Skip to content

Advertisement

BMC Cancer

What do you think about BMC? Take part in

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Clinical significance of PCDH10 promoter methylation in diffuse large B-cell lymphoma

BMC Cancer201717:815

https://doi.org/10.1186/s12885-017-3810-7

Received: 23 June 2016

Accepted: 21 November 2017

Published: 4 December 2017

Abstract

Background

PCDH10, one of the non-clustered protocadherins, is identified as a tumor suppressor gene in many tumors. Recently, promoter methylation of PCDH10 was found in diffuse large B-cell lymphoma (DLBCL) but not in normal lymph nodes, suggesting that its epigenetic aberrance is essential to the lymphomagenesis. However, there are few studies on the clinicopathological relevance and prognostic significance of PCDH10 methylation status in DLBCL.

Methods

One hundred-seven cases of DLBCL between Jan 2009 and Jul 2010 were selected to extract genomic DNA and perform bisulfite modification. Their methylation status of PCDH10 promoter were accessed by methylation-specific PCR (MSP) with methylated and unmethylated primers. Analysis of overall survival and clinicopathological correlation were conducted.

Results

PCDH10 hypermethylation were found in 54.2% (58/107) of DLBCL cases, but only 12.5% (1/8) in reactive lymph node/follicular hyperplasia. In RCHOP-treated cohort, promoter methylation of PCDH10 is an independent prognostic indicator of worse overall survival (p = 0.017; HR 4.045; 95%CI 1.287–12.711) and worse progress-free survival (p = 0.014; HR 2.977; 95%CI 1.245–7.119). Whereas, PCDH10 hypermethylation wasn’t correlated with MYC translocation and cell of origin classification using Hans model.

Conclusions

PCDH10 methylation status could serve as a valuable biomarker for risk classification, and a potential therapeutic target for demethylating drugs in DLBCL in the future.

Keywords

PCDH10Diffuse large B-cell lymphomaMethylationPrognosis

Background

PCDH10 is one of the non-clustered protocadherins encoding calcium-dependent adhesion protein, participating in multiple molecular functions, such as cell adhesion, colony formation and signaling regulation [1, 2]. It was identified as a tumor suppressor gene in many tumors, including nasopharyngeal carcinoma [2], gastric carcinoma [3] and multiple myeloma [4]. Epigenetic disruption of PCDH10 was proved to be the key event leading to the transcriptional silencing or reduction [2]. Recent studies have shown that the methylation of PCDH10 promoter could serve as a prognostic marker in gastric carcinoma [3, 5] and non-small-cell lung cancer [6].

Diffuse large B-cell lymphoma (DLBCL) is a molecular heterogeneity disease with wide spectrum of survival. Compared to the conventional CHOP chemotherapy, the prognosis of patients has been significantly improved by the addition of Rituximab, however there are still ~35% of DLBCL that are poor response [7]. International Prognostic Index (IPI) is confirmed as a robust prognostic indicator [8] but with little insight into the molecular mechanism. The cell of origin (COO) classification based on gene expression profiling shows great values of prognostic stratification [9] and clinical therapy selection [10], but it cannot be applied to the routine practice due to technical obstacles. Recently, silence or reduction of PCDH10 and its promoter methylation was found in 80%(16/20) of DLBCL samples but not in the normal lymph nodes, suggesting that epigenetic aberrance of PCDH10 is essential to the lymphomagenesis [11]. However, there are few studies on its clinicopathological relevance. Herein, our study will explore the relationship between PCDH10 methylation status and prognostic significance in our cohort of DLBCL.

Methods

Study population

One hundred and seven cases of DLBCL with formalin-fixed, paraffin-embedded (FFPE) tissue blocks, were enrolled into this study between Jan 2009 and Jul 2010. All patients were diagnosed at National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences according to the 4th edition of the WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. Treatment and prognosis data were collected using medical records review and telephone survey. Patients who received resection plus RCHOP or RCHOP alone were included in the survival analysis. Eight cases of reactive lymph node/follicular hyperplasia (RL/FH), six cases of chronic tonsillitis and nine cases of Castleman disease were selected as controls.

