As a result, the most differentially expressed genes were found to construct networks of DLBCL, TCDD and furans and hence the potential pathway presented how dioxins could cause the prog
INTRODUCTION
Research rationale
Dioxins and dioxins-like compounds pose significant health risks due to their persistent presence and long-term effects on humans and animals TCDD and furans are key representatives that can negatively impact health even at low levels through bio-magnification within the food chain These chemicals primarily exert their harmful effects by activating the aryl hydrocarbon receptor (AhR), which, upon entering the cell nucleus, can induce genetic mutations and increase the risk of carcinogenesis.
Diffuse large B-cell lymphoma (DLBCL) is the most common type of B-cell non-Hodgkin lymphoma, accounting for 40% of lymphoma cases While the exact cause of DLBCL remains unknown, previous studies have identified numerous proto-oncogenes and abnormal genes implicated in its development Understanding the biological mechanisms that activate these genes, including potential links to dioxins and dioxin-like compounds, is crucial for advancing research Bioinformatics techniques—such as gene and protein expression analysis, sequence analysis, structural bioinformatics, and network biology—play a vital role in unraveling the complex molecular pathways involved in DLBCL, supporting targeted diagnosis and treatment strategies.
Bioinformatics plays a crucial role in advancing biomedical research, yet its adoption remains limited in Vietnam compared to developed countries High-throughput sequencing and DNA microarray technologies are essential tools for identifying genetic and transcriptomic alterations involved in diffuse large B-cell lymphoma (DLBCL) and discovering prognosis biomarkers for effective lymphoma treatment These technologies help clarify how abnormal gene activation and environmental factors like dioxin exposure influence lymphoma development For example, the study “Identifying the effect of exposure TCDD and Furans on human health leading to diffuse large B lymphoma through gene-network construction” demonstrates how bioinformatics can enhance diagnosis and understanding of lymphoma pathogenesis, promoting further 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 compounds (POPs) are lipophilic chemicals linked to environmental degradation, with certain categories like organochlorine pesticides and industrial by-products containing chlorine atoms known for their highly toxic effects Despite strict regulations and bans in many countries, POPs exposure persists in the general population primarily through the consumption of fatty animal-derived foods Biomagnification causes POPs concentrations to increase along food chains, leading to higher accumulation in humans compared to the environment, posing ongoing health risks.
Persistent Organic Pollutants (POPs) accumulate in adipose tissue over time, serving as a ongoing source of exposure These chemicals are continually released from fat stores into the bloodstream and vital organs, especially those rich in lipids, leading to chronic exposure This continuous release underscores the importance of understanding adipose tissue as a reservoir for POPs and its role in long-term health effects.
POPs are characterized by their main properties, including their lipophilic nature that causes them to accumulate predominantly in lipid-rich tissues such as adipose tissue These compounds tend to bind to lipids within the body, facilitating their widespread distribution and persistence Understanding these properties is essential for assessing the environmental and health impacts of POPs (Lewis et al.).
Persistent Organic Pollutants (POPs) are typically found as complex chemical mixtures in the environment due to natural mixing processes, bioaccumulation through the food web, and their long-term retention in fatty tissues (Kortenkamp et al., 2008) These mixtures include various subclasses such as OC pesticides, polychlorinated biphenyls (PCBs), and dioxins, which are classified based on their chemical composition and environmental behavior.
Dioxins and dioxin – liked compounds
Polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs) are classified as ubiquitous persistent organic pollutants (POPs), known for their environmental persistence and toxicity These compounds belong to two of the three subclasses of halogenated aromatic hydrocarbons, specifically called dioxins and dioxin-like compounds Understanding their classification is essential for assessing their environmental impact and health risks (see Figure 2.1).
Figure 2.1: General molecular structure of polychlorinated dibenzo-p-dioxins
Dioxins and dioxin-like compounds are widespread across nearly all environments, including remote areas, due to their persistence and lipophilicity These compounds can bioaccumulate through food chains, posing potential health risks to humans and wildlife PCDDs and Fs are subclasses of halogenated aromatic hydrocarbons characterized by a benzene ring structure, with differences based on the number of oxygen rings—two for PCDDs and one for PCDFs Their biological impacts are mediated through binding to the Aryl Hydrocarbon Receptor (AHR), with their toxicity influenced by molecular shape, persistence, and receptor fit Notably, TCDD, a specific PCDD component, exhibits high affinity and strong binding to AHR, leading to toxic effects Major sources of PCDD/Fs include combustion processes, metallurgical activities, refining, and processing industries.
(3) biological and photochemical process (US National Research Council, 2006)
Polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs) pose significant health risks, including cancer, reproductive disorders, and birth defects They are known to cause immunotoxicity and are associated with various toxic endpoints such as liver diseases, thyroid dysfunction, lipid imbalances, neurotoxicity, cardiovascular problems, and metabolic disorders like diabetes (US National Research Council, 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 one of the most toxic members of the polychlorinated dibenzodioxin (PCDD) family, representing a widespread environmental contaminant It is primarily produced as a byproduct during the manufacturing of chlorophenols and chlorophenoxy herbicides TCDD can also form during burning processes, including waste incineration, metal production, and fossil fuel or wood combustion Due to its long biological half-life and low water solubility, dioxins tend to bioaccumulate in the food chain, with even small amounts leading to significant contamination TCDD exerts its toxic effects mainly by binding to the aryl hydrocarbon receptor (AhR), a mechanism observed across various mammalian species.
AhR is a basic-loop-helix/PAS transcription factor that resides in the cytoplasm, forming complexes with various proteins and lipophilic compounds, and is involved in multiple physiological processes (Agostinis et al., 2007) In the cytoplasm, it associates with pp60, which can bind to the epidermal growth factor receptor (EGFR) and activate mitogen-activated protein kinase signaling pathways Upon translocation to the nucleus, AhR heterodimerizes with the aryl hydrocarbon receptor nuclear translocator (ARNT), promoting transcription of xenobiotic response elements (XRE) and interacting with key pathways such as Wnt/beta-catenin, estrogen receptors, retinoblastoma proteins, retinoic acid pathways, NF-kB, and circadian rhythm regulators (Sorg, 2013) Evidence shows that AhR influences cell cycle regulation and proliferation, with studies linking exposure to TCDD and phenoxyl herbicides to increased cancer risk, notably a heightened risk of Non-Hodgkin lymphoma following prolonged TCDD exposure in populations in Sweden and the US (Hardell et al., 1996).
Between 1962 and 1971, approximately 45 million liters of Agent Orange contaminated with TCDD were extensively spread across South Vietnam and Cambodia to defoliate vegetation during the Vietnam War This environmentally damaging chemical exposure has led to a significant increase in cancer incidence among affected populations, with long-lasting health impacts still evident today (Stellman et al., 2003) The lingering presence of TCDD in the environment continues to pose serious health risks to communities in the region.
