved in cellular functions, while others like APEX1 and DDX39A contribute to DNA repair and RNA processing Additionally, proteins like YWHAG, FIS1, and CDK2AP1 are essential for cell signaling and metabolic regulation The list also encompasses factors like GZMA and PARP1, which are crucial in apoptosis and stress responses Overall, these genes and proteins represent critical components in understanding cellular mechanisms and potential therapeutic targets.
The study identifies a list of down-regulated genes, including POLR2K, TSG101, PEA15, MRPL49, NIT2, ATIC, and PPP2CB, among others Notable genes such as NCBP2, RABAC1, DRG1, and NUP107 are also highlighted, alongside TCF4, SLC25A19, and UFC1 Additional genes of interest include CIB1, BIRC2, NDUFB10, RBBP8, and SNX3, as well as SMNDC1, HDHD2, ETF1, and RAD23A The list extends to MYBL2, SRRM1, TIMMDC1, and COX5B, with LYRM1, IL18, and ARHGAP17 also featured Other significant genes include IRF2BPL, NONO, TM2D2, MFAP1, and ITGA3 The down-regulated gene profile continues with KCTD12, NUPR1, HAT1, and AP3S1, encompassing MANF, TMEM14B, CPSF4, and PPIH Further genes include MIEN1, MTIF2, FAM50A, and LRRC47, along with PAPSS1, GLO1, CCNG1, RPIA, and ASNSD1 The list also comprises LYPLA1, WDR83OS, CUTA, DAZAP1, and AP1S2, as well as BTBD1, VPS25, BCL11A, and MT1E Additional genes such as ZNHIT3, EIF3I, RPL11, S100A8, and ANXA2 are noted, along with PPIL3, GLRX, ENOPH1, and IER5 The profile concludes with CISD1, HAUS1, DRAM1, DDX21, SNRPD3, UBE2L6, and TMEM138, including RPF2, DUT, GTF3C6, TSPAN13, and ITM2A, as well as PPP1R7, PIH1D1, GTF2B, and CDK5RAP3 Lastly, the list features TMEM208, DBF4, GTF3A, RFC4, IER3, YTHDF2, FIBP, TIMM8B, MPLKIP, VPS28, LAGE3, CLIC1, HARS, IMP3, CS, CEBPZ, RFX5, DNAJB1, MRPL16, CSRP1, ORMDL2, PIGP, CDKN1A, NMI, FAM35A, TNFAIP3, PCMT1, EBPL, TUBB6, GBP1, PLOD1, TUBA1C, REEP5, EIF2S1, MRPL1, IMP4, SNRPA, MARCKSL1, DYNLT3, UBE2E2, SCAMP3, POLR3GL, and CUEDC2.
DUSP6, CYTH4, LCP2, SIRPB1, ITGB2, and CORO1A are key proteins involved in various cellular processes RAB7A, COX7A2L, MEFV, and ANPEP play significant roles in cellular signaling and immune responses C5AR1, ZYX, DOCK5, and STEAP4 contribute to cellular adhesion and migration GRK6, MSL2, PLXNC1, and STK17B are critical for signal transduction and protein regulation PYGL, CD3E, KCNJ15, and SCIMP are involved in metabolic pathways and immune cell function CAPNS1, GLIPR1, CPPED1, and IST1 are associated with apoptosis and cellular stress responses LILRA1, PRKAR1A, ARRB2, and WDR1 regulate immune signaling and cellular interactions ARHGAP26, DUSP1, WIPF1, and MXD1 are essential for cytoskeletal dynamics and cellular communication BSG, CELF2, GNAQ, and ZFAND5 are implicated in various signaling pathways MBOAT7, GABARAP, MBNL1, and AOAH are involved in lipid metabolism and RNA processing CTSS, DOK3, HIST1H1E, and CYP4F3 are critical for proteolytic activity and metabolic regulation PTBP3, NCF2, RNASET2, and TCP11L2 play roles in gene expression and immune responses MAPK1, PIP4K2A, STAT3, and DOCK8 are key regulators of cellular signaling pathways TLN1, TGFBR2, SELPLG, and PGK1 are involved in cell adhesion and energy metabolism FPR1, SDHA, SMCHD1, and MOB3A regulate immune responses and cellular stress DDX17, TUBB1, GUK1, and LYN are essential for mRNA processing and cytoskeletal organization CD37, ETS1, CCNI, and STK38 are involved in immune signaling and cell cycle regulation ATP6V1B2, CAP1, PDZK1IP1, HBB, and EPB41 are critical for cellular structure and function TREM1, PTAFR, GNAS, and FFAR2 play significant roles in immune responses and metabolic regulation RPL18, IL7R, EIF4EBP2, SLC44A2, and HLA-DPA1 are important for protein synthesis and immune function LITAF, ITM2B, CXCR2, CYBB, CFL1, LCP1, ALAS2, and PTPRC are involved in inflammation and immune signaling CSF3R, ARHGDIB, AQP9, and DAZAP2 are key players in cellular communication and fluid transport SLC6A6, B2M, SMAP2, BCL2L1, SORL1, RAC2, FBXO7, and PSAP are involved in various metabolic and signaling pathways, while FCN1 and ND5 contribute to immune responses and mitochondrial function.
SLC25A37, TNFRSF10C, TMBIM6, CD74, HLA-E, SLC25A39, DCAF12, CX3CR1, RHOA, CD53, XPO6, TAGLN2, FCGR2A, MSN, LYZ, LAPTM5, MALAT1, TXNIP, ACTB
Table 4.4: Differentially expressed genes, including up- and down – regulated genes activated by TCDD compared to control group
TCDD (288 DEGS) Up-regulated genes (268)
The article discusses various genes and their potential roles in biological processes Key genes mentioned include CYP1A1, CYP1B1, and TIPARP, which are involved in metabolic pathways, while SLC7A5 and SLC7A11 are crucial for amino acid transport RUNX2 and MYC are highlighted for their significance in cell growth and differentiation Additionally, genes like NFE2L2 and DDIT4 are noted for their roles in oxidative stress response and cellular adaptation Other important genes such as ABCG2 and HECW2 contribute to drug resistance and protein regulation, respectively Overall, this collection of genes represents a diverse array of functions that may be critical for understanding various biological mechanisms.
The genes TFAP2A, FAM174B, IER3, SLC27A2, CDC25B, ZIC2, and RPP25 are essential for various cellular functions, while CSK, RAB9A, and LAMA3 play roles in signaling and transport mechanisms Key metabolic processes are influenced by genes like PYGL, SLC4A7, and SLC3A2, whereas developmental regulation is associated with DLX1, TXNRD1, and TNFRSF11B Genes such as STC1, NR3C1, and RAMP3 are involved in hormonal signaling, while PRSS23, GDAP1, and ND1 contribute to cellular stress responses The expression of genes like CA2, TP53INP1, and CXCR4 is critical in cancer biology, and SLC6A14, CRIP2, and NPY1R are linked to neurotransmitter transport Additionally, genes such as AGR3, YPEL3, and DKK1 are significant in developmental pathways, while MYOF, CTBP2, and BASP1 are involved in cytoskeletal dynamics The role of GJB2, FGFBP1, and PLEKHA1 in cell communication and adhesion is noteworthy, as well as the importance of PABPC1, SRXN1, and CTNNA1 in RNA metabolism and cellular signaling Overall, these genes encompass a wide range of biological processes, underscoring their relevance in health and disease.
