Primary testicular lymphoma demonstrates an over-expression of Wilms tumor 1 gene and different mRNA and miRNA expression profiles compared to nodal diffuse large B-cell lymphoma

Primary testicular lymphoma demonstrates an over-expression of Wilms tumor 1 gene and different mRNA and miRNA expression profiles compared to nodal diffuse large B-cell lymphoma | 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 Article Primary testicular lymphoma demonstrates an over-expression of Wilms tumor 1 gene and different mRNA and miRNA expression profiles compared to nodal diffuse large B-cell lymphoma Adnan Mansoor, Ariz Akhter, Meer-Taher Shabani-Rad, Jean Deschenes, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1906454/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diffuse large B-cell lymphoma (DLBCL) shows a high degree of clinical and biological heterogeneity. Primary testicular lymphoma (PTL) is an extra nodal variant of DLBCL associated with higher risk of recurrence including contralateral testicle and central nervous system sanctuary sites. Several molecular aberrations including somatic mutation of MyD88, CD79B and upregulation of NF-Kb, PDL-1 and PDL-2 are thought to contribute to the pathogenesis and poor prognosis of PTL. However, additional biomarkers are needed that may improve the prognosis and help understand the PTL biology and possibly lead to new therapeutic targets. RNA from diagnostic tissue biopsies of PTL (n=31) and matched nodal DLBCLs (n=27) patients, were evaluated by mRNA and miRNA expression. Expression of 770 key genes were screened, utilizing nCounter Human miRNA and PAN-cancer pathway mRNA assays utilizing nCounter analysis System (Nanostring technologies). PTL and nodal DLBCL patients were comparable in age, gender, stage, and putative cell of origin (P>0.05). WT1 expression was higher in PTL compared to nodal DLBCL (> 3-fold; P= 0.0001). In addition, we found that WT1 associated pathway genes THBS4, PTPN5, PLA2G2A and IFNA17 were upregulated in PTL (>2.0-fold, P<0.005). The miRNAs targeting WT1 (hsa15a-5p, hsa-miR-16-5p, hsa-miR-361-5p, hsa-miR-27b-3p, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-132-3p, hsa-miR-128-3p) were upregulated in PTL compared to nodal DLBCL (≥2.0-fold; FDR 0.01). We also found lower expression of BMP7, LAMB3, GAS1, MMP7 and LAMC2 (>2.0- fold, P<0.01) in PTL compared to nodal DLBCL. Our study demonstrated an overexpression of WT1 in PTL compared to nodal DLBCL. We hypothesize that a select miRNA subset targets the WT1 expression and influences the PI3k/Akt pathway in PTL. Additional studies are needed to further investigate the biological role of WT1 in PTL and its potential as a therapeutic target. Figures Figure 1 Figure 2 Introduction Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of Non-Hodgkin lymphoma and has significant genetic, pathologic, and clinical heterogeneity ( 1 ). The two most common subtypes are germinal center B-cell-like DLBCL (GCB-DLBCL) and activated B-cell-like DLBCL (ABC-DLBCL) based on the cell of origin ( 2 ). Primary testicular lymphoma and primary central nervous system lymphoma are subtypes of DLBCL, localized to extra nodal organs ( 3 , 4 ). In fact, both PTL and central nervous system DLBCL are considered to originate within immune sanctuary sites, protected by blood endothelial barriers ( 3 – 6 ). PTL is an infrequent disease, accounting for only 1% of non-Hodgkin lymphoma ( 7 ). Based on gene expression profiling and immunohistochemistry-based assay, most of PTLs are considered ABC-DLBCL ( 3 , 8 ). Patients with PTL have a median age at diagnosis of 66–68 years. Although the prognosis of DLBCL improved with the addition of rituximab to CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone) chemotherapy, PTL requires additional therapies, including scrotal radiotherapy and CNS prophylaxis, due to high-risk of recurrence, especially in the CNS and the contralateral testis ( 9 , 10 ). Relapse can also occur in other extra-nodal sites, including soft tissue, lung, liver and skin ( 11 ). DLBCL of immune sanctuary sites have an enhanced somatic hypermutation of the immunoglobulin heavy chain variable regions and show a frequent loss of HLA expression ( 12 , 13 ), resulting in evasiveness of the host antitumor response. Previous studies demonstrated that somatic mutations of MyD88, CD79B and an upregulation of NF-Kb, PDL-1 and PDL2 ( 14 – 16 ), may contribute to the pathogenesis and account for the poor prognosis of PTL. Further studies are however needed to identify new biomarkers that can improve the understanding of PTL biology and prognosis, and potentially lead to discovery of new therapeutic targets. The Wilms tumor 1 ( WT1) encodes a zinc finger transcription factor that has emerged as an important regulator of normal and malignant hematopoiesis. WT1 is highly expressed at the time of embryogenesis in the development of the kidney, gonads, testis, ovary, and the mesothelial lining of the abdominal and thoracic cavity. WT1 was originally recognized as a tumor suppressor gene, but subsequent studies indicated that it may play an oncogenic function in leukemia ( 17 , 18 ), as well as solid neoplasms, including colon carcinoma, head and neck squamous cell carcinoma, and carcinomas of the pancreas, ovary and lung ( 19 – 21 ). Therefore, WT1 is a universal tumor antigen that may represent a good therapeutic target for the development of gene therapy strategies. In acute leukemia, WT1 also acts as potential prognostic factor and as a marker of minimal residual disease, as well as target of vaccination immunotherapy ( 22 ). However, to date, the role WT1 in the pathogenesis of lymphoid malignancies remains mostly unknown. In the present study, we evaluated diagnostic tissue samples from PTL patients and compared them with those from nodal DLBCL patients, diagnosed using the World Health Organization (WHO) criteria ( 1 ). We examined the differential expression of mRNA with specific reference to WT1 gene expression in these samples. We also investigated epigenetic modulation of WT1 expression, while screening a series of miRNA related to common cancer signaling pathways. Materials And Methods Patients We assembled a cohort of PTL patients (N = 31), identified in our institutional database between 1995 and 2012, for whom formalin fixed paraffin embedded (FFPE) tissue was available. This cohort was compared with a group of nodal DLBCL (n = 27) from our institution, matched for age, gender, stage and cell of origin (Table 1 ). The inclusion criteria included lack of evidence of primary or secondary CNS involvement and availability of sufficient and adequate tissue material (biopsies > 1 mm in diameter, well preserved, processed, and fixed). Histologic diagnosis of PTL and nodal DLBCL was reviewed by two hematopathologist (AM and MTSR) to ensure diagnostic accuracy, according to the 2008 WHO Classification of Tumors of Hematopoietic and Lymphoid tissues ( 1 ). This study was performed in accordance with the Declaration of Helsinki and was approved by the Health Research Ethics Board of Alberta (HREBA),( HREBA study #.CC-16-0218; REN 6 dated December, 29, 2021). Table 1 Clinical Characteristics of PTL and DLBCL patients in current cohort Parameter PTL (n = 31) nDLBCL (n = 27) P -value Age (years) Median (Range) ≥ 60 ≤ 60 68 (43–90) 23 (74%) 08 (26%) 55 (31–74) 15 (56%) 12 (44%) 0.3780 Sex Male Female 31 (100%) 00 (0%) 26 (93%) 01 (07%) 1.0000 Performance status 0–1 2–4 unknown 23 (74%) 05 (16%) 03 (10%) 18 (67%) 09 (33%) 0.7418 Stage I-II III-IV 25 (85%) 06 (15%) 10 (37%) 17 (73%) 0.8834 IPI* Low (0–1) Intermediate (2–3) High (4–5) unknown 23 (74%) 03 (10%) 03 (10%) 02 (06%) 09 (33%) 14 (52%) 04 (15%) 0.8584 Serum LDH* Normal High Missing 22 (71%) 06 (19%) 03 (09%) 12 (44%) 15 (56%) 1.0000 Cell-of –Origin (Lymph2CX) ABC type* GCB type* Intermediate type 25 (81%) 04 (13%) 02 (06%) 23 (85%) 04 (15%) 00 (00%) 0.1835 *IPI, International Prognostic Index; LDH, lactate dehydrogenase; GCB, germinal center B cell like; ABC, Activated B cell like A statement on ethics approval and consent (even where the need for approval was waived); the name of the ethics committee that approved the study and the committee’s reference number if appropriate; a statement that the study was performed in accordance with the Declaration of Helsinki. RNA extraction RNA was extracted using the Ambion Kit (ThermoFisher scientific, Waltham, MA USA), utilizing duplicate cores (1 mm), harvested off the areas with maximum tumor concentration in the diagnostic FFPE blocks. The RNA concentration was quantified using the