Methylation-specific PCR(MSP)

Genomic DNA were extracted from FFPE blocks using QIAamp DNA FFPE Tissue Kit (Qiagen, Valencia, CA), and then bisulfite-treated using EZ DNA Methylation-Gold™ Kit (Zymo Research, Irvine, CA). The MSP primers were used as previously described, including methylated forward primer (5′-TCGTTAAATAGATACGTTACGC-3′), methylated reverse primer (5′-TAAAAACTAAAAACTTTCCGCG-3′), unmethylated forward primer (5′-GTTGTTAAATAGATATGTTATGT-3′), and unmethylated reverse primer (5′-CTAAAAACTAAAAACTTTCCACA-3′) [11]. MSP was performed using standard conditions for 40 cycles. The annealing temperatures of methylation and unmethylation primers were 60 °C and 58 °C, respectively. The product lengths of methylation and unmethylation PCR were 153 bps and 156 bps, respectively. The MSP products were checked on 2% the Agarose GEL. The methylated or unmethylated result was determined according to the band produced by which primers. In order to confirm the gel results, 2 of the methylation-positive PCR products were randomly selected to perform Sanger sequencing.

Statistical analysis

The association between PCDH10 methylation status and other clinicopathological characteristics was analyzed using Chi-square test and Fisher exact test. Overall survival (OS) is defined as time from the date of pathologic diagnosis to date of death from any cause. Progress-free survival (PFS) is defined as time from the date of pathologic diagnosis to the date of the progression or death, whichever occurs first. Univariate analysis for each parameter of interests is performed using Cox proportional hazard model. Parameters with p-value <0.05 from the univariate analysis are included in the multi-variate analysis using Cox PH regression model. Kaplan-Meier method is used to estimate the survival rate and plot the survival curve. The cutoff date was Jan 1st 2014. All statistical tests were two sided with p < 0.05 as significance. All statistical analyses were conducted in the IBM SPSS Statistics v22.

Results

Patients’ characteristics

The median age of our DLBCL cohort was 55 years (range19–88 years), about 54% of them were male. Primary nodal lymphoma accounted for 57.94% of the total cases. The proportions of the 4 IPI stratification groups from low to high risk were 44.86%, 28.04%, 18.69% and 8.41% respectively. According to the Hans algorithm, 30.8% cases were classified into GCB subtype. 8.41% patients were found with MYC translocation.

Frequency of PCDH10 promoter methylation in DLBCL

One hundred-seven cases of DLBCL were analyzed using MSP, and 58 (54.2%) cases were found with PCDH10 promoter methylation (Fig. 1). The specificity of MSP was confirmed by Sanger sequencing (Fig. 1). In contrast, only 12.5% (1/8) RL/FH were found with PCDH10 methylation, and all of them demonstrated polyclonal results of IgH and TCR by clonality analysis. Among the 9 cases of Castleman diseases, only 1 was found PCDH10 methylation, and no methylation was detected in the 6 cases of chronic tonsillitis.
Fig. 1

Methylation analysis of PCDH10 promoter in DLBCL. a Representative results of PCDH10 MSP products using agarose GEL. M, methylated PCDH10 promoter (PCR product length was 153 bp); U, unmethylated PCDH10 promoter (PCR product length was 156 bp). b MSP products of methylated PCDH10 promoter were confirmed by Sanger sequencing

Correlation of PCDH10 methylation and clinicopathological characteristics

The primary nodal presentation occupied 51.72% in DLBCL with PCDH10 methylation, which was less than 65.31% in the patients without methylation. However, the correlation analysis by chi-square test didn’t show any significance (p = 0.156). The proportion of GCB subtype in methylation and non-methylation groups were 24.14% and 38.78% respectively(p = 0.102). The MYC translocation in methylation group (5.17%) was less than that in non-methylation counterpart (12.24%), while there wasn’t significantly different. Similarly, no statistical correlation of PCDH10 methylation status with age (p = 0.282), sex (p = 0.575) and IPI risk category (p = 0.683) were obtained in our cohort (Table 1 and Additional file 1: Table S1).
Table 1

Clinicopathological characters and PCDH10 methylation status

 

PCDH10 Methylation

p value

Yes (n = 58)

No (n = 49)

Age at diagnosis

 <60y

39

28

0.282

  ≥ 60y

19

21

Sex

 Male

30

28

0.575

 Female

28

21

Primary site

 Extranodal

28

17

0.156

 Nodala

30

32

IPI risk category

 Low

26

22

0.683

 Low-intermediate

15

15

 High-intermediate

13

7

 High

4

5

 

Hans Algorithm

 GCB

14

19

0.102

 Non-GCB

44

30

MYC FISH Breakapart

 No

55

43

0.296a

 Yes

3

6

aFisher test

Survival analysis of DLBCL with RCHOP treatment

Survival analysis was performed in 65 cases of patients who received RCHOP regiment with or without surgical resection. The median follow-up was 58.7 months (range 3.0–82.7 months).