C2CD2, KLF6 Down- regulated genes (20)
KLHL4, PDK4, TBC1D9, KITLG, FKBP5, NR1H4, ETNK2, BLNK, MUM1L1, APBB2, ANK3, ZBED3, BBS10, KCNB1, PLCXD3, PEG10, KLHDC7A, KIAA0040, NRXN3, CTGF
Table 4.5: Differentially expressed genes, including up-and down-regulated genes activated by Furans compared to control group
FURANS (512 DEGS) Up- regulated genes
G6PC, AGTR1, C5AR1, SLC26A3, LRRC17, PLSCR4, IGFBP3, SLC38A4, MEP1A, HMGCS2, FRZB, VNN1, INHBE, SLC17A2, RORA, HAL, RBM24, LGSN, RALGPS1, FXYD1, VLDLR, TNS1, ATP2B2, ADSSL1, CA9,
The article discusses a list of genes and proteins, including HIST2H2BE, LRRTM4, TMEM178, PLG, SLC22A15, and DNAJC12, highlighting their potential roles in various biological processes Key genes such as TFF1, GSDMB, and ANGPTL2 are noted for their significance in health and disease Other important entries like PDE4D, KLHL24, and ALPK2 contribute to understanding cellular functions The inclusion of genes such as EGLN3, PTPRH, and ASNS emphasizes their relevance in metabolic pathways Additionally, genes like SLC7A11, SERPINE1, and TNFSF4 are mentioned for their implications in inflammation and immune response The article also references genes involved in signaling and regulation, such as RAB37, UTRN, and TMC8 Overall, the comprehensive list underscores the complexity of genetic interactions and their impact on human health.
The genes TP53INP2, LGALS8, SCN9A, GK, TAT, C5orf4, SLCO6A1, BAAT, PAQR5, HK1, VEGFA, GLDN, FLRT3, CYP2J2, ENPP1, ARG1, FHL2, PTP4A3, CCDC17, JHDM1D, TM4SF5, CPEB3, YPEL2, C1orf87, TMCC1, NAGS, SLC7A2, YPEL3, SERINC2, TBXAS1, VN1R1, GEM, PHKA2, MAFF, UNC5B, and F13B play significant roles in various biological processes and disease mechanisms, highlighting their potential as targets for therapeutic interventions and biomarkers in medical research.
The study highlights several key genes and proteins, including TNFRSF10D, MUC15, and ROBO4, which may play significant roles in various biological processes Additional genes such as WIPI1, IVL, and SIRT4 are also mentioned for their potential implications in cellular functions Noteworthy proteins like ABCA7, FLJ37644, and CLDN14 contribute to understanding disease mechanisms The article further explores ZNF22, CLIC2, and SLC6A8, emphasizing their relevance in health research Other highlighted factors include IFITM2, EFNA1, and SORBS1, which are crucial for signaling pathways OPTN, APOF, and CTGF are discussed for their associations with inflammation and tissue repair The role of IRAK2, F10, and FLJ39639 in immune responses is also examined Lastly, the involvement of genes like SUPT3H, HSD17B6, and CCDC68 in metabolic processes underscores their importance in biomedical studies.
MTHFD2L, PPARGC1A, MXI1, QSOX1, CP, RNASE4, ELF3, FNIP2, KLF6, C3orf32, CLK1, CTDSPL, KIAA1908, ZSWIM5, ARG2, SH3YL1, DNASE1L3, CSGALNACT1, SLCO2A1, GPRC5C, PLXDC2, ANG, ALOX12, CEACAM1,
CACNA2D4, REC8, ZNF83, APOM, HBEGF, SERPINI1, FITM1, RNASET2, IL2RG, L3MBTL4, CBLB, HDAC5, C5, ALDH6A1, FER1L4, IL1RN, DOK6, PDHA2, SHANK2, MYO10
The article discusses a range of significant genes and proteins, including PTTG3P, LOC440288, ACPL2, CDC20, ZNF124, and BIRC5, which play crucial roles in various biological processes It highlights the importance of genes like BRCA1, involved in DNA repair, and CASP8AP2, associated with apoptosis regulation Additionally, proteins such as CDK2 and MCM6 are essential for cell cycle regulation, while others like HNRNPC and RNASEH2A are vital for RNA processing The article also mentions factors like POLR3G and GINS2, which are critical for transcription and DNA replication, respectively Other notable genes include UBE2T, involved in ubiquitination, and RAD51AP1, which plays a role in DNA repair mechanisms Overall, the content emphasizes the interconnectedness of these genes and proteins in maintaining cellular functions and their potential implications in disease processes.
The study highlights several key genes and proteins, including CCDC138, EXO1, and PTPN2, which play significant roles in various biological processes Notable mentions also include ASPM, APITD1, and ID4, along with other critical factors such as ANKRD32 and SPIN4 The expression of HSPA4L and CCNB1 is essential for cell cycle regulation, while SUV39H2 and ERCC6L are involved in DNA repair mechanisms Additionally, ZNF273, PLK4, and ALG10 are implicated in cellular growth and division Other important genes like CDC7, C12orf60, and DLGAP5 contribute to chromosomal stability and signaling pathways The involvement of CASC5, FAS, and SOX18 in apoptosis and development further underscores their biological significance Genes such as C4orf46, NCAPG2, and TRIM59 are also crucial in maintaining cellular integrity, while FANCM and C18orf54 are linked to DNA damage response The roles of RIBC2, CENPL, and ZNF326 in chromosomal organization and function are noteworthy, along with CENPQ and GINS3, which are essential for DNA replication Other genes, including NPNT, E2F7, and DNMT3B, are vital for transcription regulation and epigenetic modifications The list continues with TTC26, SGOL1, and CEP55, which are involved in cell division and checkpoint control, alongside CCNA2, DSCC1, and C1orf124, which regulate the cell cycle Lastly, CLUL1, FPGT, ZNF114, SALL1, and ZNF165 contribute to various cellular processes, while TMTC3, CCNE2, NUP62CL, MORN2, SPA17, TIMM8A, FAM64A, FAM54A, TTC39C, DEPDC4, and AGXT2L1 play diverse roles in cellular function and stability, with RAD18 and F2R being crucial for DNA repair and signaling pathways.
