Integration of Human and Viral miRNAs in Epstein-Barr Virus-Associated Tumors and Implications for Drug Repurposing

Abstract

Epstein-Barr virus (EBV) is associated with several tumors, and has substantial relevance for public health. Therapeutics innovation for EBV-related disorders is much needed. In this context, miRNAs are noncoding RNA molecules that play vital roles in EBV infection. miRNA-Seq and RNA-Seq data for EBV-associated clinical samples and cell lines have been generated, but their detailed integrative analyses, and exploitation for drug repurposing against EBV are lacking. Hence, we identified and analyzed the differentially expressed miRNAs (DEmiRs) in EBV-infected cell lines (28) and infected (28) and uninfected human tissue (20) samples using an in-house pipeline. We found significantly enriched host miRNAs like hsa-mir-3651, hsa-mir-1248, and hsa-mir-29c-3p in EBV-infected samples from EBV-associated nasopharyngeal carcinoma and Hodgkin's lymphoma, among others. Furthermore, we also identified significantly enriched novel miRNAs such as hsa-mir-29c-3p, hsa-mir-3651, and hsa-mir-98-3p, which were not previously reported in EBV-related tumors. Differentially expressed mRNAs (DEMs) were identified in EBV-infected cell lines (21) and uninfected human tissue (14) samples. We predicted and selected 1572 DEMs (upregulated) that are targeted by 547 DEmiRs (downregulated). These were further classified into essential (870) and nonessential (702) genes. Moreover, a miRNA-mRNA network was developed for the hub miRNAs. Importantly, we used the DEMs during EBV latent infection types I, II, and III to identify the candidate drugs for repurposing: Glyburide, Levodopa, Nateglinide, and Stiripentol, among others. To the best of our knowledge, this is the first integrative analyses that identified DEmiRs and DEMs as potential therapeutic targets and predicted drugs as potential candidates for repurposing against EBV-related tumors.

Introduction

Epstein-Barr virus (EBV) is a member of the herpes virus family, also known as human herpesvirus 4. It is one of the most common human viruses infecting more than 90% of the population across the globe (Tzellos and Farrell, 2012). EBV is associated with several types of malignancies like gastric carcinoma (GC) (Burke et al, 1990), Burkitt lymphoma (BL) (Epstein et al, 1964), nasopharyngeal carcinoma (NPC) (zur Hausen et al 1970), diffuse large B cell lymphoma (DLBCL) (Oyama et al, 2007), Hodgkin's lymphoma (HL) (Forte et al, 2012), and Pythorax-associated lymphoma (PAL) (Aozasa et al, 2005), among others. Currently, there is no approved antiviral drug for the EBV treatment (Andrei et al, 2019). However, few antiviral drugs like acyclovir, ganciclovir were administered, and have limited success clinically (Andrei et al, 2019; Pagano et al, 2018). So there is a need to develop new strategies to focus on EBV-related disorders.

The EBV lymphomas have three distinct latent infection stages based on viral gene expression patterns, namely, latency type I, II, and III (Kang and Kieff, 2015) having six Epstein-Barr nuclear antigens [EBNAs 1, 2, 3A, 3B, and 3C and EBNA leader protein (EBNA‑LP) ], latent membrane proteins LMP1 and LMP2 (having two isoforms, LMP2A and LMP2B), and the noncoding EBV-encoded RNAs (EBER1 and EBER2) and also viral miRNAs (Young et al, 2016). BL and GC belong to type I latency and have EBNA-1 and EBER RNA (Castillo et al, 2011; Sun et al, 2020).

The type II latency include HL and NPC having EBNA-1, LMP-2A/B, LMP-1, and EBER (Salamon et al, 2012; Tsao et al, 2017). The type III latency is observed in lymphoproliferative diseases and EBV infection-mediated lymphoblastoid cell line (LCL). In type III latency, most latent genes are expressed, that is, EBNA-1, EBNA-2, EBNA-3A, EBNA-3B, EBNA-3C, EBNA-LP, LMP-2A, LMP-2B, LMP-1, and EBER (Kang and Kieff, 2015).

miRNAs play an essential role in EBV-associated carcinomas (Cosmopoulos et al, 2009; Niu et al, 2020). EBV-encoded miRNAs have two regions, namely, the BamH I fragment A rightward transcript (BART) and the BamH I fragment H rightward open reading frame 1 (BHRF1) cluster. The BART region comprises three pre-miRNAs encoding four mature miRNAs. BHRF1 has 22 pre-miRNAs that encode 40 mature miRNAs (Pfeffer et al, 2004).

Various studies reported miRNAs reported in EBV carcinomas. For example, ebv-mir-bart-10-3p (Luo et al, 2021b), ebv-mir-bart-8-3p (Lin et al, 2022), and ebv-miR-bart-7 in NPC (Wardana et al, 2020); ebv-mir-bart-6 in EBV-positive BL (Zhang et al, 2017; Zhou et al, 2016); and ebv-mir-bart-20-5p in GC (Kang et al, 2017). Likewise, host miRNAs are also found expressed in EBV-associated carcinomas, namely, hsa-miR-155-5p in GC (Shi et al, 2020a); and hsa-miR-142, hsa-miR-127, and hsa-miR-197 in BL (Onnis et al, 2012; Zhang et al, 2017; Zhou et al, 2016). Pfeffer et al (2004) also reported that clusters of hsa-miR-183, hsa-miR-96, and hsa-miR-182 are associated with EBV-associated carcinomas (Oussaief et al, 2015). Okuno et al (2019) reported that viral miRNA (BART and BHRF) are also defective in EBV lymphomas.

In the last few years, several studies have checked the abnormal expression of EBV-encoded viral/host miRNAs (Cosmopoulos et al, 2009; Oduor et al, 2017), for example, miRNA expression between EBV-positive and EBV-negative DLBCL (Imig et al, 2011); in primary infected normal tissues and tumor biopsies (Qiu et al, 2011); in different BL cell lines namely, Mutu, Kem, and Sav (Oussaief et al, 2015). Blangero J group found miRNA and mRNA interactions in induced pluripotent stem cell reprogramming of LCLs (Kumar et al, 2019). Katano H group has studied miRNA in the clinical samples of EBV-associated lymphomas (Sakamoto et al, 2017).

Although studies are reported for viral miRNA and host miRNA for specific EBV-associated carcinoma, there is no study that identifies highly enriched or depleted common miRNAs across different EBV-associated carcinomas, which would be helpful to identify therapeutic targets. Also, we need to know the pattern of differential expressed miRNAs (DEmiRs) between EBV-infected clinical samples and cell lines. Furthermore, common miRNA targets are not exploited for drug repurposing against EBV-associated carcinomas.