Patients with methylation of PCDH10 promoter were more likely to have dismal survival. The 3-year OS rate of patients with and without PCDH10 methylation were 61.8% and 86.8% respectively (p = 0.026, Fig. 2). The 3-year PFS rate was 43.6% in patients with PCDH10 methylation, as compared to 73.3% without methylation (p = 0.019, Fig. 2).
Fig. 2

Survival curves of OS (a) and PFS (b) separated by PCDH10 promoter methylation status. Blue and green line indicate PCDH10 with and without methylation, respectively. The p-values were calculated based on univariate Cox proportional regression analysis

In the univariate survival analysis, IPI risk category and PCDH10 methylation status were significantly associated with OS, and both of them were proved to be independent prognostic indicators by multivariate analysis (Table 2). With regard to PFS, three parameters, including IPI risk category, PCDH10 methylation status and treatment, were found statistical significance using univariate analysis, and all of them were identified as independent risk predictors by multivariate analysis (Table 3 and Additional file 2: Table S2).
Table 2

Survival analysis of OS

 

OS

HR_U(95%CI)

p value

HR_M(95%CI)

p value

Age at diagnosis

 <60y

1.000

   

  ≥ 60y

1.393(0.549–3.535)

0.485

  

Sex

 Male

1.000

   

 Female

0.426(0.160–1.137)

0.088

  

Primary site

 Extranodal

1.000.

   

 Nodala

2.267(0.744-6.911)

0.150

  

IPI risk category

 Low

1.000

 

1.000

 

 Low-intermediate

2.029(0.545–7.559)

0.292

2.510(0.669–9.421)

0.173

 High-intermediate

3.385(0.976–11.746)

0.055

2.836(0.815–9.873)

0.101

 High

5.487(1.467–20.517)

0.011

6.986(1.845–26.450)

0.004

Treatment

 RCHOP

1.000

   

 Resection & RCHOP

0.152(0.020–1.145)

0.067

  

Hans Algorithm

 GCB

1.000

   

 Non-GCB

1.419(0.505–3.984)

0.506

  

PCDH10 Methylation

 No

1.000

 

1.000

 

 Yes

3.547(1.166–10.791)

0.026

4.045(1.287–12.711)

0.017

MYC FISH Breakapart

 No

1.000

   

 Yes

1.285(0.170–9.713)

0.808

  

HR_U hazard ratio by univariate analysis, HR_M hazard ratio by multivariate analysis

aDLBCL arising in spleen was considered as primary nodal lymphoma [20]

Table 3

Survival analysis of PFS

 

PFS

HR_U(95%CI)

p value

HR_M(95%CI)

p value

Age at diagnosis

 <60y

1.000

   

  ≥ 60y

1.557(0.731–3.314)

0.251

  

Sex

 Male

1.000

   

 Female

1.067(0.501–2.272)

0.867

  

Primary site

 Extranodal

1.000

   

 Nodala

2.422(0.976-6.007)

0.056

  

IPI risk category

 Low

1.000

 

1.000

 

 Low-intermediate

2.456(0.825–7.313)

0.107

2.474(0.829–7.389)

0.105

 High-intermediate

3.895(1.360–11.151)

0.011

3.687(1.259–10.795)

0.017

 High

9.202(3.156–26.832)

<0.001

8.680(2.874–26.215)

<0.001

Treatment

 RCHOP

1.000

 

1.000

 

 Resection & RCHOP

0.090(0.012–0.668)

0.018

0.119(0.016–0.903)

0.040

Hans Algorithm

 GCB

1.000

   

 Non-GCB

1.410(0.617–3.222)

0.415

  

PCDH10 Methylation

 No

1.000

 

1.000

 

 Yes

2.687(1.173–6.156)

0.019

2.977(1.245–7.119)

0.014

MYC FISH Breakapart

 No

1.000

   

 Yes

0.716(0.097–5.279)

0.743

  

HR_U hazard ratio by univariate analysis, HR_M hazard ratio by multivariate analysis

aDLBCL arising in spleen was considered as primary nodal lymphoma [20]