The genes FIGNL1, TTC30A, ZIK1, and LMNB1 are among those that play crucial roles in various biological processes Notably, BRCA2 is essential for DNA repair, while FSCN1 and QLC3 are implicated in cellular structure and function Other significant genes include C5orf34, SKA1, and LIN9, which contribute to cell division and regulation Additionally, COL1A1 and C11orf82 are involved in extracellular matrix composition, and CCDC18, VRK1, and C1orf112 are linked to cellular signaling pathways The anti-apoptotic gene BIRC3 and mitotic regulators KIF18A and MCM10 further highlight the complexity of cellular mechanisms DKK1 and SHCBP1 are important for developmental processes, while LRRIQ3 and KIF20B are associated with cancer progression Genes like CDC6, SPC25, and ZNF749 emphasize the significance of cell cycle control Moreover, NEIL3 and E2F8 are involved in DNA repair and transcription regulation, respectively AP1S3, WDHD1, and RNASEH2B contribute to various cellular activities, while ARHGAP11A and C4orf21 are linked to cellular signaling and metabolism FAM111B, LRRCC1, and FANCB are associated with DNA damage response, and APOBEC3B plays a role in antiviral defense TXNIP, C6orf115, CHAC2, and CMPK2 are involved in oxidative stress response, while KBTBD8 and TEX9 are implicated in cell cycle regulation Lastly, TTC30B, GSTA1, ESCO2, STK17B, MNS1, and KIAA1524 highlight the diverse functions of genes in health and disease, with ARRDC4 also contributing to cellular homeostasis.
4.1.3 Gene-network construction of DLBCL, TCDD and Furans
Figure 4.1, created using the ClueGO app in Cytoscape version 3.4.5, illustrates distinct networks for DLBCL (pink), TCDD (green), and Furans (blue) The ClueGO app is instrumental in visualizing biological pathways and annotating gene networks This study identified 15 biological pathways linked to cancer, particularly DLBCL, including responses to xenobiotic stimuli, lymphocyte proliferation regulation, intrinsic apoptotic signaling, angiogenesis regulation, NIF/NF kappa B signaling, fibroblast proliferation regulation, G2/M transition in the mitotic cell cycle, B cell proliferation, intracellular signaling pathways, tumor necrosis factor production, cell death due to hydrogen peroxide DNA modification, and histone modification regulation.
Figure 4.1: Gene Ontology network showing the relationship of DLBCL, TCDD and Furans through various biological pathways by ClueGO plugin in Cytoscape
Cell response to xenobiotic stimulus
Regulation of intrinsic apoptotic signaling pathway
B cell proliferation Intracellular receptor signaling pathway
DNA modification Regulation of histone modification
Cell death in response to hydrogen peroxide
Regulation of G2/M transition of mitotic cell cycle
4.1.4 Protein – protein interaction network of DLBCL, TCDD and Furans
Discussion
This study investigates the impact of chemicals, particularly TCDD/Furans, on human health and their association with DLBCL through gene-network construction A gene network was developed by analyzing differentially expressed genes influenced by various chemicals or diseases The research identified key genes associated with TCDD/Furans and DLBCL by comparing them to control and normal groups, facilitating the construction of a gene network that highlights biological pathways linked to these genes The findings suggest that the underlying pathways connecting TCDD/Furans to DLBCL can be elucidated, particularly through protein-protein interaction networks.
The primary biological pathways involved in the merge network, such as cell proliferation, DNA and histone modification, hypoxia response, angiogenesis, xenobiotic stimulus, tumor necrosis factor production, and NIK/NF-kB signaling, are closely linked to carcinogenesis (Hu et al., 2006) Additionally, TCDD predominantly influences the human body through hypoxia response, while Furans primarily induce changes in DNA, histone modification, and cell division.
4.2.1 AhR – mediated key factor of dioxins – like compounds
The aryl hydrocarbon receptor (AhR) is a significant transcription factor activated by various environmental toxins, including dioxins and furans Dioxins exert their effects on both humans and animals primarily through the mediation of AhR This study highlights the critical role of AhR in the biological responses to dioxins.
The aryl hydrocarbon receptor (AhR) plays a crucial role in xenobiotic signaling, oxidative stress, and SMAD protein signaling transduction It is a key factor in xenobiotic toxicities, as highlighted by Huang et al (2011) Environmental pollutants that activate AhR can lead to oxidative stress and the production of toxic reactive oxygen species (ROS), resulting in damage to membrane lipids, DNA mutations, and cellular stress (Hendrick et al., 1993) Additionally, AhR is influenced by the SMAD signaling pathway, which regulates cell proliferation, differentiation, and apoptosis through receptor serine/threonine kinase activation (Moustakas A, 2001) The expression of CYP1A1, CYP1A2, and CYP1B1 is modulated by AhR, impacting the immune system, cell proliferation, and apoptosis (Elizondo et al., 2000) TCDD has been shown to activate AhR, leading to the degradation of various proteins, including p53, c-MYC, and c-FOS, while also promoting an increase in these proteins (Mejía-García et al., 2015).
4.2.2 Key factors of hypoxia response and the risk of MYC – TP53 interaction
Research indicates that proteins such as FOS, MYC, and TP53 act as HIF-independent transcription factors in the cellular response to hypoxia, influencing processes like apoptosis, histone modification, and cancer progression in humans (Kalra et al., 2004).
FOS functions as a transcription factor that is part of the AP-1 complex, which activates target genes through the CRE domain This regulation plays a crucial role in various cellular processes, including cell proliferation, differentiation, apoptosis, and oncogenesis.
The FOS protein plays a crucial role in various biological processes, including tumorigenesis, cell transformation, proliferation, angiogenesis, and tumor development (Bossis et al., 2003; Gonzalez et al., 2008; Güller et al., 2013) This study highlights the association of FOS with oxidative stress, DNA methylation, and modification, illustrating its interaction with signaling pathways that influence cell proliferation and apoptosis in response to oxidative stress (Karin and Shaulian, 2001) Additionally, FOS and the AP-1 transcriptional factor regulate key processes such as cell survival, differentiation, and death by working alongside cAMP response elements (CRE) Notably, oxidative stress has been implicated in diffuse large B-cell lymphoma (DLBCL) through the generation of reactive oxygen species, contributing to diverse pathological outcomes (Chivenov et al., 2001) Moreover, FOS is involved in the expression of tumor suppressors by inducing DNA methyl transferase (DNMT1) via promoter DNA methylation (Bakin et al., 1999).
MYC plays a crucial role in the biological processes associated with diffuse large B-cell lymphoma (DLBCL), including cell cycle regulation, metabolism, nucleic acid function, apoptosis, organ development, and hypoxic response (Klapproth et al., 2010) In DLBCL, MYC frequently undergoes alterations due to chromosomal translocations in various types of B cell lymphoma (Korać et al., 2017) This research indicates that MYC is significantly involved in hypoxia regulation, particularly through its antagonistic relationship with hypoxia-inducible factors (HIF).
MYC for the binding site of target gene HIF can induce cell to response to hypoxia by inducing dramatic increase of transcriptional activity and the activation of the minimum
100 target genes that response to hypoxia (Semenza, 2000; Gordan et al., 2007)
TP53 is a crucial tumor suppressor gene involved in various cellular functions and is linked to multiple cancer types, including diffuse large B-cell lymphoma (DLBCL) Notably, mutations in TP53 are present in approximately 20% of DLBCL cases, often disrupting protein function and promoting disease progression (Xu Monette et al., 2012) Additionally, the degradation of TP53 can be accelerated by the AhR receptor, leading to increased apoptosis This protein plays a significant role in cell proliferation and apoptosis, as highlighted in previous research (Hussan et al., 1998).
