1
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Liu Z, Guo T, Yin Z, Zeng Y, Liu H, Yin H. Functional inference of long non-coding RNAs through exploration of highly conserved regions. Front Genet 2023; 14:1177259. [PMID: 37260771 PMCID: PMC10229068 DOI: 10.3389/fgene.2023.1177259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/28/2023] [Indexed: 06/02/2023] Open
Abstract
Background: Long non-coding RNAs (lncRNAs), which are generally less functionally characterized or less annotated, evolve more rapidly than mRNAs and substantially possess fewer sequence conservation patterns than protein-coding genes across divergent species. People assume that the functional inference could be conducted on the evolutionarily conserved long non-coding RNAs as they are most likely to be functional. In the past decades, substantial progress has been made in discussions on the evolutionary conservation of non-coding genomic regions from multiple perspectives. However, understanding their conservation and the functions associated with sequence conservation in relation to further corresponding phenotypic variability or disorders still remains incomplete. Results: Accordingly, we determined a highly conserved region (HCR) to verify the sequence conservation among long non-coding RNAs and systematically profiled homologous long non-coding RNA clusters in humans and mice based on the detection of highly conserved regions. Moreover, according to homolog clustering, we explored the potential function inference via highly conserved regions on representative long non-coding RNAs. On lncRNA XACT, we investigated the potential functional competence between XACT and lncRNA XIST by recruiting miRNA-29a, regulating the downstream target genes. In addition, on lncRNA LINC00461, we examined the interaction relationship between LINC00461 and SND1. This interaction or association may be perturbed during the progression of glioma. In addition, we have constructed a website with user-friendly web interfaces for searching, analyzing, and downloading to present the homologous clusters of humans and mice. Conclusion: Collectively, homolog clustering via the highly conserved region definition and detection on long non-coding RNAs, as well as the functional explorations on representative sequences in our research, would provide new evidence for the potential function of long non-coding RNAs. Our results on the remarkable roles of long non-coding RNAs would presumably provide a new theoretical basis and candidate diagnostic indicators for tumors.
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Affiliation(s)
- Zhongpeng Liu
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Tianbin Guo
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Zhuoda Yin
- TJ-YZ School of Network Science, Haikou University of Economics, Haikou, China
| | - Yanluo Zeng
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Haiwen Liu
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Hongyan Yin
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
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2
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Patra GK, Gupta D, Rout GR, Panda SK. Role of long non coding RNA in plants under abiotic and biotic stresses. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2023; 194:96-110. [PMID: 36399914 DOI: 10.1016/j.plaphy.2022.10.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 06/16/2023]
Abstract
Evolutionary processes have evolved plants to cope with several different natural stresses. Basic physiological activities of crop plants are significantly harmed by these stresses, reducing productivity and eventually leading to death. The recent advancements in high-throughput sequencing of transcriptome and expression profiling with NGS techniques lead to the innovation of various RNAs which do not code for proteins, more specifically long non-coding RNAs (lncRNAs), undergirding regulate growth, development, and the plant defence mechanism transcriptionally under stress situations. LncRNAs are a diverse set of RNAs that play key roles in various biological processes at the level of transcription, post-transcription, and epigenetics. These are thought to serve crucial functions in plant immunity and response to changes in the environment. In plants, however, just a few lncRNAs have been functionally identified. In this review, we will address recent advancements in comprehending lncRNA regulatory functions, focusing on the expanding involvement of lncRNAs in modulating environmental stress responsiveness in plants.
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Affiliation(s)
- Gyanendra K Patra
- Department of Agriculture Biotechnology, Orissa University of Agriculture and Technology, Bhubaneswar, 751 003, Odisha, India
| | - Divya Gupta
- School of Life Sciences, Central University of Rajasthan, NH 8, Bandarsindri, Ajmer, 305817, Rajasthan, India
| | - Gyana Ranjan Rout
- Department of Agriculture Biotechnology, Orissa University of Agriculture and Technology, Bhubaneswar, 751 003, Odisha, India
| | - Sanjib Kumar Panda
- School of Life Sciences, Central University of Rajasthan, NH 8, Bandarsindri, Ajmer, 305817, Rajasthan, India.
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3
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Jafari-Raddani F, Davoodi-Moghaddam Z, Yousefi AM, Ghaffari SH, Bashash D. An overview of long noncoding RNAs: Biology, functions, therapeutics, analysis methods, and bioinformatics tools. Cell Biochem Funct 2022; 40:800-825. [PMID: 36111699 DOI: 10.1002/cbf.3748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/05/2022] [Accepted: 09/06/2022] [Indexed: 12/15/2022]
Abstract
Long noncoding RNAs (lncRNAs) are a diverse class of RNAs whose functions are widespread in all branches of life and have been the focus of attention in the last decade. While a huge number of lncRNAs have been identified, there is still much work to be done and plenty to be learned. In the current review, we begin with the biogenesis and function of lncRNAs as they are involved in the different cellular processes from regulating the architecture of chromosomes to controlling translation and post-translation modifications. Questions on how overexpression, mutations, or deficiency of lncRNAs can affect the cellular status and result in the pathogenesis of various human diseases are responded to. Besides, we allocate an overview of several studies, concerning the application of lncRNAs either as diagnostic and prognostic biomarkers or novel therapeutics. We also introduce the currently available techniques to explore details of lncRNAs such as their function, cellular localization, and structure. In the last section, as exponentially growing data in this area need to be gathered and organized in comprehensive databases, we have a particular focus on presenting general and specialized databases. Taken together, with this review, we aim to provide the latest information on different aspects of lncRNAs to highlight their importance in physiopathologic states and take a step towards helping future studies.
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Affiliation(s)
- Farideh Jafari-Raddani
- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zeinab Davoodi-Moghaddam
- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amir-Mohammad Yousefi
- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed H Ghaffari
- Hematology, Oncology and Stem Cell Transplantation Research Center, Shariati Hospital, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Davood Bashash
- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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4
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Hegre SA, Samdal H, Klima A, Stovner EB, Nørsett KG, Liabakk NB, Olsen LC, Chawla K, Aas PA, Sætrom P. Joint changes in RNA, RNA polymerase II, and promoter activity through the cell cycle identify non-coding RNAs involved in proliferation. Sci Rep 2021; 11:18952. [PMID: 34556693 PMCID: PMC8460802 DOI: 10.1038/s41598-021-97909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/26/2021] [Indexed: 11/09/2022] Open
Abstract
Proper regulation of the cell cycle is necessary for normal growth and development of all organisms. Conversely, altered cell cycle regulation often underlies proliferative diseases such as cancer. Long non-coding RNAs (lncRNAs) are recognized as important regulators of gene expression and are often found dysregulated in diseases, including cancers. However, identifying lncRNAs with cell cycle functions is challenging due to their often low and cell-type specific expression. We present a highly effective method that analyses changes in promoter activity, transcription, and RNA levels for identifying genes enriched for cell cycle functions. Specifically, by combining RNA sequencing with ChIP sequencing through the cell cycle of synchronized human keratinocytes, we identified 1009 genes with cell cycle-dependent expression and correlated changes in RNA polymerase II occupancy or promoter activity as measured by histone 3 lysine 4 trimethylation (H3K4me3). These genes were highly enriched for genes with known cell cycle functions and included 57 lncRNAs. We selected four of these lncRNAs-SNHG26, EMSLR, ZFAS1, and EPB41L4A-AS1-for further experimental validation and found that knockdown of each of the four lncRNAs affected cell cycle phase distributions and reduced proliferation in multiple cell lines. These results show that many genes with cell cycle functions have concomitant cell-cycle dependent changes in promoter activity, transcription, and RNA levels and support that our multi-omics method is well suited for identifying lncRNAs involved in the cell cycle.
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Affiliation(s)
- Siv Anita Hegre
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Helle Samdal
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Antonin Klima
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Endre B Stovner
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.,K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Kristin G Nørsett
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.,Department of Biomedical Laboratory Science, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Nina Beate Liabakk
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Lene Christin Olsen
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.,Bioinformatics Core Facility-BioCore, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.,The Central Norway Regional Health Authority, St. Olavs Hospital HF, Trondheim, Norway
| | - Konika Chawla
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.,Bioinformatics Core Facility-BioCore, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Per Arne Aas
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Pål Sætrom
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway. .,Department of Computer Science, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway. .,K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway. .,Bioinformatics Core Facility-BioCore, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.
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5
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Lv Y, Huang S, Zhang T, Gao B. Application of Multilayer Network Models in Bioinformatics. Front Genet 2021; 12:664860. [PMID: 33868392 PMCID: PMC8044439 DOI: 10.3389/fgene.2021.664860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 02/26/2021] [Indexed: 11/24/2022] Open
Abstract
Multilayer networks provide an efficient tool for studying complex systems, and with current, dramatic development of bioinformatics tools and accumulation of data, researchers have applied network concepts to all aspects of research problems in the field of biology. Addressing the combination of multilayer networks and bioinformatics, through summarizing the applications of multilayer network models in bioinformatics, this review classifies applications and presents a summary of the latest results. Among them, we classify the applications of multilayer networks according to the object of study. Furthermore, because of the systemic nature of biology, we classify the subjects into several hierarchical categories, such as cells, tissues, organs, and groups, according to the hierarchical nature of biological composition. On the basis of the complexity of biological systems, we selected brain research for a detailed explanation. We describe the application of multilayer networks and chronological networks in brain research to demonstrate the primary ideas associated with the application of multilayer networks in biological studies. Finally, we mention a quality assessment method focusing on multilayer and single-layer networks as an evaluation method emphasizing network studies.
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Affiliation(s)
- Yuanyuan Lv
- Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, China
- Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
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6
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Chen Z, Shen Z, Zhang Z, Zhao D, Xu L, Zhang L. RNA-Associated Co-expression Network Identifies Novel Biomarkers for Digestive System Cancer. Front Genet 2021; 12:659788. [PMID: 33841514 PMCID: PMC8033200 DOI: 10.3389/fgene.2021.659788] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 02/25/2021] [Indexed: 01/04/2023] Open
Abstract
Cancers of the digestive system are malignant diseases. Our study focused on colon cancer, esophageal cancer (ESCC), rectal cancer, gastric cancer (GC), and rectosigmoid junction cancer to identify possible biomarkers for these diseases. The transcriptome data were downloaded from the TCGA database (The Cancer Genome Atlas Program), and a network was constructed using the WGCNA algorithm. Two significant modules were found, and coexpression networks were constructed. CytoHubba was used to identify hub genes of the two networks. GO analysis suggested that the network genes were involved in metabolic processes, biological regulation, and membrane and protein binding. KEGG analysis indicated that the significant pathways were the calcium signaling pathway, fatty acid biosynthesis, and pathways in cancer and insulin resistance. Some of the most significant hub genes were hsa-let-7b-3p, hsa-miR-378a-5p, hsa-miR-26a-5p, hsa-miR-382-5p, and hsa-miR-29b-2-5p and SECISBP2 L, NCOA1, HERC1, HIPK3, and MBNL1, respectively. These genes were predicted to be associated with the tumor prognostic reference for this patient population.
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Affiliation(s)
- Zheng Chen
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Zijie Shen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Zilong Zhang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Da Zhao
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Lijun Zhang
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
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7
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Xu L, Jiao S, Zhang D, Wu S, Zhang H, Gao B. Identification of long noncoding RNAs with machine learning methods: a review. Brief Funct Genomics 2021; 20:174-180. [PMID: 33758917 DOI: 10.1093/bfgp/elab017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 12/11/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic
| | - Shihu Jiao
- College of Chemistry, Sichuan University, Sichuan, China
| | - Dandan Zhang
- Departments of Obstetrics and Gynecology, First Affiliated Hospital of Harbin Medical University
| | - Song Wu
- Preventive Treatment of Disease Centre of Qinhuangdao Hospital of Traditional Chinese Medicine
| | - Haihong Zhang
- First Affiliated Hospital of Harbin Medical University
| | - Bo Gao
- Second Affiliated Hospital, Harbin Medical University, Harbin, China
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8
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Jiao S, Wu S, Huang S, Liu M, Gao B. Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases. Front Genet 2021; 12:665233. [PMID: 33815488 PMCID: PMC8017306 DOI: 10.3389/fgene.2021.665233] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 03/01/2021] [Indexed: 12/14/2022] Open
Abstract
Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs (ncRNAs) with a closed-loop structure that are mainly produced by variable processing of precursor mRNAs (pre-mRNAs). They are widely present in all eukaryotes and are very stable. Currently, circRNA studies have become a hotspot in RNA research. It has been reported that circRNAs constitute a significant proportion of transcript expression, and some are significantly more abundantly expressed than other transcripts. CircRNAs have regulatory roles in gene expression and critical biological functions in the development of organisms, such as acting as microRNA sponges or as endogenous RNAs and biomarkers. As such, they may have useful functions in the diagnosis and treatment of diseases. CircRNAs have been found to play an important role in the development of several diseases, including atherosclerosis, neurological disorders, diabetes, and cancer. In this paper, we review the status of circRNA research, describe circRNA-related databases and the identification of circRNAs, discuss the role of circRNAs in human diseases such as colon cancer, atherosclerosis, and gastric cancer, and identify remaining research questions related to circRNAs.