Since the IPI risk category and PCDH10 methylation were independent factors in both OS and PFS, we combined these two elements to establish a risk stratification. According to the log-rank analysis, the survival curves are greatly separated, and the p values of OS and PFS were 0.002 and <0.001, respectively. The survival of PCDH10 methylation DLBCL was worse than that of non-methylation counterpart in both IPI (1&2) and IPI (3&4) groups (Fig. 3).
Fig. 3

Survival curves of OS (a) and PFS (b) separated by the combination of IPI risk category and PCDH10 methylation status. The blue line indicates IPI (1&2) plus PCDH10 with methylation, the green line indicates IPI (1&2) plus PCDH10 without methylation, the brown line indicates IPI (3&4) plus PCDH10 with methylation and the purple line indicates IPI (3&4) plus PCDH10 without methylation

Discussion

DLBCL is a highly heterogeneous lymphoma at multiple genetic levels. Gene expression profiling identified two kinds of classifications: the cell of origin classification (GCB, ABC, TypeIII) [12] and consensus cluster classification (BCR, OxPhos, HR) [13]. Recently, genome-wide DNA methylation study based on HELP microarray revealed a distinctive epigenetic classification, in which 6 groups (A to F groups) can be categorized and many protocadherins were found to be hypermethylated [14].

PCDH10 is one of non-clustered protocadherins that belong to delta subfamily [1]. Its frequent epigenetic inactivation was reported in DLBCL [11]. In Chambwe et al. study, PCDH10 were hypermethylation in 3 out of 6 epigenetic clusters, occupying approximately 34.3% of the whole cohort [14]. And an even higher frequency (100%) in Narayan et al. study was reported by using MSP [15]. Coupled with our data of 54.2% methylation rate, PCDH10 promoter methylation could be a common event and may play an important role in lymphomagenesis. The frequency of Narayan et al. study seems to be higher than that of our study, probably because the coverage varied among those primers (The detail of coverage of our primers can be found in Additional file 3: Diagram S1).

Our study also examined the RL/FH, chronic tonsillitis and Castleman diseases, and found that the frequency of PCDH10 methylation were 12.5%, 0% and 11.1% respectively. The incidence of methylation was much lower than that of DLBCL, implying that PCDH10 promoter methylation is a characteristic of lymphoid malignancy. This result is consistent with the findings of Narayan et al. study [15].

DNA epigenetic disruption could exert crucial effects on the gene expression. Recent research has revealed several distinct signatures of DNA methylation between GCB and ABC groups of DLBCL [16]. Our study also explored the relationship between PCDH10 methylation and COO classification based on Hans algorithm. No statistical significance was achieved (p = 0.102). This result was in line with the current study using HELP microarray. The data showed that three clusters (C/E/F clusters) with PCDH10 hypermethylation didn’t show any preference to ABC or GCB subtypes, as compared with the rest PCDH10-unmethylated clusters (A/B/D clusters) [14]. Similarly, no significant correlations were found between PCDH10 promoter methylation and other clinicopathological parameters, including the IPI risk category and MYC breakapart.

Furthermore, the prognostic prediction of PCDH10 methylation revealed in many carcinomas was also investigated in our RCHOP-treated cohort [3, 5, 6]. Similar to IPI risk category, PCDH10 methylation status was identified as an independent prognostic parameter for OS and PFS. Patient with PCDH10 methylation showed more aggressive clinical behavior. Unfortunately, the detailed mechanism remained unclear. Narayan et al. study showed that B-NHL cell lines harbored PCDH10 promoter methylation were less sensitive to doxorubicin treatment. And the cell lines with homozygous methylation showed less cytotoxicity as compared with that of heterozygous methylation [15]. These results may explain the short OS and PFS observed in our PCDH10-methylated patients, since doxorubicin is also an important part of R-CHOP regime.

The tumorigenesis mechanisms of PCDH10 were most studied in the multiple myeloma(MM), while little is known in DLBCL. In MM, PCDH10 silencing was reported that can enhance migration of b-catenin to nucleus, forming the complex of b-catenin/LEF/TCF and consequently promoting the MM cell growth [4]. In DLBCL, the aberrance of Wnt/b-catenin signaling also played an essential role in the pathogenesis [17]. The nuclear localization of b-catenin was found in nearly half of lymphoma, and the mRNA and protein levels of b-catenin were higher than that of lymph nodes [18]. Herein, we postulated that methylated PCDH10 may promote lymphomagenesis by means of Wnt/b-catenin signaling. Another most likely mechanism involved was NF-κB pathway. Current study in MM demonstrated that PCDH10 could down-regulate the IKKs expression and subsequently reduce the phosphorylated IκBα, leading to the blockage of p65 translocation to nucleus [19]. Meanwhile, the NF-κB constitutional activation were also found in DLBCL, especially the ABC subtype. Thus, NF-κB pathway was speculated that may drive the PCDH10-introduced tumorigenesis in DLBCL.

