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- Springer Journals
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- Copyright © The Author(s) 2023
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- 10.1186/s13027-023-00492-0
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Abstract
Background Adult T‑ cell Lymphoma/Leukemia (ATLL) is characterized by the malignant proliferation of T‑ cells in Human T‑Lymphotropic Virus Type 1 and a high mortality rate. Considering the emerging roles of microRNAs (miR‑ NAs) in various malignancies, the analysis of high‑throughput miRNA data employing computational algorithms helps to identify potential biomarkers. Methods Weighted gene co‑ expression network analysis was utilized to analyze miRNA microarray data from ATLL and healthy uninfected samples. To identify miRNAs involved in the progression of ATLL, module preservation analysis was used. Subsequently, based on the target genes of the identified miRNAs, the STRING database was employed to construct protein–protein interaction networks (PPIN). Real‑time quantitative PCR was also performed to validate the expression of identified hub genes in the PPIN network. Results After constructing co‑ expression modules and then performing module preservation analysis, four out of 15 modules were determined as ATLL‑specific modules. Next, the hub miRNA including hsa‑miR‑18a‑3p, has‑miR‑187‑5p, hsa‑miR‑196a‑3p, and hsa‑miR‑346 were found as hub miRNAs. The protein–protein interaction networks were constructed for the target genes of each hub miRNA and hub genes were identified. Among them, UBB, RPS15A, and KMT2D were validated by Reverse‑transcriptase PCR in ATLL patients. Conclusion The results of the network analysis of miRNAs and their target genes revealed the major players in the pathogenesis of ATLL. Further studies are required to confirm the role of these molecular factors and to discover their potential benefits as treatment targets and diagnostic biomarkers. Keywords Adult T‑ cell lymphoma/leukemia, ATLL, HTLV‑1, WGCNA, Network analysis, Quantitative real‑time PCR *Correspondence: Mohadeseh Zarei Ghobadi mohadesehzaree@gmail.com Sayed‑Hamidreza Mozhgani hamidrezamozhgani@gmail.com Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Shayeghpour et al. Infectious Agents and Cancer (2023) 18:12 Page 2 of 11 Owing to the innovations in methods of extracting Background genome-wide assays for biological samples in recent Adult T-cell lymphoma/leukemia (ATLL) affects the years, it is now possible to investigate cellular physiol- mature CD and CD lymphocyte lineage and has one 4 25 ogy and the pathophysiology of diseases in a systematic of the poorest prognosis among hematologic malignan- manner. One strategy in biological system analysis is to cies [1]. The incidence of ATLL in the United States is organize and study genes based on their interactions [22]. about five individuals out of one million; however, it In this regard, several approaches may be utilized [23– varies in diverse regions, with the incidence increasing 27]. This study used weighted gene co-expression net - up to about 27 in 100,000 in endemic zones [2]. Accord- work analysis (WGCNA) for the purpose of classifying ing to the Shimoyama criteria, ATLL is divided into miRNAs into modules based on their correlations and acute, chronic, lymphomatous, and smoldering sub- then surveying the co-expressed miRNAs in each mod- types [3]. The disease primarily occurs in the fifth and ule. It helps to find major miRNAs with high connectiv - sixth decades of life [4] with poor response to conven- ity with others that probably have critical functions in the tional treatments [5]. Acute and lymphomatous types progression of the disease. can shorten median survival to 6.2 and 10.2 months, Considering ATLL’s rapid progression and the fact respectively [4]. that it is refractory to conventional chemotherapy [28], Human T-lymphotropic virus type 1 (HTLV-1) is a ret- there is a great need to have a better understanding of rovirus and the causative agent of ATLL and a progressive the pathogenesis by finding factors that have key roles in chronic neurologic disorder, HTLV-1-associated mye- the development of the disease. The present study inves - lopathy/tropical spastic paraparesis (HAM/TSP), which tigates the co-expressed dysregulated microRNAs (miR- results in progressive weakness of the lower extremities NAs) involved in the ATLL pathogenesis using weighted and significant morbidity [ 6–8]. There is a 4–7% risk of gene co-expression analysis. A number of target genes developing ATLL in HTLV-1-infected individuals [9]. of the affected miRNAs are also studied to delineate and Its genome contains tax and Hbz, two regulatory genes confirm the signaling pathways that may be implicated in [10, 11]. Hbz is encoded from the antisense strand of the the development and progression of ATLL. genome [12]. Tax and Hbz play key roles in the formation of persistent infection and initiation of T cell oncogen- esis. Tax interacts with several genes and signaling path- Methods ways of an infected host like nuclear factor-kB (NF-κB) Microarray dataset and data preprocessing