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Affiliation(s)
- Shihu Jiao
- Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Song Wu
- Director of Preventive Treatment of Disease Centre, Qinhuangdao Hospital of Traditional Chinese Medicine, Qinhuangdao, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Mingyang Liu
- Department of Internal Medicine-Oncology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
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9
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Zhang S, Zhang C, Du J, Zhang R, Yang S, Li B, Wang P, Deng W. Prediction of Lymph-Node Metastasis in Cancers Using Differentially Expressed mRNA and Non-coding RNA Signatures. Front Cell Dev Biol 2021; 9:605977. [PMID: 33644044 PMCID: PMC7905047 DOI: 10.3389/fcell.2021.605977] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/07/2021] [Indexed: 12/12/2022] Open
Abstract
Accurate prediction of lymph-node metastasis in cancers is pivotal for the next targeted clinical interventions that allow favorable prognosis for patients. Different molecular profiles (mRNA and non-coding RNAs) have been widely used to establish classifiers for cancer prediction (e.g., tumor origin, cancerous or non-cancerous state, cancer subtype). However, few studies focus on lymphatic metastasis evaluation using these profiles, and the performance of classifiers based on different profiles has also not been compared. Here, differentially expressed mRNAs, miRNAs, and lncRNAs between lymph-node metastatic and non-metastatic groups were identified as molecular signatures to construct classifiers for lymphatic metastasis prediction in different cancers. With this similar feature selection strategy, support vector machine (SVM) classifiers based on different profiles were systematically compared in their prediction performance. For representative cancers (a total of nine types), these classifiers achieved comparative overall accuracies of 81.00% (67.96-92.19%), 81.97% (70.83-95.24%), and 80.78% (69.61-90.00%) on independent mRNA, miRNA, and lncRNA datasets, with a small set of biomarkers (6, 12, and 4 on average). Therefore, our proposed feature selection strategies are economical and efficient to identify biomarkers that aid in developing competitive classifiers for predicting lymph-node metastasis in cancers. A user-friendly webserver was also deployed to help researchers in metastasis risk determination by submitting their expression profiles of different origins.
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Affiliation(s)
- Shihua Zhang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, China
| | - Cheng Zhang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, China
| | - Jinke Du
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Rui Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Shixiong Yang
- Central Laboratory, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan, China
| | - Bo Li
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Wensheng Deng
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, China
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10
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Long Non-Coding RNAs, the Dark Matter: An Emerging Regulatory Component in Plants. Int J Mol Sci 2020; 22:ijms22010086. [PMID: 33374835 PMCID: PMC7795044 DOI: 10.3390/ijms22010086] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/18/2020] [Accepted: 12/19/2020] [Indexed: 02/07/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) are pervasive transcripts of longer than 200 nucleotides and indiscernible coding potential. lncRNAs are implicated as key regulatory molecules in various fundamental biological processes at transcriptional, post-transcriptional, and epigenetic levels. Advances in computational and experimental approaches have identified numerous lncRNAs in plants. lncRNAs have been found to act as prime mediators in plant growth, development, and tolerance to stresses. This review summarizes the current research status of lncRNAs in planta, their classification based on genomic context, their mechanism of action, and specific bioinformatics tools and resources for their identification and characterization. Our overarching goal is to summarize recent progress on understanding the regulatory role of lncRNAs in plant developmental processes such as flowering time, reproductive growth, and abiotic stresses. We also review the role of lncRNA in nutrient stress and the ability to improve biotic stress tolerance in plants. Given the pivotal role of lncRNAs in various biological processes, their functional characterization in agriculturally essential crop plants is crucial for bridging the gap between phenotype and genotype.
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11
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Cheng L, Wang P, Tian R, Wang S, Guo Q, Luo M, Zhou W, Liu G, Jiang H, Jiang Q. LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res 2020; 47:D140-D144. [PMID: 30380072 PMCID: PMC6323902 DOI: 10.1093/nar/gky1051] [Citation(s) in RCA: 231] [Impact Index Per Article: 57.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 10/26/2018] [Indexed: 12/12/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) play crucial roles in regulating gene expression, and a growing number of researchers have focused on the identification of target genes of lncRNAs. However, no online repository is available to collect the information on target genes regulated by lncRNAs. To make it convenient for researchers to know what genes are regulated by a lncRNA of interest, we developed a database named lncRNA2Target to provide a comprehensive resource of lncRNA target genes in 2015. To update the database this year, we retrieved all new lncRNA-target relationships from papers published from 1 August 2014 to 30 April 2018 and RNA-seq datasets before and after knockdown or overexpression of a specific lncRNA. LncRNA2Target database v2.0 provides a web interface through which its users can search for the targets of a particular lncRNA or for the lncRNAs that target a particular gene, and is freely accessible at http://123.59.132.21/lncrna2target.
