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Zhao S, Fang S, Liu Y, Li X, Liao S, Chen J, Liu J, Zhao L, Li H, Zhou W, Shen W, Dong X, Xiang R, Wang L, Zhao Y. The long non-coding RNA NONHSAG026900 predicts prognosis as a favorable biomarker in patients with diffuse large B-cell lymphoma. Oncotarget 2018; 8:34374-34386. [PMID: 28423735 PMCID: PMC5470975 DOI: 10.18632/oncotarget.16163] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 02/24/2017] [Indexed: 12/11/2022] Open
Abstract
Long non-coding RNAs are known to be involved in cancer progression, but their biological functions and prognostic values are still largely unexplored in diffuse large B-cell lymphoma. In this study, long non-coding RNAs expression was characterized in 1,403 samples including normal and diffuse large B-cell lymphoma by repurposing 7 microarray datasets. Compared with any stage of normal B cells, NONHSAG026900 expression was significantly decreased in tumor samples. And in germinal center B-cell subtype, the significantly higher expression of NONHSAG026900 indicated it was a favorable prognosis biomarker. Then the prognostic power of NONHSAG026900 was validated with another independent dataset and NONHSAG026900 improved the predictive power of International Prognostic Index as an independent factor. Moreover, functional prediction and validation demonstrated that NONHSAG026900 could inhibit cell cycle activity to restrain tumor proliferation. These findings identified NONHSAG026900 as a novel prognostic biomarker and offered a new therapeutic target for diffuse large B-cell lymphoma patients.
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Affiliation(s)
- Shuangtao Zhao
- Department of Radiation Oncology, National Cancer Center/Cancer Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Shuangsang Fang
- The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Yanhua Liu
- The School of Medicine, Nankai University, Tianjin, China.,The Collaborative Innovation Center for Biotherapy, Nankai University, Tianjin, China.,The Tianjin Key Laboratory of Tumor Microenvironment and Neurovascular Regulation, Tianjin, China
| | - Xixi Li
- The School of Medicine, Nankai University, Tianjin, China.,Department of Pathology, Nankai University, Tianjin, China
| | - Shengyou Liao
- The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Jinwen Chen
- The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Jingjia Liu
- The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Lianhe Zhao
- The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Hui Li
- The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Wei Zhou
- The School of Medicine, Nankai University, Tianjin, China.,The Collaborative Innovation Center for Biotherapy, Nankai University, Tianjin, China.,The Tianjin Key Laboratory of Tumor Microenvironment and Neurovascular Regulation, Tianjin, China
| | - Wenzhi Shen
- The School of Medicine, Nankai University, Tianjin, China.,The Collaborative Innovation Center for Biotherapy, Nankai University, Tianjin, China.,The Tianjin Key Laboratory of Tumor Microenvironment and Neurovascular Regulation, Tianjin, China
| | - Xiaoli Dong
- The School of Medicine, Nankai University, Tianjin, China.,The Collaborative Innovation Center for Biotherapy, Nankai University, Tianjin, China.,The Tianjin Key Laboratory of Tumor Microenvironment and Neurovascular Regulation, Tianjin, China
| | - Rong Xiang
- The School of Medicine, Nankai University, Tianjin, China.,The Collaborative Innovation Center for Biotherapy, Nankai University, Tianjin, China.,The Tianjin Key Laboratory of Tumor Microenvironment and Neurovascular Regulation, Tianjin, China
| | - Luhua Wang
- Department of Radiation Oncology, National Cancer Center/Cancer Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yi Zhao
- The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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Coiffier B, Sarkozy C. Diffuse large B-cell lymphoma: R-CHOP failure-what to do? HEMATOLOGY. AMERICAN SOCIETY OF HEMATOLOGY. EDUCATION PROGRAM 2016; 2016:366-378. [PMID: 27913503 PMCID: PMC6142522 DOI: 10.1182/asheducation-2016.1.366] [Citation(s) in RCA: 172] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Although rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) is the standard treatment for patients with diffuse large B-cell lymphoma (DLBCL), ∼30% to 50% of patients are not cured by this treatment, depending on disease stage or prognostic index. Among patients for whom R-CHOP therapy fails, 20% suffer from primary refractory disease (progress during or right after treatment) whereas 30% relapse after achieving complete remission (CR). Currently, there is no good definition enabling us to identify these 2 groups upon diagnosis. Most of the refractory patients exhibit double-hit lymphoma (MYC-BCL2 rearrangement) or double-protein-expression lymphoma (MYC-BCL2 hyperexpression) which have a more aggressive clinical picture. New strategies are currently being explored to obtain better CR rates and fewer relapses. Although young relapsing patients are treated with high-dose therapy followed by autologous transplant, there is an unmet need for better salvage regimens in this setting. To prevent relapse, maintenance therapy with immunomodulatory agents such as lenalidomide is currently undergoing investigation. New drugs will most likely be introduced over the next few years and will probably be different for relapsing and refractory patients.
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Affiliation(s)
- Bertrand Coiffier
- Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre-Bénite, France
| | - Clémentine Sarkozy
- Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre-Bénite, France
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Chanthra N, Payungporn S, Chuaypen N, Piratanantatavorn K, Pinjaroen N, Poovorawan Y, Tangkijvanich P. Single Nucleotide Polymorphisms in STAT3 and STAT4 and Risk of Hepatocellular Carcinoma in Thai Patients with Chronic Hepatitis B. Asian Pac J Cancer Prev 2016; 16:8405-10. [PMID: 26745093 DOI: 10.7314/apjcp.2015.16.18.8405] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Hepatitis B virus (HBV) infection is the leading cause of hepatocellular carcinoma (HCC) development. Recent studies demonstrated that single nucleotide polymorphisms (SNPs) rs2293152 in signal transducer and activator of transcription 3 (STAT3) and rs7574865 in signal transducer and activator of transcription 4 (STAT4) are associated with chronic hepatitis B (CHB)-related HCC in the Chinese population. We hypothesized that these polymorphisms might be related to HCC susceptibility in Thai population as well. Study subjects were divided into 3 groups consisting of CHB-related HCC (n=192), CHB without HCC (n=200) and healthy controls (n=190). The studied SNPs were genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The results showed that the distribution of different genotypes for both polymorphisms were in Hardy-Weinberg equilibrium (P>0.05). Our data demonstrated positive association of rs7574865 with HCC risk when compared to healthy controls under an additive model (GG versus TT: odds ratio (OR) =2.07, 95% confidence interval (CI)=1.06-4.03, P=0.033). This correlation remained significant under allelic and recessive models (OR=1.46, 95% CI=1.09-1.96, P=0.012 and OR=1.71, 95% CI=1.13-2.59, P=0.011, respectively). However, no significant association between rs2293152 and HCC development was observed. These data suggest that SNP rs7574865 in STAT4 might contribute to progression to HCC in the Thai population.
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Affiliation(s)
- Nawin Chanthra
- Research Unit of Hepatitis and Liver Cancer, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand E-mail :
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