Conclusions

This study reveals that 54.2% of DLBCL harbored PCDH10 promoter methylation. The frequency was much higher than that of RL/FH, chronic tonsillitis and Castleman diseases. Patients with methylated PCDH10 performs more aggressive OS and PFS. Thus, we conclude that PCDH10 methylation status could serve as a valuable prognostic indicator, and a potential therapeutic target for demethylating drugs in the future.

Notes

Abbreviations

COO: 

Cell of origin

DLBCL: 

Diffuse large B-cell lymphoma

FFPE: 

Formalin-fixed, paraffin-embedded

HR: 

Hazard ratio

IPI: 

International Prognostic Index

MM: 

multiple myeloma

MSP: 

Methylation-specific PCR

OS: 

Overall survival

PFS: 

Progress-free survival

RL/FH: 

Reactive lymph node/follicular hyperplasia

Declarations

Acknowledgements

Not applicable.

Funding

This research was supported by the Capital Clinical Characteristic Application Research (Z151100004015121) from Beijing Municipal Science & Technology Commission, and Beijing Hope Run Special Fund(LC2014L13) from Cancer Foundation of China and supported by PUMC Youth Fund and the Fundamental Research Funds for the Central Universities(3332016026).

Availability of data and materials

The datasets supporting the conclusions of this article were included in the additional files.

Authors’ contributions

WH contributed to FFPE tissues collection, MSP detection, Sanger sequencing and clinical follow-up. XX contributed to clinical follow-up, data analysis and manuscript writing. LS, TQ and LG provided experiment guidance and data interpretation. JY and NL contributed to study design, coordination, discussion and manuscript editing. All authors commented on the manuscript and approve its submission for publication.

Ethics approval and consent to participate

The ethics approval of this study was obtained from the Independent Ethics Committee of Cancer Hospital, Chinese Academy of Medical Sciences, National GCP Center for Anticancer Drugs (NCC2015ST-05).

Consent for publication

All authors approved its submission for publication.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