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Affiliation(s)
- Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Pingping Wang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Rui Tian
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Song Wang
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
| | - Qinghua Guo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Meng Luo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Wenyang Zhou
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Guiyou Liu
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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12
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Ying X, Jin X, Wang P, He Y, Zhang H, Ren X, Chai S, Fu W, Zhao P, Chen C, Ma G, Liu H. Integrative Analysis for Elucidating Transcriptomics Landscapes of Glucocorticoid-Induced Osteoporosis. Front Cell Dev Biol 2020; 8:252. [PMID: 32373610 PMCID: PMC7176994 DOI: 10.3389/fcell.2020.00252] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 03/25/2020] [Indexed: 11/13/2022] Open
Abstract
Osteoporosis is the most common bone metabolic disease, characterized by bone mass loss and bone microstructure changes due to unbalanced bone conversion, which increases bone fragility and fracture risk. Glucocorticoids are clinically used to treat a variety of diseases, including inflammation, cancer and autoimmune diseases. However, excess glucocorticoids can cause osteoporosis. Herein we performed an integrated analysis of two glucocorticoid-related microarray datasets. The WGCNA analysis identified 3 and 4 glucocorticoid-related gene modules, respectively. Differential expression analysis revealed 1047 and 844 differentially expressed genes in the two datasets. After integrating differentially expressed glucocorticoid-related genes, we found that most of the robust differentially expressed genes were up-regulated. Through protein-protein interaction analysis, we obtained 158 glucocorticoid-related candidate genes. Enrichment analysis showed that these genes are significantly enriched in the osteoporosis related pathways. Our results provided new insights into glucocorticoid-induced osteoporosis and potential candidate markers of osteoporosis.
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Affiliation(s)
- Xiaoxia Ying
- School of Stomatology, Dalian Medical University, Dalian, China
| | - Xiyun Jin
- School of Life Sciences and Technology, Harbin Institute of Technology, Harbin, China
| | - Pingping Wang
- School of Life Sciences and Technology, Harbin Institute of Technology, Harbin, China
| | - Yuzhu He
- School of Stomatology, Dalian Medical University, Dalian, China
| | - Haomiao Zhang
- School of Stomatology, Dalian Medical University, Dalian, China
| | - Xiang Ren
- School of Stomatology, Dalian Medical University, Dalian, China
| | - Songling Chai
- School of Stomatology, Dalian Medical University, Dalian, China
| | - Wenqi Fu
- School of Stomatology, Dalian Medical University, Dalian, China
| | - Pengcheng Zhao
- School of Stomatology, Dalian Medical University, Dalian, China
| | - Chen Chen
- School of Stomatology, Dalian Medical University, Dalian, China
| | - Guowu Ma
- School of Stomatology, Dalian Medical University, Dalian, China
| | - Huiying Liu
- School of Stomatology, Dalian Medical University, Dalian, China
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13
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Dong H, Zhou W, Wang P, Zuo E, Ying X, Chai S, Fei T, Jin L, Chen C, Ma G, Liu H. Comprehensive Analysis of the Genetic and Epigenetic Mechanisms of Osteoporosis and Bone Mineral Density. Front Cell Dev Biol 2020; 8:194. [PMID: 32269995 PMCID: PMC7109267 DOI: 10.3389/fcell.2020.00194] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 03/09/2020] [Indexed: 01/11/2023] Open
Abstract
Osteoporosis is a skeletal disorder characterized by a systemic impairment of bone mineral density (BMD). Genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for osteoporosis and BMD. However, the vast majority of susceptibility loci are located in non-coding regions of the genome and provide limited information about the genetic mechanisms of osteoporosis. Herein we performed a comprehensive functional analysis to investigate the genetic and epigenetic mechanisms of osteoporosis and BMD. BMD and osteoporosis are found to share many common susceptibility loci, and the corresponding susceptibility genes are significantly enriched in bone-related biological pathways. The regulatory element enrichment analysis indicated that BMD and osteoporosis susceptibility loci are significantly enriched in 5′UTR and DNase I hypersensitive sites (DHSs) of peripheral blood immune cells. By integrating GWAS and expression Quantitative Trait Locus (eQTL) data, we found that 15 protein-coding genes are regulated by the osteoporosis and BMD susceptibility loci. Our analysis provides new clues for a better understanding of the pathogenic mechanisms and offers potential therapeutic targets for osteoporosis.
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Affiliation(s)
- Hui Dong
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China.,Department of Stomatology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Enjun Zuo
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Xiaoxia Ying
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Songling Chai
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Tao Fei
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Laidi Jin
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Chen Chen
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Guowu Ma
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Huiying Liu
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
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14
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Wang C, Zhao N, Yuan L, Liu X. Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers. Cells 2020; 9:E326. [PMID: 32019269 PMCID: PMC7072524 DOI: 10.3390/cells9020326] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 01/28/2020] [Accepted: 01/28/2020] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.
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Affiliation(s)
- Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Ning Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China;
| | - Linlin Yuan
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;
| | - Xiaoyan Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
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15
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Abstract
Embryonic Stem cells are widely studied to elucidate the disease and developmental processes because of their capability to differentiate into cells of any lineage, Pervasive transcription is a distinct feature of all multicellular organisms and genomic elements such as enhancers and bidirectional or unidirectional promoters regulate these processes. Thousands of loci in each species produce a class of transcripts called noncoding RNAs (ncRNAs), that are well known for their influential regulatory roles in multiple biological processes including stem cell pluripotency and differentiation. The number of lncRNA species increases in more complex organisms highlighting the importance of RNA-based control in the evolution of multicellular organisms. Over the past decade, numerous studies have shed light on lncRNA biogenesis and functional significance in the cell and the organism. In this review, we focus primarily on lncRNAs affecting the stem cell state and developmental pathways.
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Affiliation(s)
- Meghali Aich
- CSIR-Institute of Genomics & Integrative Biology, New Delhi, India; Academy of Scientific & Innovative Research, New Delhi, India
| | - Debojyoti Chakraborty
- CSIR-Institute of Genomics & Integrative Biology, New Delhi, India; Academy of Scientific & Innovative Research, New Delhi, India.
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16
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Mora A. Gene set analysis methods for the functional interpretation of non-mRNA data—Genomic range and ncRNA data. Brief Bioinform 2019; 21:1495-1508. [DOI: 10.1093/bib/bbz090] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 05/30/2019] [Accepted: 06/28/2019] [Indexed: 12/31/2022] Open
Abstract
Abstract
Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.
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Affiliation(s)
- Antonio Mora
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health - Chinese Academy of Sciences
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17
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Ahmed W, Xia Y, Li R, Bai G, Siddique KHM, Guo P. Non-coding RNAs: Functional roles in the regulation of stress response in Brassica crops. Genomics 2019; 112:1419-1424. [PMID: 31430515 DOI: 10.1016/j.ygeno.2019.08.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 07/03/2019] [Accepted: 08/16/2019] [Indexed: 12/22/2022]
Abstract
Brassica crops face a combination of different abiotic and biotic stresses in the field that can reduce plant growth and development by affecting biochemical and morpho-physiological processes. Emerging evidence suggests that non-coding RNAs (ncRNAs), especially microRNAs (miRNAs) and long ncRNAs (lncRNAs), play a significant role in the modulation of gene expression in response to plant stresses. Recent advances in computational and experimental approaches are of great interest for identifying and functionally characterizing ncRNAs. While progress in this field is limited, numerous ncRNAs involved in the regulation of gene expression in response to stress have been reported in Brassica. In this review, we summarize the modes of action and functions of stress-related miRNAs and lncRNAs in Brassica as well as the approaches used to identify ncRNAs.
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Affiliation(s)
- Waqas Ahmed
- International Crop Research Center for Stress Resistance, College of Life Sciences, Guangzhou University, Guangzhou, China
| | - Yanshi Xia
- International Crop Research Center for Stress Resistance, College of Life Sciences, Guangzhou University, Guangzhou, China
| | - Ronghua Li
- International Crop Research Center for Stress Resistance, College of Life Sciences, Guangzhou University, Guangzhou, China
| | - Guihua Bai
- United States Department of Agriculture - Agricultural Research Service, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas 66506, United States
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, LB 5005, Perth, WA 6001, Australia
| | - Peiguo Guo
- International Crop Research Center for Stress Resistance, College of Life Sciences, Guangzhou University, Guangzhou, China.
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18
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Yi J, Chen B, Yao X, Lei Y, Ou F, Huang F. Upregulation of the lncRNA MEG3 improves cognitive impairment, alleviates neuronal damage, and inhibits activation of astrocytes in hippocampus tissues in Alzheimer's disease through inactivating the PI3K/Akt signaling pathway. J Cell Biochem 2019; 120:18053-18065. [PMID: 31190362 DOI: 10.1002/jcb.29108] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 04/18/2019] [Accepted: 04/29/2019] [Indexed: 12/31/2022]
Abstract
OBJECTIVE The purpose of this study was to elucidate the expression of the long noncoding RNA (lncRNA) maternally expressed gene 3 (MEG3) in rats with Alzheimer's disease (AD) and to explore its potential mechanisms. METHODS An AD rat model was induced by microinjection of Aβ25-35 . On the first day after successful modeling, pcDNA3.1 plasmid and pcDNA3.1-MEG3 plasmid were continuously infused into the third ventricle through a micro-osmotic pump to interfere with the expression level of MEG3. The spatial learning ability and memory ability, the histopathological changes of hippocampus tissues, the ultrastructure of hippocampal neurons, astrocyte activation as well as the survival and apoptosis of hippocampal neurons in each group was observed. The expression of apoptosis, PI3/Akt signaling pathway-related proteins, glial fibrillary acidic protein, inflammatory factors, malondialdehyde, glutathione-peroxidase, and superoxide dismutase levels were determined. The deposition of amyloid beta (Aβ) in the hippocampus of rats by was observed by Aβ immunohistochemical staining. RESULTS Downregulated MEG3 was detected in the tissues of AD rats. In addition, upregulation of MEG3 contributed to an improvement of spatial learning ability and memory ability, inhibited the pathological injury and its apoptosis of hippocampal neurons, decreased Aβ positive expression, inhibited oxidative stress injury and inflammatory injury as well as the activated astrocytes in AD rats via inactivation of the PI3/Akt pathway. CONCLUSION Our study highlights that upregulation of the lncRNA MEG3 improves cognitive impairment, alleviates neuronal damage, and inhibits activation of astrocytes in hippocampus tissues in AD through inhibiting the PI3K/Akt signaling pathway.
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Affiliation(s)
- Jiping Yi
- Department of Neurology, Translational Medicine Institute, The First People's Hospital of Chenzhou, University of South China, Chenzhou, P.R. China
| | - Bin Chen
- Department of Spinal Surgery, Translational Medicine Institute, The First People's Hospital of Chenzhou, University of South China, Chenzhou, P.R. China
| | - Xiaoxi Yao
- Department of Neurology, Translational Medicine Institute, The First People's Hospital of Chenzhou, University of South China, Chenzhou, P.R. China
| | - Yuanbiao Lei
- Department of Neurology, Translational Medicine Institute, The First People's Hospital of Chenzhou, University of South China, Chenzhou, P.R. China
| | - Fuyong Ou
- Department of Neurology, Translational Medicine Institute, The First People's Hospital of Chenzhou, University of South China, Chenzhou, P.R. China
| | - Fengzhen Huang
- Department of Neurology, Translational Medicine Institute, The First People's Hospital of Chenzhou, University of South China, Chenzhou, P.R. China
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19
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Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods. Int J Mol Sci 2019; 20:ijms20061284. [PMID: 30875752 PMCID: PMC6471543 DOI: 10.3390/ijms20061284] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 03/09/2019] [Accepted: 03/11/2019] [Indexed: 01/13/2023] Open
Abstract
Non-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA–protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA–protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at https://github.com/HAN-Siyu/APINet/.
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20
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Weirick T, Militello G, Ponomareva Y, John D, Döring C, Dimmeler S, Uchida S. Logic programming to infer complex RNA expression patterns from RNA-seq data. Brief Bioinform 2019; 19:199-209. [PMID: 28011754 DOI: 10.1093/bib/bbw117] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Indexed: 12/15/2022] Open
Abstract
To meet the increasing demand in the field, numerous long noncoding RNA (lncRNA) databases are available. Given many lncRNAs are specifically expressed in certain cell types and/or time-dependent manners, most lncRNA databases fall short of providing such profiles. We developed a strategy using logic programming to handle the complex organization of organs, their tissues and cell types as well as gender and developmental time points. To showcase this strategy, we introduce 'RenalDB' (http://renaldb.uni-frankfurt.de), a database providing expression profiles of RNAs in major organs focusing on kidney tissues and cells. RenalDB uses logic programming to describe complex anatomy, sample metadata and logical relationships defining expression, enrichment or specificity. We validated the content of RenalDB with biological experiments and functionally characterized two long intergenic noncoding RNAs: LOC440173 is important for cell growth or cell survival, whereas PAXIP1-AS1 is a regulator of cell death. We anticipate RenalDB will be used as a first step toward functional studies of lncRNAs in the kidney.
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Affiliation(s)
- Tyler Weirick
- Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.,German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany.,Cardiovascular Innovation Institute, University of Louisville, 302 E Muhammad Ali Blvd, Louisville, KY, U.S.A
| | - Giuseppe Militello
- Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.,German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany.,Cardiovascular Innovation Institute, University of Louisville, 302 E Muhammad Ali Blvd, Louisville, KY, U.S.A
| | - Yuliya Ponomareva
- Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.,German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany
| | - David John
- Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.,German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany
| | - Claudia Döring
- Dr. Senckenberg Institute of Pathology, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany
| | - Stefanie Dimmeler
- Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.,German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany
| | - Shizuka Uchida
- Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, Germany.,German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany.,Cardiovascular Innovation Institute, University of Louisville, 302 E Muhammad Ali Blvd, Louisville, KY, U.S.A
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21
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Abstract
SIGNIFICANCE The concepts of junk DNA and transcriptional noise are long gone as the existence of noncoding RNAs (ncRNAs) has been tested extensively in recent years. Given that the epigenetic status of cells affects many biological processes, how ncRNAs mechanistically contribute to these processes is of great interest. Recent Advances: Recent studies show that various ncRNAs interact with epigenetic and/or transcription factors to modulate the epigenetic status of cells directly and/or indirectly. There exists growing interest in the field of cardiovascular research to understand the roles of ncRNAs. Due to the large number of ncRNAs in the mammalian genome, only a handful of ncRNAs have been functionally elucidated, which makes it difficult to understand how ncRNAs interact with protein-coding genes and their encoded proteins. CRITICAL ISSUES Although the canonical function of microRNAs (miRNAs) to inhibit the translation of protein-coding genes is well established, the number of functionally annotated long noncoding RNAs (lncRNAs) is still small, which is especially true in the heart. FUTURE DIRECTIONS Future studies must connect the epigenetic controls of various cellular phenomena by incorporating both miRNAs and lncRNAs. Antioxid. Redox Signal. 29, 832-845.
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Affiliation(s)
- Shizuka Uchida
- 1 Cardiovascular Innovation Institute, University of Louisville , Louisville, Kentucky
| | - Roberto Bolli
- 1 Cardiovascular Innovation Institute, University of Louisville , Louisville, Kentucky.,2 Institute of Molecular Cardiology, University of Louisville , Louisville, Kentucky
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22
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Lefever S, Anckaert J, Volders PJ, Luypaert M, Vandesompele J, Mestdagh P. decodeRNA- predicting non-coding RNA functions using guilt-by-association. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2017:3866795. [PMID: 29220434 PMCID: PMC5502368 DOI: 10.1093/database/bax042] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 05/01/2017] [Indexed: 12/23/2022]
Abstract
Although the long non-coding RNA (lncRNA) landscape is expanding rapidly, only a small number of lncRNAs have been functionally annotated. Here, we present decodeRNA (http://www.decoderna.org), a database providing functional contexts for both human lncRNAs and microRNAs in 29 cancer and 12 normal tissue types. With state-of-the-art data mining and visualization options, easy access to results and a straightforward user interface, decodeRNA aims to be a powerful tool for researchers in the ncRNA field. Database URL:http://www.decoderna.org
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Affiliation(s)
- Steve Lefever
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium
| | - Jasper Anckaert
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium
| | - Pieter-Jan Volders
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium
| | - Manuel Luypaert
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
| | - Jo Vandesompele
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium.,Biogazelle, Zwijnaarde, Belgium
| | - Pieter Mestdagh
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium.,Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium.,Biogazelle, Zwijnaarde, Belgium
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23
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When Long Noncoding RNAs Meet Genome Editing in Pluripotent Stem Cells. Stem Cells Int 2017; 2017:3250624. [PMID: 29333164 PMCID: PMC5733163 DOI: 10.1155/2017/3250624] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 10/25/2017] [Indexed: 11/18/2022] Open
Abstract
Most of the human genome can be transcribed into RNAs, but only a minority of these regions produce protein-coding mRNAs whereas the remaining regions are transcribed into noncoding RNAs. Long noncoding RNAs (lncRNAs) were known for their influential regulatory roles in multiple biological processes such as imprinting, dosage compensation, transcriptional regulation, and splicing. The physiological functions of protein-coding genes have been extensively characterized through genome editing in pluripotent stem cells (PSCs) in the past 30 years; however, the study of lncRNAs with genome editing technologies only came into attentions in recent years. Here, we summarize recent advancements in dissecting the roles of lncRNAs with genome editing technologies in PSCs and highlight potential genome editing tools useful for examining the functions of lncRNAs in PSCs.
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24
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Wang J, Meng X, Dobrovolskaya OB, Orlov YL, Chen M. Non-coding RNAs and Their Roles in Stress Response in Plants. GENOMICS PROTEOMICS & BIOINFORMATICS 2017; 15:301-312. [PMID: 29017967 PMCID: PMC5673675 DOI: 10.1016/j.gpb.2017.01.007] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 01/04/2017] [Accepted: 01/26/2017] [Indexed: 02/04/2023]
Abstract
Eukaryotic genomes encode thousands of non-coding RNAs (ncRNAs), which play crucial roles in transcriptional and post-transcriptional regulation of gene expression. Accumulating evidence indicates that ncRNAs, especially microRNAs (miRNAs) and long ncRNAs (lncRNAs), have emerged as key regulatory molecules in plant stress responses. In this review, we have summarized the current progress on the understanding of plant miRNA and lncRNA identification, characteristics, bioinformatics tools, and resources, and provided examples of mechanisms of miRNA- and lncRNA-mediated plant stress tolerance.
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Affiliation(s)
- Jingjing Wang
- Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xianwen Meng
- Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Oxana B Dobrovolskaya
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia; Novosibirsk State University, Novosibirsk 630090, Russia
| | - Yuriy L Orlov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia
| | - Ming Chen
- Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, China.
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25
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Zheng Y, Liu L, Shukla GC. A comprehensive review of web-based non-coding RNA resources for cancer research. Cancer Lett 2017; 407:1-8. [PMID: 28823961 DOI: 10.1016/j.canlet.2017.08.015] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 08/02/2017] [Accepted: 08/08/2017] [Indexed: 12/13/2022]
Abstract
Non-coding RNAs include many kinds of RNAs that did not encode proteins. Recent evidences reveal that ncRNAs play critical roles in initiation and progression of cancers. But it is not easy for cancer biologists and medical doctors to easily know the potential roles of ncRNAs in cancer and retrieve the information of ncRNAs under their investigations. To make the available web-based resources more accessible and understandable, we made a comprehensive review for 49 web-based resources of three types of ncRNAs, i.e., microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). We also listed some preferred resources for 6 different types of analyses related to ncRNAs.
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Affiliation(s)
- Yun Zheng
- Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China.
| | - Li Liu
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China
| | - Girish C Shukla
- Center for Gene Regulation in Health and Disease, Cleveland State University, Cleveland, OH, 44115, USA
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26
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Transcriptional Regulation of lncRNA Genes by Histone Modification in Alzheimer's Disease. BIOMED RESEARCH INTERNATIONAL 2016; 2016:3164238. [PMID: 27822470 PMCID: PMC5086382 DOI: 10.1155/2016/3164238] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 09/27/2016] [Indexed: 12/11/2022]
Abstract
Increasing studies have revealed that long noncoding RNAs (lncRNAs) are not transcriptional noise but play important roles in the regulation of a wide range of biological processes, and the dysregulation of lncRNA genes is associated with disease development. Alzheimer's disease (AD) is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. However, little is known about the roles of lncRNA genes in AD and how the lncRNA genes are transcriptionally regulated. Herein, we analyzed RNA-seq data and ChIP-seq histone modification data from CK-p25 AD model and control mice and identified 72 differentially expressed lncRNA genes, 4,917 differential peaks of H3K4me3, and 1,624 differential peaks of H3K27me3 between AD and control samples, respectively. Furthermore, we found 92 differential peaks of histone modification H3K4me3 are located in the promoter of 39 differentially expressed lncRNA genes and 8 differential peaks of histone modification H3K27me3 are located upstream of 7 differentially expressed lncRNA genes, which suggest that the majority of lncRNA genes may be transcriptionally regulated by histone modification in AD.
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27
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ANGIOGENES: knowledge database for protein-coding and noncoding RNA genes in endothelial cells. Sci Rep 2016; 6:32475. [PMID: 27582018 PMCID: PMC5007478 DOI: 10.1038/srep32475] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 08/08/2016] [Indexed: 12/29/2022] Open
Abstract
Increasing evidence indicates the presence of long noncoding RNAs (lncRNAs) is specific to various cell types. Although lncRNAs are speculated to be more numerous than protein-coding genes, the annotations of lncRNAs remain primitive due to the lack of well-structured schemes for their identification and description. Here, we introduce a new knowledge database “ANGIOGENES” (http://angiogenes.uni-frankfurt.de) to allow for in silico screening of protein-coding genes and lncRNAs expressed in various types of endothelial cells, which are present in all tissues. Using the latest annotations of protein-coding genes and lncRNAs, publicly-available RNA-seq data was analyzed to identify transcripts that are expressed in endothelial cells of human, mouse and zebrafish. The analyzed data were incorporated into ANGIOGENES to provide a one-stop-shop for transcriptomics data to facilitate further biological validation. ANGIOGENES is an intuitive and easy-to-use database to allow in silico screening of expressed, enriched and/or specific endothelial transcripts under various conditions. We anticipate that ANGIOGENES serves as a starting point for functional studies to elucidate the roles of protein-coding genes and lncRNAs in angiogenesis.
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Tripathi R, Patel S, Kumari V, Chakraborty P, Varadwaj PK. DeepLNC, a long non-coding RNA prediction tool using deep neural network. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/s13721-016-0129-2] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Long non-coding RNA Databases in Cardiovascular Research. GENOMICS PROTEOMICS & BIOINFORMATICS 2016; 14:191-9. [PMID: 27049585 PMCID: PMC4996844 DOI: 10.1016/j.gpb.2016.03.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 03/16/2016] [Accepted: 03/17/2016] [Indexed: 12/05/2022]
Abstract
With the rising interest in the regulatory functions of long non-coding RNAs (lncRNAs) in complex human diseases such as cardiovascular diseases, there is an increasing need in public databases offering comprehensive and integrative data for all aspects of these versatile molecules. Recently, a variety of public data repositories that specialized in lncRNAs have been developed, which make use of huge high-throughput data particularly from next-generation sequencing (NGS) approaches. Here, we provide an overview of current lncRNA databases covering basic and functional annotation, lncRNA expression and regulation, interactions with other biomolecules, and genomic variants influencing the structure and function of lncRNAs. The prominent lncRNA antisense noncoding RNA in the INK4 locus (ANRIL), which has been unequivocally associated with coronary artery disease through genome-wide association studies (GWAS), serves as an example to demonstrate the features of each individual database.
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Yao J, Huang JX, Lin M, Wu ZD, Yu H, Wang PC, Ye J, Chen P, Wu J, Zhao GJ. Microarray expression profile analysis of aberrant long non-coding RNAs in esophageal squamous cell carcinoma. Int J Oncol 2016; 48:2543-57. [PMID: 27035335 DOI: 10.3892/ijo.2016.3457] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2016] [Accepted: 03/01/2016] [Indexed: 11/06/2022] Open
Abstract
Increasing evidence indicates that long non-coding RNA (lncRNA) plays an important role in tumorigenesis. However, the function and regulatory mechanism of lncRNAs are still unclear in esophageal squamous cell carcinoma (ESCC). To address this challenge, we screened lncRNAs expression profiles in 3 pairs of ESCC and matched non-cancerous tissues by microarray assay and identified the relationship between lncRNAs expression in ESCC tissue and clinicopathological characteristics and prognosis of patients with ESCC. We found 182 lncRNAs that were significantly differently expressed in ESCC tissues versus the matched non-cancerous tissues. Gene ontology and pathway analysis results suggested that the primary biological processes of these genes were involved in extracellular matrix, immune responses, cell differentiation and cell proliferation. Through cis and trans analyzing, we found 4 lncRNAs (ENST00000480669, NONHSAT104436, NONHSAT126998 and NONHSAT112918) may play important roles in tumorigenesis of ESCC. The four lncRNAs were checked in 73 patients with ESCC. The results showed that they mainly related to tumor metastasis. Kaplan-Meier survival analysis showed that high expression of NONHSAT104436, NONHSAT126998 and low expression of ENST00000480669 were related to poor 3-year overall survival (P=0.003, 0.032 and 0.040, respectively). Multivariate analysis showed that NONHSAT104436 was an independent prognostic factor (P=0.017). Thus we concluded that, lncRNAs showed differently expression patterns in ESCC versus matched non-cancerous tissues, and aberrantly expressed lncRNA may play important roles in ESCC development and progression. Interestingly, the overexpression of NONHSAT104436 was tightly correlated with distant metastasis and, poor survival rate, which might indicate that NONHSAT104436 might play a very important part in ESCC tumor progression.