Nanodrop UV-VIS spectrophotometer (Nanodrop Technologies, Wilmington, DE USA) and the integrity was assessed using a Bio-analyzer 2100 and RNA Nano Chip assay (Agilent Technologies, Wilmington, DE USA). NanoString nCounter Assay Total RNA samples were processed according to the manufacturer’s protocol for the nCounter Human miRNA Expression Assay v3 kit and for gene expression PAN-cancer pathway code set, containing 770 key genes related to major pathways in cancer biology (NanoString, Seattle, WA USA). Briefly, using nCounter™ technology, miRNA and mRNA expression analysis was conducted for each sample. The probes were hybridized to 300ng of total RNA for 20 h at 65°C and were applied to the nCounterTM Prep Station for automated removal of excess probe and immobilization of probe-transcript complexes on a streptavidin-coated cartridge. The data were collected using the nCounterTM Digital Analyzer by counting the individual barcodes. miRNA/mRNA analysis. The normalization of raw data was conducted using the nSolver Analysis Software v3.0 (NanoString Technologies). miRNA raw counts were normalized with ligation control and the background level of expression for each sample was calculated using the mean level of the negative controls (plus two standard deviations of the mean). PAN cancer mRNA raw counts were normalized to internal levels of 40 reference genes. Normalized data were log2-transformed and used for further analysis. Cell-of-origin determination (Lymph2CX assay) We used 250ng of total RNA for digital gene expression profiling to determine the cell-of-origin of PTL and nodal DLBCL cases utilizing NanoString platform. The cell-of-origin was assigned using Lymph2CX- 20 gene expression-based assay (8 gene overexpressed in ABC, 7 gene overexpressed in GCB subtypes of PTL and nodal DLBCL and 5 housekeeping genes), as described by Scott et al. ( 23 , 24 ). Briefly, 250ng total RNA was hybridized with Lymph2cx code sets at 65°C for 20 hours (16-22hours). After hybridization, purification was performed in nCounter™ Prep Station for automated removal of excess probes, while immobilized target-probe complex was captured on the cartridge coated with streptavidin. This cartridge was used on nCounter™ digital analyzer for fully automated imaging and data collection, as per manufacturer instructions. miRNA target prediction To assess the potential association between differentially expressed mRNA and miRNA, we used miRTarBase (ver 6.0), Starbase (v 5.0) and DIANA TOOLS to predict potential targets of the miRNAs. Statistical analysis We used SPSS software v20.0 (IBM, Armonk, NY USA) for the statistical evaluation, and nSolver software v3.0 (NanoString Technologies) for the normalization of the raw counts. Hierarchical clustering and principal component analyses were performed on Qlucore Omics Explorer v3.2 (Lund, Sweden). Results with fold change ≥ 2.0 and p -value < 0.05 were considered significant. Results Patient characteristics The clinical characteristics of the patient cohorts in both groups are summarized in Table 1 . For PTL patients, the median age at diagnosis was 68 years (range, 43–90 years); 85% of patients had stage I-II disease, 74% had a low risk IPI score of 0–1, and 81% had ABC cell of origin. For nodal DLBCL patients, the median age was 55 years (range, 31–74 years), 37% had stage I-II disease, 33% had a low risk IPI score of 0–1, and 85% had ABC cell of origin. Hence, PTL and nodal DLBCL patients were comparable in age, gender, stage and cell of origin (p > 0.05). Overexpression of WT1 in primary testicular lymphoma patients We observed high expression of WT1 gene in PTL, compared to the nodal DLBCL, based on PAN cancer code set (Table 2 ). The principal component analysis (PCA) and the hierarchical clustering showed that WT1 expression in PTL was 3.2-fold higher ( P = 0.0001) than in nodal DLBCL (Fig. 1 ). Intermediate cases were excluded in the hierarchical clustering. In addition, WT1 associated pathway genes were also significantly higher in PTL patients: THBS4 (2.8-fold change, P = 0.004), PLA2G2A (2.7-fold change, P = 0.005), PTPN5 (2.4- fold change, P < 0.0001), and I FNA17 (2.1- fold change, P = 0.0002). We also found a set of genes that were downregulated in PTL patients compared to nodal DLBCL, including: BMP7 (2.9- fold change, P = 0.001), LAMB3 (2.8- fold change, P < 0.0001), GAS1 (2.3- fold change, P < 0.0001), MMP7 (2.2- fold change, P = 0.01) and LAMC2 (2.1- fold change, P = 0.002) The analysis of GCB and ABC cell of origin subtypes did not identify any significant differences between the analyzed cohorts (data not shown) Table 2 Differential gene expression in PTL compared to nDLBCL as analyzed by NanoString Genes Upregulated in PTL Gene Description P-value Fold change Accession no. WT1 Wilms tumor 1 0.0001 3.2 NM_005157.3 THBS4 Thrombospondin 4 0.004 2.8 NM_004302.3 PLA2G2A Phospholipase A2 group IIA 0.005 2.7 NM_145259.2 PTPN5 Protein tyrosine phosphatase, non-receptor type 5 < 0.0001 2.4 NM_001616.3 IFNA17 IFNA17 interferon alpha 17 0.0002 2.1 NM_181690.1 Gene Downregulated in PTL Gene Description P-value Fold change Accession no. BMP7 Bone morphogenetic protein 7 0.001 2.9 NM_004656.2 LAMB3 Laminin subunit beta 3 < 0.0001 2.8 NM_012342.2 GAS1 Growth arrest specific 1 < 0.0001 2.3 NM_000657.2 MMP7 Matrix metallopeptidase 7 0.01 2.2 NM_138761.3 LAMC2 Laminin subunit gamma 2 0.002 2.1 NM_004049.2 NanoString based analysis of miRNA in PTL and DLBCL We compared the miRNA signatures between PTL (n = 31) and a set of nodal DLBCL, based on the available samples. 800 miRNAs were assessed in PTL and nodal DLBCL using the NanoString platform. A supervised hierarchical clustering revealed different miRNA expression patterns in PTL and nodal DLBCL (Fig. 2 ). Overall, in PTL there was a higher expression of 122 miRNAs, with a median expression ≥ 2.0-fold change in log2 and FDR 0.01. Twenty-six miRNAs were found to be associated with WT1, PLA2G2A, GAS1, BMP7, MMP7 and LAMC2 genes, as summarized in Table 3 . Table 3 Differential miRNA expression in PTL compared to nDLBCL miRNAs P-value Fold change Target gene mirTarbase/Starbase/DIANA Tools hsa-miR-15a-5p < 0.0001 9.4 WT1 √ hsa-miR-361-5p < 0.0001 5.9 WT1 √ hsa-miR-16-5p < 0.0001 5.7 WT1 √ hsa-miR-27b-3p < 0.0001 5.7 WT1 √ hsa-miR-199a-5p < 0.0001 5.7 WT1 √ hsa-miR-199b-5p < 0.0001 4.6 WT1 √ hsa-miR-132-3p < 0.0001 4.5 WT1 √ hsa-miR-128-3p 0.0001 3.0 WT1 √ hsa-miR-127-3p 0.0001 2.9 MYC √ hsa-miR-9-5p < 0.0001 5.2 PLA2G2A √ hsa-miR-148a-3p < 0.0001 8.2 GAS1 √ hsa-miR-34a-5p < 0.0001 7.9 GAS1 √ hsa-miR-340-5p < 0.0001 3.9 GAS1 √ hsa-miR-421 < 0.0001 2.9 GAS1 √ hsa-miR-1290 < 0.0001 2.5 GAS1 √ hsa-miR-22-3p < 0.0001 7.8 BMP7 √ hsa-miR-24-3p < 0.0001 5.9 BMP7 √ hsa-miR-342-3p < 0.0001 5.6 BMP7 √ hsa-let-7b-5p < 0.0001 5.2 BMP7 √ hsa-miR-126-3p < 0.0001 7.4 MMP7 √ hsa-miR-146a-5p < 0.0001 7.8 LAMC2 √ hsa-miR-29a-3p < 0.0001 6.2 LAMC2 √ hsa-miR-29b-3p < 0.0001 4.7 LAMC2 √ hsa-miR-29c-3p < 0.0001 6.5 LAMC2 √ We also analyzed the miRNAs expression based on the cell-of origin in PTL and nodal DLBCL, but we found no significant differences of miRNA associated with the ABC and GCB subtypes in each group (data not shown). Discussion To our knowledge, this is the first study to simultaneously analyze and compare both mRNA and miRNA expression profiles in PTL and nodal DLBCL, and to implicate WT1 in PTL biology. We analyzed 770 mRNA genes (includes 13 major cancer pathway genes) and 800 miRNA genes, and we identified 37 mRNA and 123 miRNA genes that were differentially expressed in PTL and nodal DLBCL. We observed significantly higher expression of miRNAs, like hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-199b-5p, hsa-miR-132-3p, hsa-miR-128-3p in the PTL group. We also demonstrated that PTL mostly has an ABC type (81%) of gene expression, which correlates consistently with poorer prognosis ( 25 ). PTL patients have also been shown to have a low frequency of BCL2 rearrangements, higher frequency of BCL2 amplification, and higher number of BCL6 rearrangements, ( 26 – 28 ), as well as higher expression of CD44 ( 29 ) and BCL2 ( 30 ). In PTL, active STAT3 and CXCR4 signaling ( 14 ) and somatic mutations in CD79B and MyD88 (> 70%) lead to constitutive activation of NF-kb and JAK/STAT signaling ( 31 ). All these factors may explain why most of PTL patients exhibit a highly aggressive ABC type. We also found no significant differences in the gene expression profiles of ABC and GCB subtypes between the groups. Emerging evidence indicates that miRNAs likely contribute to the pathogenesis of all human malignancies ( 32 ), through