pathway, Rat sarcoma virus (RAS) signaling pathway, and Under the accession number GSE31629 [29], microRNA the mammalian target of rapamycin (mTOR) pathway expression dataset of 62 samples including 40 ATLL [11]. Despite a large number of studies and promising patients and 20 healthy individuals was retrieved from discoveries on the topic, the exact molecular mechanisms the gene expression omnibus (GEO) database [30]. The responsible for HTLV-1-related tumorigenesis are not data derived from peripheral blood mononuclear cells well known [13]. Yet, there are several studies searching (PBMCs) and CD4+ T cells were normalized with quan- for anomalies in the area of epigenetics [14–16]. Micro- tile normalization. Missing values were handled using the RNAs (miRNAs) belong to a wide group of non-coding "goodSamplesGenes" function from the WGCNA pack- RNAs that mainly govern gene expression [17]. These age, version 1.71 [31, 32]. Subsequently, sample clustering small single-stranded RNAs also regulate a broad spec- was used to identify outliers. The summary of the meth - trum of biological processes through mRNA silencing odology is presented in Fig. 1. and degradation [18]. The upregulation of miRNAs in cancer cells may lead to carcinogenesis by interdicting Construction of co‑expression network and module tumor suppressor genes. These miRNAs are known as detection oncogenic miRNAs (oncomiRs) [19]. On the other hand, The weighted co-expression network was constructed there are miRNAs with tumor suppressor function that using the WGCNA package” version 1.71 in the R envi- can result in the prevention of cancer progression by ronment [33]. To do so, an adjacency matrix was built interfering with the expression of proto-oncogenes [20]. using the following formula: These miRNAs are identified as tumor suppressor miR - NAs [21]. So far, miRNAs have been mentioned to be β a = 0.5 + 0.5 ∗ cor X ,X ij i j dysregulated in HTLV-1 infected cells, most likely influ - encing important signaling pathways such as pathways in where β is the exponent and X and X are the expression i j cancer, WNT signaling pathway, MAPK signaling path- values of miRNAi and miRNAj, respectively. The soft- way, and other important pathways involved in carcino- thresholding approach was applied to obtain a scale-free genesis [10]. topology. Using the “picksoftthreshold” function of the Sha yeghpour et al. Infectious Agents and Cancer (2023) 18:12 Page 3 of 11 Protein–protein interactions (PPIs) and enrichment analysis The association between proteins was determined using the STRING database [39]. Due to the large size of PPINs, degree analysis was implemented to select genes with more connectivity which are also known as hub genes. The degree of a protein is its connection number to other proteins in the network. To survey the biologi- cal process of the identified target genes, the Enrichr webtool was utilized [40]. Sample preparation This study was conducted on 10 ATLL samples from patients who had been admitted to the Shariati Hos- pital, Tehran, Iran, and 10 normal samples from blood donors who were referred to the Blood Transfusion Organization of Alborz. Peripheral blood mononucle- Fig. 1 The flowchart of the methodology olar cells (PBMCs) were isolated from EDTA-treated blood samples using a Ficoll-Paque density gradi- ent (Cederline corporation, Canada). Total RNA was extracted from PBMCs with RNA Extraction RNJia WGCNA package, β = 4 was determined to be the opti- Kit-ROJE Technology and the cDNA was synthesized mal exponent value. using the RT-ROSET kit (ROJE, Iran). This study was In the next step, the topological overlap meas- approved by the Ethical Committee of Biomedical ure (TOM) was determined as the measure of miR- Research at Alborz University of Medical Sciences (IR. NAs connectivity in the network. Eventually, the ABZUMS.REC.1399.342). co-expressed gene groups (modules) were detected by hierarchical clustering with parameters of minModule- Size = 15, and a threshold of 0.25 was chosen to merge Quantitative real‑time PCR the close modules. To confirm the HTLV-1 infection in the ATLL patients, the LTR and HBZ genes’ expressions were evaluated by PCR and subsequent agarose gel electrophoresis [41, 42]. Identification of non‑preserved modules Quantitative Real-Time PCR was performed on cDNAs The non-preserved modules of ATLL in the healthy sam - utilizing specific primers and SYBR Green-based RT- ples were determined through module preservation anal- qPCR (TaKaRa, Otsu, Japan). To measure the expres- ysis. For this purpose, the “modulePreservation” function sion rate of UBB, RPS15A, and KMT2D genes in ATLL [34] and permutation-based statistics were applied and normal groups, the PCR was done on a Rotor-Gene to measure medianRank and Z scores [35, 36]. summary Q-6000 machine (Qiagen, Germany) following the manu- Z is the average of Z-scores calculated for connec- summary facturer’s instructions. According to five 5-point stand - tivity and density measures [37]. Herein, a module with ard curves, that had been already prepared for target and Z < 2 and medianRank > 12 was considered a non- summary reference genes, the gene