This study aimed to address the above research gaps, and utilized the reported miRNA-Seq data from NCBI-SRA for EBV-infected clinical samples, and cell lines. Furthermore, we used an in-house pipeline as a common platform to identify DEmiRs across samples. We found transcription factors (TFs) for DEmiRs that are validated by their differentially expressed target mRNAs (DEMs) from RNA-Seq data. These DEMs are utilized for functional Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and identification of drug repurposing candidates from the DrugBank database (Fig. 1).

FIG. 1.

Detailed summary of miRNA-Seq and RNA-Seq analysis for EBV-associated tumors. EBV, Epstein-Barr virus.

Materials and Methods

Data collection of miRNA-Seq data

The data pertinent to this study were retrieved from the NCBI-Sequence Read Archive (SRA) for EBV miRNA-Seq by using the following query: ((((EBV) OR Epstein-Barr virus)) AND ((miRNA-seq) OR miRNAseq)) AND “Homo sapiens”[orgn:__txid9606]

Using this query in the advanced search option, 80 samples were found. We filtered out data not relevant to our analysis. We used 28 samples tested on 15 different EBV-infected cell lines, namely, IBL_LCLd3 (PRJNA218007), Akata (PRJNA613459), Akata_LCLd3 (PRJNA218007), BJAB (PRJNA260069), SNU719 (PRJNA613459), Mutu (PRJNA260069 and PRJNA274950), Kem (PRJNA274950), IK140508 (PRJNA260069), IM-1 (PRJNA260069), RN (PRJNA260069), LCL (PRJNA299648 and PRJDB3333), and Sav and Raji (PRJDB3333). In addition, samples from NPC cell lines, namely, EBV-associated NPC cell line and C666, were also collected from two studies that is, PRJNA753839 and PRJNA281456, respectively. The sample IDs for IBL_LCLd3 are:- SRR965653 and SRR965655; Akata:- SRR14466847, SRR14466848, and SRR14466849; Akata_LCLd3:- SRR1003025; BJAB:- SRR1563015; SNU719:- SRR14466850, SRR14466851, and SRR14466852; Mutu:- SRR1563018, SRR1563057, and SRR1793804; Kem:- SRR1793807; IK140508:- SRR1563059; IM-1:- SRR1563061; RN:- SRR1563063; LCL:- SRR2770445, SRR2770446, SRR2770447, SRR2770448, SRR2770449, SRR2770450, and DRR027359; Sav:- SRR1793810; Raji:- DRR027369; C666:- SRR1979367, and EBV- NPC:- SRR15417418.

From the above search, we found 28 clinical samples of EBV associated with six tumors, namely, AIDS-related diffuse large B cell lymphoma (ARL) (DRR027351, DRR027352, and DRR027353), EBV-positive DLBCL (ELD1) (DRR027354, DRR027355, and DRR027356), HL (DRR027357 and DRR027358), Methotrexate-associated lymphoproliferative disorder (MTX) (DRR027360, DRR027361, DRR027362, DRR027363, and DRR027364), and PAL (DRR027365, DRR027366, DRR027367, and DRR027368) reported in one study PRJDB3333. In addition, clinical samples were also found for NPC, namely, from NPC biopsy (PRJNA486534) and NPC patient sample (PRJNA753839). There are seven samples of NPC biopsy, namely, SRR7707744, SRR7707745, SRR7707746, SRR7707747, SRR7707748, SRR7707749, and SRR7707750, while two samples were taken each from metastatic NPC patient sample (fresh human NPC tumor, SRR15417422 and SRR15417423) and nonmetastatic NPC patient sample (fresh human NPC tumor, SRR15417424 and SRR15417425).

For the control group, we collected samples for human tissues like nasopharyngeal mucosal specimen (PRJNA486534), nasopharynx tissue (PRJNA391554), noncancerous immortalized nasopharyngeal epithelial cell line (PRJNA753839), cord blood (PRJNA576395, PRJNA603745, and PRJNA807665), stomach (PRJNA603337), and cancer-free plasma and serum (PRJNA634142). The sample ids for nasopharyngeal mucosal specimen are SRR7707751, SRR7707752, SRR7707753, and SRR7707754. We have one sample each for nasopharynx tissue and noncancerous immortalized nasopharyngeal epithelial cell line that is, SRR5753916 and SRR15417420, respectively. The sample ids for cord blood are SRR10244516, SRR10984944, SRR10984948, SRR10984952, SRR18052304, and SRR18052308. For the stomach, we have used two samples:- SRR10971716 and SRR10971722, while plasma and serum have three samples each, namely, SRR11820311, SRR11820312, and SRR11820313, and SRR11820355, SRR11820356, and SRR11820357, respectively.

Data collection of RNA-Seq data

RNA-Seq data were also collected for EBV infection using keywords like EBV, Epstein-Barr virus, and RNA-seq in the advanced search option of NCBI-SRA. We found four studies relevant to our work, namely, PRJNA79791 (1 sample), PRJNA299648 (6 samples), PRJNA335645 (4 samples), and PRJNA593250 (11 samples). These samples have been reported on different cell lines like, Burkitt's lymphoma cells (SRR069060); EBV-immortalized LCL (SRR2770427, SRR2770428, SRR2770429, SRR2770430, SRR2770431, and SRR2770432); MKN7 cells (SRR3957175, SRR3957176, SRR3957177, SRR10582343, SRR10582344, SRR10582345, SRR10582346, and SRR10582347); NCC24 (SRR10582348 and SRR10582349), SNU719 (SRR10582350 and SRR10582351), and GES1 (SRR11074303 and SRR11074304).

Likewise, for the control group, raw data were collected for nasopharyngeal cell (PRJNA579573), nasopharynx NP69 (PRJNA693503), cord blood (PRJNA639459), and plasma (PRJNA732398). Nasopharyngeal cell has two samples, namely, SRR10354989 and SRR10354990; nasopharynx NP69 also has two samples, namely, SRR13490218 and SRR13490219; and cord blood has five samples, namely, SRR12014762, SRR12014763, SRR12014764, SRR12014765, and SRR12014766, while plasma has five samples, namely, SRR14633547, SRR14633548, SRR14633549, SRR14633550, and SRR14633551.

Read quality assessment and preprocessing

The quality of reads was checked and adapter sequences were removed using Trimmomatic v0.39 tool (Bolger et al, 2014; Wong et al, 2018). Using this tool, reads with less than 22 and 36 bp were dropped from miRNASeq and RNA-Seq data, respectively. Furthermore, three bases were also removed from the start and end of the read.