tumor suppression and ontogenetic ( 33 ) mechanisms. We found a higher expression of specific miRNAs (miRNA-15a-5p, hsa-miR-16-5p, hsa-miR-361-5p, hsa-miR-27b-3p, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-132-3p and hsa-miR-128-3p) in PTL patients compared to nodal DLBCL. This miRNA profile has been associated with WT1 gene target. The regulatory function of these miRNA has been defined previously ( 34 , 35 ). For example, an upregulation of miRNA-361, miRNA-15a/16 suppresses the WT1 expression in non-small-cell lung carcinoma and acute leukemia, but in cervical carcinoma, miRNA-361 has been reported to be upregulated and acting as an oncogene ( 36 ). These findings indicate that the roles of miRNAs are diverse and tissue specific. Expression of hsa-miR-9-5p target PLA2G2 was also found to be enriched in immune privileged sites ( 37 ). Our study also demonstrated a higher expression of miR-127 in PTL, consistent with a previous report ( 38 ). Further, higher expression of miRNAs, like hsa-miR-148a-3p, 34a-5p, 340-5p, 421,1290 correlate with downregulation of GAS1 . Similarly, hsa-miR-22-3p, 24-3p, 342-3p, hsa-let-7b-5p downregulate BMP7 , while hsa-miR-126-3p targets MMP7. In addition, hsa-miR-146a-5p, 29a-3p, 29b-3p, 29c-3p have been found to downregulate LAMC2 . These MiRNA-mRNA target interactions have been validated by the miRTarbase, Starbase and DIANA tools databases. Hence, the miRNA expression profile in this series validates the epigenetic modulation of various genes in PTL, exemplified through the mRNA differential expression. WT1 gene was initially discovered as a tumor suppressor gene responsible for Wilms tumor, a renal carcinoma affecting primarily children ( 39 ). In the early publications, WT1 was described as a tumor suppressor gene, similar to the Bcl-2 expression in various cancer cell lines, such as HeLa (cervical cancer) ( 40 ), LNln3 (prostate cancer) ( 41 ) and DHL-4 (follicular lymphoma) ( 42 ). Other studies also supported the growth inhibitory effects of WT1 ( 43 , 44 ), and demonstrated that forced expression of WT1 results in growth arrest and differentiation of progenitor cells. However, recent studies have postulated a role of WT1 as an oncogene. For example, higher expression of WT1 in leukemia was shown to correlate with poor prognosis ( 45 ). The oncogenic role of WT1 was also established in various solid carcinomas, including colon ( 46 ), pancreas ( 47 ), ovary ( 48 ), brain ( 47 ), lung ( 49 ) and breast ( 50 ). Thus, our finding of a possible oncogenic role of WT1 in PTL is consistent with these reports. WT1 also activates the downstream PI3K/AKT pathway, as shown in lung cancer ( 51 ). Additionally, an enhanced efficacy of the chemotherapeutic agent Cisplatin (ddp) has been found following the inhibition of WT1/ PI3K/AKT pathway in lung carcinoma. We also found that downstream PI3K/AKT genes, like THBS4, PTPN5 and PLA2G2A are upregulated in PTL, indirectly supporting the involvement of an activated PI3K /AKT pathway in PTL. However, our data could not confirm an over-expression of PI3K or AKT mRNA molecules in PTL. These discrepant findings may be related to altered targets, limited target coverage in our panel or degradation of mRNA in formalin tissue. However, THBS4 expression was associated with tumor invasion in breast carcinoma ( 52 ) and was implicated in the development of hepatocellular carcinoma ( 53 ). PLA2G2A overexpression has been reported as a poor prognostic factor in rectal carcinoma ( 54 ). PTPN5 is a key regulator of signal transduction and contributor to the carcinogenesis ( 55 ). GAS1 has been shown to act as a tumor suppressor in gastric carcinoma ( 56 ) and as an inhibitor of metastasis in melanoma ( 57 ). The association between high expression of miRNA-34a downregulating the GAS1 in PTL in the current study is in line with the similar expression pattern previously shown in papillary thyroid carcinoma ( 58 ). Our study also found that genes like BMP7, MMP7, LAMB3 and LAMC2 were significantly downregulated in PTL. Aberrant expression of BMP7 and its association with the miRNA in the tumor microenvironment is associated with various cancer tissues. For example, it has been shown that an increased expression of BMP7 inhibits the growth of the normal and malignant cells ( 59 ). WT1 as a tumor antigen has been found in acute leukemias, and several studies have shown increased WT1 expression in leukemia cells compared to normal hematopoietic cells ( 60 ). Due to higher expression in various hematologic malignancies and solid tumors, WT1 may be used as a promising immunotherapy target. The development of vaccines targeting WT1 antigen depends on the characterization of peptides associated with HLA molecules on the cell surface. In this context, different WT1 epitopes have been identified with this potential ( 61 ). These observations presented in our study may open the opportunity to exploit the higher expression of WT1 gene as a target for immunotherapy in PTL. In conclusion, we found an overexpression of WT1 in PTL patients, which is a novel finding. We also found that WT1 associated pathway genes were upregulated in PTL. We postulate that a miRNA subset may target WT1 through the PI3K/AKT pathway in PTL. Further studies are needed to investigate the biological role of WT1 function in PTL, as well as the potential of WT1 signaling as a possible therapeutic target. Declarations Acknowledgement: Authors would like to acknowledge all the anatomical pathologists of Alberta Precision Laboratories (APL) who performed initial review and reporting of the diagnostic biopsies on patients included in this study. Authors also acknowledge the support provided by the APL research department in retrieval of diagnostic tissues archival material for the patients in this cohort. Conflict of Interest: Authors declare no competing financial interests in relation to the work presented. Ethics approval: This study was performed in accordance with the Declaration of Helsinki and was approved by the Health Research Ethics Board of Alberta (HREBA),(CC-16-0218; REN 6 dated December, 29, 2021). Author Contributions: AM and AA contributed equally to this work. The parent project of this study was conceived by DS and AM, who also devised all the protocols. DS designed the study, performed the patient clinical data review, and edited the final manuscript. AM designed the study, reviewed final pathology, performed the statistical and bioinformatic analyses and wrote the manuscript. AA performed experimental work, compiled data, performed QA and wrote manuscript. JD, AY, MSTR and KT contributed patients, review pathology and provided critical review and edits to the manuscript Funding: This work was supported by research grant from Alberta Cancer Foundation (grant # 25999) References Swerdlow S CE, Harris N, Jaffe E, Pileri S, Stein H, Thiele J, Vardiman J. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. International Agency for Research on Cancer. 2008;Lyon, France. Barrans SL, Crouch S, Care MA, Worrillow L, Smith A, Patmore R, et al. 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Robertus JL, Harms G, Blokzijl T, Booman M, de Jong D, van Imhoff G, et al. Specific expression of miR-17-5p and miR-127 in testicular and central nervous system diffuse large B-cell lymphoma. Mod Pathol. 2009;22(4):547–55. Haber DA, Sohn RL, Buckler AJ, Pelletier J, Call KM, Housman DE. Alternative splicing and genomic structure of the Wilms tumor gene WT1. Proc Natl Acad Sci U S A. 1991;88(21):9618–22. Hewitt SM, Hamada S, McDonnell TJ, Rauscher FJ, 3rd, Saunders GF. Regulation of the proto-oncogenes bcl-2 and c-myc by the Wilms' tumor suppressor gene WT1. Cancer Res. 1995;55(22):5386–9. Cheema SK, Mishra SK, Rangnekar VM, Tari AM, Kumar R, Lopez-Berestein G. Par-4 transcriptionally regulates Bcl-2 through a WT1-binding site on the bcl-2 promoter. J Biol Chem. 2003;278(22):19995–20005. Heckman C, Mochon E, Arcinas M, Boxer LM. The WT1 protein is a negative regulator of the normal bcl-2 allele in t(14;18) lymphomas. J Biol Chem. 1997;272(31):19609–14. Ellisen LW, Carlesso N, Cheng T, Scadden DT, Haber DA. The Wilms tumor suppressor WT1 directs stage-specific quiescence and differentiation of human hematopoietic progenitor cells. EMBO J. 2001;20(8):1897–909. Svedberg H, Richter J, Gullberg U. Forced expression of the Wilms tumor 1 (WT1) gene inhibits proliferation of human hematopoietic CD34(+) progenitor cells. Leukemia. 2001;15(12):1914–22. Inoue K, Sugiyama H, Ogawa H, Nakagawa M, Yamagami T, Miwa H, et al. WT1 as a new prognostic factor and a new marker for the detection of minimal residual disease in acute leukemia. Blood. 