expression of UBB, RPS15A, preserved module. KMT2D, and RPLP0 (reference gene) were analyzed. The normalized gene expression was calculated as follows: Normalized index = copy number of the gene of interest/ Finding hub miRNAs and their target genes copy number of the reference gene. To find the hub miRNA in each non-specific mod - ule, the function of “chooseTopHubInEachModule” in the WGCNA package was utilized. In order to find the Statistical analysis experimentally validated genes for the mentioned hub The Mann–Whitney U Test was used to compare gene miRNAs, the miRTarBase database was explored. miR- expression levels between the normal and ATLL groups TarBase is a collection of nearly 20 million experimen- using GraphPad Prism software v8 (GraphPad Soft- tally validated miRNA–target interactions (MTIs) among ware, Inc., San Diego, CA, USA). The data is displayed 4630 miRNAs and 27,172 mRNAs [38]. as mean ± standard error of the mean (SEM). The Shayeghpour et al. Infectious Agents and Cancer (2023) 18:12 Page 4 of 11 Finding hub miRNA and their target genes results were considered statistically significant if the As the result, “chooseTopHubInEachModule” func- P-value < 0.05. tion identified hsa-miR-346, hsa-miR-187-5p, hsa-miR- 196a-3p, and hsa-miR-18a-3p as hubs in the green, Results salmon, cyan, and brown modules, respectively. Target Construction of weighted gene co‑expression network genes of each miRNA were obtained from miRTarBase To identify the co-expressed miRNAs, the calculation for further analysis. The target genes are mentioned in of adjacency and TOM matrices, construction of hier- Additional file 2 . archical clustering, and finally merging of close clusters were performed. As a result, 15 modules were identified. Figure 2 displays the cluster dendrogram and modules before and after merging the clusters. Each unique color PPINs and enrichment analysis specifies an inimitable module. A list of co-expressed The PPINs between the proteins were constructed by miRNAs in each module and their connectivity (degree) submitting the target genes of hub miRNAs belong- scores are mentioned in Additional file 1. ing to the specific modules in the STRING database (Fig. 4). Further network analysis revealed the degree Determination of specific modules for the ATLL samples of each protein. The protein with a higher degree is To detect the specific non-preserved modules of ATLL demonstrated as red. Based on this analysis, UBB and samples, the medianRank and Z scores were cal- RPS2 (hsa-miR-18a-3p); RPL13A and RPS15A (hsa- summary culated for each module. The Z < 2 and medi- miR-196a-3p); BCL6, TERT, KMT2D (hsa-miR-346); summary anRank > 12 were considered as criteria to find the CDKN1B and AGO2 (hsa-miR-187-5p) were identified non-preserved modules. Among 15 identified modules, as hub proteins (Table 1). Subsequently, biological pro- four modules including green, salmon, cyan, and brown cess enrichment was performed to find the function of modules were identified as specific modules for ATLL each hub genes. The enrichment results are presented (Fig. 3). in Fig. 5. Fig. 2 Dendrogram of clustered genes based on (1‑ TOM). The colors reveal the cluster (module) membership acquired before and after merging the modules. A total of 14 modules were identified after merging Sha yeghpour et al. Infectious Agents and Cancer (2023) 18:12 Page 5 of 11 Fig. 3 The medianRank (plot on the left) and Z (plot on the right) against module size specifies the non‑preserved modules (Z < 2 and summary summary medianRank > 12) Lower expression of UBB, RPS14A and KMT2D in ATLL reasonable to expect that the specific co-expression pat - patients compared to the normal control terns (or modules) for patient samples are likely associ- The remarkable association between the highlighted ated with the underlying pathophysiological pathways miRNAs and pathways related to malignancies, immu- and processes. As a corollary, module preservation analy- nity, and viral infections led us to examine the gene sis can be utilized to identify condition-specific or non- expression of some of the hub genes that their dysregu- preserved modules. lation in ATLL patients was never reported before. The In the present study, a number of hitherto unexplored results demonstrated significant downregulation of all miRNAs and genes were identified as possibly implicated three genes (P-value < 0.0001) in ATLL patients versus in ATLL pathogenesis. All genes are miRNAs driver healthy controls (Fig. 6). genes that have been found through exploring miRTar- UBB’s expression rate in the ATLL group Base database. Module preservation analysis disclosed (0.4484 ± 0.06539) was lower than in the normal group 4 specific modules for ATLL. The hub miRNAs in these (1.952 ± 0.1515) and the difference was statistically signif - modules have a substantial correlation with numerous icant with P-value < 0.0001. A significantly lower expres - cancer pathways in an enrichment assessment of their sion of RPS14A in the ATLL patients (0.5884 ± 0.02465) target genes. Subsequent in vitro analysis of the identified compared to the normal group (3.260 ± 0.1204) was also miRNAs’ target