This study aims to explore the gene networks and pathways involved in the toxic effects of TCDD and dioxin-like furans, the most harmful compounds within PCDDs Specifically, it investigates how these substances may induce non-Hodgkin lymphoma, with a focus on diffuse large B-cell lymphoma Understanding these molecular mechanisms can provide insights into the link between dioxin exposure and lymphoma development, highlighting potential targets for diagnosis and therapy.
Figure 2.3: A schematic representation of signal transduction after TCDD/AHR interaction
Lymphoma and non – Hodgkin lymphoma
Lymphoma is a well-known type of cancer that originates from lymphoid precursor cells, first described by Thomas Hodgkin in 1832, giving the disease the name Hodgkin’s lymphoma Over time, various subtypes were identified, leading to the classification of lymphomas into two main categories: Hodgkin lymphoma and non-Hodgkin lymphoma Non-Hodgkin lymphoma predominantly involves B-cell malignancies, although T-cell and NK-cell lymphomas also exist Lymphoid neoplasms are highly diverse, reflecting the complexity of the immune system (Hussain and Harris, 1998) In Vietnam, the incidence of non-Hodgkin lymphoma has increased over the past decade, with approximately 2,700 new cases reported annually (Nguyen, 2015).
Diffuse large B-cell lymphoma (DLBCL) is the most common form of B-cell non-Hodgkin lymphoma in adults, accounting for approximately 40% of diagnoses It is classified into three major subclasses based on molecular heterogeneity: germinal center B-cell like (GCB) DLBCL, activated B-cell like (ABC) DLBCL, and primary mediastinal B-cell lymphoma GCB DLBCL originates from germinal center B cells and exhibits gene expression profiles characteristic of germinal center B lymphocytes In contrast, ABC DLBCL expresses genes typical of plasma cells, suggesting it arises from B-cells activated for differentiation into plasma cells Primary mediastinal B-cell lymphoma is believed to develop from rare B-cell populations in the thymus, with distinct gene expression patterns compared to GCB and ABC DLBCL (Rosenwald).
2.3.2 SNPs of Diffuse Large B lymphoma
Gene expression and genome sequencing are essential tools for advancing our understanding of DLBCL subclasses, the molecular mechanisms underlying chemotherapy resistance, and identifying novel molecular subtypes These approaches facilitate the discovery of new targets for drug interventions, ultimately aiding in the prevention and more effective treatment of DLBCL (Lossos et al., 2006).
Diffuse Large B-Cell Lymphoma (DLBCL) primarily arises from normal antigen-exposed B cells at various stages of differentiation, which undergo clonal expansion within the germinal centers of peripheral lymphoid organs The progression of DLBCL can occur gradually or rapidly, involving clonal evolution and extensive DNA rearrangements across subclones Genetic abnormalities, such as aberrant somatic hypermutation, chromosomal deletions, translocations, and deregulation of proto-oncogenes like BCL6, BCL2, REL, and c-MYC, contribute to the clinical and genetic heterogeneity of DLBCL These alterations often lead to dysregulated apoptosis and defective DNA repair mechanisms, driving tumor development and progression.
Several gene mutations have been identified as key drivers in the development of diffuse large B-cell lymphoma (DLBCL) Early oncogenic events often involve chromosomal translocations that target oncogenes such as BCL6, BCL2, REL, and c-MYC In addition, mutations in genes including BCL2, PRDM1, CARD11, MYD88, TNFAIP3, CREBBP, TP53, EZH2, MLL2, and others contribute to the disease's molecular heterogeneity and progression These genetic alterations play a critical role in the pathogenesis of DLBCL and are important targets for personalized treatment approaches.
MYOM2, PIM1, LYN, CD36, B2M, CD79B, MEF2B, ANKLE2, KDM2B, HNF1B, NOTCH1/2, DTX1, and MYCCD58 are frequently associated with secondary or late oncogenic events, appearing as clonally represented recurrent mutations or gene alterations that contribute to cancer progression These genetic alterations are critical in understanding the molecular mechanisms driving oncogenesis, as highlighted by Morin et al.
Research indicates that alterations in DNA repair and signaling genes impact DNA repair pathways in DLBCL tumors, contributing to lymphomagenesis by forming intermediate cancer driver events Mutations or translocations involving BCL6, BCL2, REL, or c-MYC lead to overexpression of proto-oncogenes, while mutations in TNFAIP3, CARD11, CD79A/B, MYD88, or TRAF2 activate both canonical and non-canonical NF-kB pathways, promoting tumor progression Additionally, key epigenetic reprogramming events driven by mutations in genes like TET1, MLL2, EZH2, MEF2B, EP300, and CREBBP are among the most frequent cancer drivers in DLBCL These genetic alterations facilitate tumor cell plasticity, enable escape from apoptosis, and enhance growth by regulating proto-oncogene and tumor suppressor gene expression through sustained survival and proliferative signaling pathways.
Gene - network components
DNA microarray technology is widely used to measure the expression levels of thousands of genes simultaneously There are two main microarray platforms for gene expression analysis: single-color and two-color systems Affymetrix Gene Chip arrays are among the most popular single-platform microarrays, featuring probes designed to target specific regions of mRNA transcripts, typically at the 3’ end Each probe set consists of 11 to 20 perfect match (PM) probes, usually 25 nucleotides long, along with an equal number of mismatch (MM) probes that contain a single nucleotide substitution in the center, allowing for accurate detection of gene expression levels.
DNA microarray techniques are valuable for predicting treatment success and understanding disease heterogeneity in DLBCL by analyzing five key clinical features: age, tumor stage, serum lactate dehydrogenase levels, performance status, and number of extranodal disease sites (Gohlman and Talloen, 2009) This technology is widely used for whole-genome gene expression profiling, enabling microarray-based studies that refine prognosis through molecular-level insights into DLBCL (Segal, 2005) Additionally, DNA microarrays have been employed to examine how dioxins induce changes in human B-cell gene expression, further demonstrating their versatility in cancer and immune research (Kovalova et al., 2017).
This study utilized DNA microarray techniques to analyze gene expression profiles associated with DLBCL and exposure to dioxins, such as TCDD and furans The gene expression datasets were sourced from prominent databases, including Gene Expression Omnibus (GEO) and ArrayExpress, which will be discussed in greater detail in the following 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, both of which are public repositories for high-throughput functional genomics data Array Express consists of two main components: the MIAME-supportive Array Express Repository, which archives microarray data, and the Array Express Data Warehouse, which features curated gene expression profiles that are regularly re-annotated Users can access specific samples or experiments by searching attributes such as keywords, species, array platforms, authors, journals, or accession numbers The database supports visualization of gene expression profiles using gene names, properties, and ontology terms With over 50,000 hybridizations and 1.5 million expression profiles, Array Express is a rapidly expanding resource that complies with community standards including MIAME, Microarray and Gene Expression Markup Language (MAGE-ML), and MAGE-TAB.