References

  1. Kim SY, Yasuda S, Tanaka H, Yamagata K, Kim H. Non-clustered protocadherin. Cell Adhes Migr. 2011;5(2):97–105.View ArticleGoogle Scholar
  2. Ying J, Li H, Seng TJ, Langford C, Srivastava G, Tsao SW, Putti T, Murray P, Chan AT, Tao Q. Functional epigenetics identifies a protocadherin PCDH10 as a candidate tumor suppressor for nasopharyngeal, esophageal and multiple other carcinomas with frequent methylation. Oncogene. 2006;25(7):1070–80.View ArticlePubMedGoogle Scholar
  3. Yu J, Cheng YY, Tao Q, Cheung KF, Lam CN, Geng H, Tian LW, Wong YP, Tong JH, Ying JM, et al. Methylation of protocadherin 10, a novel tumor suppressor, is associated with poor prognosis in patients with gastric cancer. Gastroenterology. 2009;136(2):640–51. e641View ArticlePubMedGoogle Scholar
  4. Xu Y, Yang Z, Yuan H, Li Z, Li Y, Liu Q, Chen J. PCDH10 inhibits cell proliferation of multiple myeloma via the negative regulation of the Wnt/β-catenin/BCL-9 signaling pathway. Oncol Rep. 2015;34(2):747–54.View ArticlePubMedGoogle Scholar
  5. Hou YC, Deng JY, Zhang RP, Xie XM, Cui JL, WP W, Hao XS, Liang H. Evaluating the clinical feasibility: the direct bisulfite genomic sequencing for examination of methylated status of protocadherin10 (PCDH10) promoter to predict the prognosis of gastric cancer. Cancer Biomarkers. 2015;15(5):567–73.View ArticlePubMedGoogle Scholar
  6. Harada H, Miyamoto K, Yamashita Y, Taniyama K, Mihara K, Nishimura M, Okada M. Prognostic signature of protocadherin 10 methylation in curatively resected pathological stage I non-small-cell lung cancer. Cancer Medicine. 2015;4(10):1536–46.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Coiffier B, Lepage E, Briere J, Herbrecht R, Tilly H, Bouabdallah R, Morel P, Van Den Neste E, Salles G, Gaulard P, et al. CHOP chemotherapy plus rituximab compared with CHOP alone in elderly patients with diffuse large-B-cell lymphoma. N Engl J Med. 2002;346(4):235–42.View ArticlePubMedGoogle Scholar
  8. Ziepert M, Hasenclever D, Kuhnt E, Glass B, Schmitz N, Pfreundschuh M, Loeffler M. Standard international prognostic index remains a valid predictor of outcome for patients with aggressive CD20+ B-cell lymphoma in the rituximab era. J Clin Oncol. 2010;28(14):2373–80.View ArticlePubMedGoogle Scholar
  9. Lenz G, Wright G, Dave SS, Xiao W, Powell J, Zhao H, Xu W, Tan B, Goldschmidt N, Iqbal J, et al. Stromal gene signatures in large-B-cell lymphomas. N Engl J Med. 2008;359(22):2313–23.View ArticlePubMedGoogle Scholar
  10. Friedberg JW. New strategies in diffuse large B-cell lymphoma: translating findings from gene expression analyses into clinical practice. Clin Cancer Res. 2011;17(19):6112–7.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Ying J, Gao Z, Li H, Srivastava G, Murray PG, Goh HK, Lim CY, Wang Y, Marafioti T, Mason DY, et al. Frequent epigenetic silencing of protocadherin 10 by methylation in multiple haematologic malignancies. Br J Haematol. 2007;136(6):829–32.View ArticlePubMedGoogle Scholar
  12. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyne RD, Muller-Hermelink HK, Smeland EB, Giltnane JM, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med. 2002;346(25):1937–47.View ArticlePubMedGoogle Scholar
  13. Monti S, Savage KJ, Kutok JL, Feuerhake F, Kurtin P, Mihm M, Wu B, Pasqualucci L, Neuberg D, Aguiar RC, et al. Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood. 2005;105(5):1851–61.View ArticlePubMedGoogle Scholar
  14. Chambwe N, Kormaksson M, Geng H, De S, Michor F, Johnson NA, Morin RD, Scott DW, Godley LA, Gascoyne RD, et al. Variability in DNA methylation defines novel epigenetic subgroups of DLBCL associated with different clinical outcomes. Blood. 2014;123(11):1699–708.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Narayan G, Xie D, Freddy AJ, Ishdorj G, Do C, Satwani P, Liyanage H, Clark L, Kisselev S, Nandula SV, et al. PCDH10 promoter hypermethylation is frequent in most histologic subtypes of mature lymphoid malignancies and occurs early in lymphomagenesis. Genes Chromosomes Cancer. 2013;52(11):1030–41.View ArticlePubMedGoogle Scholar
  16. Shaknovich R, Geng H, Johnson NA, Tsikitas L, Cerchietti L, Greally JM, Gascoyne RD, Elemento O, Melnick A. DNA methylation signatures define molecular subtypes of diffuse large B-cell lymphoma. Blood. 2010;116(20):e81–9.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Bognar MK, Vincendeau M, Erdmann T, Seeholzer T, Grau M, Linnemann JR, Ruland J, Scheel CH, Lenz P, Ott G, et al. Oncogenic CARMA1 couples NF-kappaB and beta-catenin signaling in diffuse large B-cell lymphomas. Oncogene. 2016;Google Scholar
  18. Ge X, Lv X, Feng L, Liu X, Wang X. High expression and nuclear localization of beta-catenin in diffuse large B-cell lymphoma. Mol Med Rep. 2012;5(6):1433–7.PubMedGoogle Scholar
  19. Li Z, Yang Z, Peng X, Li Y, Liu Q, Chen J. Nuclear factor-kappaB is involved in the protocadherin-10-mediated pro-apoptotic effect in multiple myeloma. Mol Med Rep. 2014;10(2):832–8.View ArticlePubMedGoogle Scholar
  20. Lopez-Guillermo A, Colomo L, Jimenez M, Bosch F, Villamor N, Arenillas L, Muntanola A, Montoto S, Gine E, Colomer D, et al. Diffuse large B-cell lymphoma: clinical and biological characterization and outcome according to the nodal or extranodal primary origin. J Clin Oncol Off J Am Soc Clin Oncol. 2005;23(12):2797–804.View ArticleGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement