GET THE APP

MicroRNAs as Biomarkers of the Response to Treatment with ABVD Sc
Journal of Leukemia

Journal of Leukemia
Open Access

ISSN: 2329-6917

+44 1300 500008

Research Article - (2015) Volume 3, Issue 4

MicroRNAs as Biomarkers of the Response to Treatment with ABVD Scheme in Hodgkin Lymphoma

Ana Virgínia Van Den Berg1,2#, Leandro Magalhães1#, Amanda Ferreira Vidal1, Aline Maria Pereira Cruz1,3 and Ândrea Ribeiro-dos-Santos1,3*
1Laboratório de Genética Humana e Médica. Universidade Federal do Pará, Belém, Brazil
2Universidade do Estado do Pará. Belém, Brazil
3Núcleo de Pesquisas em Oncologia. Universidade Federal do Pará, Belém, Brazil
#Contributed equally to this work
*Corresponding Author: Ândrea Ribeiro-dos-Santos, Laboratório de Genética Humana e Médica, Universidade Federal Do Pará, Belém, Brazil, Tel: +55 (91) 3201-7843 Email:

Abstract

Hodgkin Lymphoma (HL) is a neoplasia characterized by the restrict number of malignant cells present in the lymph node. Actual criteria in diagnostic standards don’t consider genetic and epigenetic alterations as risk factors in the development of this disease or in the response of the treatment. MicroRNAs (miRNAs) are important regulatory elements of genic expression that can be altered in the presence of cancer. New advances in the field suggest miRNAs as HL biomarkers. We evaluated the expression profiles of five miRNAs (hsa-miR-9, hsa-miR-20a, hsamiR- 21, hsa-miR-26a and hsa-miR-155) in the peripheral blood of three groups of patients: patients diagnosed with HL who had not received any radiologic or chemotherapeutic treatment; patients diagnosed with HL who had been treated with the Adriblastin, Bleomycin, Vinblastine and Dacarbazine (ABVD) chemotherapeutic scheme; and a control group consisting of healthy volunteers without HL. Our results showed that the expression profiles of hsamiR- 9, hsa-miR-21, hsa-miR-26a and hsa-miR-155 were able to significantly distinguish untreated HL patients from patients without the disease and that the hsa-miR-9, hsa-miR-21 and hsa-miR-155 expression profiles were altered by treatment with ABVD. These results suggest that miRNAs are promising blood biomarkers of HL and also a possible biomarkers of the response to ABVD treatment.

Keywords: MicroRNAs; Biomarkers; Response to treatment; ABVD scheme; Hodgkin lymphoma

Introduction

Hodgkin lymphoma (HL) is a B-cell lymphoma that occurs in the lymph nodes (predominantly those in the cervical region) and is characterized by the presence of few cancer cells, usually representing 0.1 to 10% of the total number of cells in the tissue. HL is divided in classic Hodgkin Lymphoma (cHL) which are further subdivided according to its histology, being the nodular sclerosis, mixed cellularity, lymphocyte-rich and lymphocyte-depleted subtypes; and in cases of nodular lymphocyte-predominant HL [1].

HL is one of the most common types of lymphoma with an annual incidence of 5:100.000 persons globally and 3:100.000 persons in the western world [2,3]. Despite its incidence, HL mortality is low, with a cure rate of approximately 80% [2,4]. At the present time, the standard treatment for HL is a chemotherapy scheme consisting of Adriblastin, Bleomycin, Vinblastine and Dacarbazine (ABVD), associated or not with radiotherapy. This combination has been used for over 20 years and has high efficiency and a low toxicity profile [2,4].

Even though most patients respond well to the treatment, some show no effective changes in their clinical condition or relapse after complete remission of the disease [5,6]. In the early stages of treatment, a robust tool that stratifies distinct types of patients called the International Prognostic Score (IPS) [4] has been widely used in clinical practice to identify patients that are non-responsive to treatment. However, this system does not consider genetic and epigenetic changes as factors involved in the development of the disease or in the response to treatment.

MicroRNAs (miRNAs) are endogenous small non coding RNAs (approximately 23 nt) that regulate genic expression at the post-transcriptional level by pairing with the 3’ UTR region of the messenger RNA and repressing its translation [7,8]. Because miRNAs regulate thousands of genes involved in cellular processes, such as proliferation, differentiation and apoptosis, aberrant expression or alteration of the characteristic miRNA profile of a tissue contributes to the development of various human diseases, including cancer [9-12]. MiRNAs are considered promising biomarkers of cancer, mainly because (I) they circulate in biofluids (including blood, serum and plasma); (II) they can easily be sampled and (III) they are highly stable in plasma [8,13,14].

The aim of this study was to evaluate the expression profile of five miRNAs (hsa-miR-9, hsa-miR-20a, hsa-miR-21, hsa-miR-26a and hsa-miR-155) described in the literature [15-17] in peripheral blood samples from untreated HL patients; HL patients treated with ABVD; and healthy volunteers without HL to see if these miRNAs are blood biomarkers of HL and if ABVD treatment modifies the expression profiles of these miRNAs.

Methods

Sample collection and ethical aspects

The present study comprised three sample groups: group 1-patients diagnosed with HL who had not received any radiologic or chemotherapeutic treatment (n=4); group 2-HL patients treated with the ABVD chemotherapeutic scheme, including patients from group 1 (n=7); and group 3-Healthy volunteers without HL or any other blood disease (n=14). The characteristics of the included patients are summarized in Table 1. The study followed the Declaration of Helsinki, and all patients and volunteers signed a consent form for participation in the study that had been approved by the Pará State University ethics committee CCBS-UEPA 08/2011).

Characteristics Patients
  Before Treatment (n = 4) After Treatment (n = 7)
Median Age (range) 35 (21 - 54) 29 (13 - 54)
Gender    
Male 2 4
Female 2 3
Histologic Subtype    
Nodular Sclerosis 2 5
Mixed Cellularity 2 2
Stage    
I - II 3 5
III - IV 1 2
IPS    
< 3 1 2
Does not apply 3 5
Average Time of Treatment (range) - 6.7 months (6 - 8)

Table 1: Clinical characteristics of the Hodgking Lymphoma patients, before and after the treatment with ABVD

Samples were obtained from patients at Ofir Loyola Hospital (HOL-Belém/Brazil) in two moments: Before starting the ABVD scheme and 3 months after the sixth cycle of ABVD. Blood was collected in PAXgene Blood RNA tubes (Qiagen, Germany) and stored at -80ºC until total RNA extraction.

Total RNA extraction, reverse transcription and quantitative real time PCR (qRT-PCR)

Total RNA was extracted from the 25 blood samples using a PAXgene Blood RNA kit (Qiagen, Germany) according the manufacturer's instructions. RNA was quantified using a Qubit® 2.0 fluorometer (Life Technologies, Carlsbad, CA) and was then stored at -80ºC until performing miRNA profiling.

TaqMan MicroRNA Assays (Thermo Fisher Scientific, Waltham, MA) were used to measure miRNA expression levels. Ten nanograms of total RNA was reversed transcribed using a TaqMan MicroRNA Reverse Transcription kit (Applied Biosystems, Foster City, CA) following the manufacturer's instructions. qRT-PCR was performed in a ABI Prism 7500 thermal cycler using TaqMan Universal Master Mix and TaqMan MicroRNA Assays according to the manufacturer's instructions.

To evaluate differences in the expression levels between each group, the comparative Ct method was used [18], and the endogenous control RNU24 was used to normalize the expression values. The NormFinder algorithm [19] was also used to measure the existing variation in the studied miRNAs in all studied groups, including the endogenous control.

Statistical analysis

-ΔCt values were used to verify differences in the miRNA expression levels between the (1) untreated HL patients, (2) patients with HL treated with ABVD and (3) Healthy voluntaries and 2-ΔCt values were used to present relative expression in graphic form.

The Shapiro-Wilk test was used to determine if the data followed a Gaussian distribution, and One Way ANOVA or Kruskal-Wallis were used to compare the miRNA expression levels between the groups if the data followed parametric or non-parametric distribution, respectively. Differences were considered statistically significant differences at P<0.05, and False Discovery Rate (FDR) correction was applied for multiple pairwise comparisons. All tests and graphics were done using the R statistical package (www.R-project.org).

Prediction of target genes

Target genes regulated by the studied miRNAs were predicted using two online tools: TargetCompare [20] (lghm.ufpa.br/targetcompare), which is able to determine the target genes of miRNAs and the disease that is related to those target genes and to identify simultaneous targets of two or more miRNAs; and miRTarBase [21] (http://mirtarbase.mbc.nctu.edu.tw), which predicts possible target genes of a miRNA in addition to indicating which molecular biology technique was used to validate the miRNA-gene interaction. Only genes validated with strong evidence (by a Reporter assay, Western Blot or qRT-PCR) were considered. Targets predicted by both tools that had any relation with hematological diseases or malignant neoplasias were considered in this study.

