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Tian XP, Xie D, Huang WJ, Ma SY, Wang L, Liu YH, Zhang X, Huang HQ, Lin TY, Rao HL, Li M, Liu F, Zhang F, Zhong LY, Liang L, Lan XL, Li J, Liao B, Li ZH, Tang QL, Liang Q, Shao CK, Zhai QL, Cheng RF, Sun Q, Ru K, Gu X, Lin XN, Yi K, Shuang YR, Chen XD, Dong W, Sang W, Sun C, Liu H, Zhu ZG, Rao J, Guo QN, Zhou Y, Meng XL, Zhu Y, Hu CL, Jiang YR, Zhang Y, Gao HY, He WJ, Xia ZJ, Pan XY, Lan H, Li GW, Liu L, Bao HZ, Song LY, Kang TB, Cai QQ. A gene-expression-based signature predicts survival in adults with T-cell lymphoblastic lymphoma: a multicenter study. Leukemia 2020; 34:2392-2404. [PMID: 32080345 DOI: 10.1038/s41375-020-0757-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 01/10/2020] [Accepted: 02/10/2020] [Indexed: 12/21/2022]
Abstract
We aimed to establish a discriminative gene-expression-based classifier to predict survival outcomes of T-cell lymphoblastic lymphoma (T-LBL) patients. After exploring global gene-expression profiles of progressive (n = 22) vs. progression-free (n = 28) T-LBL patients, 43 differentially expressed mRNAs were identified. Then an eleven-gene-based classifier was established using LASSO Cox regression based on NanoString quantification. In the training cohort (n = 169), high-risk patients stratified using the classifier had significantly lower progression-free survival (PFS: hazards ratio 4.123, 95% CI 2.565-6.628; p < 0.001), disease-free survival (DFS: HR 3.148, 95% CI 1.857-5.339; p < 0.001), and overall survival (OS: HR 3.790, 95% CI 2.237-6.423; p < 0.001) compared with low-risk patients. The prognostic accuracy of the classifier was validated in the internal testing (n = 84) and independent validation cohorts (n = 360). A prognostic nomogram consisting of five independent variables including the classifier, lactate dehydrogenase levels, ECOG-PS, central nervous system involvement, and NOTCH1/FBXW7 status showed significantly greater prognostic accuracy than each single variable alone. The addition of a five-miRNA-based signature further enhanced the accuracy of this nomogram. Furthermore, patients with a nomogram score ≥154.2 significantly benefited from the BFM protocol. In conclusion, our nomogram comprising the 11-gene-based classifier may make contributions to individual prognosis prediction and treatment decision-making.
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Affiliation(s)
- Xiao-Peng Tian
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, PR China
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Dan Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Wei-Juan Huang
- Department of Pharmacology, College of Pharmacy, Jinan University, Guangzhou, PR China
| | - Shu-Yun Ma
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, PR China
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Liang Wang
- Department of Hematology, Zhujiang Hospital of Southern Medical University, Guangzhou, PR China
- Department of Hematology, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
| | - Yan-Hui Liu
- Department of Pathology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, PR China
| | - Xi Zhang
- Department of Hematology, Xinqiao Hospital, Third Military Medical University, Chongqing, PR China
| | - Hui-Qiang Huang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Tong-Yu Lin
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Hui-Lan Rao
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Mei Li
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Fang Liu
- Department of Pathology, The First People's Hospital of Foshan, Foshan, PR China
| | - Fen Zhang
- Department of Pathology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, PR China
| | - Li-Ye Zhong
- Department of Hematology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, PR China
| | - Li Liang
- Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, PR China
| | - Xiao-Liang Lan
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, PR China
| | - Juan Li
- Department of Hematology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Bing Liao
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Zhi-Hua Li
- Department of Oncology, Sun-Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Qiong-Lan Tang
- Department of Oncology, Sun-Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Qiong Liang
- Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Chun-Kui Shao
- Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Qiong-Li Zhai
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, PR China
| | - Run-Fen Cheng
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, PR China
| | - Qi Sun
- Department of Pathology, Hematological Hospital of Chinese Academy of Medical Sciences, Tianjin, PR China
| | - Kun Ru
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, PR China
| | - Xia Gu
- Department of Pathology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, PR China
| | - Xi-Na Lin
- Department of Pathology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, PR China
| | - Kun Yi
- Department of Oncology, Jiangxi Provincial Cancer Hospital, Nanchang, PR China
| | - Yue-Rong Shuang
- Department of Hematology, Jiangxi Provincial Cancer Hospital, Nanchang, PR China
| | - Xiao-Dong Chen