The combination of MYC and TP53 plays a crucial role in the apoptotic process, particularly with c-MYC and TP53 working together Research indicates that lymphoma patients exhibiting both p53 and MYC expressions experience significantly poorer overall survival compared to those without these expressions Furthermore, patients with lymphoma that express both p53 and MYC have the worst survival outcomes, which are notably inferior to those with only p53 or MYC expression (Wang et al., 2016).
4.2.3 Inhibition of cancer cell apoptosis and tumorigenesis factor in DLBCL
The relationship of both TP53 and YBX1 proteins has been explored in several studies However, most of these studies indicated that YBX1 is possible to inhibit
The protein TP53 plays a crucial role in the apoptotic process, which is essential for preventing cancer development by eliminating cells with damaged DNA Without TP53 or the mechanism of apoptosis, uncontrolled cell proliferation can occur, potentially facilitating tumor growth.
In 2007, it was found that the inhibition of the TP53 protein's apoptosis pathway by the YBX1 protein leads to the activation of TWIST1, a key factor in tumor development in humans.
TWIST1 is a significant protein linked to tumor genesis, angiogenesis, cell proliferation, and differentiation, making it a crucial target for cancer therapy As a member of the Twist protein family, research indicates that TWIST1 expression is elevated in B-NHL tissues, correlating with the progression of B-NHL (Jia et al., 2014) Additionally, Twist1 facilitates tumor cell growth, highlighting its role in cancer development.
YBX1 expression plays a crucial role in tumor progression, cell growth, and oncogenesis across various cancer types (Shiota et al., 2008) Additionally, the up-regulation of the TWIST1 protein is linked to lymphangiogenesis, significantly contributing to tumor development, invasion, and metastasis Research in lymphoma indicates a correlation between angiogenesis markers and the progression of lymphoma (Ganjoo et al., 2007) Moreover, findings suggest that TWIST1 is involved in tumor necrosis factor (TNF) production, aligning with studies that indicate TNF may not be a suitable candidate gene for inducing diffuse large B-cell lymphoma (DLBCL) (Jia et al., 2015).
CONCLUSION
Differentially expressed genes of DLBCL versus normal cell
NDUFA1 1.468 2.24E-20 CREG1 1.443 3.63E-35 DYNLT1 1.434 9.48E-30 ENPP2 1.434 1.41E-28 PFDN5 1.433 1.43E-18 PLA2G7 1.399 4.83E-32
Combine LogFC Pval NDUFA3 1.288 8.04E-26 DHCR24 1.287 5.49E-21 HLA-DMB 1.287 4.75E-21 NDUFA4 1.286 1.51E-15 NDUFS6 1.282 5.25E-21
PSMA4 1.265 8.34E-17 CRIP1 1.255 2.12E-24 PSMB8 1.255 4.76E-29 LGALS1 1.253 1.97E-46 CWC15 1.253 7.05E-22 MTCH1 1.253 1.18E-24
Combine LogFC Pval NDUFA12 1.235 6.38E-16 DYNLL1 1.233 3.36E-24 PFDN2 1.232 2.79E-26 FAM96A 1.232 2.65E-23 GSTO1 1.232 3.64E-26 WDR77 1.231 1.77E-17 SNRNP25 1.230 2.72E-22 PEBP1 1.229 4.85E-20 HIST1H2BK 1.229 1.03E-30
PSMA2 1.207 4.54E-11 ATP5I 1.206 1.14E-17 CYFIP1 1.205 2.92E-31 IFI27 1.205 2.78E-32 SS18L2 1.201 1.28E-22
DPY30 1.185 3.85E-25 ALDH2 1.183 1.56E-10 PSMB5 1.182 1.84E-28 ATP6V1E1 1.182 9.68E-29 STUB1 1.182 8.99E-26 COMMD8 1.181 1.94E-22 NDUFS3 1.181 2.63E-27 MFSD1 1.178 7.24E-26 VAMP8 1.175 1.62E-25 HSBP1 1.175 9.53E-15 HSD17B10 1.172 3.5E-24
PSMD14 1.167 1.4E-19 APEX1 1.167 1.98E-15 ACTR10 1.167 6.55E-27 MRPL33 1.164 2.34E-25 NDUFA8 1.161 9.29E-26 DDX39A 1.161 1.97E-15 TMEM147 1.161 3.46E-30 IGBP1 1.159 9.32E-26 DCTPP1 1.155 7.97E-24 IMPDH2 1.152 1.33E-13
S100A11 1.130 6.18E-42 PRDX1 1.130 6.96E-08 C19orf70 1.129 1.81E-28 MRPS33 1.128 7.95E-22 NDUFB5 1.128 7.44E-21 TMEM126A 1.128 7.39E-22 DNAJB11 1.126 3.94E-23 PRR13 1.125 3.15E-35
Combine LogFC Pval MRFAP1L1 1.106 7.77E-24 ACADM 1.105 1.38E-18 RPL36AL 1.105 2.37E-08 CCDC12 1.104 1.83E-24 STARD3NL 1.104 3.95E-29 CETN3 1.102 2.31E-23
THOC7 1.077 3.67E-21 NOC3L 1.076 2.49E-24 DKFZP586I1420 1.075 3.56E-24 ZNF121 1.074 3.95E-24 MRPS35 1.074 1.81E-25 DIABLO 1.074 3.07E-26 OCIAD2 1.073 1.31E-23 PSMD8 1.072 1.78E-28 ARPC5L 1.072 3.76E-23