The GEO database, maintained by the National Center for Biotechnology Information (NCBI), is a comprehensive resource containing gene expression data generated by DNA microarray technology Designed to accommodate both raw and processed data according to MIAME standards, GEO hosts approximately one billion gene expression measurements across over 100 organisms and 1,500 laboratories To enhance data utility and facilitate effective exploration, query, and visualization, several user-friendly web applications have been developed, supporting researchers in analyzing individual studies or entire datasets (Barrett, 2004).
Meta-analysis is a statistical technique used to combine results from multiple studies, enhancing the reliability of findings, particularly in microarray research for identifying differentially expressed genes (DEGs) This method involves seven key steps: selecting suitable microarray studies, extracting and preparing data, annotating datasets, resolving probe-gene relationships, combining study estimates, and analyzing and interpreting the results Applying meta-analysis to DLBCL tissues and dioxin-exposed groups compared to normal tissues can help identify significant DEGs, providing valuable insights for further research.
The false discovery rate (FDR) is the expected proportion of false positives among all rejected hypotheses, making it a crucial metric in microanalysis This method helps estimate the likelihood of false positive findings when identifying genes that are differentially expressed Using FDR control ensures the reliability and validity of gene expression results in genomic studies (Gohlmann and Talloen)
The Benjamini and Hochberg method is widely regarded as the most popular procedure for controlling the False Discovery Rate (FDR), as utilized in this study This method adjusts p-values using the formula p = p(i) * m / i, where p(i) represents the original p-value for gene i, m is the total number of genes in the dataset, and i indicates the gene's rank when p-values are ordered.
Different gene expression analysis in microarray experiments is essential for identifying genes that are differentially expressed in disease versus normal cells Mutations in specific genes, such as the tumor suppressor gene p53, can lead to abnormal gene expression and contribute to diseases like cancer Comparing gene expression profiles between diseased and healthy cells helps to uncover target genes whose up- or down-regulation is associated with the disease, facilitating the development of targeted therapies Additionally, different gene expression analysis provides valuable insights into gene functions and protein interactions, supporting the reconstruction of gene networks, metabolic pathways, and gene annotation efforts.
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, nodes are referred as the term of “genes” or “proteins” and edges are molecular interaction, as a result, gene network represents the interaction of genes or proteins leading to a variety of biological processes The types of nodes in each network is currently divided into two distinct types, including: (1) highly- connected nodes, or hub-proteins and (2) poorly-connected nodes or non-hub proteins Hub-proteins are significantly more important that non-hubs since they have an ability to ensure the maintenance of the network It has been indicated that in protein-protein interaction network, hubs tend to be essential due to the centrality-lethality rule that shows functional importance of a node is thought to increase from its structure importance in the network, as a results, hubs tend to relate to significant biological pathways that may result in biological reaction in 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
Gene Ontology (GO) is a hierarchical system that categorizes gene products into three main functional groups: molecular functions, biological processes, and cellular components, facilitating the understanding of proteins' roles in biology (Balakrishnan, 2013) These GO terms, derived from published research, are accessible through the GO database via an annotation process, enabling researchers to explore gene functions efficiently The GO database serves as a vital resource for analyzing high-throughput datasets such as transcriptomic and proteomic studies, supporting functional, pathway, and cellular component identification to advance biological research (Pavlidis, 2004).
GO database is a valuable pathway-driven analysis tool used to identify genetic risks by analyzing single nucleotide polymorphisms (SNPs), which play a crucial role in biomarker discovery and understanding genetic associations (Holmans, 2009).
Gene Network construction tools
A network analyst website is a user-friendly tool that simplifies the process of analyzing protein-protein interaction networks through high-quality visualization Designed for efficiency, this platform is accessible to all users and is primarily focused on enhancing the performance of protein-protein interaction analyses It utilizes data generated from multiple gene expression experiments across various species, with a particular emphasis on human and mouse studies, making it a valuable resource for researchers in molecular biology.
The development of a network analyst website involves three key steps in network analysis: identifying significant genes through data processing, constructing and refining the network for mapping purposes, and conducting network analysis and visualization Each of these steps offers multiple options to enhance accuracy and effectiveness (Xia et al., 2014) In this study, Network Analyst was found to be insufficient for identifying the most prominent DEGs in DLBCL, as well as for locating dioxins and hub-proteins crucial for gene-network construction and pathway analysis.
2.5.2 Cytoscape software and plugins: ClueGO and CluePedia Apps
Cytoscape is an open-source software tool designed for visualizing and analyzing high-throughput expression data and various molecular interactions within a comprehensive conceptual framework It plays a vital role in integrating data from protein-protein, protein-DNA, and genetic interaction databases, applicable to humans and other organisms, facilitating advanced network analysis and biological research (Shannon, 2003).
ClueGO is a Cytoscape plugin designed to facilitate biological data interpretation by visualizing functional groups and pathways as networks and charts It employs the Kappa statistic to link related terms within these networks, enabling functional organization of GO terms and pathways As a powerful tool for analyzing relationships among biological functions, ClueGO enhances the understanding of complex biological data within Cytoscape (Bindea et al., 2009).
CluePedia is a versatile Cytoscape plugin used in this study to identify novel markers associated with biological pathways It enables the exploration of connections between genes, proteins, and miRNAs based on experimental data, facilitating the discovery of new pathway associations through enrichment analysis This tool offers powerful visualization capabilities for illustrative networks of gene, miRNA, and protein interactions, making it highly user-friendly and valuable for pathway analysis research (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
All necessary microarray datasets were initially gathered from two major repositories: the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/) and Array Express (http://www.ebi.ac.uk/arrayexpress), which serve as extensive public resources for gene expression data These platforms offer users flexible data mining tools, facilitating efficient analysis of millions of gene expression datasets (https://academic.oup.com/nar/article/35/suppl_1/D760/1106106/NCBI-GEO-mining-tens-of-millions-of-expression).
This study analyzed human gene expression patterns in DLBCL and their relationship to chemical exposure by retrieving relevant datasets from public repositories using keywords such as Homo sapiens, DLBCL, TCDD, and Furans The selected array files, all generated on the Affymetrix platform between September 2015 and the present, included untreated samples of both normal tissues and DLBCL tissues obtained from diverse sources A total of 10 microarray datasets—such as E-GEOD-12195, E-GEOD-83632, GSE47355, GSE56313, E-GEOD-69844, and E-GEOD-69845—were identified as suitable for this research, enabling a comprehensive analysis of gene expression in relation to chemical exposure.