Results

RNU24 does not act as a good endogenous control

The NormFinder algorithm was used to determine stability values (lower values indicate a more stable expression) for all miRNAs investigated, including the endogenous control RNU24 (Figure 1). This endogenous control had one of the highest stability values of all of the examined miRNAs, indicating that it is not a reliable control for the normalization of expression data. The NormFinder analysis also suggested that a better strategy for normalizing the data would be to use hsa-miR-155 as the endogenous control, as it presented the most stable value. To normalize hsa-miR-155 expression we used the mean values of hsa-miR-21 and hsa-miR-155 expression, also suggested by Normfinder.

leukemia-NormFinder-algorithm

Figure 1: Stability values of the investigated miRNAs generated by the NormFinder algorithm. NormFinder calculates stability values based on the inter- and intra-group variance of each miRNA candidate and the endogenous control RNU24. Lower values indicate more stable expression of a miRNA candidate among all groups studied.

hsa-miR-9, hsa-miR-21, hsa-miR-26a and hsa-miR-155 are differentially expressed in patients with Hodgkin lymphoma

After normalization of the expression data, we observed that the data followed both parametric and non-parametric distribution. ANOVA post-hoc pairwise comparisons showed statistically significant differences in the expression levels of hsa-miR-21 (P=0.0043) and hsa-miR-155 (P=0.0184) in patients from group 1 compared to patients from group 3 (Figure 2). Hsa-miR-20a was the only miRNA whose expression profile was not different between the studied groups (P=0.428). Kruskal-Wallis post-hoc pairwise comparisons showed statistically significant differences in the expression levels of hsa-miR-9 (P=0.025) and hsa-miR-26a (P=0.011).

leukemia-peripheral-blood

Figure 2: Differential expression of the investigated miRNAs in the peripheral blood of the three groups of patients. This figure shows the the 2-ΔCt values of hsa-miR-9, hsa-miR-20a, hsa-miR-21, hsamiR- 26a and hsa-miR-155 . One Way ANOVA and Kruskal-Wallis post-hoc pairwise comparisons were made with False Discovery Rate correction; * represents a significant difference of P<0.05, and **represents a significant difference of P<0.01.

Compared to group 3, hsa-miR-9 and hsa-miR-21 were up-regulated by 55- and 5.23-fold, while hsa-miR-26a and hsa-miR-155 were down-regulated by 1.35- and 2.36-fold in group 1, respectively.

ABVD treatment altered the expression of hsa-miR-9, hsa-miR-21 and hsa-miR-155

For hsa-miR-9, hsa-miR-21 and hsa-miR-155 expression, in addition to being different between untreated HL patients and the control group, it was also significantly different (P=0.025, P=0.019 and P=0.019, respectively) between untreated HL patients (group 1) and HL patients treated with ABVD (group 2). The treatment reduced the expression of this miRNA in 62-, 2.48- and 2.05-fold, respectively, not differing from the expressions observed in voluntaries without HL (group 3; P=0.5353, P=0.4654, P=0.4654, respectively; Figure 2).

Predicted target genes of hsa-miR-9, hsa-miR-21, hsa-miR-26a and hsa-miR-155

Based on the differential expressions of these four miRNAs, we used the online tools TargetCompare and miRTarBase to predict which genes those four miRNAs targeted.

Both platforms predicted the SIRT1 gene as a target gene of hsa-miR-9, and RASGRP1, TIMP3 and MTAP genes were predicted as target genes of hsa-miR-21. Because these two miRNAs are up-regulated in untreated HL patients (group 1), we searched for target genes that were shared by these two miRNAs, and the only such gene that was identified was the TGFBR2 gene.

The platforms predicted HMGA1, HMGA2, RB1 and MTDH genes as possible targets of hsa-miR-26a, and SOCS1 as a possible target of hsa-miR-155. The validated common targets of these two down-regulated miRNAs were JARID2, SMAD1, SOCS6 and EZH2.

