EBV-HSA-RegDB: A database of regulatory interactions between Epstein–Barr virus and human miRNAs

EBV-HSA-RegDB: A database of regulatory interactions between Epstein–Barr virus and human miRNAs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article EBV-HSA-RegDB: A database of regulatory interactions between Epstein–Barr virus and human miRNAs Helber Gonzales de Almeida Palheta, Amanda Ferreira Mercês, Greice de Lemos Cardoso Costa, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9558966/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Epstein-Barr virus (EBV) is a ubiquitous human herpesvirus associated with various malignancies and autoimmune diseases. MiRNAs play crucial roles in viral-host interactions by regulating gene expression post-transcriptionally. Understanding the regulatory networks between EBV-encoded miRNAs and human miRNAs is essential for elucidating viral pathogenesis and identifying therapeutic targets. We reported the EBV-HSA miRNA Regulatory Database (EBV-HSA-RegDB, https://lbcd.ufpa.br/ebvhsa/ ), an integrated bioinformatics resource that combines comparative sequence analysis, regulatory network reconstruction, experimental data integration, and functional gene enrichment analysis. The database incorporates miRNA sequences from miRBase and experimentally validated miRNA-target interactions from miRTarBase. It also advances the use of multiple sequence-comparison methods, including BLAST alignment and Hamming and Levenshtein distances, to investigate miRNA seed alignments. The tool reconstructs and enables exploration of a comprehensive regulatory network linking experimental data on EBV-encoded miRNAs, human miRNAs, and their target genes. Gene Ontology and pathway analyses using the KEGG and Reactome databases are also available. Network analysis identified key regulatory hubs and revealed convergent targeting patterns in which EBV and human miRNAs regulate the same target genes ( DICER , TP53 , E2F3 , SMAD4 , SPRY2 ) and several pairs of EBV-encoded miRNAs and their human counterparts, with high similarity in their target sequences. Functional enrichment analysis uncovered significant associations with biological processes, molecular functions, cellular components, and pathways relevant to viral infection, immune response, and cellular transformation. The EBV-HSA-RegDB provides a user-friendly, interactive platform for exploring virus-host miRNA regulatory networks. This resource facilitates hypothesis generation regarding EBV pathogenesis mechanisms and may aid in elucidating novel gene targets for EBV-associated diseases. Epstein-Barr virus miRNA regulatory network bioinformatics database virus-host interaction Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Epstein-Barr Virus (EBV) is a human herpesvirus that encodes miRNAs (EBV-miRNAs), the first miRNAs identified in viruses (Pfeffer et al., 2004 ), which play significant roles in various biological processes, particularly immune regulation and oncogenesis. The interaction between EBV-miRNAs and human miRNAs has become an area of increasing interest, particularly regarding their roles in tumorigenesis and immune evasion. Some studies show that more than half of the proteins encoded by the EBV genome are involved in this evasion, modulating the host's immune response (Ressing et al., 2015 ; Wang et al., 2022). Viruses evade host immune responses through EBV-miRNAs, which also contribute to oncogenesis. For example, they can modulate gene expression involved in apoptosis, proliferation, and immune signaling. EBV-miRNAs, such as those from the BART and BHRF1 clusters, have been shown to target host genes associated with tumorigenesis, thereby promoting the survival and proliferation of infected cells (Riley et al., 2012 ; Harold et al., 2016 ; Fan et al., 2018 ; Wang et al., 2018 ; Židovec Lepej et al., 2020 ; Kim, 2023 ). This targeting can redirect the host epigenome, favoring tumor development, particularly in cancers such as nasopharyngeal carcinoma (NPC) (Chen et al., 2010 ; Ramakrishnan et al., 2011 ; Skalsky et al., 2012 ; Zhang et al., 2018 ). This emerging evidence suggests that viral miRNAs can exhibit sequence similarity to host miRNAs, potentially allowing them to functionally mimic host regulatory molecules to hijack cellular regulatory networks, manipulate host cell biology, and evade immune surveillance. Most computational resources, such as databases, have addressed aspects of viral miRNA biology. For instance, VIRmiRNA provides curated information on experimentally validated viral miRNAs and their targets, including EBV-miRNAs (Qureshi et al., 2014 ). The miRBase offers detailed sequence and annotation data for EBV-encoded miRNAs. The VirusMINT focuses on host–virus protein interactions relevant to EBV pathogenesis and immune evasion (Chatr-aryamontri et al., 2009 ). The VISDB is an active comprehensive database of viral integration sites, emphasizing viral genomics and host–pathogen interactions over miRNA-centric regulatory mechanisms (Tang et al., 2020 ). In addition, ViRBase v3.0 advances the field by cataloging virus–host ncRNA interactions (Cheng et al., 2022 ). To our knowledge, these tools lack integrative analysis of host interactions and functional context, failing to place sequences within interaction networks or to address ncRNA-mediated regulation. This reveals a critical gap: the absence of an integrated EBV-focused miRNA resource that enables comprehensive analysis of sequence features, regulatory interactions, and biological impact. To address this gap, we developed the EBV-HSA miRNA Regulatory Database (hereafter, EBV-HSA-RegDB), an integrated bioinformatics resource that compiles comprehensive genomic sequence information on EBV and human miRNAs. The tool includes multi-method sequence similarity analyses (including BLAST, Hamming distance, and Levenshtein distance), integrates experimentally validated miRNA–target interactions, reconstructs and visualizes regulatory networks, and conducts functional enrichment analysis to identify associated biological pathways across several layers. The tool provides an interactive, user-friendly web interface to access raw data and networks. 2. Materials and Methods 2.1. Data collection and organization MiRNA sequences for EBV and Homo sapiens were obtained from miRBase (version 22.1), a comprehensive, curated database of published miRNA sequences and annotations (Kozomara et al., 2019 ). Sequences were downloaded in multi-fasta format and filtered according to organism-specific prefixes (ebv- and hsa-), yielding 44 EBV-miRNA sequences and 2,656 hsa-miRNAs. 2.2. Experimentally Validated miRNA-Target Interactions MiRNA–target gene interactions were retrieved from miRTarBase (release 2025) (Cui et al., 2025 ), a manually curated database of experimentally validated miRNA–target interactions containing over 2.5 million entries supported by strong experimental evidence, including reporter (luciferase) assays, Western blot analysis, quantitative PCR (qPCR), CLIP-seq (crosslinking and immunoprecipitation followed by sequencing), and other functional validation methods. The interactions were filtered to retain only those involving EBV-miRNAs (n = 25) or hsa-miRNAs (n = 23,058), thereby enabling the reconstruction of convergent regulatory networks. 2.3. Sequence Analysis Methods To identify sequence similarities between EBV and human miRNAs, pairwise BLAST (Basic Local Alignment Search Tool) analyses were performed using the BLASTn algorithm optimized for short sequences (Camacho et al., 2009 ), with parameters set to a word size of 4, an E-value threshold of 10 to allow detection of potential similarities, standard gap penalties, and filtering of low-complexity regions. BLAST outputs were parsed to extract query and subject identifiers, alignment length, number of mismatches, gap openings, query start and end positions, E-values, and bit scores. Biologically relevant alignments were defined using stringent criteria, including an alignment length of at least 6 nucleotides, absence of gap openings (gapopen = 0), query start position ≤ 2, and query end position ≥ 8, ensuring that the identified similarities encompass the critical miRNA seed region (nucleotides 2–8) responsible for mRNA recognition. Hamming similarity measures the number of positions at which corresponding nucleotides differ between two sequences of equal length. For miRNA pairs with identical lengths, the Hamming distance was calculated as: $$\:H\left(x,y\right)=1-\:\frac{\sum\:_{i}\left({x}_{i\:}\ne\:\:{y}_{i\:}\right)\:}{L}$$ , where x and y denote the miRNA sequences, and x i and y i represent the nucleotides at position i in each sequence, respectively. L denotes the length of the miRNA sequence. Levenshtein distance (edit distance) measures the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one sequence into another. Unlike Hamming distance, Levenshtein distance accommodates sequences of different lengths, making it suitable for comparing miRNAs with length variations. The Levenshtein distance was computed using dynamic programming with the following recurrence relation: $$\:L\left(x,y\right)=\text{min}\left\{\begin{array}{c}L(i-1,\:j)\:+\:1\\\:L(i,\:j-1)\:+\:1\\\:L(i-1,\:j-1)\:+\:cost\end{array}\right.$$ where cost = 0 if xi = yj, otherwise cost = 1. The Levenstein package is available for free at https://pypi.org/project/python-Levenshtein/ . 2.4. Reconstructing Regulatory Network Architecture The EBV regulatory network was modeled to include: Edges between EBV-miRNAs and hsa-miRNAs derived from sequence-similarity based on BLAST results; Interactions between EBV-miRNAs and their target genes were obtained from experimental interaction data; Interactions between hsa-miRNAs and their target genes, also obtained from experimental data. The experimental edges corresponded to interactions experimentally validated in miRTarBase. Network visualization was performed using PyVis (version 0.3.2), a Python library for interactive network rendering built on the Vis.js framework. 2.5. Functional and pathway enrichment analysis of miRNA networks Functional enrichment analysis was conducted programmatically using g:Profiler, a remote platform for functional profiling of gene lists ( https://pypi.org/project/gprofiler-official/ ). The analysis incorporated multiple annotation sources to comprehensively characterize the biological relevance of the identified genes. The enrichment analysis was performed for Homo sapiens genes using the g:SCS (Set Counts and Sizes) statistical framework in g:Profiler to correct for multiple testing. Statistical significance was defined as an adjusted p-value < 0.05, ensuring robust control of false-positive enrichment. Gene Ontology (GO) annotations were evaluated across three domains: Biological Process (GO:BP), describing coordinated biological programs carried out through multiple molecular activities; Molecular Function (GO:MF), referring to elemental biochemical activities at the molecular level; and Cellular Component (GO:CC), indicating the subcellular locations or structural contexts in which gene products operate. In addition to GO terms, pathway-based enrichment was assessed using curated biological pathway databases. These included the Kyoto Encyclopedia of Genes and Genomes (KEGG), which provides representations of metabolic and signaling pathways (Kanehisa and Goto, 2000 ), and Reactome, a manually curated resource of biological pathways and processes (Jassal et al., 2020 ). 