Data processing
Data analysis was performed using Network Analyst, a standard web browser-based tool for network analysis and interactive exploration The datasets were combined and categorized into three distinct groups based on sample sources: (1) DLBCL and normal tissues, (2) control and TCDD, and (3) control and Furans Initial data processing involved uploading text files to specify the organism as Homo sapiens and selecting Official Gene Symbol for ID type, followed by immediate ID conversion to identify matched or unmatched genes These files were then submitted for gene annotation to ensure label consistency across datasets Data normalization was conducted, with no normalization applied to DLBCL and normal tissues data, while log2 normalization was used for control and dioxin-treated groups to enhance variance at low intensities.
Normalized data were transformed into various expression analysis dialogues to perform gene expression analysis on individual datasets, allowing identification of differentially expressed genes (DEGs) between DLBCL, normal tissues, control, and chemical treatment groups An analysis of variance (ANOVA) was conducted with a p-value threshold adjusted using the Benjamini-Hochberg false discovery rate (FDR), set at 0.05, to determine whether genes are differentially expressed Following data summarization, four datasets of DLBCL, five datasets of TCDD, and one dataset of Furans were analyzed, as detailed in 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
The differentially expressed genes (DEGs) associated with DLBCL, TCDD, and Furans were identified using fold change thresholds of 2.0, 1.2, and 1.2, respectively (|Combine LogFC| ≥ 0.26), to select the top up- and down-regulated genes for subsequent analyses such as Gene Ontology (GO) pathway enrichment and gene network reconstruction These DEGs were uploaded into Cytoscape's ClueGO plugin to systematically analyze biological pathways, revealing key insights into their functional roles Gene network visualization using ClueGO demonstrated the biological pathways involved, with the Kappa score threshold set at 0.4 to optimize network connectivity and define functional gene groups The individual networks of DLBCL, TCDD, and Furans were merged into a comprehensive network, illustrating potential pathways by which TCDD and Furans may influence human health and contribute to the development of DLBCL.
To explore the potential link between TCDD/furan exposure and diffuse large B-cell lymphoma (DLBCL), a protein-protein interaction network was constructed to identify hub genes that may play a critical role in disease development These hub genes could be indirectly involved in various biological processes relevant to DLBCL pathogenesis (Raman et al., 2013) The analysis focused on all filtered differentially expressed genes (DEGs) associated with DLBCL and TCDD exposure, providing insights into the molecular mechanisms underlying their connection.
Furans were individually submitted to the Network Analyst website to construct their own protein-protein interaction networks Key hub proteins associated with DLBCL and two dioxin-related compounds with the highest node degree and betweenness centrality were identified and summarized for subsequent pathway analysis These hub proteins and additional target genes were integrated into the CluePedia app within Cytoscape to visualize potential pathways linking TCDD and Furans to DLBCL development The pathway network was constructed using directed edges representing gene activation and gene expression, illustrating how dioxin-related compounds may influence the progression of DLBCL in humans.
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 study utilizes data mining from the Gene Expression Omnibus and Array Express databases, selecting a total of 10 high-quality datasets that include 4 DLBCL datasets, 5 TCDD datasets, and 1 Furan dataset, all based on the Affymetrix platform and human samples In total, 809 samples were analyzed, with 243 dedicated to DLBCL, 376 to TCDD, and 30 to Furans The DLBCL samples were classified into normal tissues and tumor tissues, primarily obtained from tissues or lymph nodes, whereas TCDD and Furan samples were categorized into control and chemical exposure groups, with chemical exposure experiments mainly conducted on cell lines.
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 Network Analyst and Benjamini–Hochberg’s FDR method, a total of 1,228 differentially expressed genes (DEGs) were identified with a |fold change| ≥ 1.2 (|Combine LogFC| ≥ 0.26), including 488 DEGs in DLBCL, 288 in TCDD, and 512 in Furan The analysis revealed the number of upregulated genes in DLBCL, highlighting significant gene expression changes associated with the condition.
TCDD, Furans were 316, 268 and 217 respectively and down – regulated genes of these categories were counted for 172, 20 and 295 DEGs respectively (Tables 4.3, 4.4, 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,
This comprehensive analysis highlights key genes involved in critical cellular processes, including COX7A2, NDUFA13, NDUFAB1, and NDUFA3, which play vital roles in mitochondrial function and energy production The presence of immune-related genes such as AIM2, HLA-DMB, and TNFSF13B suggests an association with immune response and inflammation Additionally, proteins like GZMK, LGALS1, and CRIP1 are involved in apoptosis, cell signaling, and immune regulation Genes like PSMB8, PSMB6, and PSMC3 are integral to proteasome function, essential for protein degradation and cellular homeostasis Other notable genes such as CAPG and ERH are linked to cytoskeletal organization and gene expression regulation, respectively The analysis also underscores the significance of mitochondrial genes such as VDAC2, ATP5I, and DNAJA1, emphasizing their roles in maintaining cellular energy metabolism Overall, the highlighted genes are crucial for mitochondrial activity, immune response, protein processing, and gene regulation, making them valuable targets for further research in cellular health and disease mechanisms.
This comprehensive gene list includes key proteins such as COMMD8, NDUFS3, MFSD1, VAMP8, HSBP1, and HSD17B10, highlighting their roles in cellular processes like mitochondrial function, vesicle transport, and metabolic regulation Notably, genes like LSM1 and RRM1 are crucial for RNA processing and cell cycle control, while others like RSL24D1 and C14orf2 are involved in ribosomal biogenesis and protein synthesis The presence of immune-related genes such as CD19 and SLAMF8 emphasizes their importance in immune response, along with signaling molecules like GJA1 and RACGAP1 Additionally, genes like DNAJB11 and S100A11 contribute to stress response and cytoskeletal dynamics Genes such as MLH1, PARP1, and PSMC5 are vital for DNA repair and proteasome activity, ensuring genomic stability Metabolic regulators like GLA, ACADM, and GYG1 facilitate fatty acid metabolism and energy production, while others like PSMB10 and UBE2A are involved in protein degradation pathways This gene collection underpins critical functions in cell vitality, immune defense, gene expression regulation, and metabolic homeostasis, making them important targets for biomedical research and therapeutic development.
This article highlights a comprehensive list of genes, including POLR2K, TSG101, PEA15, MRPL49, and NIT2, among others, many of which are involved in critical cellular processes such as transcription regulation, mitochondrial function, and immune response Notably, the study identifies 172 genes that are down-regulated, indicating a significant impact on pathways related to cell cycle, apoptosis, and cellular metabolism Key genes like CCNG1, CDKN1A, and PIGP suggest alterations in cell proliferation and growth control mechanisms The down-regulation of genes such as EIF3I, RPL11, and TUBA1C underscores potential disruptions in protein synthesis and cytoskeletal integrity Overall, these findings provide insights into gene expression alterations associated with disease states or treatment effects, emphasizing the importance of targeted research in understanding cellular dynamics.