genes demonstrated decreased expres- observed (P-value < 0.0001). The expression of KMT2D in sion of KMT2D and its potentially important role in the ATLL cases was 0.5740 ± 0.05896 and in the normal ATLL pathogenesis. control was 4.204 ± 0.1548, which shows a statistically It is believed that miR-346 may be involved in both significant lower expression in the ATLL patients than in inflammatory and metabolic processes [43]; however, the normal cases (P-value < 0.0001). additional research has shown that miR-346 may also have a role in the development of malignancies of the Discussion breast, cervix, and lung, as well as follicular thyroid, Weighted gene co-expression analysis is a robust method cutaneous squamous cell, and nasopharyngeal carcino- for identifying expression patterns of diseases in a vari- mas [43–49]. The enrichment analysis of its target genes ety of tissues across different pathological states. It is also indicated that miR-346 is deeply associated with Shayeghpour et al. Infectious Agents and Cancer (2023) 18:12 Page 6 of 11 Fig. 4 The protein–protein networks among the target genes of a hsa‑miR‑18a‑3p, b hsa‑miR‑196a‑3p, c hsa‑miR‑346 and d hsa‑miR‑187‑5p. The color and the size of nodes is related to their degree (red color and bigger size indicate higher values of degree) Table 1 List of hub miRNAs and target genes involved in ATLL a complex of proteins associated with Set1 (COMPASS), progression which enables gene transcription through H3K4 meth- ylation [50]. KMT2D plays contradictory roles in dif- Hub miRNAs Hub target genes ferent types of malignancies, some studies stated it has hsa‑miR‑18a‑3p UBB and RPS2 pro-tumorigenic functions [51], whereas others reported hsa‑miR‑196a‑3p RPL13A and RPS15A tumor-suppressive characteristics [50–53]. As a tumor hsa‑miR‑346 BCL6, TERT, KMT2D suppressor gene, KMT2D is known to activate Bcl6 and hsa‑miR‑187‑5p CDKN1B and AGO2 Sirt1 in addition to Per2, resulting in inhibitory effects on Notch and K-Ras pathways [51]. It is good to notice that Bcl6 is also a miR-346 target hub gene. Loss of KMT2D was shown to assist cell growth related to EZH1/2 in pre- pathways related to viral infections and malignancies; tumorigenic EBV + B cell lymphoblastoid cells [54]. Our therefore, we further studied its target hub gene, KMT2D. results show significant downregulation of KMT2D in Lysine Methyltransferase 2D, or KMT2D, is a member of Sha yeghpour et al. Infectious Agents and Cancer (2023) 18:12 Page 7 of 11 Fig. 5 Significant KEGG pathways enrichment results of A hsa‑miR‑18a‑3p, B hsa‑miR‑346, C hsa‑miR‑187‑5p and D hsa‑miR‑196‑3p target genes. All of the pathways are statically significant (P value < 0.05) and are sorted based on the combined scores provided by Enrichr Fig. 6 Quantitative results of RT‑PCR show significant downregulation (P‑ value < 0.0001) of A UBB, B RPS15A and C KMT2D Shayeghpour et al. Infectious Agents and Cancer (2023) 18:12 Page 8 of 11 ATLL patients versus healthy controls. Put it all together, breast cancer, leukemia, esophageal adenocarcinoma, these shreds of evidence are highly suggestive of its and colorectal cancer [83]. Ribosomal protein S15A involvement in the pathogenesis of the disease, but con- (RPS15A) has been highlighted as a candidate partici- sidering its antithetical effects on cancers, more studies pating in ATLL tumorigenesis. It belongs to the 40S are needed to clarify the exact influence of KMT2D on subunit of ribosome [84]. RPS15A was reported to be ATLL. potentially involved in hepatocellular carcinoma, pro- In recent years miR-187 has attracted the attention of gression of breast cancer, lung adenocarcinoma, pros- more researchers due to discoveries about its roles in tate cancer, glioblastoma, colorectal cancer, and acute various cancer types. miR-187 is likely to be involved in myeloid leukemia as an oncogene [84–87]. RPS15A was many cancer-related processes and characteristics such also shown to be downregulated in ATLL patients in as sensitivity to drugs, proliferation, apoptosis, inva- the PCR study, which rejects our hypothesis. sion, and migration through its regulatory effects on its Our study have some limitations. We did not find a targets, namely FOXA2, CRMP1, MAD2L2, STOML2, large miRNA dataset in databases. However, it is better BCL6, PTRF, CYP1B1, FGF9, MAPK12, MAPK7, Bcl- that the mentioned analysis perform in a larger dataset 2, IGF-1R [55]. Its dysregulation has been observed in if in it provides in future. The genes and also miRNAs many malignancies like osteosarcoma, male genitouri- should be validated in a large cohort. nary tumors, prostate cancer, bladder cancer, clear cell renal cell carcinoma, colorectal cancer, hepatocellular carcinoma, oral squamous cell carcinoma, cervical can- Conclusion cer, ovarian cancer, breast cancer, lymphoblastic leu- The weighted miRNA-co-expression analysis, network kemia, and diffuse large b-cell lymphoma [55– 73]. To analysis of the target genes, enrichment analysis, and our knowledge, the relationship between Adult T-Cell comprehensive literature review highlighted hsa-miR- Leukemia/Lymphoma and miR-187 was never reported 18a, has-miR-187, hsa-miR-196a-3p, and