3. Results 3.1. The EBV-HSA-RegDB The EBV-HSA-RegDB was implemented as a lightweight interactive web application using Streamlit ( https://streamlit.io/ ) (version 1.28.0), a Python-based framework for developing data science applications. The EBV-HSA-RegDB supports automatic reactive view updates in response to user input and provides a comprehensive set of built-in widgets for interactive data exploration. An overview of the EBV-HSA-RegDB framework is shown in Fig. 1 . The web application interface was organized into four main functional sections designed to simplify access to raw data, sequencing similarity and alignment, network integration, and pathway information. The Sequences section presents EBV-miRNAs and HSA-miRNAs sequences in a tabular format, along with associated sequence statistics, including identifiers, descriptions, and sequence lengths. This module also enables users to export the displayed data in .csv format, supporting downstream analysis and reproducibility; The BLAST analysis section provides interactive filtering options to select EBV-miRNAs and HSA-miRNAs, enabling dynamic inspection of alignment statistics and detailed alignment information for selected miRNA pairs; The Hamming and Levenstein analysis section displays pre-computed distance metrics in tabular form and presents a scatter plot comparing Hamming and Levenstein distances across miRNA pairs. Interactive hover tooltips reveal the identities of the corresponding miRNA pairs, facilitating detailed comparative analysis; The Alignment section allows input of pairs of miRNA sequences and visualizes the alignment graphically; The Network Section comprises EBV-miRNAs and HSA-miRNAs interactions with genes, the modeled regulatory and functional enrichment analysis of genes. EBV-miRNAs were displayed as green diamonds, HSA-miRNAs as red diamonds, and target genes as blue circles. Node sizes were scaled proportionally to node degree, reflecting the number of interactions, while edges were represented as lines connecting related nodes. Interactive features such as zooming, panning, hover tooltips, and node dragging were enabled to support exploratory analysis. 4. EBV-miRNA and HSA-miRNA similarities The BLASTn analysis between EBV and human miRNAs yielded 32,976 pairwise alignments, all of which lacked gap openings, reflecting the structural constraints of short RNA sequences. Descriptive statistical analysis revealed a very high sequence identity, with a minimum of 85.7%, a mean of 99.88%, and a median of 100%, indicating that most alignments are near-perfect nucleotide matches. Alignment lengths ranged from 4 to 15 nucleotides, with a median of five nucleotides, consistent with the expected distribution for short-sequence comparisons. Notably, a substantial fraction of alignments exceeded six nucleotides in length, a threshold commonly associated with functional relevance in miRNA seed-mediated target recognition. The positional analysis of alignments within EBV miRNAs showed broad coverage across the query sequences (Table 1 , Fig. 2 ). Moreover, biologically meaningful matches were enriched in regions encompassing the canonical seed region (nucleotides 2–8). To increase biological specificity, stringent filtering criteria were applied, retaining only alignments with a minimum length of six nucleotides, zero gap openings, e-values ≤ 1, query start positions ≤ 2, and query end positions ≥ 8. These criteria ensured that retained alignments spanned the seed region, a critical feature of miRNA–target interactions. Although E-values were relatively high overall, a known limitation of BLAST for short nucleotide sequences, the bit score distribution provided a more reliable metric of alignment quality, with values ranging from 8.4 to 24.3, and higher scores corresponding to longer and more conserved matches. Most alignments exhibited 100% identity across 7 or 8 nucleotides, without insertions or deletions, and were predominantly located within positions 2–8 of the mature miRNA sequence, corresponding to the seed region. This region is the primary determinant of target recognition and binding specificity. Such perfect seed matches were observed in multiple pairs, including ebv-miR-BART22 with hsa-miR-520d-5p and hsa-miR-524-5p, ebv-miR-BHRF1-2-5p with hsa-miR-4778-5p, ebv-miR-BART4-3p with hsa-miR-499a/b-3p, ebv-miR-BART6-3p with hsa-miR-542-5p, ebv-miR-BART17-3p with hsa-miR-675-3p, and ebv-miR-BART11-5p with hsa-miR-1324. Table 1 Alignment results between Epstein–Barr virus (EBV) miRNAs and homologous human miRNAs, showing sequence identity, alignment length, mismatches, gaps, query (Q) and subject (S) start/end positions, E-value, and bit score for each predicted match. EBV miRNA Human miRNA Indetity Length Mismatch Gaps Q-Start Q-End S-Start S-End E-value Bitscore ebv-miR-BART22 hsa-miR-520d-5p 100 7 0 0 2 8 2 8 22 14.4 hsa-miR-524-5p 100 7 0 0 2 8 2 8 22 14.4 ebv-miR-BHRF1-2-5p hsa-miR-4778-5p 100 8 0 0 2 9 1 8 5.2 16.4 ebv-miR-BHRF1-3 hsa-miR-4681 100 8 0 0 2 9 1 8 5.2 16.4 ebv-miR-BART4-3p hsa-miR-499b-3p 100 7 0 0 2 8 2 8 22 14.4 hsa-miR-499a-3p 100 7 0 0 2 8 2 8 22 14.4 ebv-miR-BART5-3p hsa-miR-6829-5p 90 10 1 0 2 11 1 10 56 12.4 ebv-miR-BART6-3p hsa-miR-542-5p 100 8 0 0 1 8 2 9 5.2 16.4 ebv-miR-BART17-3p hsa-miR-675-3p 100 8 0 0 1 8 2 9 5.6 16.4 ebv-miR-BART7-5p hsa-miR-3690 100 8 0 0 1 8 2 9 5.2 16.4 hsa-miR-8058 90.90 11 1 0 2 12 1 11 21 14.4 ebv-miR-BART9-5p hsa-miR-3654 100 7 0 0 2 8 2 8 21 14.4 ebv-miR-BART11-5p hsa-miR-1324 100 7 0 0 2 8 2 8 24 14.4 hsa-miR-4692 88.88 9 1 0 1 9 1 9 371 10.4 ebv-miR-BART20-5p hsa-miR-4690-5p 100 7 0 0 2 8 2 8 19 14.4 In a subset of cases, slightly lower sequence identity (approximately 88–91%) was observed due to a single mismatch within alignments spanning 9–11 nucleotides, as seen for ebv-miR-BART5-3p/hsa-miR-6829-5p, ebv-miR-BART7-5p/hsa-miR-8058, and ebv-miR-BART11-5p/hsa-miR-4692. Nevertheless, preservation of the core seed nucleotides suggests that target recognition potential may remain largely intact, depending on the precise position of the mismatch. 4.1. The Regulatory EBV-miRNA network and co-target interactions The analyzed network comprises 153 nodes and 156 edges, exhibiting a low topological density (0.013), a characteristic of regulatory biological networks. The nodes are distributed into three main categories: 120 human miRNAs, 20 Epstein-Barr virus (EBV) miRNAs, and 13 target genes, forming an architecture in which experimental interactions occur between miRNAs and genes, and miRNA-miRNA relationships are inferred by BLAST local alignment. The network represents a functional gene-regulation relationship; its structural organization follows a hub-and-spoke pattern, in which multiple miRNAs converge on a small number of central genes. The genes act as the main hubs of the network, concentrating most of the regulatory connections. Among the most interconnected nodes, SMAD4 stands out with 43 interactions, TP53 with 32, E2F3 with 24, and DICER1 with 23, highlighting a strong centralization of the network around key regulators of the cell cycle, apoptosis, and miRNA biogenesis. The coexistence of human and viral miRNAs that regulate the same genes suggests mechanisms by which EBV may jointly or competitively modulate cellular pathways. Thus, the observed topology indicates a regulatory network highly dependent on a few central genes, consistent with cellular reprogramming processes associated with viral infection and oncogenesis. Functional enrichment analysis of host genes predicted as targets of EBV-miRNAs identified significant overrepresentation of biological processes related to apoptosis regulation, cellular differentiation, developmental pathways, and signal transduction (FDR < = 0.05) (Fig. 3 ). Among the most significantly enriched categories were processes associated with muscle cell apoptosis, positive regulation of striated muscle cell apoptosis, and cardiac muscle cell apoptosis. In addition, enrichment was observed in pathways associated with negative regulation of cell differentiation and cell population proliferation, including negative regulation of glial cell proliferation. Development-related processes were prominently represented, including tissue development and regulation of multicellular organismal development. Furthermore, genes associated with regulating the cellular response to transforming growth factor beta stimulation and with the transforming growth factor beta receptor signaling pathway were significantly enriched. Additional enrichment was detected for nuclear protein import, indicating that genes involved in nucleocytoplasmic transport are represented among predicted EBV-miRNA targets. KEGG pathway enrichment analysis revealed significant enrichment in cancer-associated and cell regulatory pathways. Among the most significantly enriched pathways were chronic myeloid leukemia and pancreatic cancer, followed by miRNAs in cancer and gastric cancer pathways. Viral infection-related pathways, including Hepatitis B, Hepatitis C, and Human T-cell leukemia virus type 1 infection, were also significantly represented. Pathways associated with cell proliferation and growth control were enriched, including the cell cycle and cellular senescence pathways. Multiple solid tumor-associated pathways were identified, comprising hepatocellular carcinoma, bladder cancer, glioma, melanoma, colorectal cancer, prostate cancer, breast cancer, and both small cell and non-small cell lung cancer. Additionally, signaling pathways involved in tumor progression and therapeutic response were enriched, including endocrine resistance and the Wnt signaling pathway. The integrative cancer-related pathway categories were also significantly enriched among EBV-miRNAs target genes. 