Discussion

There is still no consensus in the literature regarding the best method of normalizing data obtained by relative quantification of miRNAs in whole blood and plasma/serum samples. The most common endogenous controls used to normalize miRNA expression in tissues are small nucleolar RNAs (snoRNAs). Because nucleic acids were extracted from whole blood samples collected in PAXgene tubes, we expected that the chosen endogenous control (RNU24) would be stable between samples. However, the results of the NormFinder algorithm showed that RNU24 was among the miRNAs whose expressions varied the most, indicating that it is not a reliable endogenous control for the normalization of miRNA expression data. Other snoRNAs, such as RNU43, RNU44, RNU48 and RNU6B, have been shown to have highly variable expression in cancer and to introduce bias in miRNA analyses when they are used for normalization [22]. The NormFinder analysis suggested that a better strategy for normalizing the data would be to use hsa-miR-155 and the mean values of hsa-miR-21 and hsa-miR-155 expressions as endogenous controls; therefore, we decided to adopt this strategy.

We analyzed the expression profiles of hsa-miR-9, hsa-miR-20a, hsa-miR-21, hsa-miR-26a and hsa-miR-155 (identified in distinct studies in cell lines and lymph nodes to be specially altered in HL [15-17]) in peripheral blood samples in patients diagnosed with HL (before and after treatment with ABVD) and in healthy volunteers without the disease.

Our results showed that the expression profiles of hsa-miR-9 and hsa-miR-21 were up-regulated (P=0.0083 and P=0.0043, respectively) and the expression profile of hsa-miR-26a (P=0.011) was down-regulated in untreated patients with HL compared to controls. These results were similar to those observed in HRS cells found in lymph nodes of patients with HL and in cell lines of the disease [16,17]. We observed a down-regulation of hsa-miR-155 (2.36-fold; P = .0043) in the peripheral blood of untreated patients with HL and an absence of differences in the expression of hsa-miR-20a in all groups studied (P = 0.428).

Of all the studied miRNAs, hsa-miR-9, hsa-miR-21 and hsa-miR-155 were the ones whose expression profile was altered by ABVD treatment (Figure 2; P = 0.025, P = 0.019 and P = 0.019, respectively). In addition to being a characteristic biomarker of HL, hsa-miR-9, hsa-miR-21 and hsa-miR-155 may be promising new markers of ABVD treatment response as a reduction in hsa-miR-9 and hsa-miR-21 expression and an elevation in hsa-miR-155 expression were observed in treated HL patients and their expression profiles were similar and had no significant difference (P = 0.79) to the controls without HL.

Up-regulation of hsa-miR-9 has been observed in HL [15] and other types of cancer [23,24]. In ovarian cancer [25], this miRNA was found to be closely related to tumorigenesis and elevated tumor sensitivity to treatment with cisplatin and other chemotherapeutic drugs. Although the positive correlations between the up-regulation of hsa-miR-9 expression and treatment response in ovarian cancer and HL may not involve the same mechanisms, this observed correlation could explain why HL is a cancer type that has one of the highest cure rates and lower treatment toxicity rates [26] and supports the hypothesis that this miRNA may be a good biomarker of ABVD treatment response.

The SIRT1 gene was identified as a possible target of hsa-miR-9, and its expression in HL would be reduced by the high regulatory activity of this miRNA. Yang et al. [27] made a similar observation in gastric cancer: decreased expression of this gene was associated with an elevated proliferation rate and high clonal potential.

Up-regulation of the expression of hsa-miR-21 in the blood circulation and in solid tumors has been previously observed in various studies and in several types of cancer, including HL [28-32]. A recent meta-analysis of 36 studies regarding circulating hsa-miR-21 expression [33] revealed that up-regulation of this miRNA was able to distinguish patients with cancer from patients without cancer, with 78% specificity and 82% sensitivity. We observed similar results to those of Jones et al. [34]: we found the circulating level of hsa-miR-21 to be up-regulated in untreated patients with HL, indicating the importance of this miRNA in the general mechanism of carcinogenesis. As elevated expression of hsa-miR-21 in the blood/plasma has also been observed at the site of origin of the tumor, this miRNA is a promising diagnostic biomarker for the development of malignant neoplasias.

Elevated levels of hsa-miR-21 and hsa-miR-30D were associated with chemoresistance in L428 Hodgkin Lymphoma cell line [6]. When transfected with hsa-miR-21 inhibitors, L428 cells presented a slight increase in apoptosis rates and when treated with the hsa-miR-21 inhibitors in combination with doxorubicin the cells presented a major increase in apoptosis. This also suggests that hsa-miR-21 has an important role in the dynamics of treatment response to chemotherapeutics.

One of the predicted target genes of hsa-miR-21 was the TIMP3 gene (a tumor suppressor that induces programmed cell death), and low expression of this gene as well as the up-regulation of hsa-miR-21 have already been observed in gastric cancer [35,36]. These data suggest that the changes in the expressions of these two genes might be related and reinforces the role of hsa-miR-21 in cancer development.

The TGFBR2 gene, a common target of hsa-miR-9 and hsa-miR-21, was previously shown to be down-regulated in B cell lymphoma [37], and this gene is involved in growth suppression. Reduction in the level of TBRII protein allows the cell to grow without limitation because it compromises growth suppression signaling.

This is the first study to evaluate down-regulated circulating miRNAs in the blood of patients with HL, and hsa-miR-26a expression was found to be reduced by 1.35-fold compared with the control group. This down-regulated expression profile has also been observed in HRS cells in lymph nodes of patients with HL [16], in non-Hodgkin lymphomas, such as natural killer/T cell lymphoma [38] and in solid tumors, such as breast cancer [39]. In breast cancer cell lines, the down-regulation of hsa-miR-26a was found to be related to the up-regulation of the HMGA1 gene, which consequently caused increased proliferation.

The other miRNA that was down-regulated in the blood of untreated patients with HL was hsa-miR-155, and this is not consistent with the findings of Jones et al. [34]; however, this discrepancy can be explained by the differences in the strategies used for nucleic acid extraction from the blood between these two studies. We extracted RNA from whole blood, which included the RNA present in circulating T cells. Huffacker et al. [40] observed that hsa-miR-155 expression in T cells was closely related to antitumoral activity: mice that had hsa-miR-155 deficiency in their T CD4+ and CD8+ cells had limited antitumoral activity. Because the hsa-miR-155 expression profile in untreated HL patients was reduced compared to healthy volunteers without the disease, we can speculate that a similar mechanism to that observed in the mouse study occurs during the development of HL, as immune system evasion is one of the hallmarks of cancer [41].

Analysis of miRNA expression profiles in the peripheral blood of patients with HL has potential for use in clinical practice when the expression levels of most of the miRNAs examined are altered in the presence of the disease. Of all five studied miRNAs, hsa-miR-9, hsa-miR-21, hsa-miR-26a and hsa-miR-155 were differentially expressed in HL patients that had not received any radiologic or chemotherapeutic treatment compared to the healthy control group without the disease. This difference is important because the differential expression of these miRNAs can separate these two groups.

In addition to the potential of the miRNAs hsa-miR-9, hsa-miR-21 and hsa-miR-155 as biomarkers of HL, they were also found to be promising biomarkers of ABVD treatment response. Thus, these data suggest that it should have a tracking on the expression levels of these miRNAs in the patients considered remitted after the treatment to verify its efficiency and risks in the relapse of the disease.

Acknowledgments

This work is part of the Rede de Pesquisa em Genômica Populacional Humana (Biocomputacional-Protocol no. 3381/2013/CAPES). Financial support: CAPES; PROPESP/UFPA-FADESP; MS/Decit, CNPq and FAPESPA. Ândrea Ribeiro-dos-Santos is supported by CNPq/Produtividade; The authors confirm that the funder had no influence over the study design, content of the article, or selection of this journal.

Authorship

Contribution: AVVDB and ARS conceived and designed the study; AVVDB collected samples from the patients; LM, AMPC and AV realized the experiments; LM analysed the data; LM and ARS wrote the first draft of the manuscript; AVVDB and AV contributed to the final version of the manuscript.

Conflict of interest disclosure: The authors declare that no competing interests exist.