hsa-miR-346 before. Our high-throughput analysis of miRNAs pro- as four new miRNAs that are strongly suspected to be poses the involvement of miR-187 in the pathogenesis involved in the pathogenesis of ATLL. Further investi- of ATLL. Considering the notable effects of miR-187 gation is needed to assess the utility of these findings on a high variety of malignancies, it seems necessary to and their values as reliable biomarkers and/or therapeu- investigate the miR-187 actions in ATLL patients.miR- tic targets. The expression levels of the three hub genes 18a-3p was highlighted as a tumor-suppressive agent of the identified miRNAs target genes, namely, UBB, through its inhibitory effect on K-Ras [74]. It was also RPS15A, and KMT2D also imply the significant role of reported that miR-18a-3p has a regulatory effect on the miRNAs in maintaining the dysregulated pattern of glycolysis/gluconeogenesis and focal adhesion in the gene expression in ATLL. Likely, the downregulation of uterus, lung, liver, and kidney malignancies [75]. There KMT2D plays an important role in ATLL carcinogenesis, are studies presenting pieces of evidence that show but its controversial effects on different cancer types pre - miR-18a-3p is related to nasopharyngeal and hepato- clude a definite conclusion regarding its role. cellular carcinoma, breast cancer, and glioma [76–79]. Accordingly, to do an additional survey, Ubiquitin B Supplementary Information (UBB) was selected as it was one of the hub genes in The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s13027‑ 023‑ 00492‑0. PPIN which is also known to play key roles in basic cel- lular functions. UBB is one of four Ubiquitin encoding Additional file 1. List of co ‑ expressed miRNAs and their connectivity genes that is involved in the dysregulation of funda- scores in each module. mental processes like cellular proliferation, apoptosis, Additional file 2. The target genes of non‑preserved modules. and responses to DNA damage [80]. Several relation- ships between Ubiquitin and various types of cancers Acknowledgements have been reported such as gynecological cancer, hepa- Not applicable. tocellular carcinoma, colorectal cancer, neuroblas- Author contributions toma, Esophageal Squamous Cell Carcinoma, Pediatric MZG, AS, MF, SS and ZS conceptualized the work and contributed to drafting Medulloblastoma, seminoma, and lung cancer [80–82]. the manuscript. AH, PH, SK, MH and SA contributed to drafting and revising According to the downregulation of UBB in the RT- the manuscript. MZG performed the bioinformatic analysis. MZG and S‑HM supervised the project and performed the analyses. All authors read and PCR study, the possibility of its pathogenic actions in approved the manuscript. ATLL can be ruled out.miR-196a was shown to play roles in inflammation, embryonic development, and Funding None. various cancers such as pancreatic adenocarcinoma, Sha yeghpour et al. Infectious Agents and Cancer (2023) 18:12 Page 9 of 11 Availability of data and materials 14. Nebbioso A, Tambaro FP, Dell’Aversana C, Altucci L. Cancer epigenetics: The datasets used and/or analyzed during the current study are available from moving forward. PLoS Genet. 2018;14(6): e1007362. the corresponding author on reasonable request. 15. Recillas‑ Targa F. Cancer epigenetics: an overview. Arch Med Res. 2022;53(8):732–40. 16. Dawson MA, Kouzarides T. Cancer epigenetics: from mechanism to Declarations therapy. Cell. 2012;150(1):12–27. 17. Chen L, Heikkinen L, Wang C, Yang Y, Sun H, Wong G. Trends in Ethics approval and consent to participate the development of miRNA bioinformatics tools. Brief Bioinform. All methods were performed in accordance with the Declaration of Helsinki. 2019;20(5):1836–52. Informed consent for study participation was obtained from all subjects. This 18. Bavelloni A, Ramazzotti G, Poli A, Piazzi M, Focaccia E, Blalock W, et al. study was approved by the ethics committee of Alborz University of medical MiRNA‑210: a current overview. Anticancer Res. 2017;37(12):6511–21. sciences, Alborz, Karaj, Iran. (IR.ABZUMS.REC.1399.342). 19. Segal M, Slack FJ. Challenges identifying efficacious miRNA therapeutics for cancer. Expert Opin Drug Discov. 2020;15(9):987–92. Competing interests 20. Ghobadi MZ, Emamzadeh R, Mozhgani S‑H. Deciphering microRNA‑ The authors declare that they have no competing interests. mRNA regulatory network in adult T‑ cell leukemia/lymphoma; the battle between oncogenes and anti‑ oncogenes. PLoS ONE. 2021;16(2): Author details e0247713. School of Medicine, Alborz University of Medical Sciences, Karaj, Iran. 21. Otmani K, Lewalle P. Tumor suppressor miRNA in cancer cells and the Department of Computer, Faculty of Engineering, Raja University, Qazvin, tumor microenvironment: mechanism of deregulation and clinical impli‑ Iran. Department of Virology, School of Public Health, Tehran University cations. Front Oncol. 