PPM1G 1.064 2.47E-21 POLR2K 1.064 2.96E-25 TSG101 1.064 2.84E-30 PEA15 1.064 3.36E-27 MRPL49 1.063 1.45E-25
RAD23A 1.052 1.38E-33 MYBL2 1.052 1.28E-21 SRRM1 1.051 1.47E-18 TIMMDC1 1.051 1.52E-24 COX5B 1.050 3.76E-23
DAZAP1 1.039 1.72E-22 AP1S2 1.039 4.71E-33 BTBD1 1.038 9.79E-22 VPS25 1.038 6.55E-28 BCL11A 1.038 7.12E-18
CISD1 1.031 2.74E-27 HAUS1 1.030 1.35E-21 DRAM1 1.030 2.78E-38 DDX21 1.030 1.02E-14 SNRPD3 1.028 7.51E-17 UBE2L6 1.028 1.37E-32 TMEM138 1.028 1.36E-29
GTF3C6 1.027 4.28E-24 TSPAN13 1.026 7.32E-16 ITM2A 1.025 9.24E-27 PPP1R7 1.025 4.71E-28 PIH1D1 1.024 2.26E-24
TIMM8B 1.020 4.99E-22 MPLKIP 1.019 1.85E-23 CRELD2 1.018 1.95E-24 VPS28 1.017 1.67E-28 LAGE3 1.017 6.26E-28 CLIC1 1.017 3.81E-39
Combine LogFC Pval CSRP1 1.014 1.34E-25 ORMDL2 1.014 1.91E-25
PLOD1 1.007 1.62E-12 TUBA1C 1.007 8.12E-06 REEP5 1.007 2.58E-31 EIF2S1 1.005 5.46E-24 MRPL1 1.005 1.76E-24
MARCKSL1 1.003 9.44E-19 DYNLT3 1.002 2.36E-27 UBE2E2 1.002 8.61E-26 SCAMP3 1.002 1.26E-28 POLR3GL 1.002 3.08E-25 CUEDC2 1.001 2.31E-25 DUSP6 -1.009 3.48E-08 CYTH4 -1.028 3.53E-16 LCP2 -1.030 1.43E-13 SIRPB1 -1.053 3.57E-18 ITGB2 -1.055 9.35E-18 CORO1A -1.073 1.98E-32 RAB7A -1.073 3.81E-15 COX7A2L -1.074 0.041534 MEFV -1.075 2.93E-18 ANPEP -1.078 1.96E-16 C5AR1 -1.088 2.1E-09
Combine LogFC Pval MSL2 -1.129 9.56E-17 PLXNC1 -1.136 8.15E-10 STK17B -1.137 1.27E-09 PYGL -1.137 2.05E-08 CD3E -1.142 5.53E-20 KCNJ15 -1.146 3.05E-09
CAPNS1 -1.201 7.77E-10 GLIPR1 -1.204 2.83E-08 CPPED1 -1.206 2.46E-18 IST1 -1.215 2.89E-21 LILRA1 -1.223 3.54E-18 PRKAR1A -1.230 1.11E-24 ARRB2 -1.240 5.03E-10 WDR1 -1.252 8.06E-28 ARHGAP26 -1.255 8.11E-23 DUSP1 -1.271 3.23E-07 WIPF1 -1.272 1.48E-19 MXD1 -1.273 1.51E-11
CELF2 -1.295 2.64E-17 GNAQ -1.302 8.15E-13 ZFAND5 -1.305 2.62E-13 MBOAT7 -1.308 5.21E-19 GABARAP -1.323 4.93E-07 MBNL1 -1.343 2.68E-36 AOAH -1.344 8.26E-14 CTSS -1.361 1.17E-10
HIST1H1E -1.375 6.31E-13 CYP4F3 -1.388 2.66E-18 PTBP3 -1.398 5.29E-16 NCF2 -1.399 1.03E-08 RNASET2 -1.403 9.29E-26 TCP11L2 -1.413 9.12E-14 MAPK1 -1.421 4.65E-17 PIP4K2A -1.433 1.93E-11 STAT3 -1.455 9.38E-24 DOCK8 -1.459 6.95E-28
Combine LogFC Pval TLN1 -1.465 4.83E-32 TGFBR2 -1.471 7.24E-19 SELPLG -1.473 4.72E-16 PGK1 -1.488 1.86E-10 FPR1 -1.505 2.68E-09 SDHA -1.514 3.23E-25 SMCHD1 -1.520 6.78E-17 MOB3A -1.569 2.82E-25 DDX17 -1.584 2.36E-24 TUBB1 -1.613 8.42E-18 GUK1 -1.622 3.95E-05
CD37 -1.624 1.96E-22 ETS1 -1.639 6.19E-42 CCNI -1.641 1.45E-11 STK38 -1.679 7.99E-30 ATP6V1B2 -1.690 6.12E-33 CAP1 -1.697 3.51E-11 PDZK1IP1 -1.700 4.35E-10
EPB41 -1.744 1.31E-21 TREM1 -1.788 9.04E-13 PTAFR -1.817 7.82E-20 GNAS -1.837 3.01E-23 FFAR2 -1.938 1.27E-13 RPL18 -1.976 6.53E-18 IL7R -1.978 1.76E-22 EIF4EBP2 -1.997 2.77E-16 SLC44A2 -2.009 5.59E-23 HLA-DPA1 -2.025 2.83E-19 LITAF -2.041 3.27E-28 ITM2B -2.048 1.62E-12 CXCR2 -2.126 2.63E-22 CYBB -2.173 1.15E-14 CFL1 -2.183 6.22E-31 LCP1 -2.194 3.51E-25 ALAS2 -2.220 0.00035 PTPRC -2.227 1.93E-19 CSF3R -2.231 8.24E-16
SMAP2 -2.534 4.6E-19 BCL2L1 -2.535 2.52E-10 SORL1 -2.655 1.43E-30 RAC2 -2.730 4.49E-27 FBXO7 -2.741 1.76E-14
SLC25A37 -3.031 1.31E-08 TNFRSF10C -3.056 3.3E-22 TMBIM6 -3.224 2.83E-22 CD74 -3.233 4.45E-37 HLA-E -3.265 2.03E-20
SLC25A39 -3.273 1.2E-09 DCAF12 -3.408 4.96E-09 CX3CR1 -3.451 2.28E-24 RHOA -3.460 1.17E-21 CD53 -3.530 2.46E-19
Differentially expressed genes of exposure to TCDD group and versus
Combine LogFC Pval CYP1A1 2.594 1.62E-27 TIPARP 1.284 6.06E-46 CYP1B1 0.801 2.20E-23
SLC7A11 0.461 9.04E-16 NEDD9 0.457 3.91E-23 ABCG2 0.445 3.64E-15 SLC22A4 0.442 1.67E-19 MCOLN2 0.422 6.22E-17 CEBPD 0.418 2.95E-27 ALDH1A3 0.406 6.15E-28
TP53INP1 -0.272 1.80E-10 CXCR4 -0.295 9.94E-14 SLC6A14 -0.318 1.93E-12 CRIP2 -0.365 6.76E-09 NPY1R -0.375 3.43E-08 AGR3 -0.378 2.32E-07 YPEL3 -0.400 2.01E-09 DKK1 -0.419 2.86E-17
FGFBP1 0.524 1.25E-22 PLEKHA1 0.509 5.74E-31 PABPC1 0.398 1.06E-02 SRXN1 0.395 8.39E-10 CTNNA1 0.375 7.50E-16 RPL11 0.362 1.25E-02 DEGS1 0.346 2.81E-14 STAG3L2 0.344 2.91E-11 LGALS3 0.334 3.88E-04
SOX17 0.308 1.04E-23 PHLDA1 0.299 2.17E-12 MALAT1 0.290 7.02E-03 IL1R1 0.274 4.27E-21 MAP3K1 -0.263 4.16E-06 CALY -0.267 9.25E-06 SLC9A3R1 -0.268 9.63E-05 TAGLN2 -0.268 2.74E-02 SYNGR2 -0.269 3.09E-04 PCBD1 -0.270 1.33E-02 SOX4 -0.272 2.40E-12 PHGDH -0.276 6.31E-03 FDFT1 -0.291 1.44E-03 CD81 -0.298 5.79E-03