Indexing and alignment process

For miRNASeq data, index file was generated for human and EBV miRNAs obtained from miRBase using bowtie2 tool. Indexing is a preprocessing step that compresses the genome size by making it simple for searching sequences in the genome. For the alignment process, bowtie2 tool is used to align reads with the index file of human and EBV miRNAs (Langmead and Salzberg, 2012). Using bowtie2, one mismatch with length 22 bp was allowed for local alignment on miR-Seq reads. After obtaining the aligned reads, samtools were used to quantify the read count for each miRNA (Li et al, 2009; Ungerleider et al, 2021).

For the RNA-Seq data, trimmed RNA-Seq reads were aligned against the human genome hg38 index file (Homo_sapiens.GRCh38.105). Reads with more than 32 bp were allowed along with one mismatch. Mapped mRNA reads were then quantified using htseq-count tool (Anders et al, 2015).

Differential expression analysis of miRNAs and mRNAs

After obtaining the read counts, DESeq2 algorithm was used to perform data normalization and DEmiR analysis between different carcinoma-associated cell lines, as well as clinical samples (Kim et al, 2019). This algorithm is an R-based bioconductor package that uses “DESeq” functions to estimate the size factors, dispersions, gene-wise dispersion, and model-fit and testing (Love et al, 2014). miRNAs having adjusted p value (padj) with <0.01 and log2-fold change (log2FC) >1.5 were observed as the most significant DEmiRs. In addition, DEM analysis was also performed for EBV carcinoma samples compared to healthy tissues using similar parameters.

DEmiR and DEM visualization through principal component analysis and EnhancedVolcano plot

DEmiRs and DEMs identified from DESeq2 methods were visualized using principal component analysis (PCA) plots generated from “plotPCA,” which is a built-in DESeq2 function in R (McCarthy et al, 2017). Volcano plots were also developed using “enhancedvolcano” (https://github.com/kevinblighe/EnhancedVolcano) package in R to visualize the DEmiRs and DEMs based on log2FC and p-value.

Target gene prediction for DEmiRs and their intersection with DEMs

Target genes were predicted using miRTarBase database (Huang et al, 2020) and miRDB (Chen and Wang, 2020). This miRTarBase database has >25000 experimentally validated target genes for different miRNAs. The miRDB database has 29,161 unique gene targets for 2656 human mature miRNAs. We identified common genes between targets of DEmiRs and DEMs during EBV infection. We made a venn diagram using an online tool (https://bioinfogp.cnb.csic.es/tools/venny/) in which intersection represents common genes.

Identification of TFs for DEmiRs

TFs of DEmiRs of common targets were also predicted using the FunRich tool (Fonseka et al, 2021; Xu et al, 2021). In this study, top 10 TFs of DEmiRs were identified.

Function and pathway enrichment analysis of common targets between DEmiRs and DEMs

GO and KEGG pathway enrichment analysis was performed to identify functional relationships of common genes categorizing cellular component (CC), biological process (BP) and molecular function (MF). The analysis was performed using R package “cluster Profiler” (Yu et al, 2012) and “enrich plot” having p-value <0.05.

Identification of essential and nonessential genes in common targets

Essential and nonessential genes from common targets were identified using the database of essential genes (DEG, http://origin.tubic.org/deg/public/index.php/) database (Luo et al, 2021a; Zhang et al, 2004). DEG database contains essential genes of different organisms like human, bacteria, and archaea. The essential genes are important for the host organism to survive under specific conditions (Bartha et al, 2018).

Protein-protein interaction network and identification of hub DEMs

For protein-protein interaction (PPI), STRING database v11.5 (https://string-db.org/) was used to check the interactions between target genes or proteins (Szklarczyk et al, 2019). Furthermore, we used cytoscape software to view the PPI network using the STRING plugin. Next, hub DEMs were identified from the PPI network using the cytoHubba plugin in the cytoscape v3.9.1 software (Chin et al, 2014).

Identification of hub DEmiRs

Hub DEmiRs were identified based on the number of nodes that interact with the targets. In our analysis, miRNAs having more than 50 target genes were considered hub miRNAs. We developed the hub DEmiR-DEM network using the cytoscape v3.9.1 software (Shannon et al, 2003).

Identification of repurposed drugs targeting hub DEMs

Repurposed drugs were identified for nonessential genes using the DrugBank database (Wishart et al, 2008). DrugBank is a freely accessible online resource containing information about the drugs and their targets. This resource currently has more than 14,000 drug entries, including approved small molecule drugs, biologics (proteins, peptides, vaccines, and allergenic), nutraceuticals, and experimental drugs.

This research was conducted under the overall research ethics oversight of the authors' institutions.

Results

In this study, we identified the miRNAs of significance for EBV-associated tumors to determine a new strategy for its treatment. We predicted the DEmiRs, their targets, TFs, hub miRNAs, hub mRNAs, and PPI network, as well as constructing the miRNA-mRNA network.

Identification of DEmiRs

The DEmiRs was identified among EBV-infected samples, cell lines, and EBV-uninfected human tissue. The miRNA expression was checked in six different categories as summarized in Figure 2.

FIG. 2.

Flow chart for miRNA-Seq analysis for EBV-associated tumors.

DEmiRs among clinical samples of six EBV tumors

Differential expression analysis was performed among clinical samples of six EBV tumors namely, ARL, ELD1, HL, MTX, PAL, and NPC using the “one-vs-other” approach from BioProject id: PRJDB3333, PRJNA486534, and PRJNA753839. Expression of each carcinoma was checked against the remaining five tumors using the padj ≤0.01 and log2FC ≥1.5 or ≤ −1.5 like “NPC vs Other EBV tumors” and “ARL vs Other EBV tumors” (ELD1, HL, MTX, and PAL), as given in Table 1. In “NPC vs Other EBV tumors,” 272 DEmiRs were obtained, which include 108 upregulated and 145 downregulated human DEmiRs, while there are 11 upregulated and 8 downregulated EBV DEmiRs. In “ARL vs Other EBV tumors,” 29 DEmiRs were obtained, which have 10 upregulated and 19 downregulated human DEmiRs.

Table 1.