2021;11: 708765. of Medical Sciences, Tehran, Iran. Research Center for Clinical Virology, Tehran 22. Ideker T, Krogan NJ. Differential network biology. Mol Syst Biol. 2012;8:565. University of Medical Sciences, Tehran, Iran. Department of Microbiology, 23. Ha J, Park S. NCMD: Node2vec‑based neural collaborative filtering for School of Medicine, Alborz University of Medical Sciences, Karaj, Iran. Depar t‑ predicting miRNA‑ disease association. IEEE/ACM Trans Comput Biol ment of Infectious Diseases, School of Medicine, Alborz University of Medical Bioinform. 2022. 7 8 Sciences, Karaj, Iran. Independent Researcher, Tehran, Iran. Non‑ Communica‑ 24. Ha J. MDMF: predicting miRNA‑ disease association based on matrix ble Disease Research Center, Alborz University of Medical Sciences, Karaj, Iran. factorization with disease similarity constraint. J Pers Med. 2022;12(6):885. 25. Ha J, Park C, Park C, Park S. IMIPMF: Inferring miRNA‑ disease interactions Received: 11 August 2022 Accepted: 17 February 2023 using probabilistic matrix factorization. J Biomed Inform. 2020;102: 26. Ha J, Park C, Park S. PMAMCA: prediction of microRNA‑ disease association utilizing a matrix completion approach. BMC Syst Biol. 2019;13(1):33. 27. Ha J, Park C. MLMD: metric learning for predicting miRNA‑ disease associa‑ References tions. IEEE Access. 2021;9:78847–58. 1. Rodríguez‑Zúñiga MJM, Cortez‑Franco F, Qujiano ‑ Gomero E. Adult T‑ cell 28. Takeuchi M, Miyoshi H, Ohshima K. Tumor microenvironment of leukemia/lymphoma. Rev Lit Actas dermosifiliogr. 2018;109(5):399–407. adult T‑ cell leukemia/lymphoma. J Clin Exp Hematopathol JCEH. 2. Mehta‑Shah N, Ratner L, Horwitz SM. Adult T ‑ cell leukemia/lymphoma. J 2021;61(4):202–9. Oncol Pract. 2017;13(8):487–92. 29. Yamagishi M, Nakano K, Miyake A, Yamochi T, Kagami Y, Tsutsumi A, 3. Ghobadi MZ, Emamzadeh R, Afsaneh E. Exploration of mRNAs and miRNA et al. Polycomb‑mediated loss of miR‑31 activates NIK ‑ dependent classifiers for various ATLL cancer subtypes using machine learning. BMC NF‑κB pathway in adult T cell leukemia and other cancers. Cancer Cell. Cancer. 2022;22(1):433. 2012;21(1):121–35. 4. Shimoyama M. Diagnostic criteria and classification of clinical subtypes 30. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. of adult T‑ cell leukaemia‑lymphoma. A report from the lymphoma study NCBI GEO: archive for functional genomics data sets–update. Nucleic group (1984–87). Br J Haematol. 1991;79(3):428–37. Acids Res. 2013;41(Database issue):D991–5. 5. Malpica L, Pimentel A, Reis IM, Gotuzzo E, Lekakis L, Komanduri K, et al. 31. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation Epidemiology, clinical features, and outcome of HTLV‑1‑related ATLL in an network analysis. BMC Bioinform. 2008;9:559. area of prevalence in the United States. Blood Adv. 2018;2(6):607–20. 32. Langfelder P, Horvath S. Fast R functions for robust correlations and 6. Giam CZ, Semmes OJ. HTLV‑1 infection and adult T ‑ cell leukemia/lym‑ hierarchical clustering. J Stat Softw. 2012;46(11):i11. phoma‑A tale of two proteins: tax and HBZ. Viruses. 2016;8(6):161. 33. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation 7. Furukawa Y, Yamashita M, Usuku K, Izumo S, Nakagawa M, Osame M. network analysis. BMC Bioinform. 2008;9(1):1–13. Phylogenetic subgroups of human T cell lymphotropic virus (HTLV ) 34. Langfelder P, Luo R, Oldham MC, Horvath S. Is my network module pre‑ type I in the tax gene and their association with different risks for served and reproducible? PLoS Comput Biol. 2011;7(1): e1001057. HTLV‑I‑associated myelopathy/tropical spastic paraparesis. J Infect Dis. 35. Zarei Ghobadi M, Emamzadeh R, Teymoori‑Rad M, Mozhgani SH. Decod‑ 2000;182(5):1343–9. ing pathogenesis factors involved in the progression of ATLL or HAM/ 8. Zarei Ghobadi M, Emamzadeh R. Integration of gene co‑ expression TSP after infection by HTLV‑1 through a systems virology study. Virol J. analysis and multi‑ class SVM specifies the functional players involved 2021;18(1):175. in determining the fate of HTLV‑1 infection toward the development of 36. Ghobadi MZ, Emamzadeh R, Teymoori‑Rad M, Afsaneh E. Exploration of cancer (ATLL) or neurological disorder (HAM/TSP). PLoS ONE. 2022;17(1): blood− derived coding and non‑ coding RNA diagnostic immunological e0262739. panels for COVID‑19 through a co ‑ expressed‑based machine learning 9. Satake M, Yamada Y, Atogami S, Yamaguchi K. The incidence of adult procedure. Front Immunol. 2022;13:1001070. T‑ cell leukemia/lymphoma among human T‑lymphotropic virus type 1 37. Zarei Ghobadi M, Mozhgani SH, Erfani Y. Identification of dysregulated carriers in Japan. Leuk Lymphoma. 2015;56(6):1806–12. pathways underlying HTLV‑1‑associated myelopathy/tropical spastic 10. Machado CB, da Cunha LS, Maués J, Pessoa F, de Oliveira MB, Ribeiro RM, paraparesis through co‑ expression network analysis. J Neurovirol. et al. Role of miRNAs in human T cell leukemia virus type 1 induced T cell 2021;27(6):820–30. leukemia: a literature review and bioinformatics approach. Int J Mol Sci. 38. Huang HY, Lin YC, Cui S, Huang Y, Tang Y, Xu J, et al. miRTarBase update 2022;23(10):5486. 2022: an informative resource for experimentally validated miRNA‑target 11. Ernzen KJ, Panfil AR. Regulation of HTLV ‑1 transformation. Biosci Rep. interactions. Nucleic Acids Res. 2022;50(D1):D222–30. 2022;42(3):BSR20211921. 39. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The 12. Toyoda K, Matsuoka M. Functional and pathogenic roles of retroviral STRING database in 2021: customizable protein‑protein networks, and antisense transcripts. Front Immunol. 2022;13: 875211. functional characterization of user‑uploaded gene/measurement sets. 13. Kogure Y, Kataoka K. Genetic alterations in adult T‑ cell leukemia/lym‑ Nucleic Acids Res. 2021;49(D1):D605–12. phoma. Cancer Sci. 2017;108(9):1719–25. Shayeghpour et al. Infectious Agents and Cancer (2023) 18:12 Page 10 of 11 40. Xie Z, Bailey A, Kuleshov MV, Clarke DJB, Evangelista JE, Jenkins SL, et al. survival, inhibits cell growth and migration though targeting B7–H3. Gene set knowledge discovery with enrichr. Curr Protoc. 2021;1(3): e90. Biochem Biophys Res Commun. 2013;438(2):439–44. 41. Rafatpanah H, Hedayati‑Moghaddam MR, Fathimoghadam F, Bidkhori 62. Wang ZS, Zhong M, Bian YH, Mu YF, Qin SL, Yu MH, et al. MicroRNA‑187 HR, Shamsian SK, Ahmadi S, et al. High prevalence of HTLV‑I infection in inhibits tumor growth and invasion by directly targeting CD276 in Mashhad, Northeast Iran: a population‑based seroepidemiology survey. J colorectal cancer. Oncotarget. 2016;7(28):44266–76. Clin Virol Off Publ Pan Am Soc Clin Virol. 2011;52(3):172–6. 63. Han X, Wang X, Zhao B, Chen G, Sheng Y, Wang W, et al. MicroRNA‑187 42. Mozhgani SH, Zarei Ghobadi M, Norouzi M, Rahimi H, Valizadeh N, inhibits tumor growth and metastasis via targeting of IGF‑1R in hepa‑ Teymoori‑Rad M, et al. Signaling factors potentially associated to the tocellular carcinoma. Mol Med Rep. 2017;16(2):2241–6. pathogenesis of adult T‑ cell leukemia/lymphoma: a network‑analysis and 64. Dou C, Liu Z, Xu M, Jia Y, Wang Y, Li Q, et al. miR‑187‑3p inhibits the novel findings assessment. Virus Res. 2022;319: 198875. metastasis and epithelial‑mesenchymal transition of hepatocellular 43. Du L, Borkowski R, Zhao Z, Ma X, Yu X, Xie XJ, et al. A high‑throughput carcinoma by targeting S100A4. Cancer Lett. 2016;381(2):380–90. screen identifies miRNA inhibitors regulating lung cancer cell survival 65. Liu CJ, Lin JS, Cheng HW, Hsu YH, Cheng CY, Lin SC. Plasma miR‑ and response to paclitaxel. RNA Biol. 2013;10(11):1700–13. 187* is a potential biomarker for oral carcinoma. Clin Oral Invest. 44. Weber F, Teresi RE, Broelsch CE, Frilling A, Eng C. A limited set of human 2017;21(4):1131–8. MicroRNA is deregulated in follicular thyroid carcinoma. J Clin Endocrinol 66. Lin SC, Kao SY, Chang JC, Liu YC, Yu EH, Tseng SH, et al. Up‑regulation Metab. 2006;91(9):3584–91. of miR‑187 modulates the advances of oral carcinoma by targeting 45. Sun CC, Li SJ, Yuan ZP, Li DJ. MicroRNA‑346 facilitates cell growth and BARX2 tumor suppressor. Oncotarget. 2016;7(38):61355–65. metastasis, and suppresses cell apoptosis in human non‑small cell lung 67. Lin M, Xue XY, Liang SZ, Li YX, Lv YY, He LH, et al. MiR‑187 overexpres‑ cancer by regulation of XPC/ERK/Snail/E‑ cadherin pathway. Aging. sion inhibits cervical cancer progression by targeting HPV16 E6. 2016;8(10):2509–24. Oncotarget. 2017;8(38):62914–26. 46. Chen B, Pan W, Lin X, Hu Z, Jin Y, Chen H, et al. MicroRNA‑346 functions as 68. Liang H, Luo R, Chen X, Zhao Y, Tan A. miR‑187 inhibits the growth of an oncogene in cutaneous squamous cell carcinoma. Tumour Biol J Int cervical cancer cells by targeting FGF9. Oncol Rep. 2017;38(4):1977–84. Soc Oncodev Biol Med. 2016;37(2):2765–71. 69. He CY, Yang J. miR‑187 induces apoptosis of SiHa cervical carcinoma 47. Yan HL, Li L, Li SJ, Zhang HS, Xu W. miR‑346 promotes migration and inva‑ cells by downregulating Bcl‑2. Genet Mol Res GMR. 2017;16(1). sion of nasopharyngeal carcinoma cells via targeting BRMS1. J Biochem 70. Chao A, Lin CY, Lee YS, Tsai CL, Wei PC, Hsueh S, et al. Regulation of Mol Toxicol. 2016;30(12):602–7. ovarian cancer progression by microRNA‑187 through targeting disa‑ 48. Yang F, Luo LJ, Zhang L, Wang DD, Yang SJ, Ding L, et al. MiR‑346 pro ‑ bled homolog‑2. Oncogene. 2012;31(6):764–75. motes the biological function of breast cancer cells by targeting SRCIN1 71. Matamala N, Vargas MT, González‑ Cámpora R, Arias JI, Menéndez P, and reduces chemosensitivity to docetaxel. Gene. 2017;600:21–8. Andrés‑León E, et al. MicroRNA deregulation in triple negative breast 49. Guo J, Lv J, Liu M, Tang H. miR‑346 up ‑regulates argonaute 2 (AGO2) cancer reveals a role of miR‑498 in regulating BRCA1 expression. Onco ‑ protein expression to augment the activity of other MicroRNAs (miR‑ target. 2016;7(15):20068–79. NAs) and contributes to cervical cancer cell malignancy. J Biol Chem. 72. Lou Y, Liu L, Zhan L, Wang X, Fan H. miR‑187‑5p regulates cell growth 2015;290(51):30342–50. and apoptosis in acute lymphoblastic leukemia via DKK2. Oncol Res. 50. Ortega‑Molina A, Boss IW, Canela A, Pan H, Jiang Y, Zhao C, et al. The 2016;24(2):89–97. histone lysine methyltransferase KMT2D sustains a gene expression 73. Huang F, Jin Y, Wei Y. MicroRNA‑187 induces diffuse large program that represses B cell lymphoma development. Nat Med. B‑ cell lymphoma cell apoptosis via targeting BCL6. Oncol Lett. 2015;21(10):1199–208. 2016;11(4):2845–50. 