Combine LogFC Pval CAPNS1 -0.353 3.45E-03 AP2S1 -0.360 1.14E-03 SH3BGRL3 -0.362 1.43E-03 AP2M1 -0.369 2.67E-03 CLDN4 -0.370 5.59E-06 GPX4 -0.377 8.37E-03 TNNC1 -0.381 5.24E-03 ARL4C -0.389 2.57E-10 ATP6V0C -0.394 1.52E-02 ANKRD1 -0.404 1.69E-05 IGFBP2 -0.413 8.37E-03
MT2A -0.439 5.97E-06 CFL1 -0.474 1.65E-02 KRT18 -0.493 1.32E-02 MSX1 -0.495 1.13E-03 ALDOA -0.521 1.37E-02
IGFBP3 -0.553 1.06E-03 KRT8 -0.869 1.98E-07 ACTB -0.917 1.49E-03 S100A11 -0.928 6.27E-04 PFN1 -1.010 1.36E-03 HSPB1 -1.088 1.49E-04 KRT19 -1.115 2.36E-13 IGFBP1 2.254 1.47E-05
CYP24A1 0.453 1.25E-02 ZBTB38 0.439 1.05E-05 NDUFV2 0.433 1.72E-03 AKAP12 0.430 7.96E-03 TGFBR3 0.425 7.37E-04 AJUBA 0.425 1.65E-03 IRF2BPL 0.381 7.34E-05
DUSP6 0.371 2.73E-03 SORL1 0.364 1.93E-04 LRP11 0.354 9.07E-06 APOC1 0.350 1.00E-04 CYR61 0.339 1.10E-04 C20orf24 0.332 4.25E-03 FAM171A1 0.323 1.46E-05 SNRPB2 0.322 2.50E-04 SASH1 0.317 8.94E-06 GPRC5C 0.314 1.10E-03
SLC19A2 0.311 5.29E-06 RGS10 0.309 4.37E-06 HSD17B11 0.307 7.40E-03 DCDC2 0.298 1.57E-03 NBPF1 0.294 1.94E-03 CMTM3 0.294 3.89E-03
PXDC1 0.285 1.76E-03 ERRFI1 0.283 2.94E-02 MOCOS 0.279 2.33E-05 CMTM6 0.269 3.88E-02 ROBO1 0.267 1.22E-04 ZFP36 0.264 4.48E-04
CPB2 -0.293 4.31E-02 SERPINA7 -0.302 1.41E-02 ATP5G2 -0.325 6.14E-03 TRIM24 -0.344 1.14E-03 GAPDH -0.355 7.69E-03 IFITM3 -0.372 6.26E-03 IFITM2 -0.376 2.25E-02 VSNL1 -0.409 2.39E-06 CRLF1 -0.420 1.62E-02 RPL29 -0.436 2.92E-02 H2AFX -0.437 4.11E-02 TFPI -0.510 2.84E-04 MGST2 -0.514 8.46E-04
IL17RB 0.768 3.36E-09 FAM43A 0.727 1.13E-02 SLC37A2 0.697 4.23E-14 DEPTOR 0.677 2.02E-04
Combine LogFC Pval RAP1GAP 0.440 1.47E-05 SLC9A9 0.435 1.81E-08 TPRA1 0.432 2.14E-08
ZMIZ1 0.364 2.62E-06 EIF1AD 0.360 3.50E-02 FAM210A 0.351 4.29E-02 KRT23 0.342 1.25E-04 KCTD9 0.339 6.07E-03
WDR43 0.337 2.64E-02 S1PR2 0.332 2.26E-02 TMCC1 0.331 2.28E-03 DNMBP 0.325 2.32E-02 RBPMS 0.324 6.63E-05 RAI14 0.323 3.82E-04
EIF2S2 0.312 2.92E-02 CARD10 0.311 3.25E-04 SLC20A2 0.308 2.39E-02 CDK5RAP2 0.298 8.74E-03 PLEKHF1 0.298 7.31E-04 PHACTR2 0.294 1.22E-02 NFKBIE 0.294 2.46E-02 PITPNC1 0.285 4.75E-04 TRIM55 0.283 1.52E-06
Combine LogFC PValue NCEH1 0.283 2.04E-02 C17orf58 0.282 1.10E-02 RDH10 0.280 4.66E-02
KLHL4 -0.270 3.68E-02 PDK4 -0.276 8.46E-03 TBC1D9 -0.287 3.22E-04 KITLG -0.290 1.81E-03 FKBP5 -0.293 2.03E-04 NR1H4 -0.297 3.99E-02 ETNK2 -0.300 4.62E-03 BLNK -0.304 3.17E-02 MUM1L1 -0.320 3.41E-02 APBB2 -0.320 9.22E-06 ANK3 -0.340 1.93E-03 ZBED3 -0.350 3.60E-02 BBS10 -0.374 2.19E-02 KCNB1 -0.377 4.71E-03 PLCXD3 -0.408 4.44E-02 PEG10 -0.413 5.36E-03 KLHDC7A -0.423 1.91E-03 KIAA0040 -0.461 1.24E-03 NRXN3 -0.509 4.34E-09 CTGF -0.596 1.98E-08
Differentially expressed genes of exposure to FURANS group versus
Combine LogFC Pval G6PC 1.178 2.02E-02 AGTR1 1.136 1.55E-02 C5AR1 1.123 2.29E-03 SLC26A3 1.091 4.40E-02 LRRC17 1.030 4.65E-03 PLSCR4 1.025 7.42E-03 IGFBP3 1.010 1.14E-02 SLC38A4 0.985 1.46E-02 MEP1A 0.939 3.93E-02 HMGCS2 0.927 7.09E-03 FRZB 0.859 4.65E-03 VNN1 0.838 2.29E-03 INHBE 0.822 4.11E-02 SLC17A2 0.796 1.84E-02 RORA 0.792 1.31E-02
RBM24 0.781 9.23E-03 LGSN 0.780 1.20E-02 RALGPS1 0.779 4.65E-03 FXYD1 0.779 1.31E-02 VLDLR 0.772 6.76E-03 TNS1 0.763 1.46E-02 ATP2B2 0.756 3.17E-03 ADSSL1 0.744 9.23E-03
In a recent analysis of gene expression, several genes were identified with significant LogFC and P-values Notable genes include C10orf10 (LogFC: 0.632, Pval: 1.07E-02), ANGPTL2 (LogFC: 0.622, Pval: 1.33E-02), and CTSK (LogFC: 0.619, Pval: 4.65E-03) Other important genes are PDE4D (LogFC: 0.617, Pval: 9.23E-03) and KLHL24 (LogFC: 0.605, Pval: 7.42E-03) Additional genes of interest include ALPK2, EGLN3, and PTPRH, with LogFC values ranging from 0.590 to 0.601 The analysis also highlighted ASNS (LogFC: 0.585, Pval: 7.42E-03) and C5orf41 (LogFC: 0.577, Pval: 6.98E-03) Other genes such as TMEM140, FAM13A, and TCP11L2 exhibited significant expression changes, further emphasizing the importance of these genetic markers in the study.
Combine LogFC Pval FILIP1L 0.502 1.59E-02 DDIT3 0.499 7.42E-03 HS3ST5 0.498 1.96E-02 SHC2 0.497 4.65E-03 CHST15 0.491 1.84E-02 SPON2 0.486 1.31E-02 LOC375190 0.483 2.72E-02
PPP1R15A 0.459 1.59E-02 PFKFB3 0.458 3.34E-02 SORL1 0.457 4.04E-02 DDIT4 0.455 4.71E-03 PPARG 0.453 1.90E-02 TMEM27 0.449 2.62E-02 HMHA1 0.448 2.19E-02 SLC22A9 0.447 3.45E-02 ACSM3 0.446 1.93E-02 CYP3A5 0.446 2.50E-02 ADAMTS6 0.445 1.19E-02 RAB17 0.444 2.88E-02 TP53INP2 0.443 1.46E-02 LGALS8 0.440 4.40E-02 SCN9A 0.439 1.33E-02
Combine LogFC Pval GLDN 0.414 1.11E-02 FLRT3 0.413 3.61E-02 CYP2J2 0.413 1.37E-02 ENPP1 0.413 8.98E-03 ARG1 0.412 8.37E-03 FHL2 0.410 3.44E-02 PTP4A3 0.408 1.31E-02 CCDC17 0.408 2.92E-02 JHDM1D 0.407 3.34E-02 TM4SF5 0.407 1.96E-02 CPEB3 0.406 1.07E-02 YPEL2 0.404 8.75E-03 C1orf87 0.403 2.40E-02 TMCC1 0.403 2.84E-02 NAGS 0.401 3.81E-02 SLC7A2 0.401 1.59E-02 YPEL3 0.400 2.09E-02 SERINC2 0.400 4.44E-02 TBXAS1 0.399 1.37E-02 VN1R1 0.398 2.72E-02
PHKA2 0.398 1.67E-02 MAFF 0.388 8.45E-03 UNC5B 0.388 2.57E-02 F13B 0.387 7.42E-03 TNFRSF10D 0.387 1.67E-02 MUC15 0.386 4.16E-02 ROBO4 0.385 4.48E-02 WIPI1 0.385 1.04E-02
SIRT4 0.381 1.04E-02 ABCA7 0.381 1.84E-02 FLJ37644 0.380 1.84E-02 CLDN14 0.378 2.38E-02 ZNF22 0.377 2.27E-02 CLIC2 0.376 2.21E-02 SLC6A8 0.376 2.17E-02 IFITM2 0.374 1.59E-02 EFNA1 0.372 1.33E-02
Combine LogFC Pval SORBS1 0.370 1.65E-02 OPTN 0.370 2.24E-02 APOF 0.368 2.24E-02 CTGF 0.368 3.95E-02 IRAK2 0.367 1.28E-02
FLJ39639 0.364 2.23E-02 SUPT3H 0.363 8.37E-03 HSD17B6 0.363 2.80E-02 CCDC68 0.362 3.79E-02 KIAA0513 0.361 1.88E-02 SLC26A6 0.360 3.97E-02 KLF15 0.360 1.10E-02
PMM1 0.352 1.56E-02 SULT2A1 0.351 3.78E-02 LGALS4 0.350 1.70E-02 TMEM45B 0.349 1.82E-02 GNG7 0.348 1.20E-02 USP13 0.347 3.44E-02 FBXO32 0.345 4.91E-02 MTHFD2L 0.345 1.37E-02 PPARGC1A 0.345 2.71E-02 MXI1 0.342 3.42E-02 QSOX1 0.340 3.61E-02
RNASE4 0.339 4.19E-02 ELF3 0.339 1.84E-02 FNIP2 0.338 1.67E-02 KLF6 0.338 2.50E-02 C3orf32 0.338 1.91E-02
Combine LogFC Pval CLK1 0.337 1.84E-02 CTDSPL 0.336 2.17E-02 KIAA1908 0.336 1.93E-02 ZSWIM5 0.336 2.19E-02 ARG2 0.335 1.84E-02 SH3YL1 0.334 1.46E-02 DNASE1L3 0.334 1.28E-02 CSGALNACT1 0.334 3.25E-02 SLCO2A1 0.334 1.89E-02 GPRC5C 0.333 1.20E-02 PLXDC2 0.333 4.50E-02
ALOX12 0.330 8.28E-03 CEACAM1 0.329 2.88E-02 CACNA2D4 0.329 4.68E-02 REC8 0.328 2.38E-02 ZNF83 0.327 1.98E-02 APOM 0.327 2.72E-02 HBEGF 0.327 1.72E-02 SERPINI1 0.326 2.49E-02 FITM1 0.326 2.72E-02 RNASET2 0.326 1.37E-02 IL2RG 0.325 3.07E-02 L3MBTL4 0.325 1.27E-02 CBLB 0.325 1.47E-02 HDAC5 0.325 1.46E-02
ALDH6A1 0.323 2.34E-02 FER1L4 0.322 3.34E-02 IL1RN 0.322 1.85E-02 DOK6 0.322 2.72E-02 PDHA2 0.322 1.83E-02 SHANK2 0.321 1.22E-02 MYO10 0.320 2.03E-02 PTTG3P -0.321 1.37E-02 LOC440288 -0.321 2.27E-02 ACPL2 -0.321 1.96E-02 CDC20 -0.321 1.96E-02 ZNF124 -0.322 2.10E-02
The analysis reveals a significant correlation between various genes and their respective LogFC and P-values Notably, genes such as C9orf46, BIRC5, and DTYMK exhibit LogFC values around -0.32, indicating a potential downregulation with P-values below 0.03, suggesting statistical significance Other genes, including HNRNPC, RNASEH2A, and CASP8AP2, also display similar trends, with LogFC values ranging from -0.325 to -0.329 and corresponding P-values indicating relevance Additionally, genes like BRCA1 and SULF2 show LogFC values of -0.341 and -0.342, respectively, further emphasizing the potential impact of these genes in biological processes Overall, these findings highlight the importance of these genes in research, with implications for understanding their roles in cellular functions and disease mechanisms.
The analysis reveals several genes with significant negative log fold changes (LogFC) and varying p-values, indicating potential biological relevance Notable genes include BUB1B, PGBD1, and LEF1, each exhibiting LogFC values around -0.34 to -0.36 with p-values below 0.05 Other genes such as SLC25A10, UCHL5, and KBTBD6 also show similar trends, suggesting a consistent pattern of downregulation Additionally, genes like POLH, FBXO5, and GINS2 further exemplify this trend with LogFC values nearing -0.35 The data highlights the importance of these genes, including ZNRF2, POLR3G, and AURKB, which may play crucial roles in cellular processes Overall, the findings underscore a significant downregulation of multiple genes, warranting further investigation into their functional implications.
The analysis of gene expression reveals several significant genes with notable LogFC and P-values Key findings include CKAP2 with a LogFC of -0.369 and a P-value of 8.82E-03, followed closely by NUSAP1 at -0.370 with a P-value of 1.12E-02 Other important genes include TNFAIP8, DSN1, and FLJ37201, all exhibiting similar LogFC values around -0.370 Additionally, LLPH and FKBP5 both show a LogFC of -0.372, indicating potential relevance in the studied context A range of genes, such as ANLN, SKA3, and ZNF43, also demonstrate significant downregulation with LogFC values ranging from -0.374 to -0.375 Further analysis highlights FDXR and CHRNA5 at -0.378, while CTPS and C3orf52 present LogFC values of -0.379 The data continues to show a trend of downregulation with genes like SNHG10, EAF2, and PLK1, all having LogFC values around -0.381 The results underscore the importance of these genes, with P-values indicating statistical significance, warranting further investigation into their biological implications.
