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Sharma S, Houfani AA, Foster LJ. Pivotal functions and impact of long con-coding RNAs on cellular processes and genome integrity. J Biomed Sci 2024; 31:52. [PMID: 38745221 PMCID: PMC11092263 DOI: 10.1186/s12929-024-01038-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 04/30/2024] [Indexed: 05/16/2024] Open
Abstract
Recent advances in uncovering the mysteries of the human genome suggest that long non-coding RNAs (lncRNAs) are important regulatory components. Although lncRNAs are known to affect gene transcription, their mechanisms and biological implications are still unclear. Experimental research has shown that lncRNA synthesis, subcellular localization, and interactions with macromolecules like DNA, other RNAs, or proteins can all have an impact on gene expression in various biological processes. In this review, we highlight and discuss the major mechanisms through which lncRNAs function as master regulators of the human genome. Specifically, the objective of our review is to examine how lncRNAs regulate different processes like cell division, cell cycle, and immune responses, and unravel their roles in maintaining genomic architecture and integrity.
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Affiliation(s)
- Siddhant Sharma
- Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Aicha Asma Houfani
- Michael Smith Laboratories and Department of Biochemistry and Molecular Biology, University of British Columbia, 2185 E Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Leonard J Foster
- Michael Smith Laboratories and Department of Biochemistry and Molecular Biology, University of British Columbia, 2185 E Mall, Vancouver, BC, V6T 1Z4, Canada.
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2
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Xie M, Xie R, Wang H. LPI-IBWA: Predicting lncRNA-protein interactions based on an improved Bi-Random walk algorithm. Methods 2023; 220:98-105. [PMID: 37972912 DOI: 10.1016/j.ymeth.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/14/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023] Open
Abstract
Many studies have shown that long-chain noncoding RNAs (lncRNAs) are involved in a variety of biological processes such as post-transcriptional gene regulation, splicing, and translation by combining with corresponding proteins. Predicting lncRNA-protein interactions is an effective approach to infer the functions of lncRNAs. The paper proposes a new computational model named LPI-IBWA. At first, LPI-IBWA uses similarity kernel fusion (SKF) to integrate various types of biological information to construct lncRNA and protein similarity networks. Then, a bounded matrix completion model and a weighted k-nearest known neighbors algorithm are utilized to update the initial sparse lncRNA-protein interaction matrix. Based on the updated lncRNA-protein interaction matrix, the lncRNA similarity network and the protein similarity network are integrated into a heterogeneous network. Finally, an improved Bi-Random walk algorithm is used to predict novel latent lncRNA-protein interactions. 5-fold cross-validation experiments on a benchmark dataset showed that the AUC and AUPR of LPI-IBWA reach 0.920 and 0.736, respectively, which are higher than those of other state-of-the-art methods. Furthermore, the experimental results of case studies on a novel dataset also illustrated that LPI-IBWA could efficiently predict potential lncRNA-protein interactions.
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Affiliation(s)
- Minzhu Xie
- College of Information Science and Engineering, Hunan Normal University, China.
| | - Ruijie Xie
- College of Information Science and Engineering, Hunan Normal University, China.
| | - Hao Wang
- College of Information Science and Engineering, Hunan Normal University, China.
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3
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González‐Moro I, Garcia‐Etxebarria K, Mendoza LM, Fernández‐Jiménez N, Mentxaka J, Olazagoitia‐Garmendia A, Arroyo MN, Sawatani T, Moreno‐Castro C, Vinci C, Op de Beek A, Cnop M, Igoillo‐Esteve M, Santin I. LncRNA ARGI Contributes to Virus-Induced Pancreatic β Cell Inflammation Through Transcriptional Activation of IFN-Stimulated Genes. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300063. [PMID: 37382191 PMCID: PMC10477904 DOI: 10.1002/advs.202300063] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/30/2023] [Indexed: 06/30/2023]
Abstract
Type 1 diabetes (T1D) is a complex autoimmune disease that develops in genetically susceptible individuals. Most T1D-associated single nucleotide polymorphisms (SNPs) are located in non-coding regions of the human genome. Interestingly, SNPs in long non-coding RNAs (lncRNAs) may result in the disruption of their secondary structure, affecting their function, and in turn, the expression of potentially pathogenic pathways. In the present work, the function of a virus-induced T1D-associated lncRNA named ARGI (Antiviral Response Gene Inducer) is characterized. Upon a viral insult, ARGI is upregulated in the nuclei of pancreatic β cells and binds to CTCF to interact with the promoter and enhancer regions of IFNβ and interferon-stimulated genes, promoting their transcriptional activation in an allele-specific manner. The presence of the T1D risk allele in ARGI induces a change in its secondary structure. Interestingly, the T1D risk genotype induces hyperactivation of type I IFN response in pancreatic β cells, an expression signature that is present in the pancreas of T1D patients. These data shed light on the molecular mechanisms by which T1D-related SNPs in lncRNAs influence pathogenesis at the pancreatic β cell level and opens the door for the development of therapeutic strategies based on lncRNA modulation to delay or avoid pancreatic β cell inflammation in T1D.
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Affiliation(s)
- Itziar González‐Moro
- Department of Biochemistry and Molecular BiologyUniversity of the Basque CountryLeioa48940Spain
- Biocruces Bizkaia Health Research InstituteBarakaldo48903Spain
| | - Koldo Garcia‐Etxebarria
- Biodonostia Health Research InstituteGastrointestinal Genetics GroupSan Sebastián20014Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd)Barcelona08036Spain
| | - Luis Manuel Mendoza
- Department of Biochemistry and Molecular BiologyUniversity of the Basque CountryLeioa48940Spain
| | - Nora Fernández‐Jiménez
- Biocruces Bizkaia Health Research InstituteBarakaldo48903Spain
- Department of GeneticsPhysical Anthropology and Animal PhysiologyUniversity of the Basque CountryLeioa48940Spain
| | - Jon Mentxaka
- Department of Biochemistry and Molecular BiologyUniversity of the Basque CountryLeioa48940Spain
- Biocruces Bizkaia Health Research InstituteBarakaldo48903Spain
| | - Ane Olazagoitia‐Garmendia
- Department of Biochemistry and Molecular BiologyUniversity of the Basque CountryLeioa48940Spain
- Biocruces Bizkaia Health Research InstituteBarakaldo48903Spain
| | - María Nicol Arroyo
- ULB Center for Diabetes ResearchUniversité Libre de BruxellesBrussels1070Belgium
| | - Toshiaki Sawatani
- ULB Center for Diabetes ResearchUniversité Libre de BruxellesBrussels1070Belgium
| | | | - Chiara Vinci
- ULB Center for Diabetes ResearchUniversité Libre de BruxellesBrussels1070Belgium
| | - Anne Op de Beek
- ULB Center for Diabetes ResearchUniversité Libre de BruxellesBrussels1070Belgium
| | - Miriam Cnop
- ULB Center for Diabetes ResearchUniversité Libre de BruxellesBrussels1070Belgium
- Division of EndocrinologyErasmus HospitalUniversité Libre de BruxellesBrussels1070Belgium
| | | | - Izortze Santin
- Department of Biochemistry and Molecular BiologyUniversity of the Basque CountryLeioa48940Spain
- Biocruces Bizkaia Health Research InstituteBarakaldo48903Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)Instituto de Salud Carlos IIIMadrid28029Spain
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4
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Wozniak M, Czyz M. lncRNAs-EZH2 interaction as promising therapeutic target in cutaneous melanoma. Front Mol Biosci 2023; 10:1170026. [PMID: 37325482 PMCID: PMC10265524 DOI: 10.3389/fmolb.2023.1170026] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/23/2023] [Indexed: 06/17/2023] Open
Abstract
Melanoma is the most lethal skin cancer with increasing incidence worldwide. Despite a great improvement of diagnostics and treatment of melanoma patients, this disease is still a serious clinical problem. Therefore, novel druggable targets are in focus of research. EZH2 is a component of the PRC2 protein complex that mediates epigenetic silencing of target genes. Several mutations activating EZH2 have been identified in melanoma, which contributes to aberrant gene silencing during tumor progression. Emerging evidence indicates that long non-coding RNAs (lncRNAs) are molecular "address codes" for EZH2 silencing specificity, and targeting lncRNAs-EZH2 interaction may slow down the progression of many solid cancers, including melanoma. This review summarizes current knowledge regarding the involvement of lncRNAs in EZH2-mediated gene silencing in melanoma. The possibility of blocking lncRNAs-EZH2 interaction in melanoma as a novel therapeutic option and plausible controversies and drawbacks of this approach are also briefly discussed.