4. Discussions In this work, we systematically curated EBV-miRNA sequences and co-target events using the human genome and experimental interaction data to propose the EBV-HSA-RegDB tool, aiming to fill gaps in pathogen-host interactions. The EBV-miRNA sequence similarity in the seed region of HSA-miRNAs supports the hypothesis of functional molecular mimicry, in which viral miRNAs can exploit host regulatory networks by converging shared or partially overlapping mRNA targets. Such mimicry could allow EBV to modulate essential host cellular pathways, including proliferation, apoptosis, and immune responses, through mechanisms analogous to regulation mediated by endogenous miRNAs. This indicates that the EBV can exploit post-transcriptional networks already established in the infected host cell. The hypothesis that EBV-miRNAs share sequence characteristics with specific human miRNAs, enabling convergent regulatory effects on common target genes, contributes to regulatory mimicry between host and virus. This strategy allows integration into the cellular regulatory network, favoring persistence and maintenance of viral latency. Within this framework, the EBV-HSA-RegDB reveals biologically relevant interactions. In this context, the potential interaction between EBV-miR-BHRF1-1 and the TP53 gene warrants particular attention. The TP53 gene encodes the p53 protein, a central transcription factor in maintaining genomic integrity, playing a role in inducing cell cycle stop, DNA repair, and apoptosis in response to stress or genetic damage. The modulation of this pathway by viral miRNAs represents a critical point of molecular interference, given p53's role in regulating cell proliferation and preventing neoplastic transformation. Experimental evidence suggests that miR-BHRF1-1 can influence components of the p53-associated pathway in chronic lymphocytic leukemia cell lines, altering the balance between cell survival and death (Mo et al., 2018 ). Similarly, the database highlights the interaction between EBV-miR-BART6-5p and the SMAD4 gene, a central mediator of the TGF-β signaling pathway, which is widely recognized for its role in tumor suppression across various cancer types. This gene acts in the transduction of antiproliferative and pro-apoptotic signals and regulates the expression of genes involved in cell cycle control. The loss or functional reduction of SMAD4 has been associated with tumor progression, increased invasiveness, and worse clinical outcomes in several neoplasms (Zhao et al., 2024 ). Beyond well-characterized genes, the platform also enables the identification of less explored targets. Figure 2 shows some genes such as SPRY2 and PDLIM7 that have shown possible interactions with EBV miRNA, but to date, there are no validated studies for these genes. Targetome studies of EBV-miRNAs identify these target genes as promising candidates for future investigations, possibly due to their roles in cell-signaling pathways involved in proliferative processes (Fachko et al., 2024 ; Skalsky et al., 2012 ). The potential interaction of EBV-miR-BART6-5p with the DICER1 gene has significant biological relevance. This gene encodes an endoribonuclease critical for the biogenesis of mature and functionally active miRNAs. Thus, its modulation can have amplified or diminished effects on the activity of this ribonuclease, compromising the maturation of tumor-suppressor miRNAs, destabilizing the balance between pro- and anti-proliferative miRNAs, and promoting extensive remodeling of the transcriptomic landscape (Abusalah et al., 2022 ; Fan et al., 2018 ; Iizasa et al., 2020 ; Liu et al., 2023 ; Notarte et al., 2021 ; Zhang et al., 2018 ) These findings support the presence of second-order regulatory mechanisms, in which EBV miRNAs influence not only specific downstream targets such as TP53 or SMAD4 but also upstream regulators that govern entire gene networks. This amplifies the biological significance of viral interference and underscores the complexity of virus–host interactions represented by EBV-HSA-RegDB. Consistent with this network-level perspective, additional targets identified in the database include E2F3 , associated with proliferation control, and STIM1 , associated with intracellular signaling. These findings reinforce the hypothesis that viral miRNAs act at multiple regulatory levels (Skalsky et al., 2012 ). Instead of exclusively targeting isolated genes, these miRNAs may interfere with critical cellular networks, promoting functional reprogramming that favors both latency and, in certain contexts, tumor progression. This global miRNA imbalance has already been observed in several neoplasms associated with EBV infection, such as nasopharyngeal carcinoma, gastric cancer, and Hodgkin's lymphoma (Abusalah et al., 2022 ; Fan et al., 2018 ; Liu et al., 2023 ; Notarte et al., 2021 ; Zhang et al., 2018 ). Importantly, descriptive and positional statistical analyses indicate that the similarities observed between EBV and human miRNAs are not random artifacts of short-sequence alignment, but rather are enriched for high-identity, gapless matches that overlap functionally relevant seed regions. These hypotheses broaden understanding of virus-host interactions and offer insight into the contribution of viral miRNAs to the persistence of infection and EBV-associated tumorigenesis. Furthermore, network analysis within the platform identified BALF5 , TOMM22 , and IPO7 as genes targeted exclusively by EBV-encoded miRNAs, with no evidence of overlap with human miRNAs, according to the databases and criteria used. This may suggest specific regulatory pathways or methodological limitations, such as prediction criteria, confidence filters, or availability of experimental data. Our study emphasizes that many of the novel EBV-miRNA interactions with HSA-miRNAs remain based on predictive or sequence-similarity analyses. Therefore, while EBV-HSA-RegDB provides a comprehensive and integrative framework for hypothesis generation, further investigations and experimental data are needed to establish causality and the magnitude of the regulatory effect on co-target genes. In conclusion, The EBV-HSA-RegDB provides a comprehensive, integrated resource for exploring Epstein-Barr virus-host miRNA regulatory networks. Through multi-method sequence analysis, network reconstruction, and functional enrichment, the database reveals: a) sequence-level similarities between EBV-encoded miRNAs and human miRNAs suggesting potential molecular mimicry; b) convergent targeting patterns where viral and host miRNAs regulate common genes; c) functional enrichment in pathways critical for viral infection, immune response, and cellular transformation; and d) network architecture highlighting regulatory co-targeting pathways. The interactive web interface enables researchers to explore these relationships, generate hypotheses, and identify candidates for experimental validation. As our understanding of virus-host interactions continues to evolve, resources like this database will play increasingly important roles in translating genomic information into therapeutic strategies. The EBV-HSA-RegDB is a valuable tool for the virology, immunology, and cancer research communities to dissect EBV pathogenesis and contribute to the development of novel diagnostic and therapeutic approaches for EBV-associated diseases. Declarations Acknowledgments We thank the research team of miRBase and miRTarBase for maintaining these essential resources. We also acknowledge the open-source bioinformatics community for developing the tools that made this work possible. Author Contributions H.G.A.P. and G.S.A contributed to software implementation, database development, and computational infrastructure. All authors contributed to the data analysis. G.L.C.C. and G.S.A contributed equally to the study conception and design. The first draft of the manuscript was written by A.F.M., G.L.C.C., and G.S.A. All authors read and approved the final manuscript. Funding G.S.A was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (404498/2025-6, 308432/2025-8) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). Data Availability All data supporting the findings of this study are available in the paper and online in the EBV-HSA-RegDB, freely accessible at https://lbcd.ufpa.br/ebvhsa/. Raw sequence data were obtained from miRBase (https://www.mirbase.org/) and miRTarBase (https://mirtarbase.cuhk.edu.cn/). Competing interests The authors have no financial or non-financial interests to disclose. Artificial Intelligence Disclosure Grammarly was used to help correct grammar and orthography within word. References Abusalah MAH, Irekeola AA, Hanim Shueb R, Jarrar M, Yean Yean C (2022) Prognostic Epstein-Barr Virus (EBV) miRNA biomarkers for survival outcome in EBV-associated epithelial malignancies: Systematic review and meta-analysis. PLoS ONE 17:e0266893. https://doi.org/10.1371/journal.pone.0266893 Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421. https://doi.org/10.1186/1471-2105-10-421 Chatr-aryamontri A, Ceol A, Peluso D, Nardozza A, Panni S, Sacco F, Tinti M, Smolyar A, Castagnoli L, Vidal M, Cusick ME, Cesareni G (2009) VirusMINT: a viral protein interaction database. Nucleic Acids Res 37:D669–D673. https://doi.org/10.1093/nar/gkn739 Chen S-J, Chen G-H, Chen Y-H, Liu C-Y, Chang K-P, Chang Y-S, Chen H-C (2010) Characterization of Epstein-Barr Virus miRNAome in Nasopharyngeal Carcinoma by Deep Sequencing. PLoS ONE 5:e12745. https://doi.org/10.1371/journal.pone.0012745 Cheng J, Lin Y, Xu L, Chen K, Li Q, Xu K, Ning L, Kang J, Cui T, Huang Y, Zhao X, Wang D, Li Y, Su X, Yang B (2022) ViRBase v3.0: a virus and host ncRNA-associated interaction repository with increased coverage and annotation. Nucleic Acids Res 50:D928–D933. https://doi.org/10.1093/nar/gkab1029 Cui S, Yu S, Huang H-Y, Lin Y-C-D, Huang Y, Zhang B, Xiao J, Zuo H, Wang J, Li Z, Li G, Ma J, Chen B, Zhang H, Fu J, Wang L, Huang H-D (2025) miRTarBase 2025: updates to the collection of experimentally validated microRNA–target interactions. Nucleic Acids Res 53:D147–D156. https://doi.org/10.1093/nar/gkae1072 Fachko DN, Goff B, Chen Y, Skalsky RL (2024) Functional Targets for Epstein-Barr Virus BART MicroRNAs in B Cell Lymphomas. Cancers 16:3537. https://doi.org/10.3390/cancers16203537 Fan C, Tang Y, Wang J, Xiong F, Guo C, Wang Y, Xiang B, Zhou M, Li, Xiayu, Wu X, Li Y, Li X, Li G, Xiong W, Zeng Z (2018) The emerging role of Epstein-Barr virus encoded microRNAs in nasopharyngeal carcinoma. J Cancer 9:2852–2864. https://doi.org/10.7150/jca.25460 Harold C, Cox D, Riley KJ (2016) Epstein-Barr viral microRNAs target caspase 3. Virol J 13:145. https://doi.org/10.1186/s12985-016-0602-7 Iizasa H, Kim H, Kartika AV, Kanehiro Y, Yoshiyama H (2020) Role of Viral and Host microRNAs in Immune Regulation of Epstein-Barr Virus-Associated Diseases. Front Immunol 11:367. https://doi.org/10.3389/fimmu.2020.00367 Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, Sidiropoulos K, Cook J, Gillespie M, Haw R (2020) others, The reactome pathway knowledgebase. Nucleic acids research 48, D498–D503 Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30 Kim SY (2023) Personalized Explanations for Early Diagnosis of Alzheimer’s Disease Using Explainable Graph Neural Networks With Population Graphs. https://doi.org/10.3390/bioengineering10060701 . Bioengineering Kozomara A, Birgaoanu M, Griffiths-Jones S (2019) miRBase: from microRNA sequences to function. Nucleic Acids Res 47:D155–D162. https://doi.org/10.1093/nar/gky1141 Liu T, Zhou X, Zhang Z, Qin, Yutao, Wang R, Qin Y, Huang Y, Mo Y, Huang T (2023) The role of EBV-encoded miRNA in EBV-associated gastric cancer. Front Oncol 13:1204030. https://doi.org/10.3389/fonc.2023.1204030 Mo X, Wei F, Tong Y, Ding L, Zhu Q, Du S, Tan F, Zhu C, Wang Y, Yu Q, Liu Y, Robertson ES, Yuan Z, Cai Q (2018) Lactic Acid Downregulates Viral MicroRNA To Promote Epstein-Barr Virus-Immortalized B Lymphoblastic Cell Adhesion and Growth. J Virol 92:e00033–e00018. https://doi.org/10.1128/JVI.00033-18 Notarte KI, Senanayake S, Macaranas I, Albano PM, Mundo L, Fennell E, Leoncini L, Murray P (2021) MicroRNA and Other Non-Coding RNAs in Epstein–Barr Virus-Associated Cancers. Cancers 13:3909. https://doi.org/10.3390/cancers13153909 Pfeffer S, Zavolan M, Grässer FA, Chien M, Russo JJ, Ju J, John B, Enright AJ, Marks D, Sander C, Tuschl T (2004) Identification of Virus-Encoded MicroRNAs. Science 304:734–736. https://doi.org/10.1126/science.1096781 Qureshi A, Thakur N, Monga I, Thakur A, Kumar M (2014) VIRmiRNA: a comprehensive resource for experimentally validated viral miRNAs and their targets. Database 2014. https://doi.org/10.1093/database/bau103 Ramakrishnan R, Donahue H, Garcia D, Tan J, Shimizu N, Rice AP, Ling PD (2011) Epstein-Barr Virus BART9 miRNA Modulates LMP1 Levels and Affects Growth Rate of Nasal NK T Cell Lymphomas. PLoS ONE 6:e27271. https://doi.org/10.1371/journal.pone.0027271 Ressing ME, Van Gent M, Gram AM, Hooykaas MJG, Piersma SJ, Wiertz EJHJ (2015) Immune Evasion by Epstein-Barr Virus. In: Münz C (ed) Epstein Barr Virus Volume 2, Current Topics in Microbiology and Immunology. Springer International Publishing, Cham, pp 355–381. https://doi.org/10.1007/978-3-319-22834-1_12 Riley KJ, Rabinowitz GS, Yario TA, Luna JM, Darnell RB, Steitz JA (2012) EBV and human microRNAs co-target oncogenic and apoptotic viral and human genes during latency. EMBO J 31:2207–2221. https://doi.org/10.1038/emboj.2012.63 Skalsky RL, Corcoran DL, Gottwein E, Frank CL, Kang D, Hafner M, Nusbaum JD, Feederle R, Delecluse H-J, Luftig MA, Tuschl T, Ohler U, Cullen BR (2012) The Viral and Cellular MicroRNA Targetome in Lymphoblastoid Cell Lines. PLoS Pathog 8:e1002484. https://doi.org/10.1371/journal.ppat.1002484 Tang D, Li B, Xu T, Hu R, Tan D, Song X, Jia P, Zhao Z (2020) VISDB: a manually curated database of viral integration sites in the human genome. Nucleic Acids Res 48:D633–D641. https://doi.org/10.1093/nar/gkz867 Wang J, Ge J, Wang Y, Xiong F, Guo J, Jiang X, Zhang L, Deng X, Gong Z, Zhang S, Yan Q, He Y, Li X, Shi L, Guo C, Wang F, Li Z, Zhou M, Xiang B, Li Y, Xiong W, Zeng Z 2022. EBV miRNAs BART11 and BART17-3p promote immune escape through the enhancer-mediated transcription of PD-L1. Nat Commun 13, 866. https://doi.org/10.1038/s41467-022-28479-2 Wang M, Yu F, Wu W, Wang Y, Ding H, Qian L (2018) Epstein-Barr virus-encoded microRNAs as regulators in host immune responses. Int J Biol Sci 14:565–576. https://doi.org/10.7150/ijbs.24562 Zhang J, Huang T, Zhou Y, Cheng ASL, Yu J, To KF, Kang W (2018) The oncogenic role of Epstein–Barr virus-encoded micro RNA s in Epstein–Barr virus‐associated gastric carcinoma. J Cell Mol Medi 22:38–45. https://doi.org/10.1111/jcmm.13354 Zhao X, Huang X, Dang C, Wang X, Qi Y, Li H (2024) The Epstein-Barr virus-miRNA-BART6-5p regulates TGF-β/SMAD4 pathway to induce glycolysis and enhance proliferation and metastasis of gastric cancer cells. OR 32:999–1009. https://doi.org/10.32604/or.2024.046679 Židovec Lepej S, Matulić M, Gršković P, Pavlica M, Radmanić L, Korać P (2020) miRNAs: EBV Mechanism for Escaping Host’s Immune Response and Supporting Tumorigenesis. Pathogens 9:353. https://doi.org/10.3390/pathogens9050353 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers invited by journal 08 May, 2026 Editor assigned by journal 02 May, 2026 Submission checks completed at journal 02 May, 2026 First submitted to journal 28 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9558966","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":640412827,"identity":"70fff631-f2ee-4e35-a72f-89d35c23306e","order_by":0,"name":"Helber Gonzales de Almeida Palheta","email":"","orcid":"","institution":"Federal University of Para","correspondingAuthor":false,"prefix":"","firstName":"Helber","middleName":"Gonzales de Almeida","lastName":"Palheta","suffix":""},{"id":640412828,"identity":"3cc63627-dda8-4f47-bb47-64c8bbd52d29","order_by":1,"name":"Amanda Ferreira Mercês","email":"","orcid":"","institution":"Federal University of Para","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"Ferreira","lastName":"Mercês","suffix":""},{"id":640412835,"identity":"84ea6073-9f1c-465f-9d0d-99dd1f2387a3","order_by":2,"name":"Greice de Lemos Cardoso Costa","email":"","orcid":"","institution":"Federal University of Para","correspondingAuthor":false,"prefix":"","firstName":"Greice","middleName":"de Lemos Cardoso","lastName":"Costa","suffix":""},{"id":640412839,"identity":"0354dcc4-b5ee-4d7e-94cf-04be5def6a51","order_by":3,"name":"Gilderlanio Santana de Araújo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3PrwvCQBTA8XcczHL2JxvsX9iKP2DD6L9xMtg/YLRMBkvKqsE/YlUwnBxotAozmJYM2gwGb4JBw85ouG+6O/jw3gGYTP8YpQkC9NUByBkC9YJaQmqCNaEexL8QeBMAC38i7ojMjrcNur0W3U0ZP7lgL86NxJckHSwr9NepFZeMV37i7L1mkpLMZgJJIVm3bN+l2jNuXuxFHgKHNZkwLoda4lJFQOC4JlSRsZZ4VP1lLjAqpBV1VryKMmenmZLL7fEugrA4yO31wk9hbmeaKeLzLsBqBmpK8k1MJpPJ9N0TPI9D7X06OdcAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Para","correspondingAuthor":true,"prefix":"","firstName":"Gilderlanio","middleName":"Santana","lastName":"de Araújo","suffix":""}],"badges":[],"createdAt":"2026-04-29 01:09:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9558966/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9558966/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109473087,"identity":"1a1f1e13-1cf6-450c-a79e-33e82af1a7f5","added_by":"auto","created_at":"2026-05-18 13:33:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":274709,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the EBV-HSA-RegDB workflow.\u003c/strong\u003e The pipeline integrates Epstein–Barr virus (EBV)-encoded miRNAs and human (HSA) miRNAs as input, together with sequence repositories and experimentally validated interaction data. Sequence analysis includes similarity assessment using Hamming and Levenshtein distances, as well as global and local alignments performed with the Needleman–Wunsch algorithm and BLAST, respectively. These data are integrated into regulatory networks linking EBV miRNAs, human miRNAs, and their gene targets. Functional enrichment analysis is conducted using g:Profiler, incorporating Gene Ontology, KEGG, and Reactomepathways, with multiple-testing correction (False Discovery Rate). The outputs include regulatory network visualizations, alignment plots, enriched biological pathways, and sequence similarity profiles, enabling systems-level interpretation of EBV–host miRNA interactions.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9558966/v1/d3d6a8b04b4d20016a5e6ed9.png"},{"id":109473088,"identity":"983c74d1-2071-4628-a2e4-ce56979a834f","added_by":"auto","created_at":"2026-05-18 13:33:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":301414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal sequence alignment (Needleman–Wunsch) between EBV-encoded and human miRNAs. Aligned nucleotides are displayed in a two-row format, with matching positions highlighted in dark green and mismatches in dark red. Vertical connectors indicate identical nucleotide positions between EBV and human sequences. Gaps introduced during alignment reflect optimal scoring under the defined penalty scheme.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9558966/v1/3ff350830795cd995d7d15f5.png"},{"id":109473089,"identity":"0258b121-9275-42dd-a9c1-cff22cbf3d83","added_by":"auto","created_at":"2026-05-18 13:33:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":757139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2. This miRNA-gene interaction network is constructed from experimentally validated interactions obtained from miRTarBase, integrated with filtered similarity results (BLAST). The diamond-shaped nodes represent miRNAs, with human miRNAs (hsa-miRNAs) shown in dark red and EBV miRNAs in dark green. Target genes are represented by light blue circles. The size of the nodes is proportional to the degree of connectivity, indicating the number of interactions associated with each element. The edges represent miRNA-gene interactions described in the database. On the platform, the visualization is interactive, allowing the inspection of attributes such as node type and degree, and the network is available for download in GraphML format for further analysis and customization in other software.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9558966/v1/7574acbbfbae06f2c010a4db.png"},{"id":109760525,"identity":"80a33605-655b-41c5-a93b-18f52cf89991","added_by":"auto","created_at":"2026-05-22 07:28:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of host genes predicted as targets of EBV-encoded miRNAs based on Gene Ontology Biological Process (GO:BP) and KEGG pathway analyses.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9558966/v1/bd80a690eb8c161fc437a1df.png"},{"id":109799871,"identity":"9fc5ac3f-f757-4c5d-94db-6a804d44d190","added_by":"auto","created_at":"2026-05-22 15:34:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1457127,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9558966/v1/ef278a3c-d211-4eea-be6f-51c55f785e72.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"EBV-HSA-RegDB: A database of regulatory interactions between Epstein–Barr virus and human miRNAs","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEpstein-Barr Virus (EBV) is a human herpesvirus that encodes miRNAs (EBV-miRNAs), the first miRNAs identified in viruses (Pfeffer et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which play significant roles in various biological processes, particularly immune regulation and oncogenesis. The interaction between EBV-miRNAs and human miRNAs has become an area of increasing interest, particularly regarding their roles in tumorigenesis and immune evasion. Some studies show that more than half of the proteins encoded by the EBV genome are involved in this evasion, modulating the host's immune response (Ressing et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al., 2022).\u003c/p\u003e \u003cp\u003eViruses evade host immune responses through EBV-miRNAs, which also contribute to oncogenesis. For example, they can modulate gene expression involved in apoptosis, proliferation, and immune signaling. EBV-miRNAs, such as those from the BART and BHRF1 clusters, have been shown to target host genes associated with tumorigenesis, thereby promoting the survival and proliferation of infected cells (Riley et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Harold et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Židovec Lepej et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This targeting can redirect the host epigenome, favoring tumor development, particularly in cancers such as nasopharyngeal carcinoma (NPC) (Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ramakrishnan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Skalsky et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This emerging evidence suggests that viral miRNAs can exhibit sequence similarity to host miRNAs, potentially allowing them to functionally mimic host regulatory molecules to hijack cellular regulatory networks, manipulate host cell biology, and evade immune surveillance.\u003c/p\u003e \u003cp\u003eMost computational resources, such as databases, have addressed aspects of viral miRNA biology. For instance, VIRmiRNA provides curated information on experimentally validated viral miRNAs and their targets, including EBV-miRNAs (Qureshi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The miRBase offers detailed sequence and annotation data for EBV-encoded miRNAs. The VirusMINT focuses on host\u0026ndash;virus protein interactions relevant to EBV pathogenesis and immune evasion (Chatr-aryamontri et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The VISDB is an active comprehensive database of viral integration sites, emphasizing viral genomics and host\u0026ndash;pathogen interactions over miRNA-centric regulatory mechanisms (Tang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, ViRBase v3.0 advances the field by cataloging virus\u0026ndash;host ncRNA interactions (Cheng et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo our knowledge, these tools lack integrative analysis of host interactions and functional context, failing to place sequences within interaction networks or to address ncRNA-mediated regulation. This reveals a critical gap: the absence of an integrated EBV-focused miRNA resource that enables comprehensive analysis of sequence features, regulatory interactions, and biological impact. To address this gap, we developed the EBV-HSA miRNA Regulatory Database (hereafter, EBV-HSA-RegDB), an integrated bioinformatics resource that compiles comprehensive genomic sequence information on EBV and human miRNAs. The tool includes multi-method sequence similarity analyses (including BLAST, Hamming distance, and Levenshtein distance), integrates experimentally validated miRNA\u0026ndash;target interactions, reconstructs and visualizes regulatory networks, and conducts functional enrichment analysis to identify associated biological pathways across several layers. The tool provides an interactive, user-friendly web interface to access raw data and networks.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data collection and organization\u003c/h2\u003e \u003cp\u003eMiRNA sequences for EBV and Homo sapiens were obtained from miRBase (version 22.1), a comprehensive, curated database of published miRNA sequences and annotations (Kozomara et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Sequences were downloaded in multi-fasta format and filtered according to organism-specific prefixes (ebv- and hsa-), yielding 44 EBV-miRNA sequences and 2,656 hsa-miRNAs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimentally Validated miRNA-Target Interactions\u003c/h2\u003e \u003cp\u003eMiRNA\u0026ndash;target gene interactions were retrieved from miRTarBase (release 2025) (Cui et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), a manually curated database of experimentally validated miRNA\u0026ndash;target interactions containing over 2.5\u0026nbsp;million entries supported by strong experimental evidence, including reporter (luciferase) assays, Western blot analysis, quantitative PCR (qPCR), CLIP-seq (crosslinking and immunoprecipitation followed by sequencing), and other functional validation methods. The interactions were filtered to retain only those involving EBV-miRNAs (n\u0026thinsp;=\u0026thinsp;25) or hsa-miRNAs (n\u0026thinsp;=\u0026thinsp;23,058), thereby enabling the reconstruction of convergent regulatory networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sequence Analysis Methods\u003c/h2\u003e \u003cp\u003eTo identify sequence similarities between EBV and human miRNAs, pairwise BLAST (Basic Local Alignment Search Tool) analyses were performed using the BLASTn algorithm optimized for short sequences (Camacho et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), with parameters set to a word size of 4, an E-value threshold of 10 to allow detection of potential similarities, standard gap penalties, and filtering of low-complexity regions. BLAST outputs were parsed to extract query and subject identifiers, alignment length, number of mismatches, gap openings, query start and end positions, E-values, and bit scores.\u003c/p\u003e \u003cp\u003eBiologically relevant alignments were defined using stringent criteria, including an alignment length of at least 6 nucleotides, absence of gap openings (gapopen\u0026thinsp;=\u0026thinsp;0), query start position\u0026thinsp;\u0026le;\u0026thinsp;2, and query end position\u0026thinsp;\u0026ge;\u0026thinsp;8, ensuring that the identified similarities encompass the critical miRNA seed region (nucleotides 2\u0026ndash;8) responsible for mRNA recognition.\u003c/p\u003e \u003cp\u003eHamming similarity measures the number of positions at which corresponding nucleotides differ between two sequences of equal length. For miRNA pairs with identical lengths, the Hamming distance was calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:H\\left(x,y\\right)=1-\\:\\frac{\\sum\\:_{i}\\left({x}_{i\\:}\\ne\\:\\:{y}_{i\\:}\\right)\\:}{L}$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere x and y denote the miRNA sequences, and x\u003csub\u003ei\u003c/sub\u003e and y\u003csub\u003ei\u003c/sub\u003e represent the nucleotides at position \u003cem\u003ei\u003c/em\u003e in each sequence, respectively. \u003cem\u003eL\u003c/em\u003e denotes the length of the miRNA sequence.\u003c/p\u003e \u003cp\u003eLevenshtein distance (edit distance) measures the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one sequence into another. Unlike Hamming distance, Levenshtein distance accommodates sequences of different lengths, making it suitable for comparing miRNAs with length variations. The Levenshtein distance was computed using dynamic programming with the following recurrence relation:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:L\\left(x,y\\right)=\\text{min}\\left\\{\\begin{array}{c}L(i-1,\\:j)\\:+\\:1\\\\\\:L(i,\\:j-1)\\:+\\:1\\\\\\:L(i-1,\\:j-1)\\:+\\:cost\\end{array}\\right.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere cost\u0026thinsp;=\u0026thinsp;0 if xi\u0026thinsp;=\u0026thinsp;yj, otherwise cost\u0026thinsp;=\u0026thinsp;1. The Levenstein package is available for free at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pypi.org/project/python-Levenshtein/\u003c/span\u003e\u003cspan address=\"https://pypi.org/project/python-Levenshtein/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Reconstructing Regulatory Network Architecture\u003c/h2\u003e \u003cp\u003eThe EBV regulatory network was modeled to include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eEdges between EBV-miRNAs and hsa-miRNAs derived from sequence-similarity based on BLAST results;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInteractions between EBV-miRNAs and their target genes were obtained from experimental interaction data;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInteractions between hsa-miRNAs and their target genes, also obtained from experimental data.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe experimental edges corresponded to interactions experimentally validated in miRTarBase. Network visualization was performed using PyVis (version 0.3.2), a Python library for interactive network rendering built on the Vis.js framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Functional and pathway enrichment analysis of miRNA networks\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis was conducted programmatically using g:Profiler, a remote platform for functional profiling of gene lists (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pypi.org/project/gprofiler-official/\u003c/span\u003e\u003cspan address=\"https://pypi.org/project/gprofiler-official/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The analysis incorporated multiple annotation sources to comprehensively characterize the biological relevance of the identified genes. The enrichment analysis was performed for Homo sapiens genes using the g:SCS (Set Counts and Sizes) statistical framework in g:Profiler to correct for multiple testing. Statistical significance was defined as an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ensuring robust control of false-positive enrichment. Gene Ontology (GO) annotations were evaluated across three domains: Biological Process (GO:BP), describing coordinated biological programs carried out through multiple molecular activities; Molecular Function (GO:MF), referring to elemental biochemical activities at the molecular level; and Cellular Component (GO:CC), indicating the subcellular locations or structural contexts in which gene products operate. In addition to GO terms, pathway-based enrichment was assessed using curated biological pathway databases. These included the Kyoto Encyclopedia of Genes and Genomes (KEGG), which provides representations of metabolic and signaling pathways (Kanehisa and Goto, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), and Reactome, a manually curated resource of biological pathways and processes (Jassal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. The EBV-HSA-RegDB\u003c/h2\u003e \u003cp\u003eThe EBV-HSA-RegDB was implemented as a lightweight interactive web application using Streamlit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://streamlit.io/\u003c/span\u003e\u003cspan address=\"https://streamlit.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (version 1.28.0), a Python-based framework for developing data science applications. The EBV-HSA-RegDB supports automatic reactive view updates in response to user input and provides a comprehensive set of built-in widgets for interactive data exploration. An overview of the EBV-HSA-RegDB framework is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The web application interface was organized into four main functional sections designed to simplify access to raw data, sequencing similarity and alignment, network integration, and pathway information.\u003c/p\u003e \u003cp\u003e \u003col style=\"list-style-type:lower-alpha;\"\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Sequences section presents EBV-miRNAs and HSA-miRNAs sequences in a tabular format, along with associated sequence statistics, including identifiers, descriptions, and sequence lengths. This module also enables users to export the displayed data in .csv format, supporting downstream analysis and reproducibility;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe BLAST analysis section provides interactive filtering options to select EBV-miRNAs and HSA-miRNAs, enabling dynamic inspection of alignment statistics and detailed alignment information for selected miRNA pairs;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Hamming and Levenstein analysis section displays pre-computed distance metrics in tabular form and presents a scatter plot comparing Hamming and Levenstein distances across miRNA pairs. Interactive hover tooltips reveal the identities of the corresponding miRNA pairs, facilitating detailed comparative analysis;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Alignment section allows input of pairs of miRNA sequences and visualizes the alignment graphically;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Network Section comprises EBV-miRNAs and HSA-miRNAs interactions with genes, the modeled regulatory and functional enrichment analysis of genes. EBV-miRNAs were displayed as green diamonds, HSA-miRNAs as red diamonds, and target genes as blue circles. Node sizes were scaled proportionally to node degree, reflecting the number of interactions, while edges were represented as lines connecting related nodes. Interactive features such as zooming, panning, hover tooltips, and node dragging were enabled to support exploratory analysis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e4. EBV-miRNA and HSA-miRNA similarities\u003c/h3\u003e\n\u003cp\u003eThe BLASTn analysis between EBV and human miRNAs yielded 32,976 pairwise alignments, all of which lacked gap openings, reflecting the structural constraints of short RNA sequences. Descriptive statistical analysis revealed a very high sequence identity, with a minimum of 85.7%, a mean of 99.88%, and a median of 100%, indicating that most alignments are near-perfect nucleotide matches. Alignment lengths ranged from 4 to 15 nucleotides, with a median of five nucleotides, consistent with the expected distribution for short-sequence comparisons. Notably, a substantial fraction of alignments exceeded six nucleotides in length, a threshold commonly associated with functional relevance in miRNA seed-mediated target recognition.\u003c/p\u003e \u003cp\u003eThe positional analysis of alignments within EBV miRNAs showed broad coverage across the query sequences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, biologically meaningful matches were enriched in regions encompassing the canonical seed region (nucleotides 2\u0026ndash;8). To increase biological specificity, stringent filtering criteria were applied, retaining only alignments with a minimum length of six nucleotides, zero gap openings, e-values\u0026thinsp;\u0026le;\u0026thinsp;1, query start positions\u0026thinsp;\u0026le;\u0026thinsp;2, and query end positions\u0026thinsp;\u0026ge;\u0026thinsp;8. These criteria ensured that retained alignments spanned the seed region, a critical feature of miRNA\u0026ndash;target interactions. Although E-values were relatively high overall, a known limitation of BLAST for short nucleotide sequences, the bit score distribution provided a more reliable metric of alignment quality, with values ranging from 8.4 to 24.3, and higher scores corresponding to longer and more conserved matches.\u003c/p\u003e \u003cp\u003eMost alignments exhibited 100% identity across 7 or 8 nucleotides, without insertions or deletions, and were predominantly located within positions 2\u0026ndash;8 of the mature miRNA sequence, corresponding to the seed region. This region is the primary determinant of target recognition and binding specificity. Such perfect seed matches were observed in multiple pairs, including ebv-miR-BART22 with hsa-miR-520d-5p and hsa-miR-524-5p, ebv-miR-BHRF1-2-5p with hsa-miR-4778-5p, ebv-miR-BART4-3p with hsa-miR-499a/b-3p, ebv-miR-BART6-3p with hsa-miR-542-5p, ebv-miR-BART17-3p with hsa-miR-675-3p, and ebv-miR-BART11-5p with hsa-miR-1324.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlignment results between Epstein\u0026ndash;Barr virus (EBV) miRNAs and homologous human miRNAs, showing sequence identity, alignment length, mismatches, gaps, query (Q) and subject (S) start/end positions, E-value, and bit score for each predicted match.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBV miRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman miRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndetity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMismatch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGaps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ-Start\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ-End\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS-Start\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS-End\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eE-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eBitscore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eebv-miR-BART22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-520d-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-524-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebv-miR-BHRF1-2-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-4778-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebv-miR-BHRF1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-4681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eebv-miR-BART4-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-499b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-499a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebv-miR-BART5-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-6829-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebv-miR-BART6-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-542-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebv-miR-BART17-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-675-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eebv-miR-BART7-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-3690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-8058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebv-miR-BART9-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eebv-miR-BART11-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-1324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-4692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eebv-miR-BART20-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-4690-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn a subset of cases, slightly lower sequence identity (approximately 88\u0026ndash;91%) was observed due to a single mismatch within alignments spanning 9\u0026ndash;11 nucleotides, as seen for ebv-miR-BART5-3p/hsa-miR-6829-5p, ebv-miR-BART7-5p/hsa-miR-8058, and ebv-miR-BART11-5p/hsa-miR-4692. Nevertheless, preservation of the core seed nucleotides suggests that target recognition potential may remain largely intact, depending on the precise position of the mismatch.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. The Regulatory EBV-miRNA network and co-target interactions\u003c/h2\u003e \u003cp\u003eThe analyzed network comprises 153 nodes and 156 edges, exhibiting a low topological density (0.013), a characteristic of regulatory biological networks. The nodes are distributed into three main categories: 120 human miRNAs, 20 Epstein-Barr virus (EBV) miRNAs, and 13 target genes, forming an architecture in which experimental interactions occur between miRNAs and genes, and miRNA-miRNA relationships are inferred by BLAST local alignment. The network represents a functional gene-regulation relationship; its structural organization follows a hub-and-spoke pattern, in which multiple miRNAs converge on a small number of central genes.\u003c/p\u003e \u003cp\u003eThe genes act as the main hubs of the network, concentrating most of the regulatory connections. Among the most interconnected nodes, \u003cem\u003eSMAD4\u003c/em\u003e stands out with 43 interactions, \u003cem\u003eTP53\u003c/em\u003e with 32, \u003cem\u003eE2F3\u003c/em\u003e with 24, and \u003cem\u003eDICER1\u003c/em\u003e with 23, highlighting a strong centralization of the network around key regulators of the cell cycle, apoptosis, and miRNA biogenesis. The coexistence of human and viral miRNAs that regulate the same genes suggests mechanisms by which EBV may jointly or competitively modulate cellular pathways. Thus, the observed topology indicates a regulatory network highly dependent on a few central genes, consistent with cellular reprogramming processes associated with viral infection and oncogenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctional enrichment analysis of host genes predicted as targets of EBV-miRNAs identified significant overrepresentation of biological processes related to apoptosis regulation, cellular differentiation, developmental pathways, and signal transduction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the most significantly enriched categories were processes associated with muscle cell apoptosis, positive regulation of striated muscle cell apoptosis, and cardiac muscle cell apoptosis. In addition, enrichment was observed in pathways associated with negative regulation of cell differentiation and cell population proliferation, including negative regulation of glial cell proliferation. Development-related processes were prominently represented, including tissue development and regulation of multicellular organismal development. Furthermore, genes associated with regulating the cellular response to transforming growth factor beta stimulation and with the transforming growth factor beta receptor signaling pathway were significantly enriched. Additional enrichment was detected for nuclear protein import, indicating that genes involved in nucleocytoplasmic transport are represented among predicted EBV-miRNA targets.\u003c/p\u003e \u003cp\u003eKEGG pathway enrichment analysis revealed significant enrichment in cancer-associated and cell regulatory pathways. Among the most significantly enriched pathways were chronic myeloid leukemia and pancreatic cancer, followed by miRNAs in cancer and gastric cancer pathways. Viral infection-related pathways, including Hepatitis B, Hepatitis C, and Human T-cell leukemia virus type 1 infection, were also significantly represented. Pathways associated with cell proliferation and growth control were enriched, including the cell cycle and cellular senescence pathways. Multiple solid tumor-associated pathways were identified, comprising hepatocellular carcinoma, bladder cancer, glioma, melanoma, colorectal cancer, prostate cancer, breast cancer, and both small cell and non-small cell lung cancer. Additionally, signaling pathways involved in tumor progression and therapeutic response were enriched, including endocrine resistance and the Wnt signaling pathway. The integrative cancer-related pathway categories were also significantly enriched among EBV-miRNAs target genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eIn this work, we systematically curated EBV-miRNA sequences and co-target events using the human genome and experimental interaction data to propose the EBV-HSA-RegDB tool, aiming to fill gaps in pathogen-host interactions. The EBV-miRNA sequence similarity in the seed region of HSA-miRNAs supports the hypothesis of functional molecular mimicry, in which viral miRNAs can exploit host regulatory networks by converging shared or partially overlapping mRNA targets. Such mimicry could allow EBV to modulate essential host cellular pathways, including proliferation, apoptosis, and immune responses, through mechanisms analogous to regulation mediated by endogenous miRNAs. This indicates that the EBV can exploit post-transcriptional networks already established in the infected host cell. The hypothesis that EBV-miRNAs share sequence characteristics with specific human miRNAs, enabling convergent regulatory effects on common target genes, contributes to regulatory mimicry between host and virus. This strategy allows integration into the cellular regulatory network, favoring persistence and maintenance of viral latency.\u003c/p\u003e \u003cp\u003eWithin this framework, the EBV-HSA-RegDB reveals biologically relevant interactions. In this context, the potential interaction between EBV-miR-BHRF1-1 and the \u003cem\u003eTP53\u003c/em\u003e gene warrants particular attention. The \u003cem\u003eTP53\u003c/em\u003e gene encodes the p53 protein, a central transcription factor in maintaining genomic integrity, playing a role in inducing cell cycle stop, DNA repair, and apoptosis in response to stress or genetic damage. The modulation of this pathway by viral miRNAs represents a critical point of molecular interference, given p53's role in regulating cell proliferation and preventing neoplastic transformation. Experimental evidence suggests that miR-BHRF1-1 can influence components of the p53-associated pathway in chronic lymphocytic leukemia cell lines, altering the balance between cell survival and death (Mo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the database highlights the interaction between EBV-miR-BART6-5p and the \u003cem\u003eSMAD4\u003c/em\u003e gene, a central mediator of the TGF-β signaling pathway, which is widely recognized for its role in tumor suppression across various cancer types. This gene acts in the transduction of antiproliferative and pro-apoptotic signals and regulates the expression of genes involved in cell cycle control. The loss or functional reduction of \u003cem\u003eSMAD4\u003c/em\u003e has been associated with tumor progression, increased invasiveness, and worse clinical outcomes in several neoplasms (Zhao et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond well-characterized genes, the platform also enables the identification of less explored targets. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows some genes such as \u003cem\u003eSPRY2\u003c/em\u003e and \u003cem\u003ePDLIM7\u003c/em\u003e that have shown possible interactions with EBV miRNA, but to date, there are no validated studies for these genes. Targetome studies of EBV-miRNAs identify these target genes as promising candidates for future investigations, possibly due to their roles in cell-signaling pathways involved in proliferative processes (Fachko et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Skalsky et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe potential interaction of EBV-miR-BART6-5p with the \u003cem\u003eDICER1\u003c/em\u003e gene has significant biological relevance. This gene encodes an endoribonuclease critical for the biogenesis of mature and functionally active miRNAs. Thus, its modulation can have amplified or diminished effects on the activity of this ribonuclease, compromising the maturation of tumor-suppressor miRNAs, destabilizing the balance between pro- and anti-proliferative miRNAs, and promoting extensive remodeling of the transcriptomic landscape (Abusalah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Iizasa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Notarte et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThese findings support the presence of second-order regulatory mechanisms, in which EBV miRNAs influence not only specific downstream targets such as \u003cem\u003eTP53\u003c/em\u003e or \u003cem\u003eSMAD4\u003c/em\u003e but also upstream regulators that govern entire gene networks. This amplifies the biological significance of viral interference and underscores the complexity of virus\u0026ndash;host interactions represented by EBV-HSA-RegDB.\u003c/p\u003e \u003cp\u003eConsistent with this network-level perspective, additional targets identified in the database include \u003cem\u003eE2F3\u003c/em\u003e, associated with proliferation control, and \u003cem\u003eSTIM1\u003c/em\u003e, associated with intracellular signaling. These findings reinforce the hypothesis that viral miRNAs act at multiple regulatory levels (Skalsky et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Instead of exclusively targeting isolated genes, these miRNAs may interfere with critical cellular networks, promoting functional reprogramming that favors both latency and, in certain contexts, tumor progression. This global miRNA imbalance has already been observed in several neoplasms associated with EBV infection, such as nasopharyngeal carcinoma, gastric cancer, and Hodgkin's lymphoma (Abusalah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Notarte et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, descriptive and positional statistical analyses indicate that the similarities observed between EBV and human miRNAs are not random artifacts of short-sequence alignment, but rather are enriched for high-identity, gapless matches that overlap functionally relevant seed regions. These hypotheses broaden understanding of virus-host interactions and offer insight into the contribution of viral miRNAs to the persistence of infection and EBV-associated tumorigenesis.\u003c/p\u003e \u003cp\u003eFurthermore, network analysis within the platform identified \u003cem\u003eBALF5\u003c/em\u003e, \u003cem\u003eTOMM22\u003c/em\u003e, and \u003cem\u003eIPO7\u003c/em\u003e as genes targeted exclusively by EBV-encoded miRNAs, with no evidence of overlap with human miRNAs, according to the databases and criteria used. This may suggest specific regulatory pathways or methodological limitations, such as prediction criteria, confidence filters, or availability of experimental data.\u003c/p\u003e \u003cp\u003eOur study emphasizes that many of the novel EBV-miRNA interactions with HSA-miRNAs remain based on predictive or sequence-similarity analyses. Therefore, while EBV-HSA-RegDB provides a comprehensive and integrative framework for hypothesis generation, further investigations and experimental data are needed to establish causality and the magnitude of the regulatory effect on co-target genes.\u003c/p\u003e \u003cp\u003eIn conclusion, The EBV-HSA-RegDB provides a comprehensive, integrated resource for exploring Epstein-Barr virus-host miRNA regulatory networks. Through multi-method sequence analysis, network reconstruction, and functional enrichment, the database reveals: a) sequence-level similarities between EBV-encoded miRNAs and human miRNAs suggesting potential molecular mimicry; b) convergent targeting patterns where viral and host miRNAs regulate common genes; c) functional enrichment in pathways critical for viral infection, immune response, and cellular transformation; and d) network architecture highlighting regulatory co-targeting pathways. The interactive web interface enables researchers to explore these relationships, generate hypotheses, and identify candidates for experimental validation. As our understanding of virus-host interactions continues to evolve, resources like this database will play increasingly important roles in translating genomic information into therapeutic strategies. The EBV-HSA-RegDB is a valuable tool for the virology, immunology, and cancer research communities to dissect EBV pathogenesis and contribute to the development of novel diagnostic and therapeutic approaches for EBV-associated diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the research team of miRBase and miRTarBase for maintaining these essential resources. We also acknowledge the open-source bioinformatics community for developing the tools that made this work possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.G.A.P. and G.S.A contributed to software implementation, database development, and computational infrastructure. All authors contributed to the data analysis. G.L.C.C. and G.S.A contributed equally to the study conception and design. The first draft of the manuscript was written by A.F.M., G.L.C.C., and G.S.A. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.S.A was supported by the Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq) (404498/2025-6, 308432/2025-8) and Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available in the paper and online in the EBV-HSA-RegDB, freely accessible at https://lbcd.ufpa.br/ebvhsa/. Raw sequence data were obtained from miRBase (https://www.mirbase.org/) and miRTarBase (https://mirtarbase.cuhk.edu.cn/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial Intelligence Disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrammarly was used to help correct grammar and orthography within word.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbusalah MAH, Irekeola AA, Hanim Shueb R, Jarrar M, Yean Yean C (2022) Prognostic Epstein-Barr Virus (EBV) miRNA biomarkers for survival outcome in EBV-associated epithelial malignancies: Systematic review and meta-analysis. PLoS ONE 17:e0266893. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0266893\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0266893\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2105-10-421\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-10-421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatr-aryamontri A, Ceol A, Peluso D, Nardozza A, Panni S, Sacco F, Tinti M, Smolyar A, Castagnoli L, Vidal M, Cusick ME, Cesareni G (2009) VirusMINT: a viral protein interaction database. Nucleic Acids Res 37:D669\u0026ndash;D673. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkn739\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkn739\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S-J, Chen G-H, Chen Y-H, Liu C-Y, Chang K-P, Chang Y-S, Chen H-C (2010) Characterization of Epstein-Barr Virus miRNAome in Nasopharyngeal Carcinoma by Deep Sequencing. PLoS ONE 5:e12745. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0012745\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0012745\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng J, Lin Y, Xu L, Chen K, Li Q, Xu K, Ning L, Kang J, Cui T, Huang Y, Zhao X, Wang D, Li Y, Su X, Yang B (2022) ViRBase v3.0: a virus and host ncRNA-associated interaction repository with increased coverage and annotation. Nucleic Acids Res 50:D928\u0026ndash;D933. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkab1029\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkab1029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui S, Yu S, Huang H-Y, Lin Y-C-D, Huang Y, Zhang B, Xiao J, Zuo H, Wang J, Li Z, Li G, Ma J, Chen B, Zhang H, Fu J, Wang L, Huang H-D (2025) miRTarBase 2025: updates to the collection of experimentally validated microRNA\u0026ndash;target interactions. Nucleic Acids Res 53:D147\u0026ndash;D156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkae1072\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkae1072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFachko DN, Goff B, Chen Y, Skalsky RL (2024) Functional Targets for Epstein-Barr Virus BART MicroRNAs in B Cell Lymphomas. Cancers 16:3537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers16203537\u003c/span\u003e\u003cspan address=\"10.3390/cancers16203537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan C, Tang Y, Wang J, Xiong F, Guo C, Wang Y, Xiang B, Zhou M, Li, Xiayu, Wu X, Li Y, Li X, Li G, Xiong W, Zeng Z (2018) The emerging role of Epstein-Barr virus encoded microRNAs in nasopharyngeal carcinoma. J Cancer 9:2852\u0026ndash;2864. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/jca.25460\u003c/span\u003e\u003cspan address=\"10.7150/jca.25460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarold C, Cox D, Riley KJ (2016) Epstein-Barr viral microRNAs target caspase 3. Virol J 13:145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12985-016-0602-7\u003c/span\u003e\u003cspan address=\"10.1186/s12985-016-0602-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIizasa H, Kim H, Kartika AV, Kanehiro Y, Yoshiyama H (2020) Role of Viral and Host microRNAs in Immune Regulation of Epstein-Barr Virus-Associated Diseases. Front Immunol 11:367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2020.00367\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2020.00367\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, Sidiropoulos K, Cook J, Gillespie M, Haw R (2020) others, The reactome pathway knowledgebase. Nucleic acids research 48, D498\u0026ndash;D503\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27\u0026ndash;30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SY (2023) Personalized Explanations for Early Diagnosis of Alzheimer\u0026rsquo;s Disease Using Explainable Graph Neural Networks With Population Graphs. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/bioengineering10060701\u003c/span\u003e\u003cspan address=\"10.3390/bioengineering10060701\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Bioengineering\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKozomara A, Birgaoanu M, Griffiths-Jones S (2019) miRBase: from microRNA sequences to function. Nucleic Acids Res 47:D155\u0026ndash;D162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gky1141\u003c/span\u003e\u003cspan address=\"10.1093/nar/gky1141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu T, Zhou X, Zhang Z, Qin, Yutao, Wang R, Qin Y, Huang Y, Mo Y, Huang T (2023) The role of EBV-encoded miRNA in EBV-associated gastric cancer. Front Oncol 13:1204030. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2023.1204030\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2023.1204030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMo X, Wei F, Tong Y, Ding L, Zhu Q, Du S, Tan F, Zhu C, Wang Y, Yu Q, Liu Y, Robertson ES, Yuan Z, Cai Q (2018) Lactic Acid Downregulates Viral MicroRNA To Promote Epstein-Barr Virus-Immortalized B Lymphoblastic Cell Adhesion and Growth. J Virol 92:e00033\u0026ndash;e00018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/JVI.00033-18\u003c/span\u003e\u003cspan address=\"10.1128/JVI.00033-18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNotarte KI, Senanayake S, Macaranas I, Albano PM, Mundo L, Fennell E, Leoncini L, Murray P (2021) MicroRNA and Other Non-Coding RNAs in Epstein\u0026ndash;Barr Virus-Associated Cancers. Cancers 13:3909. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers13153909\u003c/span\u003e\u003cspan address=\"10.3390/cancers13153909\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePfeffer S, Zavolan M, Gr\u0026auml;sser FA, Chien M, Russo JJ, Ju J, John B, Enright AJ, Marks D, Sander C, Tuschl T (2004) Identification of Virus-Encoded MicroRNAs. Science 304:734\u0026ndash;736. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1096781\u003c/span\u003e\u003cspan address=\"10.1126/science.1096781\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQureshi A, Thakur N, Monga I, Thakur A, Kumar M (2014) VIRmiRNA: a comprehensive resource for experimentally validated viral miRNAs and their targets. Database 2014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/database/bau103\u003c/span\u003e\u003cspan address=\"10.1093/database/bau103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamakrishnan R, Donahue H, Garcia D, Tan J, Shimizu N, Rice AP, Ling PD (2011) Epstein-Barr Virus BART9 miRNA Modulates LMP1 Levels and Affects Growth Rate of Nasal NK T Cell Lymphomas. PLoS ONE 6:e27271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0027271\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0027271\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRessing ME, Van Gent M, Gram AM, Hooykaas MJG, Piersma SJ, Wiertz EJHJ (2015) Immune Evasion by Epstein-Barr Virus. In: M\u0026uuml;nz C (ed) Epstein Barr Virus Volume 2, Current Topics in Microbiology and Immunology. Springer International Publishing, Cham, pp 355\u0026ndash;381. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-22834-1_12\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-22834-1_12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiley KJ, Rabinowitz GS, Yario TA, Luna JM, Darnell RB, Steitz JA (2012) EBV and human microRNAs co-target oncogenic and apoptotic viral and human genes during latency. EMBO J 31:2207\u0026ndash;2221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/emboj.2012.63\u003c/span\u003e\u003cspan address=\"10.1038/emboj.2012.63\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkalsky RL, Corcoran DL, Gottwein E, Frank CL, Kang D, Hafner M, Nusbaum JD, Feederle R, Delecluse H-J, Luftig MA, Tuschl T, Ohler U, Cullen BR (2012) The Viral and Cellular MicroRNA Targetome in Lymphoblastoid Cell Lines. PLoS Pathog 8:e1002484. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.ppat.1002484\u003c/span\u003e\u003cspan address=\"10.1371/journal.ppat.1002484\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang D, Li B, Xu T, Hu R, Tan D, Song X, Jia P, Zhao Z (2020) VISDB: a manually curated database of viral integration sites in the human genome. Nucleic Acids Res 48:D633\u0026ndash;D641. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkz867\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkz867\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Ge J, Wang Y, Xiong F, Guo J, Jiang X, Zhang L, Deng X, Gong Z, Zhang S, Yan Q, He Y, Li X, Shi L, Guo C, Wang F, Li Z, Zhou M, Xiang B, Li Y, Xiong W, Zeng Z 2022. EBV miRNAs BART11 and BART17-3p promote immune escape through the enhancer-mediated transcription of PD-L1. Nat Commun 13, 866. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-022-28479-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-28479-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Yu F, Wu W, Wang Y, Ding H, Qian L (2018) Epstein-Barr virus-encoded microRNAs as regulators in host immune responses. Int J Biol Sci 14:565\u0026ndash;576. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/ijbs.24562\u003c/span\u003e\u003cspan address=\"10.7150/ijbs.24562\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Huang T, Zhou Y, Cheng ASL, Yu J, To KF, Kang W (2018) The oncogenic role of Epstein\u0026ndash;Barr virus-encoded micro RNA s in Epstein\u0026ndash;Barr virus‐associated gastric carcinoma. J Cell Mol Medi 22:38\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jcmm.13354\u003c/span\u003e\u003cspan address=\"10.1111/jcmm.13354\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao X, Huang X, Dang C, Wang X, Qi Y, Li H (2024) The Epstein-Barr virus-miRNA-BART6-5p regulates TGF-β/SMAD4 pathway to induce glycolysis and enhance proliferation and metastasis of gastric cancer cells. OR 32:999\u0026ndash;1009. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.32604/or.2024.046679\u003c/span\u003e\u003cspan address=\"10.32604/or.2024.046679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŽidovec Lepej S, Matulić M, Gršković P, Pavlica M, Radmanić L, Korać P (2020) miRNAs: EBV Mechanism for Escaping Host\u0026rsquo;s Immune Response and Supporting Tumorigenesis. Pathogens 9:353. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/pathogens9050353\u003c/span\u003e\u003cspan address=\"10.3390/pathogens9050353\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-virology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arvi","sideBox":"Learn more about [Archives of Virology](https://www.springer.com/journal/705)","snPcode":"705","submissionUrl":"https://submission.nature.com/new-submission/705/3","title":"Archives of Virology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Epstein-Barr virus, miRNA, regulatory network, bioinformatics database, virus-host interaction","lastPublishedDoi":"10.21203/rs.3.rs-9558966/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9558966/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEpstein-Barr virus (EBV) is a ubiquitous human herpesvirus associated with various malignancies and autoimmune diseases. MiRNAs play crucial roles in viral-host interactions by regulating gene expression post-transcriptionally. Understanding the regulatory networks between EBV-encoded miRNAs and human miRNAs is essential for elucidating viral pathogenesis and identifying therapeutic targets. We reported the EBV-HSA miRNA Regulatory Database (EBV-HSA-RegDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lbcd.ufpa.br/ebvhsa/\u003c/span\u003e\u003cspan address=\"https://lbcd.ufpa.br/ebvhsa/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an integrated bioinformatics resource that combines comparative sequence analysis, regulatory network reconstruction, experimental data integration, and functional gene enrichment analysis. The database incorporates miRNA sequences from miRBase and experimentally validated miRNA-target interactions from miRTarBase. It also advances the use of multiple sequence-comparison methods, including BLAST alignment and Hamming and Levenshtein distances, to investigate miRNA seed alignments. The tool reconstructs and enables exploration of a comprehensive regulatory network linking experimental data on EBV-encoded miRNAs, human miRNAs, and their target genes. Gene Ontology and pathway analyses using the KEGG and Reactome databases are also available. Network analysis identified key regulatory hubs and revealed convergent targeting patterns in which EBV and human miRNAs regulate the same target genes (\u003cem\u003eDICER\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eE2F3\u003c/em\u003e, \u003cem\u003eSMAD4\u003c/em\u003e, \u003cem\u003eSPRY2\u003c/em\u003e) and several pairs of EBV-encoded miRNAs and their human counterparts, with high similarity in their target sequences. Functional enrichment analysis uncovered significant associations with biological processes, molecular functions, cellular components, and pathways relevant to viral infection, immune response, and cellular transformation. The EBV-HSA-RegDB provides a user-friendly, interactive platform for exploring virus-host miRNA regulatory networks. This resource facilitates hypothesis generation regarding EBV pathogenesis mechanisms and may aid in elucidating novel gene targets for EBV-associated diseases.\u003c/p\u003e","manuscriptTitle":"EBV-HSA-RegDB: A database of regulatory interactions between Epstein–Barr virus and human miRNAs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 13:33:42","doi":"10.21203/rs.3.rs-9558966/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"9678492946690567249682837525897787728","date":"2026-05-14T15:59:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137479268518587924767972124691692435083","date":"2026-05-13T11:12:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242838870030785473039414431891051756772","date":"2026-05-08T11:06:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-08T09:39:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-02T12:37:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-02T12:37:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Virology","date":"2026-04-29T01:00:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-virology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arvi","sideBox":"Learn more about [Archives of Virology](https://www.springer.com/journal/705)","snPcode":"705","submissionUrl":"https://submission.nature.com/new-submission/705/3","title":"Archives of Virology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5207916f-c8a5-4bd2-b6d6-cf3d6b9a391b","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"9678492946690567249682837525897787728","date":"2026-05-14T15:59:49+00:00","index":31,"fulltext":""},{"type":"reviewerAgreed","content":"137479268518587924767972124691692435083","date":"2026-05-13T11:12:34+00:00","index":30,"fulltext":""},{"type":"reviewerAgreed","content":"242838870030785473039414431891051756772","date":"2026-05-08T11:06:47+00:00","index":26,"fulltext":""},{"type":"reviewersInvited","content":"19","date":"2026-05-08T09:39:40+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T13:33:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 13:33:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9558966","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9558966","identity":"rs-9558966","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}