1994;84(9):3071–9. Oji Y, Inohara H, Nakazawa M, Nakano Y, Akahani S, Nakatsuka S, et al. Overexpression of the Wilms' tumor gene WT1 in head and neck squamous cell carcinoma. Cancer Sci. 2003;94(6):523–9. Oji Y, Suzuki T, Nakano Y, Maruno M, Nakatsuka S, Jomgeow T, et al. Overexpression of the Wilms' tumor gene W T1 in primary astrocytic tumors. Cancer Sci. 2004;95(10):822–7. Barbolina MV, Adley BP, Shea LD, Stack MS. Wilms tumor gene protein 1 is associated with ovarian cancer metastasis and modulates cell invasion. Cancer. 2008;112(7):1632–41. Oji Y, Miyoshi S, Maeda H, Hayashi S, Tamaki H, Nakatsuka S, et al. Overexpression of the Wilms' tumor gene WT1 in de novo lung cancers. Int J Cancer. 2002;100(3):297–303. Miyoshi Y, Ando A, Egawa C, Taguchi T, Tamaki Y, Tamaki H, et al. High expression of Wilms' tumor suppressor gene predicts poor prognosis in breast cancer patients. Clin Cancer Res. 2002;8(5):1167–71. Wang X, Gao P, Lin F, Long M, Weng Y, Ouyang Y, et al. Wilms' tumour suppressor gene 1 (WT1) is involved in the carcinogenesis of Lung cancer through interaction with PI3K/Akt pathway. Cancer Cell Int. 2013;13(1):114. McCart Reed AE, Song S, Kutasovic JR, Reid LE, Valle JM, Vargas AC, et al. Thrombospondin-4 expression is activated during the stromal response to invasive breast cancer. Virchows Arch. 2013;463(4):535–45. Niu J, Lin Y, Liu P, Yu Y, Su C, Wang X. Microarray analysis on the lncRNA expression profile in male hepatocelluar carcinoma patients with chronic hepatitis B virus infection. Oncotarget. 2016;7(46):76169–80. He HL, Lee YE, Shiue YL, Lee SW, Lin LC, Chen TJ, et al. PLA2G2A overexpression is associated with poor therapeutic response and inferior outcome in rectal cancer patients receiving neoadjuvant concurrent chemoradiotherapy. Histopathology. 2015;66(7):991–1002. Korff S, Woerner SM, Yuan YP, Bork P, von Knebel Doeberitz M, Gebert J. Frameshift mutations in coding repeats of protein tyrosine phosphatase genes in colorectal tumors with microsatellite instability. BMC Cancer. 2008;8:329. Wang H, Zhou X, Zhang Y, Zhu H, Zhao L, Fan L, et al. Growth arrest-specific gene 1 is downregulated and inhibits tumor growth in gastric cancer. FEBS J. 2012;279(19):3652–64. Gobeil S, Zhu X, Doillon CJ, Green MR. A genome-wide shRNA screen identifies GAS1 as a novel melanoma metastasis suppressor gene. Genes Dev. 2008;22(21):2932–40. Ma Y, Qin H, Cui Y. MiR-34a targets GAS1 to promote cell proliferation and inhibit apoptosis in papillary thyroid carcinoma via PI3K/Akt/Bad pathway. Biochem Biophys Res Commun. 2013;441(4):958–63. Notting I, Buijs J, Mintardjo R, van der Horst G, Vukicevic S, Lowik C, et al. Bone morphogenetic protein 7 inhibits tumor growth of human uveal melanoma in vivo. Invest Ophthalmol Vis Sci. 2007;48(11):4882–9. Tsuboi A, Oka Y, Ogawa H, Elisseeva OA, Li H, Kawasaki K, et al. Cytotoxic T-lymphocyte responses elicited to Wilms' tumor gene WT1 product by DNA vaccination. J Clin Immunol. 2000;20(3):195–202. Oka Y, Tsuboi A, Fujiki F, Li Z, Nakajima H, Hosen N, et al. WT1 peptide vaccine as a paradigm for "cancer antigen-derived peptide"-based immunotherapy for malignancies: successful induction of anti-cancer effect by vaccination with a single kind of WT1 peptide. Anticancer Agents Med Chem. 2009;9(7):787–97. Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-1906454","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":125320099,"identity":"07436db7-f684-4263-abbd-1586ba04d6c5","order_by":0,"name":"Adnan Mansoor","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-6902-3935","institution":"University of Calgary","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Adnan","middleName":"","lastName":"Mansoor","suffix":""},{"id":125320100,"identity":"e7b55007-e5f0-4959-a715-5c63b3b9dfb0","order_by":1,"name":"Ariz Akhter","email":"","orcid":"","institution":"Calgary Lab Services","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ariz","middleName":"","lastName":"Akhter","suffix":""},{"id":125320101,"identity":"f57db13b-b4b9-40a9-bfde-452ee0403221","order_by":2,"name":"Meer-Taher Shabani-Rad","email":"","orcid":"","institution":"1.\tDepartment of Pathology \u0026 Laboratory Medicine, University of Calgary,","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meer-Taher","middleName":"","lastName":"Shabani-Rad","suffix":""},{"id":125320102,"identity":"12086093-f59e-4747-9928-cc5978a8499f","order_by":3,"name":"Jean Deschenes","email":"","orcid":"","institution":"University of Alberta","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jean","middleName":"","lastName":"Deschenes","suffix":""},{"id":125320103,"identity":"44072542-a121-4703-9c08-075491718637","order_by":4,"name":"Asli Yilmaz","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Asli","middleName":"","lastName":"Yilmaz","suffix":""},{"id":125320104,"identity":"4566638d-2dfe-488b-aa3c-e306b0143096","order_by":5,"name":"Kiril Trpkov","email":"","orcid":"https://orcid.org/0000-0003-3142-8846","institution":"University of Calgary and Alberta Precision Labs","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kiril","middleName":"","lastName":"Trpkov","suffix":""},{"id":125320105,"identity":"0fa47e9a-cbe5-477f-917b-7d60b2f093a6","order_by":6,"name":"Douglas Stewart","email":"","orcid":"","institution":"University of Calgary, Tom Baker Cancer Centre","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Douglas","middleName":"","lastName":"Stewart","suffix":""}],"badges":[],"createdAt":"2022-07-28 17:51:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1906454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1906454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24679361,"identity":"0193dd99-3a28-4642-adb8-3f64efe4a01d","added_by":"auto","created_at":"2022-08-02 16:55:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":363364,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering-based heat map reflecting differential gene expression between PTL and nodal DLBCL RNA samples.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure1MansooretalPTLGEP.png","url":"https://assets-eu.researchsquare.com/files/rs-1906454/v1/023e4ffc612e0a06d583089e.png"},{"id":24679362,"identity":"d8cbc4de-396c-49fc-9282-d67b55daef0a","added_by":"auto","created_at":"2022-08-02 16:55:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1439094,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering-based heat map reflecting differential expression of selected miRNA expression between PTL and nodal DLBCL samples.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure2MansooretalPTLGEP.png","url":"https://assets-eu.researchsquare.com/files/rs-1906454/v1/518a80a4e1c8bd1bba076259.png"},{"id":24875355,"identity":"912a933e-3da7-4e84-beff-5120bf20697a","added_by":"auto","created_at":"2022-08-06 18:30:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1163833,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1906454/v1/d3c2801b-d024-443d-9436-e80fa35676ce.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Primary testicular lymphoma demonstrates an over-expression of Wilms tumor 1 gene and different mRNA and miRNA expression profiles compared to nodal diffuse large B-cell lymphoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) is the most common subtype of Non-Hodgkin lymphoma and has significant genetic, pathologic, and clinical heterogeneity (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The two most common subtypes are germinal center B-cell-like DLBCL (GCB-DLBCL) and activated B-cell-like DLBCL (ABC-DLBCL) based on the cell of origin (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Primary testicular lymphoma and primary central nervous system lymphoma are subtypes of DLBCL, localized to extra nodal organs (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In fact, both PTL and central nervous system DLBCL are considered to originate within immune sanctuary sites, protected by blood endothelial barriers (\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePTL is an infrequent disease, accounting for only 1% of non-Hodgkin lymphoma (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Based on gene expression profiling and immunohistochemistry-based assay, most of PTLs are considered ABC-DLBCL (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Patients with PTL have a median age at diagnosis of 66\u0026ndash;68 years. Although the prognosis of DLBCL improved with the addition of rituximab to CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone) chemotherapy, PTL requires additional therapies, including scrotal radiotherapy and CNS prophylaxis, due to high-risk of recurrence, especially in the CNS and the contralateral testis (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Relapse can also occur in other extra-nodal sites, including soft tissue, lung, liver and skin (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDLBCL of immune sanctuary sites have an enhanced somatic hypermutation of the immunoglobulin heavy chain variable regions and show a frequent loss of HLA expression (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), resulting in evasiveness of the host antitumor response. Previous studies demonstrated that somatic mutations of \u003cem\u003eMyD88, CD79B\u003c/em\u003e and an upregulation of \u003cem\u003eNF-Kb, PDL-1\u003c/em\u003e and PDL2 (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), may contribute to the pathogenesis and account for the poor prognosis of PTL. Further studies are however needed to identify new biomarkers that can improve the understanding of PTL biology and prognosis, and potentially lead to discovery of new therapeutic targets.