- Department of Pathology, General Hospital of Guangzhou Military Command of PLA, Guangzhou, PR China
| | - Wei Dong
- Department of Hematology, Shunde Hospital of Southern Medical University, Shunde, PR China
| | - Wei Sang
- Department of Hematology, The First Affiliated Hospital of Xuzhou Medical University, Xuzhou, PR China
| | - Cai Sun
- Department of Pathology, The First Affiliated Hospital of Xuzhou Medical University, Xuzhou, PR China
| | - Hui Liu
- Department of Pathology, The First Affiliated Hospital of Xuzhou Medical University, Xuzhou, PR China
| | - Zhi-Gang Zhu
- Department of Hematology and Oncology, Guangzhou First People's Hospital, Guangzhou, PR China
| | - Jun Rao
- Department of Hematology, Xinqiao Hospital, Third Military Medical University, Chongqing, PR China
| | - Qiao-Nan Guo
- Department of Pathology, Xinqiao Hospital, Third Military Medical University, Chongqing, PR China
| | - Ying Zhou
- Department of Medical Oncology, Jiangmen Central Hospital, Jiangmen, PR China
| | - Xiang-Ling Meng
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Yong Zhu
- Department of Gastrointestinal Surgery, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Chang-Lu Hu
- Department of Medical Oncology, Anhui Provincial Cancer Hospital, Hefei, PR China
| | - Yi-Rong Jiang
- Department of Hematology, The First People's Hospital of Dongguan, Dongguan, PR China
| | - Ying Zhang
- Department of Oncology, Affiliated Hospital of Guangdong Medical University, Guangzhou, PR China
| | - Hong-Yi Gao
- Department of Pathology, Guangdong Province Hospital for Women and Children Health Care, Guangzhou, PR China
| | - Wen-Jun He
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, PR China
| | - Zhong-Jun Xia
- Department of Hematology, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Xue-Yi Pan
- Department of Hematology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, PR China
| | - Hai Lan
- Department of Hematology, Shunde Affiliated Hospital of Guangzhou University of Chinese Medicine, Shunde, PR China
| | - Guo-Wei Li
- Department of Hematology, Huizhou Municipal Central Hospital, Huizhou, PR China
| | - Lu Liu
- Department of Lymphoma And Hematology, Jilin Cancer Hospital, Changchun, PR China
| | - Hui-Zheng Bao
- Department of Lymphoma And Hematology, Jilin Cancer Hospital, Changchun, PR China
| | - Li-Yan Song
- Department of Pharmacology, College of Pharmacy, Jinan University, Guangzhou, PR China
| | - Tie-Bang Kang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Qing-Qing Cai
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, PR China.
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, PR China.
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Shi R, Wei Z, Zhu D, Fu N, Wang C, Yin S, Liang Y, Xing J, Wang X, Wang Y. Baicalein attenuates monocrotaline-induced pulmonary arterial hypertension by inhibiting vascular remodeling in rats. Pulm Pharmacol Ther 2017; 48:124-135. [PMID: 29133079 DOI: 10.1016/j.pupt.2017.11.003] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 11/01/2017] [Accepted: 11/09/2017] [Indexed: 12/21/2022]
Abstract
BACKGROUND Pulmonary arterial hypertension (PAH) is a devastating cardiopulmonary disorder characterized by elevated pulmonary arterial pressure (PAP) and right ventricular hypertrophy (RVH) driven by progressive vascular remodeling. Reversing adverse vascular remodeling is an important concept in the treatment of PAH. Endothelial injury, inflammation, and oxidative stress are three main contributors to pulmonary vascular remodeling. Baicalein is a natural flavonoid that has been shown to possess anti-proliferative, anti-inflammatory, anti-oxidative, and cardioprotective properties. We hypothesized that baicalein may prevent the progression of PAH and preserve the right heart function by inhibiting pulmonary arterial remodeling. METHODS Male Sprague-Dawley rats were distributed randomly into 4 groups: control, monocrotaline (MCT)-exposed, and MCT-exposed plus baicalein treated rats (50 and 100 mg/kg/day for 2 weeks). Hemodynamic changes, RVH, and lung morphological features were examined on day 28. Apoptosis was determined by TUNEL staining, and the mRNA levels of tumor necrosis factor alpha (TNF-α), interleukin-1β (IL-1β), and IL-6 were detected by qRT-PCR. The changes in oxidative indicators, including malondialdehyde (MDA), superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) were measured using corresponding commercial kits. The levels of Bax, Bcl-2, and cleaved caspase-3, and the activation of mitogen-activated protein kinase (MAPK) and NF-κB were assessed by western blotting. RESULTS MCT induced an increase in hemodynamic parameters and RVH, which were attenuated by baicalein treatment. Baicalein also blocked MCT-induced pulmonary arterial remodeling. The levels of apoptotic (Bax/Bcl-2 ratio and cleaved caspase-3) and inflammatory (IL-6, TNF-α, and IL-1β) biomarkers in lung tissue were lower in baicalein-treated groups. Baicalein also decreased MDA level, and increased SOD and GSH-Px activity in rat pulmonary tissue. Furthermore, baicalein inhibited MCT-induced activation of the MAPK and NF-κB pathways. CONCLUSION Baicalein ameliorates MCT-induced PAH by inhibiting pulmonary arterial remodeling at least partially via the MAPK and NF-κB pathways in rats.
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Affiliation(s)
- Ruizan Shi
- Department of Pharmacology, Shanxi Medical University, Taiyuan, 030001, China.
| | - Zehui Wei
- Department of Pharmacology, Shanxi Medical University, Taiyuan, 030001, China
| | - Diying Zhu
- Department of Pharmacology, Shanxi Medical University, Taiyuan, 030001, China
| | - Naijie Fu
- Department of Pharmacology, Shanxi Medical University, Taiyuan, 030001, China
| | - Chang Wang
- Department of Pharmacology, Shanxi Medical University, Taiyuan, 030001, China
| | - Sha Yin
- Department of Pharmacology, Shanxi Medical University, Taiyuan, 030001, China
| | - Yueqin Liang
- Medical Functional Experimental Center, Shanxi Medical University, Taiyuan, 030001, China
| | - Jianfeng Xing
- Medical Functional Experimental Center, Shanxi Medical University, Taiyuan, 030001, China
| | - Xuening Wang
- Department of Cardiovascular Surgery, Shanxi Academy of Medical Sciences, Shanxi Dayi Hospital, Taiyuan, 030032, China
| | - Yan Wang
- Department of Pharmacology, Shanxi Medical University, Taiyuan, 030001, China
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Blauwendraat C, Francescatto M, Gibbs JR, Jansen IE, Simón-Sánchez J, Hernandez DG, Dillman AA, Singleton AB, Cookson MR, Rizzu P, Heutink P. Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe. Genome Med 2016; 8:65. [PMID: 27287230 PMCID: PMC4903003 DOI: 10.1186/s13073-016-0320-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 05/19/2016] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Expression quantitative trait loci (eQTL) analysis is a powerful method to detect correlations between gene expression and genomic variants and is widely used to interpret the biological mechanism underlying identified genome wide association studies (GWAS) risk loci. Numerous eQTL studies have been performed on different cell types and tissues of which the majority has been based on microarray technology. METHODS We present here an eQTL analysis based on cap analysis gene expression sequencing (CAGEseq) data created from human postmortem frontal lobe tissue combined with genotypes obtained through genotyping arrays, exome sequencing, and CAGEseq. Using CAGEseq as an expression profiling technique combined with these different genotyping techniques allows measurement of the molecular effect of variants on individual transcription start sites and increases the resolution of eQTL analysis by also including the non-annotated parts of the genome. RESULTS We identified 2410 eQTLs and show that non-coding transcripts are more likely to contain an eQTL than coding transcripts, in particular antisense transcripts. We provide evidence for how previously identified GWAS loci for schizophrenia (NRGN), Parkinson's disease, and Alzheimer's disease (PARK16 and MAPT loci) could increase the risk for disease at a molecular level. Furthermore, we demonstrate that CAGEseq improves eQTL analysis because variants obtained from CAGEseq are highly enriched for having a functional effect and thus are an efficient method towards the identification of causal variants. CONCLUSION Our data contain both coding and non-coding transcripts and has the added value that we have identified eQTLs for variants directly adjacent to TSS. Future eQTL studies would benefit from combining CAGEseq with RNA sequencing for a more complete interpretation of the transcriptome and increased understanding of eQTL signals.
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Affiliation(s)
- Cornelis Blauwendraat
- Applied Genomics for Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
| | - Margherita Francescatto
- Genome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - J Raphael Gibbs
- Laboratory of Neurogenetics, National Institute on Aging (NIA), Bethesda, Maryland, USA.,Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
| | - Iris E Jansen
- Genome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Department of Clinical Genetics, VU University Medical Center (VUmc), Amsterdam, The Netherlands
| | - Javier Simón-Sánchez
- Genome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Dena G Hernandez
- Laboratory of Neurogenetics, National Institute on Aging (NIA), Bethesda, Maryland, USA
| | - Allissa A Dillman
- Laboratory of Neurogenetics, National Institute on Aging (NIA), Bethesda, Maryland, USA
| | - Andrew B Singleton
- Laboratory of Neurogenetics, National Institute on Aging (NIA), Bethesda, Maryland, USA
| | - Mark R Cookson
- Laboratory of Neurogenetics, National Institute on Aging (NIA), Bethesda, Maryland, USA
| | - Patrizia Rizzu
- Applied Genomics for Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Peter Heutink
- Genome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Department of Clinical Genetics, VU University Medical Center (VUmc), Amsterdam, The Netherlands.,Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
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Imbeault M, Giguère K, Ouellet M, Tremblay MJ. Exon level transcriptomic profiling of HIV-1-infected CD4(+) T cells reveals virus-induced genes and host environment favorable for viral replication. PLoS Pathog 2012; 8:e1002861. [PMID: 22876188 PMCID: PMC3410884 DOI: 10.1371/journal.ppat.1002861] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2012] [Accepted: 06/30/2012] [Indexed: 01/01/2023] Open
Abstract
HIV-1 is extremely specialized since, even amongst CD4+ T lymphocytes (its major natural reservoir in peripheral blood), the virus productively infects only a small proportion of cells under an activated state. As the percentage of HIV-1-infected cells is very low, most studies have so far failed to capture the precise transcriptomic profile at the whole-genome scale of cells highly susceptible to virus infection. Using Affymetrix Exon array technology and a reporter virus allowing the magnetic isolation of HIV-1-infected cells, we describe the host cell factors most favorable for virus establishment and replication along with an overview of virus-induced changes in host gene expression occurring exclusively in target cells productively infected with HIV-1. We also establish that within a population of activated CD4+ T cells, HIV-1 has no detectable effect on the transcriptome of uninfected bystander cells at early time points following infection. The data gathered in this study provides unique insights into the biology of HIV-1-infected CD4+ T cells and identifies genes thought to play a determinant role in the interplay between the virus and its host. Furthermore, it provides the first catalogue of alternative splicing events found in primary human CD4+ T cells productively infected with HIV-1. Some previous studies have monitored HIV-1-induced gene expression in various host cell targets and tissues but the discrimination between productively infected cells and uninfected bystander cells represents a technical challenge yet to be solved. Consequently, data interpretation has always been biased towards the transcriptional response of a majority of uninfected bystander cells that were exposed to soluble factors released by virus-infected cells. Following the design of a unique and innovative molecular tool to identify cells productively infected with HIV-1 and the description of an efficient magnetic beads-based technique to separate them from uninfected bystander cells, we undertake this challenge and perform the first comparative whole-genome transcriptomic and large-scale proteomic profiling of both HIV-1-infected and uninfected bystander CD4+ T cells. We demonstrate herein that HIV-1- infected and uninfected bystander cells display distinctive transcriptomic signatures which might permit to identify new susceptibility and resistance factors.
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Affiliation(s)
- Michaël Imbeault
- Centre de Recherche en Infectiologie, Centre Hospitalier Universitaire de Québec - CHUL, Faculté de Médecine, Université Laval, Québec City, Québec, Canada
| | - Katia Giguère
- Centre de Recherche en Infectiologie, Centre Hospitalier Universitaire de Québec - CHUL, Faculté de Médecine, Université Laval, Québec City, Québec, Canada
| | - Michel Ouellet
- Centre de Recherche en Infectiologie, Centre Hospitalier Universitaire de Québec - CHUL, Faculté de Médecine, Université Laval, Québec City, Québec, Canada
| | - Michel J. Tremblay
- Centre de Recherche en Infectiologie, Centre Hospitalier Universitaire de Québec - CHUL, Faculté de Médecine, Université Laval, Québec City, Québec, Canada
- Département de Microbiologie-Infectiologie et Immunologie, Faculté de Médecine, Université Laval, Québec City, Québec, Canada
- * E-mail:
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