Differentially Expressed Upregulated and Downregulated miRNAs Among Clinical Samples Using “One-vs-Other” Approach

miRNA	baseMean	log2FC	lfcSE	padj
Upregulated (padj ≤0.01, FC ≥1.5)
NPC vs. other EBV tumors
hsa-mir-200c-3p	2335.13	9.09	0.71	0.0000
hsa-mir-200a-5p	181.09	8.52	0.68	0.0000
hsa-mir-184	160.66	8.16	0.92	0.0000
hsa-mir-200b-3p	2572.58	7.80	0.92	0.0000
hsa-mir-96-5p	177.49	7.64	0.89	0.0000
ARL vs. other EBV tumors
hsa-mir-135a-5p	3.76	5.11	1.16	0.0007
hsa-mir-629-3p	2.04	3.97	1.13	0.0105
hsa-mir-451a	601.09	3.72	1.06	0.0102
hsa-mir-18a-5p	42.23	3.52	0.90	0.0028
hsa-mir-664a-3p	39.89	3.46	0.81	0.0009
ELD1 vs. other EBV tumors
hsa-mir-326	4.36	3.67	0.97	0.0046
hsa-mir-146b-3p	51.97	3.42	0.59	0.0000
hsa-mir-532-3p	4.77	3.41	0.98	0.0096
hsa-mir-1273h-3p	8.63	3.25	0.81	0.0017
hsa-mir-874-3p	26.95	2.93	0.84	0.0096
MTX vs. other EBV tumors
hsa-mir-190a-5p	5.96	3.61	1.01	0.0083
PAL vs. other EBV tumors
hsa-mir-330-5p	24.75	2.98	0.78	0.0048
hsa-mir-181d-5p	94.63	1.92	0.56	0.0140
Downregulated (padj ≤0.01, FC ≤ −1.5)
NPC vs. other EBV tumors
hsa-mir-3651	9.84	−8.35	0.83	0.0000
hsa-mir-3677-3p	1.60	−7.43	1.13	0.0000
hsa-mir-548bb-5p	1.24	−7.31	1.74	0.0002
hsa-mir-133a-3p	27.99	−6.96	0.80	0.0000
hsa-mir-138-5p	72.85	−6.95	0.69	0.0000
ARL vs. other EBV tumors
hsa-mir-203a-3p	260.67	−21.77	2.37	0.0000
hsa-mir-200a-5p	181.09	−21.27	2.63	0.0000
hsa-mir-184	160.66	−21.10	2.72	0.0000
hsa-mir-200a-3p	336.06	−19.47	2.35	0.0000
hsa-mir-205-5p	3064.71	−11.44	2.71	0.0010
ELD1 vs. other EBV tumors
hsa-mir-205-5p	3064.71	−25.29	2.70	0.0000
hsa-mir-200a-3p	336.06	−22.23	2.35	0.0000
hsa-mir-203a-3p	260.67	−21.71	2.37	0.0000
hsa-mir-200a-5p	181.09	−21.36	2.64	0.0000
hsa-mir-200b-3p	2572.58	−11.36	2.52	0.0004
MTX vs. other EBV tumors
hsa-mir-184	160.66	−22.98	2.07	0.0000
hsa-mir-200a-5p	181.09	−8.63	2.01	0.0010
hsa-mir-200c-3p	2335.13	−7.72	1.69	0.0006
hsa-mir-3529-3p	359.03	−6.99	1.40	0.0001
hsa-mir-7-5p	387.87	−6.73	1.60	0.0012
PAL vs. other EBV tumors
hsa-mir-184	160.66	−20.55	2.40	0.0000
hsa-mir-200a-5p	181.09	−19.78	2.31	0.0000
hsa-mir-200c-3p	2335.13	−9.39	2.04	0.0003
hsa-mir-7-5p	387.87	−6.87	1.93	0.0093
hsa-mir-3529-3p	359.03	−6.00	1.67	0.0093
HL vs. other EBV tumors
hsa-mir-200a-5p	181.09	−21.25	3.23	0.0000
hsa-mir-184	160.66	−21.08	3.34	0.0000

EBV, Epstein-Barr virus; NPC, nasopharyngeal carcinoma; PAL, Pythorax-associated lymphoma; HL, Hodgkin's lymphoma.

In “ELD1 vs Other EBV tumors,” 27 DEmiRs were achieved, which include 10 upregulated and 17 downregulated human DEmiRs. For “MTX vs Other EBV tumors,” 27 DEmiRs were achieved, which include 1 upregulated and 26 downregulated human DEmiRs. For “PAL vs Other EBV tumors,” only 13 DEmiRs were found, which contain 2 upregulated and 11 downregulated human DEmiRs. In case of “HL vs Other EBV tumors,” only two downregulated human DEmiRs were found. The complete list of DEmiRs among clinical samples is listed from Supplementary Tables S1–S6.

EBV tumors versus EBV-uninfected human tissues

DEmiRs were also determined in “EBV tumors vs EBV-Uninfected Human Tissues” by using the same parameters. We obtained 635 DEmiRs, which include 11 upregulated and 583 downregulated human DEmiRs. Furthermore, it also has 41 upregulated EBV DEmiRs. The top five significantly enriched human DEmiRs are listed in Table 2. The complete list of DEmiRs found for “EBV tumors vs EBV-Uninfected Human Tissues” is given in Supplementary Table S7.

Table 2.

Top Five Differentially Expressed Upregulated and Downregulated miRNAs in Different Categories