\u003c/p\u003e \u003cp\u003eThe Wilms tumor 1 (\u003cem\u003eWT1)\u003c/em\u003e encodes a zinc finger transcription factor that has emerged as an important regulator of normal and malignant hematopoiesis. \u003cem\u003eWT1\u003c/em\u003e is highly expressed at the time of embryogenesis in the development of the kidney, gonads, testis, ovary, and the mesothelial lining of the abdominal and thoracic cavity. \u003cem\u003eWT1\u003c/em\u003e was originally recognized as a tumor suppressor gene, but subsequent studies indicated that it may play an oncogenic function in leukemia (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), as well as solid neoplasms, including colon carcinoma, head and neck squamous cell carcinoma, and carcinomas of the pancreas, ovary and lung (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Therefore, \u003cem\u003eWT1\u003c/em\u003e is a universal tumor antigen that may represent a good therapeutic target for the development of gene therapy strategies. In acute leukemia, \u003cem\u003eWT1\u003c/em\u003e also acts as potential prognostic factor and as a marker of minimal residual disease, as well as target of vaccination immunotherapy (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, to date, the role \u003cem\u003eWT1\u003c/em\u003e in the pathogenesis of lymphoid malignancies remains mostly unknown.\u003c/p\u003e \u003cp\u003eIn the present study, we evaluated diagnostic tissue samples from PTL patients and compared them with those from nodal DLBCL patients, diagnosed using the World Health Organization (WHO) criteria (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). We examined the differential expression of mRNA with specific reference to \u003cem\u003eWT1\u003c/em\u003e gene expression in these samples. We also investigated epigenetic modulation of \u003cem\u003eWT1\u003c/em\u003e expression, while screening a series of miRNA related to common cancer signaling pathways.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe assembled a cohort of PTL patients (N\u0026thinsp;=\u0026thinsp;31), identified in our institutional database between 1995 and 2012, for whom formalin fixed paraffin embedded (FFPE) tissue was available. This cohort was compared with a group of nodal DLBCL (n\u0026thinsp;=\u0026thinsp;27) from our institution, matched for age, gender, stage and cell of origin (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The inclusion criteria included lack of evidence of primary or secondary CNS involvement and availability of sufficient and adequate tissue material (biopsies\u0026thinsp;\u0026gt;\u0026thinsp;1 mm in diameter, well preserved, processed, and fixed). Histologic diagnosis of PTL and nodal DLBCL was reviewed by two hematopathologist (AM and MTSR) to ensure diagnostic accuracy, according to the 2008 WHO Classification of Tumors of Hematopoietic and Lymphoid tissues (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This study was performed in accordance with the Declaration of Helsinki and was approved by the Health Research Ethics Board of Alberta (HREBA),( HREBA study #.CC-16-0218; REN 6 dated December, 29, 2021).\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\u003eClinical Characteristics of PTL and DLBCL patients in current cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTL\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enDLBCL\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003cp\u003eMedian (Range)\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (43\u0026ndash;90)\u003c/p\u003e \u003cp\u003e23 (74%)\u003c/p\u003e \u003cp\u003e08 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (31\u0026ndash;74)\u003c/p\u003e \u003cp\u003e15 (56%)\u003c/p\u003e \u003cp\u003e12 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (100%)\u003c/p\u003e \u003cp\u003e00 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (93%)\u003c/p\u003e \u003cp\u003e01 (07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance status\u003c/p\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003cp\u003e2\u0026ndash;4\u003c/p\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (74%)\u003c/p\u003e \u003cp\u003e05 (16%)\u003c/p\u003e \u003cp\u003e03 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (67%)\u003c/p\u003e \u003cp\u003e09 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003cp\u003eI-II\u003c/p\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (85%)\u003c/p\u003e \u003cp\u003e06 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (37%)\u003c/p\u003e \u003cp\u003e17 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPI*\u003c/p\u003e \u003cp\u003eLow (0\u0026ndash;1)\u003c/p\u003e \u003cp\u003eIntermediate (2\u0026ndash;3)\u003c/p\u003e \u003cp\u003eHigh (4\u0026ndash;5)\u003c/p\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (74%)\u003c/p\u003e \u003cp\u003e03 (10%)\u003c/p\u003e \u003cp\u003e03 (10%)\u003c/p\u003e \u003cp\u003e02 (06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e09 (33%)\u003c/p\u003e \u003cp\u003e14 (52%)\u003c/p\u003e \u003cp\u003e04 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum LDH*\u003c/p\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (71%)\u003c/p\u003e \u003cp\u003e06 (19%)\u003c/p\u003e \u003cp\u003e03 (09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (44%)\u003c/p\u003e \u003cp\u003e15 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell-of \u0026ndash;Origin (Lymph2CX)\u003c/p\u003e \u003cp\u003eABC type*\u003c/p\u003e \u003cp\u003eGCB type*\u003c/p\u003e \u003cp\u003eIntermediate type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (81%)\u003c/p\u003e \u003cp\u003e04 (13%)\u003c/p\u003e \u003cp\u003e02 (06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (85%)\u003c/p\u003e \u003cp\u003e04 (15%)\u003c/p\u003e \u003cp\u003e00 (00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*IPI, International Prognostic Index; LDH, lactate dehydrogenase; GCB, germinal center B cell like; ABC, Activated B cell like\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e A statement on ethics approval and consent (even where the need for approval was waived); the name of the ethics committee that approved the study and the committee\u0026rsquo;s reference number if appropriate; a statement that the study was performed in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction\u003c/h2\u003e \u003cp\u003eRNA was extracted using the Ambion Kit (ThermoFisher scientific, Waltham, MA USA), utilizing duplicate cores (1 mm), harvested off the areas with maximum tumor concentration in the diagnostic FFPE blocks. The RNA concentration was quantified using the Nanodrop UV-VIS spectrophotometer (Nanodrop Technologies, Wilmington, DE USA) and the integrity was assessed using a Bio-analyzer 2100 and RNA Nano Chip assay (Agilent Technologies, Wilmington, DE USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eNanoString nCounter Assay\u003c/h2\u003e \u003cp\u003eTotal RNA samples were processed according to the manufacturer\u0026rsquo;s protocol for the nCounter Human miRNA Expression Assay v3 kit and for gene expression PAN-cancer pathway code set, containing 770 key genes related to major pathways in cancer biology (NanoString, Seattle, WA USA). Briefly, using nCounter\u0026trade; technology, miRNA and mRNA expression analysis was conducted for each sample. The probes were hybridized to 300ng of total RNA for 20 h at 65\u0026deg;C and were applied to the nCounterTM Prep Station for automated removal of excess probe and