This article highlights key genes involved in immune response, inflammation, and cell signaling, including DUSP6, CYTH4, LCP2, SIRPB1, ITGB2, and CORO1A These genes play crucial roles in cellular processes such as immune cell activation, signal transduction, and inflammatory responses, with notable mentions of RAB7A, COX7A2L, and MEFV Additionally, the gene panel features proteins like ANPEP, C5AR1, ZYX, and DOCK5, which are essential for pathogen recognition and cellular adhesion Genes such as GNAQ, MAPK1, and STAT3 regulate key signaling pathways influencing cell growth and immune regulation The list also includes important markers like CD3E, IL7R, and CD37, crucial for immune cell differentiation and activity Further genes like GABARAP, MBNL1, and AOAH are involved in autophagy and immune modulation, while others such as HBB, HLA-DPA1, and SLC44A2 are linked to hematopoiesis and antigen presentation This comprehensive gene profile underscores their significance in immune surveillance, inflammation, and cellular communication, emphasizing their relevance in understanding immune-related diseases and potential therapeutic targets.
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)
Key genes such as CYP1A1, CYP1B1, and TIPARP play crucial roles in xenobiotic metabolism and the body's response to environmental toxins SLC7A5, SLC7A11, and SLC22A4 are essential transporters involved in amino acid and solute transport, influencing cellular nutrient uptake and detoxification processes Transcription factors like RUNX2, FOSL2, and MYC regulate gene expression related to cell growth, differentiation, and cancer progression Genes such as ABCG2 and NEDD9 are implicated in drug resistance and metastasis, highlighting their significance in cancer biology Additionally, signaling pathway components including FZD7, HECW2, and ST3GAL1 contribute to cell signaling, adhesion, and immune response Epigenetic and oxidative stress regulators like AHRR, NFE2L2, and DDIT4 modulate cellular responses to environmental stressors and oxidative damage Other notable genes such as ALDH1A3, CEBPD, and MB21D2 influence cell differentiation, immune regulation, and cellular proliferation, underscoring their importance in health and disease contexts.
This comprehensive gene expression profile highlights key genes involved in various cellular processes, including TFAP2A, CDC25B, ZIC2, and RPP25, which play vital roles in cell cycle regulation, differentiation, and oncogenesis Notably, genes like TP53INP1, GDF15, and S100A16 are associated with tumor suppression and cellular stress responses, while factors such as VEGFA, EGFR, and FGF are crucial for angiogenesis and tissue growth The presence of transport and metabolic genes like SLC27A2, PYGL, and SLC4A7 emphasizes their importance in metabolic regulation, whereas structural and adhesion-related genes such as GJB2, CLDN4, and DSP contribute to cellular architecture and communication Additionally, immune-related genes including IL1R1, CCL20, and IL17RB underscore the relevance of immune response pathways This gene set provides insights into mechanisms underlying cellular proliferation, differentiation, metabolic processes, and immune functions, making it valuable for research into disease pathways and therapeutic targets Optimized for SEO, this gene profile underscores key molecular players in health and disease, facilitating targeted research and clinical applications.
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
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,
HIST2H2BE, LRRTM4, TMEM178, PLG, SLC22A15, DNAJC12, TFF1, GSDMB, C10orf10, ANGPTL2, CTSK, PDE4D, KLHL24, ALPK2, EGLN3, PTPRH, ASNS, C5orf41, TMEM140, FAM13A, TCP11L2, EDN1, IFITM1, NT5E, NDRG1,
OSTalpha, SLC7A11, SERPINE1, TNFSF4, RAB37, UTRN, TMC8, KCNE1L,
SPAG4, BDH1, ARSG, STC2, GATM, CYP27B1, MAOA, FILIP1L, DDIT3,
HS3ST5, SHC2, CHST15, SPON2, LOC375190, TTR, PIM1, CBS, N4BP2L1,
FUT1, HIVEP2, XAF1, F7, HPN, PPP1R15A, PFKFB3, SORL1, DDIT4, PPARG, TMEM27, HMHA1, SLC22A9, ACSM3, CYP3A5, ADAMTS6, RAB17,
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, F13B,
TNFRSF10D, MUC15, ROBO4, WIPI1, IVL, SIRT4, ABCA7, FLJ37644,
CLDN14, ZNF22, CLIC2, SLC6A8, IFITM2, EFNA1, SORBS1, OPTN, APOF, CTGF, IRAK2, F10, FLJ39639, SUPT3H, HSD17B6, CCDC68, KIAA0513,
SLC26A6, KLF15, F5, PTPN14, FEZ1, P4HA1, NUDT9P1, PXK, PFKM, ZNF75A, CFB, PMM1, SULT2A1, LGALS4, TMEM45B, GNG7, USP13, FBXO32,
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,
PTTG3P, LOC440288, ACPL2, CDC20, ZNF124, C9orf46, BIRC5, DTYMK,
This article highlights key genes and their potential roles in cellular processes and disease mechanisms Notable genes such as HNRNPC, RNASEH2A, and CASP8AP2 are involved in RNA processing and apoptotic pathways, which are essential for maintaining cellular health Genes like NUF2, SMC4, and CCDC113 play critical roles in chromosome segregation and cell division, impacting genomic stability Other important genes such as ARNTL2, RECQL, and ATP8B1 are linked to circadian regulation, DNA repair, and membrane transport, respectively Additionally, genes including BRCA1, LEF1, and PIGX are associated with tumor suppression, transcription regulation, and lipid metabolism, providing insights into cancer biology The diversity of these genes reflects their significance in basic cellular functions and their potential as targets for therapeutic intervention Incorporating these gene insights can enhance the understanding of complex biological systems and disease pathways.
TMEM107, ZWINT, CEP152, GPNMB, CENPA, POLR3G, ZNRF2, POLH,
FBXO5, GINS2, and VAMP4 are key genes involved in cell cycle regulation and tumor progression, highlighting their significance in cancer research Genes like YEATS4, GK5, and IMMP1L play crucial roles in cellular function and gene expression regulation, making them important for understanding genetic mechanisms The presence of H2AFX, ALG10B, and C12orf4 indicates their involvement in DNA repair and protein processing, which are vital for maintaining genomic stability CDKN3, RRM2, and CDR2L are essential in controlling cell proliferation and are potential biomarkers for cancer prognosis MYBL1, GUF1, and KIAA1704 contribute to cell growth and differentiation, emphasizing their role in developmental biology AURKB, ZNF682, and HDX are associated with mitotic processes and chromatin remodeling, underpinning their importance in cell division HS3ST3B1, DBR1, and UBR7 are linked to glycosylation and ubiquitination pathways, critical for post-translational modifications GTSE1, RRM1, and SMC2 regulate mitosis and DNA synthesis, with implications for tumorigenesis CKAP2, NUSAP1, and TNFAIP8 are involved in spindle assembly and apoptosis, key processes in cancer suppression DSN1, FLJ37201, and LLPH participate in chromosome segregation and cellular signaling pathways FKBP5, KIAA1586, and ANLN influence protein folding, cell cycle, and cytokinesis, respectively SKA3, ZNF43, and QTRTD1 are vital for kinetochore function and gene regulation, affecting cellular stability FDXR, CHRNA5, and CTPS are associated with mitochondrial function, neurotransmission, and nucleotide biosynthesis, crucial for cellular metabolism C3orf52, SNHG10, and EAF2 serve roles in gene expression, RNA processing, and tumor suppression Finally, PLK1, CDC25A, and TRIP13 are key regulators of cell cycle progression and mitotic checkpoints, making them important targets in cancer therapy.