miRNA	baseMean	log2FC	IfcSE	padj
Upregulated (padj ≤0.01, FC ≥1.5)
EBV tumors vs. EBV-uninfected human tissues
hsa-mir-3651	6.07	3.86	0.99	0.00035999
hsa-mir-1248	24.23	3.15	0.68	0.00001863
hsa-mir-29c-3p	64.56	3.04	0.65	0.00001313
hsa-mir-10b-5p	6688.26	2.77	0.49	0.00000009
hsa-mir-455-5p	198.20	2.34	0.69	0.00219984
EBV tumors + EBV-infected cell lines vs. EBV-uninfected human tissues
hsa-mir-142-5p	1002.89	5.06	0.71	0.0000
hsa-mir-3651	13.11	3.72	0.78	0.0000
hsa-mir-155-5p	21467.10	2.98	0.69	0.0001
hsa-mir-182-5p	4386.42	2.75	0.59	0.0000
hsa-mir-148a-5p	130.30	2.51	0.53	0.0000
EBV tumors vs. EBV-infected cell lines
hsa-mir-224-5p	422.34	11.88	1.01	0.00000000
hsa-mir-452-5p	67.03	10.80	1.12	0.00000000
hsa-mir-145-5p	211.43	9.42	0.63	0.00000000
hsa-mir-199a-5p	371.89	9.11	0.48	0.00000000
hsa-mir-6510-3p	59.59	9.04	1.26	0.00000000
EBV-infected cell lines vs. EBV-uninfected human tissues
hsa-mir-142-5p	3142.35	6.84	0.68	0.00000000
hsa-mir-192-5p	11524.62	5.63	0.77	0.00000000
hsa-mir-3651	14.42	4.54	0.77	0.00000002
hsa-mir-155-5p	40783.49	3.79	0.91	0.00009759
hsa-mir-182-5p	8431.46	3.71	0.71	0.00000070
Downregulated (padj ≤0.01, FC ≤ −1.5)
EBV tumors vs. EBV-uninfected human tissues
hsa-mir-6775-5p	226.73	−30.00	2.80	0.0000
hsa-mir-658	289.25	−30.00	2.80	0.0000
hsa-mir-3197	74231.51	−30.00	2.95	0.0000
hsa-mir-4793-5p	528.58	−30.00	2.95	0.0000
hsa-mir-6756-5p	498.61	−30.00	2.95	0.0000
EBV tumors + EBV-infected cell lines vs. EBV-uninfected human tissues
hsa-mir-8084	104.45	−30.00	2.79	0.000000
hsa-mir-635	121.23	−30.00	2.81	0.000000
hsa-mir-6849-5p	135.45	−30.00	2.83	0.000000
hsa-mir-6817-5p	181.97	−30.00	2.85	0.000000
hsa-mir-6775-5p	457.19	−30.00	2.93	0.000000
EBV tumors vs. EBV-infected cell lines
hsa-mir-192-5p	7478.39	−6.36	0.62	0.00000000
hsa-mir-10395-3p	28.65	−4.90	0.70	0.00000000
hsa-mir-142-5p	1819.88	−4.78	0.62	0.00000000
hsa-mir-378cc	136.28	−4.71	0.39	0.00000000
hsa-mir-4485-3p	20.00	−4.45	0.86	0.00000324
EBV-infected cell lines vs. EBV-uninfected human tissues
hsa-mir-7848-3p	181.22	−30.00	2.05	0.00000000
hsa-mir-6817-5p	178.87	−30.00	2.64	0.00000000
hsa-mir-6765-5p	222.20	−30.00	2.64	0.00000000
hsa-mir-6775-5p	399.51	−30.00	2.72	0.00000000
hsa-mir-619-3p	200.67	−30.00	2.92	0.00000000

PCA plots were also developed for miRNA-seq data to determine valuable information about the data variation. The variability for “EBV tumors vs EBV-Uninfected Human Tissues” sample in PCA analysis is 78% (Supplementary Fig. S1a). Volcano plots represent DEmiRs that are statistically significant based on log10 (padj) ≤0.01. y-axis represents the negative padj value, where higher the data point, smaller is the padj. Red dots represent those miRNAs that have log2FC ≥1.5 or ≤ −1.5 and p-value ≤0.01. Human miRNAs like hsa-miR-195-5p and hsa-miR-29c-3p and EBV-encoded miRNAs like ebv-miR-bart14-3p and ebv-miR-bart9-5p, among others, are statistically significant, as given in Supplementary Figure S1b.

EBV tumors + EBV-infected cell lines versus EBV-uninfected human tissues

The differential expression was also checked for EBV tumors with EBV-infected cell lines against the EBV-uninfected human tissues. Nine hundred forty-four DEmiRs were obtained, which include 13 upregulated and 891 downregulated human DEmiRs. Furthermore, it has 40 upregulated EBV DEmiRs. Results of top five upregulated and downregulated human DEmiRs are provided in Table 2. The complete list of DEmiRs is listed in Supplementary Table S8.

The variability for “EBV tumors + EBV-infected Cell lines vs EBV-Uninfected Human Tissues” sample is 66% (Fig. 3a). While generating the volcano plot, we found human miRNAs like hsa-miR-142-5p, and EBV-encoded miRNAs like ebv-miR-bart6-3p and ebv-miR-bart8-5p, among others, as statistically significant miRNAs (Fig. 3b).

FIG. 3.

(a) PCA plot of “EBV tumors + EBV-infected Cell lines vs EBV-Uninfected Human Tissues” Sample, (b) Volcano plot for “EBV Tumors + EBV-infected Cell lines vs EBV-Uninfected Human Tissues” using enhanced volcano package (fold change cutoff, 1.5; p-value cutoff, 0.01). PCA, principal component analysis.

EBV tumors versus EBV-infected cell lines

We also examined miRNA expression in “EBV tumors vs EBV-infected Cell lines” samples using the same parameters. Two hundred twenty-four DEmiRs were obtained, which have 148 upregulated and 75 downregulated human DEmiRs, while only one upregulated EBV DEmiR was obtained. The list of top five highly enriched DEmiRs is given in Table 2. The complete list of DEmiRs is provided in Supplementary Table S9. The PCA analysis represents 41% variability for “EBV tumors vs EBV-infected Cell lines” (Supplementary Fig. S2a). From the volcano plot, we have shown some statistically significant miRNAs in red dots like hsa-miR-224-5p and hsa-miR-452-5p (Supplementary Fig. S2b).

EBV-infected cell lines versus EBV-uninfected human tissues

In “EBV-infected Cell lines vs EBV-Uninfected Human Tissues” samples, 1029 DEmiRs were obtained, which had 27 upregulated and 961 downregulated human DEmiRs, and 41 upregulated EBV DEmiRs were achieved. Top five highly enriched human DEmiRs are given in Table 2. The complete list of DEmiRs is given in Supplementary Table S10.

The PCA analysis shows 72% of variability for “EBV-infected Cell lines vs EBV-Uninfected Human Tissues” sample (Supplementary Fig. S3a), while the volcano plot shows statistically significant miRNAs in red dots, namely, hsa-miR-142-5p and hsa-miR- 3651 and EBV-encoded miRNAs like ebv-miR-bart6-5p and ebv-miR-bart2-5p, among others (Supplementary Fig. S3b).

DEmiRs among samples of 15 different EBV cell lines

Differential expression analysis was also performed among the samples of EBV-infected cell lines using the “one-vs-other” approach. In “IBL_LCLd3 cell line vs Other Cell Lines,” 51 downregulated DEmiRs were found, which include 15 human miRNAs and 36 EBV miRNAs (Supplementary Table S11). In “LCL cell line vs Other Cell Lines,” 72 DEmiRs were obtained, which had 29 upregulated and 39 downregulated human miRNAs. The remaining four are EBV-encoded downregulated miRNAs (Supplementary Table S12). “SNU719 cell line vs Other Cell Lines” has 93 DEmiRs having 27 upregulated and 63 downregulated human miRNAs.

The remaining three are EBV-based downregulated miRNAs (Supplementary Table S13). In “Akata cell line vs Other Cell Lines,” 86 DEmiRs were obtained, which include 8 upregulated and 67 downregulated human miRNAs, as well as 11 downregulated EBV miRNAs (Supplementary Table S14). In “Mutu cell line vs Other Cell Lines,” there are 37 DEmiRs, which include 4 upregulated and 32 downregulated human miRNAs, while there is only one downregulated EBV miRNA (Supplementary Table S15). “Sav cell line vs Other Cell Lines” has 44 DEmiRs containing 2 upregulated and 41 downregulated human miRNAs, as well as 1 downregulated EBV encoded miRNA (Supplementary Table S16). “Kem cell line vs Other Cell Lines” has 20 DEmiRs with 6 upregulated and 14 downregulated human miRNAs (Supplementary Table S17).

In “BJAB cell line vs Other Cell Lines,” 46 downregulated DEmiRs were obtained, which include 14 miRNAs for humans and 32 downregulated miRNAs of EBV (Supplementary Table S18). “RN cell line vs Other Cell Lines” has 41 downregulated DEmiRs, which include 15 human miRNAs and 26 EBV miRNAs (Supplementary Table S19). For “Raji cell line vs Other Cell Lines,” there are 19 downregulated DEmiRs, which include 14 human and 5 EBV-encoded miRNAs (Supplementary Table S20). In “IM-1 cell line vs Other Cell Lines,” nine downregulated human miRNAs were found (Supplementary Table S21). In “IK140508 vs Other Cell Lines,” four downregulated human miRNAs were found (Supplementary Table S22).

Differentially expressed novel miRNAs in all categories of EBV-associated tumors

During DEmiR analysis, we identified novel miRNAs that were earlier not studied for carcinomas and were found common in “EBV tumors vs EBV-Uninfected Human Tissues,” “EBV tumors + EBV-Infected Cell lines vs EBV-Uninfected Human Tissues,” and “EBV-infected Cell lines vs EBV-Uninfected Human Tissues” samples. Based on the average log2FC for three samples, we found 554 DEmiRs that include six upregulated and 548 downregulated human miRNAs. List of significantly enriched novel miRNAs is given in Table 3. The complete list of novel miRNAs is given in Supplementary Table S23.

Table 3.

Significantly Enriched Novel miRNAs Based on the Average log2FC Value

MiRNA	Average log2FC	Expression
Upregulated
hsa-mir-142-5p	4.62	Upregulated
hsa-mir-3651	4.04	Upregulated
hsa-mir-155-5p	2.91	Upregulated
hsa-mir-182-5p	2.89	Upregulated
hsa-mir-148a-5p	2.53	Upregulated
hsa-mir-29c-3p	2.48	Upregulated
Downregulated
hsa-mir-4793-5p	30.00	Downregulated
hsa-mir-6756-5p	30.00	Downregulated
hsa-mir-6775-5p	30.00	Downregulated
hsa-mir-6798-5p	30.00	Downregulated
hsa-mir-8069	29.99	Downregulated
hsa-mir-8089	29.82	Downregulated
hsa-mir-1324	29.76	Downregulated
hsa-mir-8060	29.75	Downregulated
hsa-mir-619-3p	29.70	Downregulated
hsa-mir-6817-5p	29.65	Downregulated

Identification of DEMs

A total of 12377 DEMs were identified, which includes 2966 upregulated and 9411 downregulated mRNAs. C6orf15, SPANXB1, and CLDN6 are highly enriched DEMs. RNY3P1, PF4, and RNY3 are top three downregulated DEMs. The list of top five upregulated and downregulated mRNAs is given in Table 4. The complete list of DEMs is given in Supplementary Table S24.

Table 4.

Top Five Differentially Expressed mRNAs in Epstein-Barr Virus-Infected Cell Lines Versus Control Group

Ensembl ID	Gene name	log2FC	IfcSE	padj
Upregulated (padj ≤0.01, FC ≥1.5)
ENSG00000204542	C6orf15	26.31	1.95	0.000000
ENSG00000227234	SPANXB1	26.13	1.91	0.000000
ENSG00000184697	CLDN6	12.24	1.36	0.000000
ENSG00000155495	MAGEC1	10.03	1.69	0.000000
ENSG00000106541	AGR2	9.58	1.7	0.000000
Downregulated (padj ≤0.01, FC ≤ −1.5)
ENSG00000201955	RNY3P1	−30	2.35	0.000000
ENSG00000163737	PF4	−30	2.4	0.000000
ENSG00000202354	RNY3	−30	2.58	0.000000
ENSG00000207061	Y_RNA	−30	2.83	0.000000
ENSG00000207387	Y_RNA	−30	2.98	0.000000

Target gene prediction for DEmiRs and their intersection with DEMs

Target genes of 554 DEmiRs were predicted using miRTarBase and miRDB databases. We found 12,017 target genes in both the database for 547 downregulated miRNAs as listed in Supplementary Table S25. In DEMs, we have 2966 upregulated mRNAs. We compared 12017 target genes with 2966 upregulated DEMs and found 1572 common targets found in 527 miRNAs, as shown in venn diagram (Supplementary Fig. S4). The list of 1572 common mRNAs is provided in Supplementary Table S26.

Identification of essential and nonessential genes in common targets

We also identified essential and nonessential genes from 1572 common targets using DEG (http://origin.tubic.org/deg/public/index.php/) database (Luo et al, 2021a; Zhang et al, 2004). We found 870 genes essential for humans and the remaining 702 genes are nonessential for humans, but essential for the EBV infection, as given in Supplementary Table S27.

Identification of TFs for DEmiRs

The top 10 TFs of 527 downregulated DEmiRs of nonessential genes are FOXA1, RREB1, ZFP161, NKX6-1, NFIC, MEF2A, POU2F1, SP4, SP1, and EGR1 (Supplementary Fig. S5). The complete list of TFs is given in Supplementary Table S28.

Function and pathway enrichment analysis of common targets between DEmiRs and DEMs

GO annotations were performed to understand the biological functions of 702 nonessential genes. Using p-value cutoff <0.05, we found these targets were significantly enriched in cell adhesion, response to chemical, response to organic substance, and tissue development in BP group. In the CC group, targets were highly enriched in groups like extracellular region, extracellular space, cell periphery, and plasma membrane. In the MF group, targets were mostly enriched in molecular function regulator activity, signaling receptor binding, and signaling receptor and activator activity (Fig. 4a–c). In the KEGG analysis, the targets were significantly enriched in pathways like cytokine-cytokine receptor interaction, human cytomegalovirus infection, and lipid and atherosclerosis, among others, as shown in Figure 4d.

FIG. 4.