immobilization of probe-transcript complexes on a streptavidin-coated cartridge. The data were collected using the nCounterTM Digital Analyzer by counting the individual barcodes. miRNA/mRNA analysis. The normalization of raw data was conducted using the nSolver Analysis Software v3.0 (NanoString Technologies). miRNA raw counts were normalized with ligation control and the background level of expression for each sample was calculated using the mean level of the negative controls (plus two standard deviations of the mean). PAN cancer mRNA raw counts were normalized to internal levels of 40 reference genes. Normalized data were log2-transformed and used for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCell-of-origin determination (Lymph2CX assay)\u003c/h2\u003e \u003cp\u003eWe used 250ng of total RNA for digital gene expression profiling to determine the cell-of-origin of PTL and nodal DLBCL cases utilizing NanoString platform. The cell-of-origin was assigned using Lymph2CX- 20 gene expression-based assay (8 gene overexpressed in ABC, 7 gene overexpressed in GCB subtypes of PTL and nodal DLBCL and 5 housekeeping genes), as described by Scott et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Briefly, 250ng total RNA was hybridized with Lymph2cx code sets at 65\u0026deg;C for 20 hours (16-22hours). After hybridization, purification was performed in nCounter\u0026trade; Prep Station for automated removal of excess probes, while immobilized target-probe complex was captured on the cartridge coated with streptavidin. This cartridge was used on nCounter\u0026trade; digital analyzer for fully automated imaging and data collection, as per manufacturer instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003emiRNA target prediction\u003c/h2\u003e \u003cp\u003eTo assess the potential association between differentially expressed mRNA and miRNA, we used miRTarBase (ver 6.0), Starbase (v 5.0) and DIANA TOOLS to predict potential targets of the miRNAs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used SPSS software v20.0 (IBM, Armonk, NY USA) for the statistical evaluation, and nSolver software v3.0 (NanoString Technologies) for the normalization of the raw counts. Hierarchical clustering and principal component analyses were performed on Qlucore Omics Explorer v3.2 (Lund, Sweden). Results with fold change\u0026thinsp;\u0026ge;\u0026thinsp;2.0 and \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eThe clinical characteristics of the patient cohorts in both groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For PTL patients, the median age at diagnosis was 68 years (range, 43\u0026ndash;90 years); 85% of patients had stage I-II disease, 74% had a low risk IPI score of 0\u0026ndash;1, and 81% had ABC cell of origin. For nodal DLBCL patients, the median age was 55 years (range, 31\u0026ndash;74 years), 37% had stage I-II disease, 33% had a low risk IPI score of 0\u0026ndash;1, and 85% had ABC cell of origin. Hence, PTL and nodal DLBCL patients were comparable in age, gender, stage and cell of origin (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOverexpression of WT1 in primary testicular lymphoma patients\u003c/h2\u003e \u003cp\u003eWe observed high expression of \u003cem\u003eWT1\u003c/em\u003e gene in PTL, compared to the nodal DLBCL, based on PAN cancer code set (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The principal component analysis (PCA) and the hierarchical clustering showed that \u003cem\u003eWT1\u003c/em\u003e expression in PTL was 3.2-fold higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) than in nodal DLBCL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Intermediate cases were excluded in the hierarchical clustering. In addition, \u003cem\u003eWT1\u003c/em\u003e associated pathway genes were also significantly higher in PTL patients: \u003cem\u003eTHBS4\u003c/em\u003e (2.8-fold change, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), \u003cem\u003ePLA2G2A\u003c/em\u003e (2.7-fold change, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), \u003cem\u003ePTPN5\u003c/em\u003e (2.4- fold change, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and I\u003cem\u003eFNA17\u003c/em\u003e (2.1- fold change, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002). We also found a set of genes that were downregulated in PTL patients compared to nodal DLBCL, including: \u003cem\u003eBMP7\u003c/em\u003e (2.9- fold change, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), \u003cem\u003eLAMB3\u003c/em\u003e (2.8- fold change, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), \u003cem\u003eGAS1\u003c/em\u003e (2.3- fold change, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), \u003cem\u003eMMP7\u003c/em\u003e (2.2- fold change, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and \u003cem\u003eLAMC2\u003c/em\u003e (2.1- fold change, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) The analysis of GCB and ABC cell of origin subtypes did not identify any significant differences between the analyzed cohorts (data not shown)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential gene expression in PTL compared to nDLBCL as analyzed by NanoString\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eGenes Upregulated in PTL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFold change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccession no.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWilms tumor 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_005157.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHBS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThrombospondin 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_004302.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLA2G2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhospholipase A2 group IIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_145259.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTPN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein tyrosine phosphatase, non-receptor type 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_001616.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFNA17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFNA17\u0026nbsp;interferon alpha 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_181690.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGene Downregulated in PTL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFold change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccession no.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBone morphogenetic protein 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_004656.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAMB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaminin subunit beta 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_012342.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth arrest specific 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_000657.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatrix metallopeptidase 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_138761.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAMC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaminin subunit gamma 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_004049.2\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\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNanoString based analysis of miRNA in PTL and DLBCL\u003c/h2\u003e \u003cp\u003eWe compared the miRNA signatures between PTL (n\u0026thinsp;=\u0026thinsp;31) and a set of nodal DLBCL, based on the available samples. 800 miRNAs were assessed in PTL and nodal DLBCL using the NanoString platform. A supervised hierarchical clustering revealed different miRNA expression patterns in PTL and nodal DLBCL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, in PTL there was a higher expression of 122 miRNAs, with a median expression\u0026thinsp;\u0026ge;\u0026thinsp;2.0-fold change in log2 and FDR 0.01. Twenty-six miRNAs were found to be associated with \u003cem\u003eWT1, PLA2G2A, GAS1, BMP7, MMP7\u003c/em\u003e and \u003cem\u003eLAMC2\u003c/em\u003e genes, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential miRNA expression in PTL compared to nDLBCL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFold change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTarget gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emirTarbase/Starbase/DIANA Tools\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-15a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-361-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-16-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-27b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-199a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-199b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-132-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-128-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-127-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMYC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-9-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePLA2G2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-148a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGAS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-34a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGAS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-340-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGAS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGAS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-1290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGAS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-22-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-24-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-342-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-let-7b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-126-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMMP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-146a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLAMC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-29a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLAMC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-29b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLAMC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLAMC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\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\u003eWe also analyzed the miRNAs expression based on the cell-of origin in PTL and nodal DLBCL, but we found no significant differences of miRNA associated with the ABC and GCB subtypes in each group (data not shown).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to simultaneously analyze and compare both mRNA and miRNA expression profiles in PTL and nodal DLBCL, and to implicate \u003cem\u003eWT1\u003c/em\u003e in PTL biology. We analyzed 770 mRNA genes (includes 13 major cancer pathway genes) and 800 miRNA genes, and we identified 37 mRNA and 123 miRNA genes that were differentially expressed in PTL and nodal DLBCL. We observed significantly higher expression of miRNAs, like hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-199b-5p, hsa-miR-132-3p, hsa-miR-128-3p in the PTL group.\u003c/p\u003e \u003cp\u003eWe also demonstrated that PTL mostly has an ABC type (81%) of gene expression, which correlates consistently with poorer prognosis (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). PTL patients have also been shown to have a low frequency of BCL2 rearrangements, higher frequency of BCL2 amplification, and higher number of BCL6 rearrangements, (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), as well as higher expression of CD44 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and BCL2 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In PTL, active STAT3 and CXCR4 signaling (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and somatic mutations in \u003cem\u003eCD79B\u003c/em\u003e and \u003cem\u003eMyD88\u003c/em\u003e (\u0026gt;\u0026thinsp;70%) lead to constitutive activation of \u003cem\u003eNF-kb\u003c/em\u003e and \u003cem\u003eJAK/STAT\u003c/em\u003e signaling (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). All these factors may explain why most of PTL patients exhibit a highly aggressive ABC type. We also found no significant differences in the gene expression profiles of ABC and GCB subtypes between the groups.\u003c/p\u003e \u003cp\u003eEmerging evidence indicates that miRNAs likely contribute to the pathogenesis of all human malignancies (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), through tumor suppression and ontogenetic (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) mechanisms. We found a higher expression of specific miRNAs (miRNA-15a-5p, hsa-miR-16-5p, hsa-miR-361-5p, hsa-miR-27b-3p, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-132-3p and hsa-miR-128-3p) in PTL patients compared to nodal DLBCL. This miRNA profile has been associated with \u003cem\u003eWT1\u003c/em\u003e gene target. The regulatory function of these miRNA has been defined previously (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). For example, an upregulation of miRNA-361, miRNA-15a/16 suppresses the \u003cem\u003eWT1\u003c/em\u003e expression in non-small-cell lung carcinoma and acute leukemia, but in cervical carcinoma, miRNA-361 has been reported to be upregulated and acting as an oncogene (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These findings indicate that the roles of miRNAs are diverse and tissue specific. Expression of hsa-miR-9-5p target \u003cem\u003ePLA2G2\u003c/em\u003e was also found to be enriched in immune privileged sites (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Our study also demonstrated a higher expression of miR-127 in PTL, consistent with a previous report (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Further, higher expression of miRNAs, like hsa-miR-148a-3p, 34a-5p, 340-5p, 421,1290 correlate with downregulation of \u003cem\u003eGAS1\u003c/em\u003e. Similarly, hsa-miR-22-3p, 24-3p, 342-3p, hsa-let-7b-5p downregulate \u003cem\u003eBMP7\u003c/em\u003e, while hsa-miR-126-3p targets \u003cem\u003eMMP7.\u003c/em\u003e In addition, hsa-miR-146a-5p, 29a-3p, 29b-3p, 29c-3p have been found to downregulate \u003cem\u003eLAMC2\u003c/em\u003e. These MiRNA-mRNA target interactions have been validated by the miRTarbase, Starbase and DIANA tools databases. Hence, the miRNA expression profile in this series validates the epigenetic modulation of various genes in PTL, exemplified through the mRNA differential expression.\u003c/p\u003e \u003cp\u003e \u003cem\u003eWT1\u003c/em\u003e gene was initially discovered as a tumor suppressor gene responsible for Wilms tumor, a renal carcinoma affecting primarily children (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). In the early publications, \u003cem\u003eWT1\u003c/em\u003e was described as a tumor suppressor gene, similar to the Bcl-2 expression in various cancer cell lines, such as HeLa (cervical cancer) (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), LNln3 (prostate cancer) (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) and DHL-4 (follicular lymphoma) (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Other studies also supported the growth inhibitory effects of \u003cem\u003eWT1\u003c/em\u003e (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), and demonstrated that forced expression of \u003cem\u003eWT1\u003c/em\u003e results in growth arrest and differentiation of progenitor cells. However, recent studies have postulated a role of \u003cem\u003eWT1\u003c/em\u003e as an oncogene. For example, higher expression of \u003cem\u003eWT1\u003c/em\u003e in leukemia was shown to correlate with poor prognosis (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The oncogenic role of \u003cem\u003eWT1\u003c/em\u003e was also established in various solid carcinomas, including colon (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), pancreas (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), ovary (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), brain (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), lung (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) and breast (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Thus, our finding of a possible oncogenic role of \u003cem\u003eWT1\u003c/em\u003e in PTL is consistent with these reports. \u003cem\u003eWT1\u003c/em\u003e also activates the downstream PI3K/AKT pathway, as shown in lung cancer (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Additionally, an enhanced efficacy of the chemotherapeutic agent Cisplatin (ddp) has been found following the inhibition of WT1/ PI3K/AKT pathway in lung carcinoma. We also found that downstream PI3K/AKT genes, like \u003cem\u003eTHBS4, PTPN5\u003c/em\u003e and \u003cem\u003ePLA2G2A\u003c/em\u003e are upregulated in PTL, indirectly supporting the involvement of an activated PI3K /AKT pathway in PTL. However, our data could not confirm an over-expression of PI3K or AKT mRNA molecules in PTL. These discrepant findings may be related to altered targets, limited target coverage in our panel or degradation of mRNA in formalin tissue. However, \u003cem\u003eTHBS4\u003c/em\u003e expression was associated with tumor invasion in breast carcinoma (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) and was implicated in the development of hepatocellular carcinoma (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). \u003cem\u003ePLA2G2A\u003c/em\u003e overexpression has been reported as a poor prognostic factor in rectal carcinoma (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). \u003cem\u003ePTPN5\u003c/em\u003e is a key regulator of signal transduction and contributor to the carcinogenesis (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). \u003cem\u003eGAS1\u003c/em\u003e has been shown to act as a tumor suppressor in gastric carcinoma (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) and as an inhibitor of metastasis in melanoma (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The association between high expression of miRNA-34a downregulating the \u003cem\u003eGAS1\u003c/em\u003e in PTL in the current study is in line with the similar expression pattern previously shown in papillary thyroid carcinoma (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Our study also found that genes like \u003cem\u003eBMP7, MMP7, LAMB3\u003c/em\u003e and \u003cem\u003eLAMC2\u003c/em\u003e were significantly downregulated in PTL. Aberrant expression of \u003cem\u003eBMP7\u003c/em\u003e and its association with the miRNA in the tumor microenvironment is associated with various cancer tissues. For example, it has been shown that an increased expression of \u003cem\u003eBMP7\u003c/em\u003e inhibits the growth of the normal and malignant cells (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eWT1\u003c/em\u003e as a tumor antigen has been found in acute leukemias, and several studies have shown increased \u003cem\u003eWT1\u003c/em\u003e expression in leukemia cells compared to normal hematopoietic cells (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Due to higher expression in various hematologic malignancies and solid tumors, \u003cem\u003eWT1\u003c/em\u003e may be used as a promising immunotherapy target. The development of vaccines targeting WT1 antigen depends on the characterization of peptides associated with HLA molecules on the cell surface. In this context, different \u003cem\u003eWT1\u003c/em\u003e epitopes have been identified with this potential (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). These observations presented in our study may open the opportunity to exploit the higher expression of \u003cem\u003eWT1\u003c/em\u003e gene as a target for immunotherapy in PTL.\u003c/p\u003e \u003cp\u003eIn conclusion, we found an overexpression of \u003cem\u003eWT1\u003c/em\u003e in PTL patients, which is a novel finding. We also found that WT1 associated pathway genes were upregulated in PTL. We postulate that a miRNA subset may target \u003cem\u003eWT1\u003c/em\u003e through the \u003cem\u003ePI3K/AKT\u003c/em\u003e pathway in PTL. Further studies are needed to investigate the biological role of \u003cem\u003eWT1\u003c/em\u003e function in PTL, as well as the potential of \u003cem\u003eWT1\u003c/em\u003e signaling as a possible therapeutic target.\u003c/p\u003e"},{"header":"Declarations ","content":"\u003cp\u003eAcknowledgement:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors would like to acknowledge all the anatomical pathologists of Alberta Precision Laboratories (APL) \u0026nbsp;who performed initial review and reporting of the diagnostic biopsies on patients included in this study. Authors also acknowledge the support provided by the APL research department in retrieval of diagnostic tissues archival material for the patients in this cohort.\u003c/p\u003e\n\u003cp\u003eConflict of Interest:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing financial interests in relation to the work presented.\u003c/p\u003e\n\u003cp\u003eEthics approval:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was performed in accordance with the Declaration of Helsinki and was approved by the Health Research Ethics Board of Alberta (HREBA),(CC-16-0218; REN 6 dated December, 29, 2021).\u003c/p\u003e\n\u003cp\u003eAuthor Contributions:\u003c/p\u003e\n\u003cp\u003eAM and AA contributed equally to this work. The parent project of this study was conceived by DS and AM, who also devised all the protocols. DS designed the study, performed the patient clinical data review, and edited the final manuscript. AM designed the study, reviewed final pathology, performed the statistical and bioinformatic analyses and wrote the manuscript. AA performed experimental work, compiled data, performed QA and wrote manuscript. JD, AY, MSTR and KT contributed patients, review pathology and provided critical review and edits to the manuscript\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by research grant from\u0026nbsp;Alberta Cancer Foundation (grant # 25999)\u0026nbsp;\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSwerdlow S CE, Harris N, Jaffe E, Pileri S, Stein H, Thiele J, Vardiman J. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. 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Anticancer Agents Med Chem. 2009;9(7):787\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1906454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1906454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) shows a high degree of clinical and biological heterogeneity. Primary testicular lymphoma (PTL) is an extra nodal variant of DLBCL associated with higher risk of recurrence including contralateral testicle and central nervous system sanctuary sites. Several molecular aberrations including somatic mutation of \u003cem\u003eMyD88, CD79B\u003c/em\u003e and upregulation of \u003cem\u003eNF-Kb, PDL-1\u003c/em\u003e and \u003cem\u003ePDL-2\u003c/em\u003e are thought to contribute to the pathogenesis and poor prognosis of PTL. However, additional biomarkers are needed that may improve the prognosis and help understand the PTL biology and possibly lead to new therapeutic targets. RNA from diagnostic tissue biopsies of PTL (n=31) and matched nodal DLBCLs (n=27) patients, were evaluated by mRNA and miRNA expression. Expression of 770 key genes were screened, utilizing nCounter Human miRNA and PAN-cancer pathway mRNA assays utilizing nCounter analysis System (Nanostring technologies). PTL and nodal DLBCL patients were comparable in age, gender, stage, and putative cell of origin (P\u0026gt;0.05).\u003cem\u003e WT1\u003c/em\u003e expression was higher in PTL compared to nodal DLBCL (\u0026gt; 3-fold; P= 0.0001). In addition, we found that \u003cem\u003eWT1 \u003c/em\u003eassociated pathway genes \u003cem\u003eTHBS4, PTPN5, PLA2G2A\u003c/em\u003e and \u003cem\u003eIFNA17\u003c/em\u003e were upregulated in PTL (\u0026gt;2.0-fold, P\u0026lt;0.005). The miRNAs targeting \u003cem\u003eWT1 \u003c/em\u003e(hsa15a-5p, hsa-miR-16-5p, hsa-miR-361-5p, hsa-miR-27b-3p, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-132-3p, hsa-miR-128-3p) were upregulated in PTL compared to nodal DLBCL (≥2.0-fold; FDR 0.01). We also found lower expression of \u003cem\u003eBMP7, LAMB3, GAS1, MMP7 and LAMC2\u003c/em\u003e (\u0026gt;2.0- fold, P\u0026lt;0.01) in PTL compared to nodal DLBCL. Our study demonstrated an overexpression of \u003cem\u003eWT1\u003c/em\u003e in PTL compared to nodal DLBCL. We hypothesize that a select miRNA subset targets the \u003cem\u003eWT1\u003c/em\u003e expression and influences the \u003cem\u003ePI3k/Akt\u003c/em\u003e pathway in PTL. Additional studies are needed to further investigate the biological role of \u003cem\u003eWT1\u003c/em\u003e in PTL and its potential as a therapeutic target.\u003c/p\u003e","manuscriptTitle":"Primary testicular lymphoma demonstrates an over-expression of Wilms tumor 1 gene and different mRNA and miRNA expression profiles compared to nodal diffuse large B-cell lymphoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-02 16:55:22","doi":"10.21203/rs.3.rs-1906454/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54e5f2f0-7790-4746-8085-23b380f0e879","owner":[],"postedDate":"August 2nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-08-06T18:30:32+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-02 16:55:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1906454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1906454","identity":"rs-1906454","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}