GBP3, CENPE, CENPM, FANCI, POLQ, OSR2, KIF14, BCHE, SLC38A6, TTF2, FRK, C12orf75, HAT1, LOC439949, SUV39H1, DTL, ZNF267, C3orf64, CCDC34, CEP120, UEVLD, SVIP, RP2, MCM9, GALNT7, MND1, C1orf216, CDCA2,
PTGR1, POLE3, RFC2, CEP70, CASP6, FAM102B, GINS4, HMMR, C14orf142, XRCC4, UBE2T, NCAPG, ZWILCH, CENPN, SGOL2, CENPH, CCDC99,
ZNF107, GINS1, COQ3, TMEM194, WDR67, KIF15, STIL, NDC80, CHML,
RBL1, SMTNL2, RFC3, SPARC, CCDC15, PIK3R3, NCBP1, OBFC2B, TCF19, ZNF597, NUPL1, RFC5, RAD51AP1, CENPK, C12orf32, KIAA1009, KIF23,
RPGRIP1L, MDM1, TEX15, C3orf14, PBK, C3orf70, DUSP19, CEP78, HELLS, PIH1D2, KIF11, CENPI, MICAL2, TK1, KIF20A, GPR32, OIP5,POLE2,
CCDC138, EXO1, PTPN2, CXorf57, ASPM, APITD1, ID4, ANKRD32, SPIN4, HSPA4L, CCNB1, SUV39H2, ERCC6L, ZNF273, PLK4, ALG10, CDC7,
This article highlights the significance of various genes such as C12orf60, DLGAP5, CASC5, FAS, SOX18, and C4orf46, which play crucial roles in cellular processes Additionally, genes like NCAPG2, TRIM59, FANCM, and C18orf54 are essential for maintaining genome stability and regulating cell division Key regulators including RIBC2, CENPL, ZNF326, and CENPQ are involved in chromosome segregation and genomic integrity The presence of GINS3, NPNT, and E2F7 underscores their importance in DNA replication and cell cycle control Epigenetic regulators such as DNMT3B and SALL1 contribute to gene expression modulation Other notable genes like TTC26, SGOL1, CEP55, CCNA2, and DSCC1 are critical for mitosis and chromosomal stability The list also includes C1orf124, CLUL1, FPGT, ZNF114, ZNF165, TMTC3, and CCNE2, emphasizing their roles in cellular metabolism and regulation Furthermore, NUP62CL, MORN2, SPA17, and TIMM8A are involved in nuclear transport and mitochondrial function The expression of FAM64A, FAM54A, TTC39C, and DEPDC4 highlights their relevance in cell proliferation and differentiation, while AGXT2L1, RAD18, and F2R are key in DNA repair pathways and signal transduction Collectively, these genes are interconnected in maintaining cellular homeostasis, emphasizing their importance in health and disease contexts.
The list highlights key genes such as FIGNL1, TTC30A, ZIK1, LMNB1, PACRGL, BRCA2, FSCN1, and QLC3, which play crucial roles in cellular processes Additionally, genes like C5orf34, SKA1, LIN9, and COL1A1 are integral to cell cycle regulation and structural integrity The presence of C11orf82, CCDC18, VRK1, and C1orf112 emphasizes their importance in genomic stability and signaling pathways Genes including BIRC3, KIF18A, MCM10, and DKK1 are involved in apoptosis, cell division, and developmental functions Other significant genes such as SHCBP1, LRRIQ3, KIF20B, CDC6, and SPC25 are associated with mitotic progression and DNA replication The list also features ZNF749, NEIL3, E2F8, and AP1S3, which are linked to gene regulation and DNA repair mechanisms WDHD1, RNASEH2B, ARHGAP11A, C4orf21, and FAM111B further contribute to genomic stability, cellular metabolism, and disease pathways, underscoring their importance in maintaining cellular health and integrity.
LRRCC1, FANCB, APOBEC3B, TXNIP, C6orf115, CHAC2, CMPK2, KBTBD8, TEX9, TTC30B, GSTA1, ESCO2, STK17B, MNS1, KIAA1524, ARRDC4
4.1.3 Gene-network construction of DLBCL, TCDD and Furans
Figure 4.1 was generated using the ClueGO app within Cytoscape software version 3.4.5, illustrating distinct gene networks with color coding: pink for DLBCL, green for TCDD, and blue for Furans The ClueGO app effectively visualizes biological pathways alongside gene function annotation, highlighting 15 key pathways such as cell response to xenobiotic stimulus, regulation of lymphocyte proliferation, and intrinsic apoptotic signaling pathways Other significant pathways include regulation of angiogenesis, NF-κB signaling, regulation of fibroblast proliferation, G2/M transition of the mitotic cycle, B cell proliferation, intracellular signaling, tumor necrosis factor production, cell death in response to hydrogen peroxide, DNA modification, and histone modification regulation These pathways are closely related to cancer development, particularly in the context of DLBCL.
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
In our analysis of protein-protein interaction networks across three distinct datasets—DLBCL, TCDD, and Furans—we identified key hub proteins by filtering high-degree and highly connected nodes The number of hub proteins identified in each network was 55 for DLBCL, 62 for TCDD, and 49 for Furans, as detailed in Table 4.6 This highlights the critical role of these hub proteins in maintaining network connectivity and potential biological significance.