Top 10 GO terms of common mRNAs in (a) BP, (b) CC, (c) MF, and (d) Top 10 KEGG enriched pathways of 702 nonessential genes. BP, biological process; CC, cellular component GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function.

PPI network and identification of hub DEMs

The STRING database was used to find the interactions of nonessential genes between the genes and filter out the noninteracting genes. The processed data from STRING can be visualized in the Cytoscape software. The PPI network contains 589 nodes and 1684 edges. For further analysis in the cytoscape, hub DEMs were identified from the PPI network of nonessential genes that are important in the EBV infection. Using the Maximal Clique Centrality algorithm in the cytoHubba plugin, top 50 DEMs with the maximum value were considered hub DEMs (Chin et al, 2014) (Supplementary Table S29).

Identification of hub DEmiRs

Hub DEmiRs were identified based on the maximum number of targets shared by a miRNA. We considered miRNAs with more than 25 targets as hub miRNAs. The grid network of hub miRNAs with their targets is given in Figure 5. Yellow color represents miRNAs; pink color represents a target shared by one miRNA; green color targets shared by two miRNAs; orange color targets have three miRNAs; and blue color targets shared with four miRNAs, whereas purple color targets share five or more miRNAs.

FIG. 5.

(a) Construction of hub DEmiR network in cytoscape. The network is developed between the miRNAs and their targets. Yellow color represents miRNAs; pink color represents a target shared by one miRNA; green color targets shared by two miRNAs; orange color targets have three miRNAs; and blue color targets shared with four miRNAs, whereas purple color targets share five or more miRNAs. (b) Zoomed image of the network (a). DEmiR, differentially expressed miRNA.

Identification of repurposed drugs targeting nonessential genes

Repurposed drugs were identified for 702 nonessential genes of humans, but essential in the EBV infections using the DrugBank database. We found 639 unique repurposed drugs for 152 targets. In case of hub DEMs, repurposed drugs were found for eight genes. The complete list of repurposed drugs is provided in Supplementary Table S30.

Identification of nonessential genes in different stages of latent infection

Nonessential genes were also identified in different latent infection types I, II, and III (Kang and Kieff, 2015; Young et al, 2016). For type I, we have 12 samples from SNU-719, Mutu, Akata, Sav, and Kem. For type II, we have 20 samples from HL, MTX, and NPC, while type III has 24 samples from IBL_LCLd3, IK140508, Raji, Akata_LCLd3, LCL, RN, IM-1, ARL, ELD1, and PAL. We identified 665 nonessential genes of humans from latent infection type I, 527 from latent infection type II, and 483 from latent infection type III, as given in Supplementary Table S31, while there are 305 overlapping nonessential genes in all three latent infection types (Supplementary Table S31).

Identification of repurposed drugs targeting nonessential human genes in different stages of latent infection of EBV

Repurposed drugs were identified for 305 overlapping genes that are essential in EBV infection in all three latent types. We found 401 unique repurposed drugs for 75 targets. The complete list of repurposed drugs is provided in Supplementary Table S32, whereas identification of repurposed drugs targeting hub DEMs provided a list of 639 unique repurposed drugs for 152 targets in which DEMs were identified using EBV-infected samples against the healthy controls (Supplementary Table S30). On comparing both the approaches, we found 71 genes were common and targeted by 397 unique repurposed drugs. The complete list of repurposed drugs is given in Supplementary Table S33, while representative repurposed drugs are given in Table 5.

Table 5.

List of Predicted Drugs as Potential Candidates for Repurposing Against Epstein-Barr Virus-Related Tumors Targeting Latent Infection Types I, II, and III

Drugbank Id	Drug name	Drug group
DB11638	Artenimol	Approved, experimental, investigational
DB05254	Fibrinolysin	Investigational
DB08896	Regorafenib	Approved
DB09488	Acrivastine	Approved
DB00210	Adapalene	Approved
DB06766	Alcaftadine	Approved
DB09302	Alirocumab	Approved
DB00009	Alteplase	Approved, investigational
DB01016	Glyburide	Approved
DB01235	Levodopa	Approved
DB00737	Meclizine	Approved
DB00902	Methdilazine	Approved
DB06148	Mianserin	Approved, investigational
DB00491	Miglitol	Approved
DB00370	Mirtazapine	Approved
DB14011	Nabiximols	Investigational
DB00731	Nateglinide	Approved, investigational
DB00768	Olopatadine	Approved
DB01173	Orphenadrine	Approved
DB09330	Osimertinib	Approved
DB01267	Paliperidone	Approved
DB00213	Pantoprazole	Approved
DB01619	Phenindamine	Approved
DB01620	Pheniramine	Approved
DB05316	Pimavanserin	Approved, investigational
DB08860	Pitavastatin	Approved
DB06153	Pizotifen	Approved
DB00457	Prazosin	Approved
DB00777	Propiomazine	Approved
DB01224	Quetiapine	Approved
DB00912	Repaglinide	Approved, investigational
DB00015	Reteplase	Approved, investigational
DB08864	Rilpivirine	Approved
DB00740	Riluzole	Approved, investigational
DB14840	Ripretinib	Approved
DB06228	Rivaroxaban	Approved
DB11614	Rupatadine	Approved
DB09118	Stiripentol	Approved
DB09389	Norgestrel	Approved
DB09079	Nintedanib	Approved

Discussion

EBV is associated with a wide range of infections in the lymphoid and epithelial regions like BL, NPC, and GC, among others. miRNA expression has been evaluated in different tumors or cell lines related to EBV infection in previous studies (Marquitz et al, 2014; Sakamoto et al, 2017). In this study, a comprehensive integrative analysis approach was used to identify DEmiRs in EBV carcinomas and cell lines. These DEmiRs are critical for the virus and can play a vital role in the EBV infection. The analysis was performed among EBV tumors, and EBV-infected cell lines against the EBV-Uninfected Human Tissues.

In the course of the miRNA-Seq analysis, we found six distinctive human miRNAs significantly enriched in EBV tumors and the EBV-infected cell lines against the EBV-Uninfected Human Tissues. Out of six miRNAs, four miRNAs, namely, hsa-mir-142-5p, hsa-mir-155-5p, hsa-mir-148a-5p, and hsa-mir-182-5p, have previously been reported in EBV-related disorders like GC (Shi et al, 2020a; Zhang et al, 2011), plasmablastic lymphoma (Ambrosio et al, 2017), and NPC (Shi et al, 2020b).