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Affiliation(s)
- Michal Wozniak
- Department of Molecular Biology of Cancer, Medical University of Lodz, Lodz, Poland
| | - Malgorzata Czyz
- Department of Molecular Biology of Cancer, Medical University of Lodz, Lodz, Poland
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Lin L, Chen R, Zhu Y, Xie W, Jing H, Chen L, Zou M. SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA-disease associations. Front Microbiol 2023; 13:1093615. [PMID: 36713213 PMCID: PMC9874942 DOI: 10.3389/fmicb.2022.1093615] [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: 11/09/2022] [Accepted: 11/30/2022] [Indexed: 01/13/2023] Open
Abstract
Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA-disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations. However, these methods do not consider the use of microbe-disease association information to enrich disease similarity, and also do not make more use of similarity information in the decomposition process. To address these issues, we here propose a correction-based similarity-constrained probability matrix decomposition method (SCCPMD) to predict lncRNA-disease associations. The microbe-disease associations are first used to enrich the disease semantic similarity matrix, and then the logistic function is used to correct the lncRNA and disease similarity matrix, and then these two corrected similarity matrices are added to the probability matrix decomposition as constraints to finally predict the potential lncRNA-disease associations. The experimental results show that SCCPMD outperforms the five advanced comparison algorithms. In addition, SCCPMD demonstrated excellent prediction performance in a case study for breast cancer, lung cancer, and renal cell carcinoma, with prediction accuracy reaching 80, 100, and 100%, respectively. Therefore, SCCPMD shows excellent predictive performance in identifying unknown lncRNA-disease associations.
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Affiliation(s)
- Lieqing Lin
- Center of Campus Network & Modern Educational Technology, Guangdong University of Technology, Guangzhou, China
| | - Ruibin Chen
- School of Computer, Guangdong University of Technology, Guangzhou, China
| | - Yinting Zhu
- School of Computer, Guangdong University of Technology, Guangzhou, China
| | - Weijie Xie
- School of Computer, Guangdong University of Technology, Guangzhou, China
| | - Huaiguo Jing
- Sports Department, Guangdong University of Technology, Guangzhou, China,*Correspondence: Huaiguo Jing,
| | - Langcheng Chen
- Center of Campus Network & Modern Educational Technology, Guangdong University of Technology, Guangzhou, China,Langcheng Chen,
| | - Minqing Zou
- Department of Experiment Teaching, Guangdong University of Technology, Guangzhou, China
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6
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Wang J, Wang M, Shao J, Liu Z, Fu C, Chen G, Zhao K, Li H, Sun W, Jia X, Chen S, Lai S. Combined analysis of differentially expressed lncRNAs and miRNAs in liver tissues of high-fat fed rabbits by transcriptome sequencing. Front Genet 2022; 13:1000574. [PMID: 36276943 PMCID: PMC9585185 DOI: 10.3389/fgene.2022.1000574] [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: 07/22/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
High-fat diet could lead to a series of metabolic diseases, including obesity, and its mechanism is not clear. In this study, the rabbit individuals were fed with high-fat diet, the liver tissues were collected, high-throughput sequencing technology was used to reveal the expression of lncRNA and miRNA difference, and the molecular regulation mechanism of lncRNA-miRNA. A total of 24,615 DE lncRNAs and 52 DE miRNAs were identified, including 15 novel discovered DE miRNAs (5 upregulated and 10 downregulated). Furthermore, five miRNAs and three mRNAs were verified by qRT-PCR, and the results showed that the expression of the DE miRNAs and DE lncRNAs in the two groups was consistent with our sequencing results. GO and KEGG analyzed 7,57,139 target genes respectively, enriching the pathways related to lipid metabolism, including mucin O-glycan biosynthesis pathway, insulin resistance and glucagon signaling pathway. Moreover, 65 targeting relationships were obtained. Among them, LOC103348122/miR-450a-5p, LOC103350359/miR-450a-3p and LOC103350429/miR-148a-5p were proposed the first time. Significantly, LOC103348122/miR-450a-5p and LOC103350429/miR-148a-5p were related to lipid metabolism in the liver. This study is of great significance to the CeRNA regulatory network related to lipid metabolism in the liver of rabbits, and provides a basis for understanding hepatic steatosis in rabbits.