Table 4.6: Lists of hub proteins containing in DLBCL, TCDD and Furans networks
EGFR, UBC, JUN, MYC, SOS1, PABPC1, RPL11, RPS7, ACTB, RUNX2, H2AFX, EIF2S2, MAP3K1, CDK6, RPL29, NFE2L2
RPS27A, SFN, GAPDH, TP53, CSK, CTNNA1
MAPK1, RHOA, RACK1, CREBBP, CTNNB1, ESR1, PRKCA, CCNB1, EIF4A1, FOS, CDK4, AKT1, RPS12, CDC42, UBB, PXN,, RUVBL1,
SNRPB2, CDC37, STAT3, RAP1B, SIRT1, RPS5, EIF5, TAGLN2, EFTUD2, RNPS1, SNRPF,, HSPA8, EIF2AK4, PRKCD, PPM1D, TJP1, EIF3G, UBE2N, TRIM28, CDC6, AHR, GNAS
This article highlights key genes involved in cell cycle regulation and DNA repair, including CDK2, PLK1, and CDC6, which are essential for cell proliferation The importance of tumor suppressors such as BRCA1, TP53, and RB1 is emphasized due to their roles in maintaining genomic stability and preventing cancer development Additionally, mitotic regulators like AURKB, CDC20, and NDC80 are crucial for proper chromosome segregation, while proteins such as UBC, UBB, and RPS27A are involved in ubiquitination and protein turnover Epigenetic modifiers including HDAC5, EP300, and CREBBP influence gene expression, affecting cellular functions Other significant genes like CDK4, ATR, FANCM, and MCM6 contribute to DNA replication and repair mechanisms, highlighting their potential as targets in cancer therapy The roles of structural and regulatory proteins such as CENPA, RPA1, and NPM1 further underline the complexity of cellular regulation essential for healthy cell cycle progression and genomic integrity.
HDAC3, CCNH, STAG2, PSMA4, DMC1, PSMA3, RSRC1, RPS27, DDX20, YWHAH, YWHAE, RUVBL1, ESR1
Discussion
This study examines the impact of chemicals, particularly TCDD/Furans, on human health by exploring their potential role in causing DLBCL through gene-network analysis The gene network was constructed based on the interaction of differentially expressed genes activated by chemicals or diseases, highlighting key molecular pathways involved By selecting the most significantly altered genes associated with TCDD/Furans and DLBCL, the research identifies biological pathways potentially linking these chemicals to lymphoma development Notably, the findings suggest that protein-protein interaction networks can reveal the underlying mechanisms by which TCDD/Furans contribute to DLBCL, providing valuable insights into disease etiology and potential therapeutic targets.
The main biological pathways involved in this merge network—including cell proliferation, DNA and histone modification, response to hypoxia, angiogenesis, xenobiotic stimulus, tumor necrosis factor production, and NIK/NF-kB signaling—are generally linked to carcinogenesis (Hu et al., 2006) Figure 4.1.1 indicates that TCDD primarily impacts the human body by inducing responses to hypoxia (green zone), whereas Furans mainly cause alterations in DNA, histone modifications, and cell division processes (blue zone).
4.2.1 AhR – mediated key factor of dioxins – like compounds
Aryl hydrocarbon receptor (AhR) is a well-known transcription factor that is activated by environmental toxic chemicals such as furans and dioxins Dioxins are known to exert their harmful effects on humans and animals primarily through the activation of AhR This study highlights the crucial role of AhR in mediating the toxicological impact of dioxins, emphasizing its significance in environmental health research.
AhR plays a key role in xenobiotic signaling, oxidative stress, and SMAD protein transduction pathways It is a vital factor in mediating xenobiotic toxicity (Huang et al., 2011), with environmental pollutants activating AhR to induce oxidative stress and reactive oxygen species (ROS) production, leading to membrane lipid damage, DNA mutations, and cellular stress (Hendrick et al., 1993) Additionally, AhR interacts with the SMAD signaling pathway, which regulates cell proliferation, differentiation, and apoptosis through receptor serine/threonine kinases (Moustakas, 2001) AhR modulates the expression of CYP1A1, CYP1A2, and CYP1B1 genes, impacting immune responses, cell growth, and programmed cell death (Elizondo et al., 2000) TCDD, a dioxin, activates AhR to promote the degradation of proteins such as p53, c-MYC, and c-FOS, and can also increase their expression levels (Mejía-García et al., 2015).
4.2.2 Key factors of hypoxia response and the risk of MYC – TP53 interaction
Proteins such as FOS, MYC, and TP53 act as HIF-independent transcription factors in the cellular response to hypoxia, playing key roles in inducing apoptosis, modifying histones, and promoting cancer development in humans (Kalra et al., 2004).
FOS is a transcriptional factor that forms the AP-1 complex, which activates target genes through the CRE domain This regulation plays a crucial role in controlling various cellular processes such as cell proliferation, differentiation, apoptosis, and oncogenesis.
FOS plays a vital role in processes such as tumorigenesis, cell transformation, proliferation, angiogenesis, and tumor development It is associated with oxidative stress, DNA methylation, and cellular modifications, influencing both physiological and pathological H & O & production The FOS protein and AP-1 transcription factor regulate key biological functions including cell proliferation, death, survival, and differentiation by interacting with cAMP response elements (CRE) Oxidative stress can contribute to diseases like DLBCL by generating reactive oxygen species that promote pathological changes Additionally, FOS acts as a tumor suppressor by inducing DNA methyltransferase (DNMT1), leading to promoter DNA methylation and gene regulation, highlighting its complex role in cancer biology.
MYC plays a crucial role in DLBCL by regulating diverse biological processes such as cell cycle control, metabolism, nucleic acid interactions, apoptosis, organ development, and hypoxia response (Klapproth et al., 2010) In diffuse large B-cell lymphoma (DLBCL), MYC alterations are common and often result from chromosomal translocations across various B cell lymphoma subtypes (Korać et al., 2017) Recent studies indicate that MYC is involved in hypoxia regulation through its interaction with HIF, highlighting its significance in tumor microenvironment adaptation.
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 extensively involved in cancer development, including diffuse large B-cell lymphoma (DLBCL) Mutations in TP53, which occur in approximately 20% of DLBCL cases, often disrupt protein function and drive disease progression (Xu Monette et al., 2012) Increased degradation of TP53 can result from elevated levels of the AhR receptor, leading to the initiation of apoptotic processes As a key regulator of cell proliferation and apoptosis, TP53 plays a vital role in preventing tumor growth, and its mutation status is a significant factor in cancer prognosis.
The combination of MYC and TP53 plays a crucial role in the apoptotic process, particularly involving c-MYC alongside TP53 Studies have shown that lymphoma patients expressing both p53 and MYC tend to have a worse overall survival rate compared to those without these expressions Furthermore, patients with lymphoma exhibiting concurrent p53 and MYC expression have the poorest prognosis, with significantly reduced survival compared to patients expressing only p53 or MYC (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 apoptotic process, in which the tumor suppressor protein TP53 plays a crucial role, is essential for preventing cancer development by eliminating cells with damaged DNA (Zhang et al., 2003) Apoptosis and programmed cell death serve as vital defense mechanisms to remove genetically modified cells, reducing the risk of tumor formation Without functioning TP53 or proper apoptotic pathways, uncontrolled cell proliferation can occur, potentially leading to tumor development (Elmore) Maintaining the integrity of apoptosis is therefore critical in cancer prevention and ensuring healthy cell regulation.
The inhibition of the TP53 apoptosis pathway by YBX1 protein may contribute to tumor progression, as it leads to the activation of TWIST1, a key factor in cancer development This mechanism potentially explains the study's findings, highlighting the role of YBX1 in suppressing cell death and promoting oncogenesis through TWIST1 activation.
TWIST1 is a well-known protein involved in tumor genesis, angiogenesis, cell proliferation, and differentiation, making it a critical target for cancer therapy As a member of the Twist protein family, TWIST1’s overexpression has been linked to the progression of B-cell non-Hodgkin lymphoma (B-NHL), with studies indicating higher levels of TWIST1 in B-NHL tissues compared to normal tissues (Jia et al., 2014) Additionally, Twist1 promotes tumor cell growth by regulating key pathways that facilitate cancer development and progression.
YBX1 expression plays a significant role in promoting tumor progression, cell growth, and oncogenesis across various cancers (Shiota et al., 2008) Additionally, the up-regulated protein TWIST1 can activate lymphangiogenesis, which is crucial for tumor development, invasion, and metastasis Studies on lymphoma have demonstrated a correlation between angiogenesis markers and disease progression (Ganjoo et al., 2007) Moreover, research indicates that TWIST1 is involved in tumor necrosis factor (TNF) production, supporting findings that TNF may be an inadequate candidate gene for inducing diffuse large B-cell lymphoma (Jia et al., 2015).
CONCLUSION
Differentially expressed genes of DLBCL versus normal cell
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
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
RPL36AL 1.105 2.37E-08 CCDC12 1.104 1.83E-24 STARD3NL 1.104 3.95E-29
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
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
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
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
Differentially expressed genes of exposure to TCDD group and versus
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
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
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
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
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
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
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
Differentially expressed genes of exposure to FURANS group versus
Combine LogFC Pval C10orf10 0.632 1.07E-02 ANGPTL2 0.622 1.33E-02
PDE4D 0.617 9.23E-03 KLHL24 0.605 7.42E-03 ALPK2 0.601 1.93E-02 EGLN3 0.590 1.04E-02 PTPRH 0.589 7.21E-03
NDRG1 0.547 2.39E-02 MAGI2 0.547 1.63E-02 OSTalpha 0.542 4.19E-02 SLC7A11 0.541 2.09E-02 SERPINE1 0.537 4.16E-02 TNFSF4 0.537 1.12E-02 RAB37 0.535 1.72E-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
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
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
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
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
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
Combine LogFC Pval BUB1B -0.342 1.07E-02 PGBD1 -0.342 3.51E-02 LEF1 -0.343 1.75E-02 SLC25A10 -0.345 2.53E-02 UCHL5 -0.345 1.28E-02 KBTBD6 -0.345 2.41E-02 SDF2L1 -0.345 9.87E-03 TMEM107 -0.346 1.07E-02 ZWINT -0.347 7.42E-03 CEP152 -0.347 3.57E-02 GPNMB -0.347 1.12E-02 CENPA -0.347 1.28E-02 POLR3G -0.348 1.65E-02 ZNRF2 -0.350 4.37E-02 POLH -0.351 1.12E-02 FBXO5 -0.351 2.04E-02 GINS2 -0.353 1.82E-02 VAMP4 -0.354 3.51E-02 YEATS4 -0.354 4.83E-02
IMMP1L -0.356 1.91E-02 H2AFX -0.356 1.50E-02 ALG10B -0.358 2.21E-02 C12orf4 -0.358 3.80E-02 CDKN3 -0.358 8.37E-03 RRM2 -0.361 1.04E-02 CDR2L -0.361 4.93E-02 MYBL1 -0.361 1.14E-02 GUF1 -0.361 3.26E-02 KIAA1704 -0.361 1.88E-02 AURKB -0.362 4.72E-02 ZNF682 -0.362 3.64E-02
HS3ST3B1 -0.364 1.18E-02 DBR1 -0.366 2.55E-02 UBR7 -0.366 7.42E-03 GTSE1 -0.367 1.88E-02 RRM1 -0.367 1.31E-02 SMC2 -0.368 1.28E-02
Combine LogFC Pval CCDC34 -0.395 1.30E-02 CEP120 -0.396 1.24E-02 UEVLD -0.398 4.58E-02 SVIP -0.398 9.23E-03
This study highlights several genes with significant negative correlations, indicating their potential role in disease progression Notably, MCM9, GALNT7, and MND1 show strong negative associations with p-values below 0.05, suggesting their involvement in molecular pathways relevant to the condition analyzed Other key genes such as C1orf216, CDCA2, and PTGR1 also demonstrate significant negative correlations, emphasizing their importance in understanding the disease mechanism Additionally, genes like POLE3, RFC2, and CEP70 are identified as closely linked, offering insights into cellular processes affected The research underscores the potential of these genes—such as CASP6, GINS4, and HMMR—to serve as biomarkers or therapeutic targets, based on their significant negative correlations and their roles in cell cycle regulation, DNA repair, and apoptosis These findings provide a foundational understanding for future investigations into targeted interventions and precision medicine strategies.
Combine LogFC Pval ANKRD32 -0.488 1.88E-02 SPIN4 -0.489 1.75E-02 HSPA4L -0.489 1.28E-02 CCNB1 -0.490 1.06E-02 SUV39H2 -0.491 1.28E-02 ERCC6L -0.492 1.04E-02 ZNF273 -0.493 2.21E-02 PLK4 -0.493 1.06E-02 ALG10 -0.493 2.50E-02 CDC7 -0.495 2.40E-02 C12orf60 -0.499 1.96E-02 DLGAP5 -0.501 1.67E-02 CASC5 -0.502 4.65E-03
SOX18 -0.504 1.64E-02 C4orf46 -0.505 1.17E-02 NCAPG2 -0.509 1.12E-02 TRIM59 -0.513 8.91E-03 FANCM -0.514 4.65E-03 C18orf54 -0.515 4.65E-03 RIBC2 -0.518 2.53E-02 CENPL -0.521 2.90E-02 ZNF326 -0.528 9.23E-03 CENPQ -0.529 5.36E-03 GINS3 -0.533 1.28E-02 NPNT -0.536 4.80E-02 E2F7 -0.536 1.06E-02 DNMT3B -0.537 9.87E-03 LOC100128191 -0.537 9.23E-03 TTC26 -0.539 9.55E-03 SGOL1 -0.540 2.39E-02 CEP55 -0.540 8.28E-03 CCNA2 -0.544 1.07E-02 DSCC1 -0.546 1.04E-02 C1orf124 -0.547 1.77E-02 CLUL1 -0.548 3.17E-03 FPGT -0.550 2.74E-02 ZNF114 -0.550 3.79E-02 SALL1 -0.550 1.35E-02
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
No Label Degree Betweeness No Label Degree Betweeness
Hub proteins of TCDD network
No Label Degree Betweeness No Label Degree Betweeness