However, the remaining two miRNAs reported in our study, that is, hsa-mir-29c-3p and hsa-mir-3651, are novel miRNAs since these were not reported earlier for EBV tumors. Likewise, from 548 downregulated human miRNAs, we have found only a few miRNAs, that is, hsa-mir-675-5p (Li et al, 2022), hsa-mir-671-5p (Borze et al, 2011), hsa-mir-608 (Qiu et al, 2015), hsa-mir-423-5p (Te et al, 2010), hsa-mir-372-5p (Chen et al, 2021), hsa-mir-221-3p (Ayoubian et al, 2019), hsa-mir-185-5p (Deng et al, 2016), and hsa-mir-199a-5p (Fink et al, 2014) reported earlier for EBV infection, mainly NPC, while the remaining human miRNAs (540 miRNAs) do not have any report for EBV tumors and hence may be considered novel.

The combination of TFs and miRNAs helps in implementing the appropriate biological events and developmental processes (Martinez and Walhout, 2009; Samad et al, 2017). We identified TFs for 527 downregulated DEmiRs of nonessential genes (Supplementary Table S28). From the top 10 TFs, we found some TFs causing EBV infection in the lytic cycle in different studies, for example, FOXA1 found in the ‘EBV infection’ molecular pathway (Hossain et al, 2022), NFIC (Wang et al, 2020c) affecting the viral replication, MEF2A (Jiang et al, 2014) coinciding EBV-nuclear antigen 3C, SP1 (Tsai et al, 2011; Wu et al, 2019) resulting in EBV reactivation, and EGR1, also in EBV lytic reactivation (Gorres et al, 2016; Ye et al, 2010), while the remaining TFs were not reported during EBV infection.

Likewise, in the GO analysis, we found some essential processes that play a crucial role in the EBV infection like cell adhesion (Gregory et al, 1988; Liu et al, 2005; Mo et al, 2018); tissue development (Luther et al, 2002); intermediate filament (Sairenji et al, 1987); cell surface (Johnson et al, 1981; Wroblewski et al, 2002); plasma membrane (Arancia et al, 1986; Vazirabadi et al, 2003); and G protein-coupled receptor (Tsutsumi et al, 2021), among others. Also, pathways like “human cytomegalovirus infection” are associated with EBV in ulcerative colitis (Ciccocioppo et al, 2021); apical periodontitis lesions (Jakovljevic et al, 2015); and “atherosclerosis” (Musiani et al, 1990); “miRNA in cancer” pathway (Ramayanti et al, 2019) was identified in the EBV infection.

From the PPI network of target genes, we identified the top 50 hub DEMs from the nonessential genes using the cytoHubba plugin. We screened out some nonessential hub genes earlier reported in the EBV tumors. They are CDC25C (Zhang et al, 2018); CCL2 (Indari et al, 2021); IL10 (Tang et al, 2021); IFITM1 (Hussein and Akula, 2017); ICAM1 (Busson et al, 1992); PLOD3 (Shire et al, 2021); CXCL10 (Maggio et al, 2002); and DLGAP5 (Xing et al, 2022), while the remaining DEMs were not reported earlier for EBV tumors. Furthermore, we also found FDA drugs for eight hub DEMs, that is, PLOD3, PKMYT1, P4HB, OAS1, MELK, IL10, CXCL10, and CCL2 (Supplementary Table S30). Therefore, these DEMs can also be used as therapeutic targets for EBV infection.

In our drug repurposing analysis, we found several molecules that were experimentally validated for EBV infection. They are Fluorouracil, Gemcitabine, Dasatinib, Zidovudine, Indomethacin, Mercaptopurine, and Vandetanib (Supplementary Table S33). Kim et al, (2015) found that ebv-miR-BART20-5p increased the chemoresistance to 5-fluorouracil. Dargart et al, (2012) reported that Dasatinib is an effective therapeutic molecule for the treatment of EBV-associated disorder. Kurokawa et al, (2005) reported that Zidovudine is a therapeutic target for treating BL. Likewise, Kim et al, (2017) found that Vandetanib can be developed as a novel therapeutic drug target to control EBV-infected retina neovascular disease.

Simultaneously, we identified a number of novel drugs that were not earlier reported for EBV infection in the literature (Table 5), for example, Glyburide, Levodopa, Nateglinide, and Stiripentol. These drugs were used in treating different diseases, like glyburide for the treatment of diabetes mellitus (Affres et al., 2021) and breast cancer (Bircsak et al., 2016); levodopa in HIV-mediated parkinsonism (Devine et al., 2018; Melis et al., 2021); nateglinide for type 2 diabetes mellitus (Juurinen et al., 2009; Naushad et al., 2022), and stiripentol for Dravet syndrome (Buck and Goodkin, 2019; Inoue and Ohtsuka, 2015). Moreover, Regorafenib, drug targeting two genes, was used for treating colorectal cancer (Grothey et al., 2013; Wang et al., 2020a). Furthermore, artenimol, drug targeting three genes, is used in the treatment of malaria (Pull et al., 2019).

Conclusions

We performed an integrative analysis to identify the differentially expressed host and viral miRNAs (DEmiRs) in six EBV tumors (ARL, ELD1, HL, MTX, PAL, and NPC) using an in-house pipeline. We identified DEmiRs among EBV tumors, cell lines, and uninfected human samples (hsa-mir-29c-3p, hsa-mir-3651, hsa-mir-98-3p, and hsa-mir-6775-5p). Downregulated DEmiR targets were extracted from miRTarBase and miRDB. In addition, we identified DEMs of EBV-infected cells in comparison to the control group. The upregulated DEMs of corresponding downregulated DEmiRs were categorized into essential and nonessential human genes (MELK, PBK, and CDCA2).

These upregulated nonessential human genes were used for subsequent GO and KEGG pathway analysis. We predicted various TFs such as RREB1, ZFP161, NKX6-1, POU2F1, and SP4 for downregulated DEmiRs. Furthermore, these overlapping DEMs were used to identify drug candidates for repurposing and targeting EBV latent infection types I, II, and III: Glyburide, Levodopa, Nateglinide, and Stiripentol, among others. Therefore, this is the first integrative analysis, to the best of our knowledge, in regard to the potential therapeutic targets and drug repurposing candidates against the EBV tumors. This study provides a better understanding of the role of miRNAs in EBV pathogenesis, as well as new insights that inform therapeutics innovation.

Authors' Contribution

M.K. conceived, designed, and supervised this study, data analysis, and interpretation and article writing. A.T. performed data collection, miRNAseq data analysis pipeline, data analysis, and interpretation and article writing.

Footnotes

Author Disclosure Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding Information

This work was supported by the grants from the CSIR-Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR) (STS038), and ICMR Fellowship (SRF) to Anamika Thakur (ICMR-ISRM/11(18)/2017).

Supplementary Material

Abbreviations Used

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