References

  1. Chisté M, Vrotsos E, Zamora C, Martinez A (2013) Chronic lymphocytic leukemia/small lymphocytic lymphoma involving the aortic valve.Ann DiagnPathol 17: 295-297.
  2. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, et al. (2015) Global cancer statistics, 2012.CA Cancer J Clin 65: 87-108.
  3. Küppers R, Engert A, Hansmann ML (2012) Hodgkin lymphoma.J Clin Invest 122: 3439-3447.
  4. Moccia AA, Donaldson J, Chhanabhai M, Hoskins PJ, Klasa RJ, et al. (2012) International Prognostic Score in advanced-stage Hodgkin's lymphoma: altered utility in the modern era.J ClinOncol 30: 3383-3388.
  5. Bonadonna G, Zucali R, Monfardini S, De Lena M, Uslenghi C (1975) Combination chemotherapy of Hodgkin's disease with adriamycin, bleomycin, vinblastine, and imidazole carboxamide versus MOPP.Cancer 36: 252-259.
  6. Sánchez-Espiridión B, Martín-Moreno AM, Montalbán C, Figueroa V, Vega F, et al. (2013) MicroRNA signatures and treatment response in patients with advanced classical Hodgkin lymphoma.Br J Haematol 162: 336-347.
  7. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions.Cell 136: 215-233.
  8. Ribeiro-dos-Santos Â, Cruz AMP, Darnet S (2012) Deep Sequencing of MicroRNAs in Cancer: Expression Profiling and Its Applications1:523-546.
  9. Ahmad J, Hasnain SE, Siddiqui MA, Ahamed M, Musarrat J, et al. (2013) MicroRNA in carcinogenesis & cancer diagnostics: a new paradigm.Indian J Med Res 137: 680-694.
  10. Lujambio A, Lowe SW (2012) The microcosmos of cancer.Nature 482: 347-355.
  11. Assumpção MB, Moreira FC, Hamoy IG, Magalhães L, Vidal A, et al. (2015) High-Throughput miRNA Sequencing Reveals a Field Effect in Gastric Cancer and Suggests an Epigenetic Network Mechanism.BioinformBiol Insights 9: 111-117.
  12. Darnet S, Moreira FC, Hamoy IG, Burbano R, Khayat A, et al. (2015) High-Throughput Sequencing of miRNAs Reveals a Tissue Signature in Gastric Cancer and Suggests Novel Potential Biomarkers.BioinformBiol Insights 9: 1-8.
  13. Tavallaie R, De Almeida SR, Gooding JJ (2015) Toward biosensors for the detection of circulating microRNA as a cancer biomarker: an overview of the challenges and successes.Wiley Interdiscip Rev NanomedNanobiotechnol 7: 580-592.
  14. Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, et al. (2008) Circulating microRNAs as stable blood-based markers for cancer detection.ProcNatlAcadSci U S A 105: 10513-10518.
  15. Gibcus JH, Tan LP, Harms G, Schakel RN, de Jong D, et al. (2009) Hodgkin lymphoma cell lines are characterized by a specific miRNA expression profile.Neoplasia 11: 167-176.
  16. Navarro A, Gaya A, Martinez A, Urbano-Ispizua A, Pons A, et al. (2008) MicroRNA expression profiling in classic Hodgkin lymphoma.Blood 111: 2825-2832.
  17. Pieter Van Vlierberghe, An De Weer, Pieter Mestdagh, Tom Feys, Katleen De Preter, et al. (2009) Comparison of miRNA profiles of microdissected Hodgkin/Reed-Sternberg cells and Hodgkin cell lines versus CD77+ B-cells reveals a distinct subset of differentially expressed miRNAs. British Journal of Haematology147:686-690.
  18. Schmittgen TD, Livak KJ (2008) Analyzing real-time PCR data by the comparative C(T) method.Nat Protoc 3: 1101-1108.
  19. Andersen CL, Jensen JL, Ørntoft TF (2004) Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets.Cancer Res 64: 5245-5250.
  20. Moreira FC, Dustan B, Hamoy IG, Ribeiro-Dos-Santos AM, Dos Santos AR (2014) TargetCompare: A web interface to compare simultaneous miRNAs targets.Bioinformation 10: 602-605.
  21. Hsu SD, Tseng YT, Shrestha S, Lin YL, Khaleel A, et al. (2014)miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 42: D78-85.
  22. Gee HE, Buffa FM, Camps C, Ramachandran A, Leek R, et al. (2011) The small-nucleolar RNAs commonly used for microRNA normalisation correlate with tumour pathology and prognosis.Br J Cancer 104: 1168-1177.