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Affiliation(s)
- Juan Yao
- Department of Oncology, Taizhou People's Hospital Affiliated to Nantong and Jiangsu University, Taizhou, Jiangsu 225300, P.R. China
| | - Jun-Xing Huang
- Department of Oncology, Taizhou People's Hospital Affiliated to Nantong and Jiangsu University, Taizhou, Jiangsu 225300, P.R. China
| | - Mei Lin
- Department of Experiment Center, Taizhou People's Hospital Affiliated to Nantong and Jiangsu University, Taizhou, Jiangsu 225300, P.R. China
| | - Zheng-Dong Wu
- Department of Oncology, Taizhou People's Hospital Affiliated to Nantong and Jiangsu University, Taizhou, Jiangsu 225300, P.R. China
| | - Hong Yu
- Department of Pathology, Taizhou People's Hospital Affiliated to Nantong and Jiangsu University, Taizhou, Jiangsu 225300, P.R. China
| | - Peng-Cheng Wang
- Department of Thoracic Surgery, Taizhou People's Hospital Affiliated to Nantong and Jiangsu University, Taizhou, Jiangsu 225300, P.R. China
| | - Jun Ye
- Department of Experiment Center, Taizhou People's Hospital Affiliated to Nantong and Jiangsu University, Taizhou, Jiangsu 225300, P.R. China
| | - Ping Chen
- Department of Pathology, Taizhou People's Hospital Affiliated to Nantong and Jiangsu University, Taizhou, Jiangsu 225300, P.R. China
| | - Jing Wu
- Department of Oncology, The People's Hospital of Deyang, Chengdu University of TCM, Deyang, Sichuan 618000, P.R. China
| | - Guo-Jun Zhao
- Department of Oncology, Taizhou People's Hospital Affiliated to Nantong and Jiangsu University, Taizhou, Jiangsu 225300, P.R. China
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Yotsukura S, duVerle D, Hancock T, Natsume-Kitatani Y, Mamitsuka H. Computational recognition for long non-coding RNA (lncRNA): Software and databases. Brief Bioinform 2016; 18:9-27. [DOI: 10.1093/bib/bbv114] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 12/10/2015] [Indexed: 01/22/2023] Open
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Long Noncoding RNAs as New Architects in Cancer Epigenetics, Prognostic Biomarkers, and Potential Therapeutic Targets. BIOMED RESEARCH INTERNATIONAL 2015; 2015:320214. [PMID: 26448935 PMCID: PMC4584070 DOI: 10.1155/2015/320214] [Citation(s) in RCA: 103] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2014] [Accepted: 05/06/2015] [Indexed: 12/27/2022]
Abstract
Recent advances in genome-wide analysis have revealed that 66% of the genome is actively transcribed into noncoding RNAs (ncRNAs) while less than 2% of the sequences encode proteins. Among ncRNAs, high-resolution microarray and massively parallel sequencing technologies have identified long ncRNAs (>200 nucleotides) that lack coding protein function. LncRNAs abundance, nuclear location, and diversity allow them to create in association with protein interactome, a complex regulatory network orchestrating cellular phenotypic plasticity via modulation of all levels of protein-coding gene expression. Whereas lncRNAs biological functions and mechanisms of action are still not fully understood, accumulating data suggest that lncRNAs deregulation is pivotal in cancer initiation and progression and metastatic spread through various mechanisms, including epigenetic effectors, alternative splicing, and microRNA-like molecules. Mounting data suggest that several lncRNAs expression profiles in malignant tumors are associated with prognosis and they can be detected in biological fluids. In this review, we will briefly discuss characteristics and functions of lncRNAs, their role in carcinogenesis, and their potential usefulness as diagnosis and prognosis biomarkers and novel therapeutic targets.
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Khorkova O, Hsiao J, Wahlestedt C. Basic biology and therapeutic implications of lncRNA. Adv Drug Deliv Rev 2015; 87:15-24. [PMID: 26024979 PMCID: PMC4544752 DOI: 10.1016/j.addr.2015.05.012] [Citation(s) in RCA: 247] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Revised: 05/11/2015] [Accepted: 05/21/2015] [Indexed: 12/11/2022]
Abstract
Long non-coding RNAs (lncRNA), a class of non-coding RNA molecules recently identified largely due to the efforts of FANTOM, and later GENCODE and ENCODE consortia, have been a subject of intense investigation in the past decade. Extensive efforts to get deeper understanding of lncRNA biology have yielded evidence of their diverse structural and regulatory roles in protecting chromosome integrity, maintaining genomic architecture, X chromosome inactivation, imprinting, transcription, translation and epigenetic regulation. Here we will briefly review the recent studies in the field of lncRNA biology focusing mostly on mammalian species and discuss their therapeutic implications.
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MESH Headings
- Animals
- Chromosomal Instability
- Epigenesis, Genetic
- Evolution, Molecular
- Gene Expression Regulation/drug effects
- Gene Expression Regulation/genetics
- Genetic Diseases, Inborn/diagnosis
- Genetic Diseases, Inborn/genetics
- Genetic Diseases, Inborn/therapy
- Humans
- Neoplasms/diagnosis
- Neoplasms/genetics
- Neoplasms/therapy
- Oligonucleotides, Antisense/administration & dosage
- Oligonucleotides, Antisense/therapeutic use
- RNA Processing, Post-Transcriptional
- RNA, Long Noncoding/blood
- RNA, Long Noncoding/genetics
- RNA, Long Noncoding/urine
- Species Specificity
- Telomere/genetics
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Affiliation(s)
- O Khorkova
- OPKO Health Inc., 10320 USA Today Way, Miramar, FL 33025, USA
| | - J Hsiao
- OPKO Health Inc., 10320 USA Today Way, Miramar, FL 33025, USA
| | - C Wahlestedt
- Center for Therapeutic Innovation and the Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1501 NW 10th Avenue, Miami 33136, FL, USA.
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