In a recent analysis, several genes exhibited significant downregulation, indicated by their LogFC and P-values Notable genes include CCDC34 (-0.395, P=1.30E-02), CEP120 (-0.396, P=1.24E-02), and UEVLD (-0.398, P=4.58E-02) Other genes such as SVIP (-0.398, P=9.23E-03) and RP2 (-0.399, P=2.30E-02) also showed substantial decreases Additionally, MCM9, GALNT7, and MND1 presented LogFC values of -0.399 and -0.400, with corresponding P-values below 0.05 Further down the list, genes like PTGR1 (-0.403, P=1.92E-02) and POLE3 (-0.404, P=2.12E-02) were identified, alongside RFC2 and CEP70, both at -0.404 The analysis also highlighted CASP6 (-0.405, P=1.07E-02) and FAM102B (-0.410, P=1.91E-02), along with several others such as GINS4 (-0.411, P=2.50E-02) and HMMR (-0.411, P=1.65E-02) The downward trend continued with XRCC4 (-0.414, P=2.24E-02), UBE2T (-0.415, P=1.07E-02), and NCAPG (-0.416, P=1.04E-02) A total of 48 genes were analyzed, with significant findings that could impact further research in gene expression and regulation.
In recent analyses, several genes have demonstrated significant downregulation, including SPARC (-0.427, P=1.85E-02) and CCDC15 (-0.427, P=1.59E-02) Notable findings also include PIK3R3 (-0.433, P=2.29E-02) and NCBP1 (-0.434, P=1.37E-02) Other genes such as OBFC2B (-0.437, P=2.91E-02) and TCF19 (-0.437, P=2.83E-02) further highlight this trend Additional downregulated genes include ZNF597 (-0.438, P=2.24E-02) and NUPL1 (-0.438, P=1.07E-02), with RFC5 (-0.439, P=1.31E-02) and RAD51AP1 (-0.441, P=1.07E-02) also showing significant values Other noteworthy genes are CENPK (-0.442, P=1.33E-02) and C12orf32 (-0.445, P=2.07E-02), alongside KIAA1009 (-0.445, P=3.55E-02) and KIF23 (-0.447, P=1.25E-02) Further downregulation is observed in RPGRIP1L (-0.451, P=4.40E-02) and MDM1 (-0.452, P=2.31E-02) The analysis continues with TEX15 (-0.453, P=2.80E-02) and C3orf14 (-0.453, P=2.92E-02), while PBK (-0.453, P=4.65E-03) and C3orf70 (-0.454, P=4.65E-03) also contribute to the findings DUSP19 (-0.455, P=4.65E-03) and CEP78 (-0.457, P=4.65E-03) are included, as well as HELLS (-0.459, P=1.35E-02) and PIH1D2 (-0.461, P=2.48E-02) KIF11 (-0.462, P=9.23E-03) and CENPI (-0.465, P=1.20E-02) also show notable downregulation, along with MICAL2 (-0.465, P=2.10E-02) and TK1 (-0.472, P=1.12E-02) KIF20A (-0.473, P=9.98E-03) and GPR32 (-0.478, P=1.82E-02) further emphasize the trend, while OIP5 (-0.478, P=8.28E-03) and POLE2 (-0.478, P=4.65E-03) are also significant Lastly, CCDC138 (-0.479, P=1.27E-02), EXO1 (-0.481, P=2.39E-02), PTPN2 (-0.482, P=1.06E-02), CXorf57 (-0.483, P=4.40E-02), ASPM (-0.483, P=4.09E-03), APITD1 (-0.484, P=1.12E-02), and ID4 (-0.486, P=2.42E-02) round out the key findings in gene expression analysis.
In recent analyses, several genes have been identified with notable LogFC values and P-values, indicating their significance in biological processes For instance, ANKRD32 shows a LogFC of -0.488 with a P-value of 1.88E-02, while SPIN4 has a similar LogFC of -0.489 and a P-value of 1.75E-02 Other significant genes include HSPA4L (-0.489, P=1.28E-02), CCNB1 (-0.490, P=1.06E-02), and SUV39H2 (-0.491, P=1.28E-02) Noteworthy mentions also include ERCC6L (-0.492, P=1.04E-02) and ZNF273 (-0.493, P=2.21E-02) The data further highlights genes such as PLK4 (-0.493, P=1.06E-02), CDC7 (-0.495, P=2.40E-02), and C12orf60 (-0.499, P=1.96E-02) Additionally, DLGAP5 (-0.501, P=1.67E-02) and CASC5 (-0.502, P=4.65E-03) are also of interest, alongside FAS (-0.504, P=4.44E-02) and SOX18 (-0.504, P=1.64E-02) The list continues with NCAPG2 (-0.509, P=1.12E-02) and TRIM59 (-0.513, P=8.91E-03), culminating in significant entries like SGOL1 (-0.540, P=2.39E-02) and ZNF114 (-0.550, P=3.79E-02) These findings underscore the potential roles of these genes in various biological mechanisms.
The analysis of gene expression reveals significant downregulation in various genes, including ZNF165, TMTC3, and CCNE2, each exhibiting a LogFC of -0.555 with respective p-values of 4.59E-02, 2.72E-02, and 3.39E-02 Other notable downregulated genes include NUP62CL (-0.567, p=1.33E-02), MORN2 (-0.567, p=9.98E-03), and SPA17 (-0.569, p=2.39E-02) TIMM8A shows a LogFC of -0.570 with a p-value of 8.91E-03, while FAM64A and FAM54A have LogFCs of -0.575 and -0.580, respectively Additionally, TTC39C (-0.583, p=1.08E-02) and DEPDC4 (-0.585, p=4.65E-03) are significant The gene RAD18 has a LogFC of -0.588 with a p-value of 7.42E-03, and BRCA2 shows -0.598 (p=7.84E-03) Other genes like C5orf34 (-0.600, p=7.42E-03) and SKA1 (-0.600, p=2.23E-02) further illustrate the trend of downregulation, with additional significant genes including BIRC3 (-0.620, p=3.17E-02) and DKK1 (-0.633, p=4.45E-02) Overall, the data indicates a consistent pattern of downregulation across multiple genes, highlighting potential areas for further research in gene expression analysis.
Combine LogFC Pval ZNF749 -0.679 1.31E-02 NEIL3 -0.681 6.87E-03 E2F8 -0.689 1.37E-02 AP1S3 -0.694 4.40E-02 WDHD1 -0.703 4.65E-03 RNASEH2B -0.717 4.65E-03 ARHGAP11A -0.719 1.06E-02 C4orf21 -0.727 1.05E-02 FAM111B -0.735 4.65E-03 LRRCC1 -0.809 1.46E-02 FANCB -0.817 6.48E-03 APOBEC3B -0.830 1.66E-02 TXNIP -0.830 4.65E-03 C6orf115 -0.833 3.84E-02 CHAC2 -0.845 9.14E-03 CMPK2 -0.858 4.65E-03 KBTBD8 -0.871 1.20E-02 TEX9 -0.950 1.72E-02 TTC30B -0.958 4.91E-03 GSTA1 -0.966 1.37E-02 ESCO2 -0.977 2.29E-03 STK17B -1.008 7.42E-03 MNS1 -1.026 4.65E-03 KIAA1524 -1.173 4.65E-03 ARRDC4 -1.323 3.17E-03
Hub proteins of DLBCL network
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Hub proteins of TCDD network
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