  23. Xie D, Shang C, Zhang H, Guo Y, Tong X (2015) Up-regulation of miR-9 target CBX7 to regulate invasion ability of bladder transitional cell carcinoma.Med SciMonit 21: 225-230.
  24. Wu S, Jia S, Xu P (2014) MicroRNA-9 as a novel prognostic biomarker in human laryngeal squamous cell carcinoma.Int J ClinExp Med 7: 5523-5528.
  25. Zhao HM, Wei W, Sun YH, Gao JH, Wang Q, et al. (2015) MicroRNA-9 promotes tumorigenesis and mediates sensitivity to cisplatin in primary epithelial ovarian cancer cells.TumourBiol 36: 6867-6873.
  26. Cuccaro A, Bartolomei F, Cupelli E, Galli E, Giachelia M, et al. (2014) Prognostic factors in hodgkin lymphoma.Mediterr J Hematol Infect Dis 6: e2014053.
  27. Yang Q, Wang B, Gao W, Huang S, Liu Z, et al. (2013) SIRT1 is downregulated in gastric cancer and leads to G1-phase arrest via NF-κB/Cyclin D1 signaling.Mol Cancer Res 11: 1497-1507.
  28. Xu XM, Qian JC, Deng ZL, Cai Z, Tang T, et al. (2012) Expression of miR-21, miR-31, miR-96 and miR-135b is correlated with the clinical parameters of colorectal cancer.OncolLett 4: 339-345.
  29. Vimalraj S, Miranda PJ, Ramyakrishna B, Selvamurugan N (2013) Regulation of breast cancer and bone metastasis by microRNAs.Dis Markers 35: 369-387.
  30. Busacca S, Germano S, De Cecco L, Rinaldi M, Comoglio F, et al. (2010) MicroRNA signature of malignant mesothelioma with potential diagnostic and prognostic implications.Am J Respir Cell MolBiol 42: 312-319.
  31. Ma X, Yoshimoto K, Guan Y, Hata N, Mizoguchi M, et al. (2012) Associations between microRNA expression and mesenchymal marker gene expression in glioblastoma.NeuroOncol 14: 1153-1162.
  32. Golestaneh AF, Atashi A, Langroudi L, Shafiee A, Ghaemi N, et al. (2012) miRNAs expressed differently in cancer stem cells and cancer cells of human gastric cancer cell line MKN-45.Cell BiochemFunct 30: 411-418.
  33. Wu K, Li L, Li S (2015) Circulating microRNA-21 as a biomarker for the detection of various carcinomas: an updated meta-analysis based on 36 studies.TumourBiol 36: 1973-1981.
  34. Jones K, Nourse JP, Keane C, Bhatnagar A, Gandhi MK (2014) Plasma microRNA are disease response biomarkers in classical Hodgkin lymphoma.Clin Cancer Res 20: 253-264.
  35. Guan Z, Zhang J, Song S, Dai D (2013) Promoter methylation and expression of TIMP3 gene in gastric cancer.DiagnPathol 8: 110.
  36. Osawa S, Shimada Y, Sekine S, Okumura T, Nagata T, et al. (2011) MicroRNA profiling of gastric cancer patients from formalin-fixed paraffin-embedded samples.OncolLett 2: 613-619.
  37. Chen G, Ghosh P, Osawa H, Sasaki CY, Rezanka L, et al. (2007) Resistance to TGF-beta 1 correlates with aberrant expression of TGF-beta receptor II in human B-cell lymphoma cell lines.Blood 109: 5301-5307.
  38. Ng SB, Yan J, Huang G, Selvarajan V, Tay JL, et al. (2011) Dysregulated microRNAs affect pathways and targets of biologic relevance in nasal-type natural killer/T-cell lymphoma.Blood 118: 4919-4929.
  39. Zhao XX, Yuan QZ, Mu DP, Sun DW, Bo QA, et al. (2015) MicroRNA-26a inhibits proliferation by targeting high mobility group AT-hook 1 in breast cancer.Int J ClinExpPathol 8: 368-373.
  40. Huffaker TB, Hu R, Runtsch MC, Bake E, Chen X, et al. (2012) Epistasis between microRNAs 155 and 146a during T cell-mediated antitumor immunity.Cell Rep 2: 1697-1709.
  41. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation.Cell 144: 646-674.
Citation: Ana Virgínia Van Den Berg, Leandro Magalhães, Amanda Ferreira Vidal, Aline Maria Pereira Cruz, Ândrea Ribeiro-dos-Santos (2015) MicroRNAs as Biomarkers of the Response to Treatment with ABVD Scheme in Hodgkin Lymphoma. J Leuk 3:200.

Copyright: © 2015 Ana Virgínia VDB, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Top