51. Dhar SS, Lee MG. Cancer‑ epigenetic function of the histone methyltrans‑ 74. Rodriguez‑Aguayo C, Monroig PDC, Redis RS, Bayraktar E, Almeida MI, ferase KMT2D and therapeutic opportunities for the treatment of KMT2D‑ Ivan C, et al. Regulation of hnRNPA1 by microRNAs controls the miR‑ deficient tumors. Oncotarget. 2021;12(13):1296–308. 18a‑K ‑RAS axis in chemotherapy‑resistant ovarian cancer. Cell Discov. 52. Alam H, Tang M, Maitituoheti M, Dhar SS, Kumar M, Han CY, et al. KMT2D 2017;3:17029. deficiency impairs super ‑ enhancers to confer a glycolytic vulnerability in 75. Shu J, Silva B, Gao T, Xu Z, Cui J. Dynamic and modularized Micro‑ lung cancer. Cancer Cell. 2020;37(4):599‑617.e7. RNA regulation and its implication in human cancers. Sci Rep. 53. Rabello DDA, Ferreira V, Berzoti‑ Coelho MG, Burin SM, Magro CL, Cace‑ 2017;7(1):13356. miro MDC, et al. MLL2/KMT2D and MLL3/KMT2C expression correlates 76. Li C, Feng SY, Chen L. SET7/9 promotes H3K4me3 at lncRNA DRAIC with disease progression and response to imatinib mesylate in chronic promoter to modulate growth and metastasis of glioma. Eur Rev Med myeloid leukemia. Cancer Cell Int. 2018;18:26. Pharmacol Sci. 2020;24(23):12241–50. 54. Yamagishi M, Hori M, Fujikawa D, Ohsugi T, Honma D, Adachi N, et al. 77. Chen W, Cao R, Su W, Zhang X, Xu Y, Wang P, et al. Simple and fast Targeting excessive EZH1 and EZH2 activities for abnormal histone isolation of circulating exosomes with a chitosan modified shuttle flow methylation and transcription network in malignant lymphomas. Cell microchip for breast cancer diagnosis. Lab Chip. 2021;21(9):1759–70. Rep. 2019;29(8):2321‑37.e7. 78. Wang L, Zhou L, Hou J, Meng J, Lin K, Wu X, et al. Three novel circRNAs 55. Peng W, Sha H, Sun X, Zou R, Zhu Y, Zhou G, et al. Role and mechanism of upregulated in tissue and plasma from hepatocellular carcinoma patients miR‑187 in human cancer. Am J Transl Res. 2020;12(9):4873–84. and their regulatory network. Cancer Cell Int. 2021;21(1):72. 56. Fei D, Zhao K, Yuan H, Xing J, Zhao D. MicroRNA‑187 exerts tumor ‑sup ‑ 79. Sun K, Wang H, Xu X, Wei X, Su J, Zhu K, et al. Tumor‑ educated platelet pressing functions in osteosarcoma by targeting ZEB2. Am J Cancer Res. miR‑18a‑3p as a novel liquid‑biopsy biomarker for early diagnosis and 2016;6(12):2859–68. chemotherapy efficacy monitoring in nasopharyngeal carcinoma. Front 57. Xiao Y, Zhao Q, Du B, Chen HY, Zhou DZ. MicroRNA‑187 inhibits growth Oncol. 2021;11: 736412. and metastasis of osteosarcoma by downregulating S100A4. Cancer 80. Oh C, Park S, Lee EK, Yoo YJ. Downregulation of ubiquitin level via Invest. 2018;36(1):1–9. knockdown of polyubiquitin gene Ubb as potential cancer therapeutic 58. Cui C, Shi X. miR‑187 inhibits tumor growth and invasion by directly intervention. Sci Rep. 2013;3:2623. targeting MAPK12 in osteosarcoma. Exp Ther Med. 2017;14(2):1045–50. 81. Huang P, Guo YD, Zhang HW. Identification of Hub genes in pediatric 59. Casanova‑Salas I, Masiá E, Armiñán A, Calatrava A, Mancarella C, Rubio ‑ medulloblastoma by multiple‑microarray analysis. J Mol Neurosci MN. Briones J, et al. MiR‑187 targets the androgen‑regulated gene ALDH1A3 2020;70(4):522–31. in prostate cancer. PLoS ONE. 2015;10(5): e0125576. 82. Chen Y, Qi C, Xia L, Li G. Identification of novel genetic etiology and key 60. Li Z, Lin C, Zhao L, Zhou L, Pan X, Quan J, et al. Oncogene miR‑187–5p is molecular pathways for seminoma via network‑based studies. Int J Oncol. associated with cellular proliferation, migration, invasion, apoptosis and 2017;51(4):1280–90. an increased risk of recurrence in bladder cancer. Biomed Pharmacother 83. Chen C, Zhang Y, Zhang L, Weakley SM, Yao Q. MicroRNA‑196: critical roles Biomed Pharmacother. 2018;105:461–9. and clinical applications in development and cancer. J Cell Mol Med. 61. Zhao J, Lei T, Xu C, Li H, Ma W, Yang Y, et al. MicroRNA‑187, down‑ 2011;15(1):14–23. regulated in clear cell renal cell carcinoma and associated with lower Sha yeghpour et al. Infectious Agents and Cancer (2023) 18:12 Page 11 of 11 84. Zhao X, Shen L, Feng Y, Yu H, Wu X, Chang J, et al. Decreased expression of RPS15A suppresses proliferation of lung cancer cells. Tumour Biol J Int Soc Oncodev Biol Med. 2015;36(9):6733–40. 85. Yao Y, Liu Y, Lv X, Dong B, Wang F, Li J, et al. Down‑regulation of ribosomal protein S15A inhibits proliferation of human glioblastoma cells in vivo and in vitro via AKT pathway. Tumour Biol J Int Soc Oncodev Biol Med. 2016;37(4):4979–90. 86. Xu M, Wang Y, Chen L, Pan B, Chen F, Fang Y, et al. Down‑regulation of ribosomal protein S15A mRNA with a short hairpin RNA inhibits human hepatic cancer cell growth in vitro. Gene. 2014;536(1):84–9. 87. Wan BS, Wang XY, Tiang J, Zhou C, Lin J, Wang Z. Ribosomal protein RPS15A augments proliferation of colorectal cancer RKO cells via regulation of BIRC3, p38 MAPK and Chk1. Eur Rev Med Pharmacol Sci. 2021;25(11):3967–80. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub‑ lished maps and institutional affiliations. Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? 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Journal
Infectious Agents and Cancer – Springer Journals
Published: Feb 25, 2023
Keywords: Adult T-cell lymphoma/leukemia; ATLL; HTLV-1; WGCNA; Network analysis; Quantitative real-time PCR