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Affiliation(s)
- Jie Wang
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Meigui Wang
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Jiahao Shao
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Zheliang Liu
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Chong Fu
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Guanhe Chen
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Kaisen Zhao
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Hong Li
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Wenqiang Sun
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Xianbo Jia
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Shiyi Chen
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Songjia Lai
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, China
- *Correspondence: Songjia Lai,
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Huang D, An J, Zhang L, Liu B. Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction. BMC Bioinformatics 2022; 23:299. [PMID: 35879658 PMCID: PMC9316361 DOI: 10.1186/s12859-022-04843-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A large number of evidences from biological experiments have confirmed that miRNAs play an important role in the progression and development of various human complex diseases. However, the traditional experiment methods are expensive and time-consuming. Therefore, it is a challenging task that how to develop more accurate and efficient methods for predicting potential associations between miRNA and disease. RESULTS In the study, we developed a computational model that combined heterogeneous graph convolutional network with enhanced layer for miRNA-disease association prediction (HGCNELMDA). The major improvement of our method lies in through restarting the random walk optimized the original features of nodes and adding a reinforcement layer to the hidden layer of graph convolutional network retained similar information between nodes in the feature space. In addition, the proposed approach recalculated the influence of neighborhood nodes on target nodes by introducing the attention mechanism. The reliable performance of the HGCNELMDA was certified by the AUC of 93.47% in global leave-one-out cross-validation (LOOCV), and the average AUCs of 93.01% in fivefold cross-validation. Meanwhile, we compared the HGCNELMDA with the state‑of‑the‑art methods. Comparative results indicated that o the HGCNELMDA is very promising and may provide a cost‑effective alternative for miRNA-disease association prediction. Moreover, we applied HGCNELMDA to 3 different case studies to predict potential miRNAs related to lung cancer, prostate cancer, and pancreatic cancer. Results showed that 48, 50, and 50 of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, the HGCNELMDA is a reliable method for predicting disease-related miRNAs. CONCLUSIONS The results of the HGCNELMDA method in the LOOCV (leave-one-out cross validation, LOOCV) and 5-cross validations were 93.47% and 93.01%, respectively. Compared with other typical methods, the performance of HGCNELMDA is higher. Three cases of lung cancer, prostate cancer, and pancreatic cancer were studied. Among the predicted top 50 candidate miRNAs, 48, 50, and 50 were verified in the biological database HDMMV2.0. Therefore; this further confirms the feasibility and effectiveness of our method. Therefore, this further confirms the feasibility and effectiveness of our method. To facilitate extensive studies for future disease-related miRNAs research, we developed a freely available web server called HGCNELMDA is available at http://124.221.62.44:8080/HGCNELMDA.jsp .
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Affiliation(s)
- Dan Huang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 21116, Jiangsu, China
| | - JiYong An
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 21116, Jiangsu, China.
| | - Lei Zhang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 21116, Jiangsu, China.
| | - BaiLong Liu
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 21116, Jiangsu, China
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Deng L, Han X, Wang Z, Nie X, Bian J. The Landscape of Noncoding RNA in Pulmonary Hypertension. Biomolecules 2022; 12:biom12060796. [PMID: 35740920 PMCID: PMC9220981 DOI: 10.3390/biom12060796] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/31/2022] [Accepted: 06/02/2022] [Indexed: 02/08/2023] Open
Abstract
The transcriptome of pulmonary hypertension (PH) is complex and highly genetically heterogeneous, with noncoding RNA transcripts playing crucial roles. The majority of RNAs in the noncoding transcriptome are long noncoding RNAs (lncRNAs) with less circular RNAs (circRNAs), which are two characteristics gaining increasing attention in the forefront of RNA research field. These noncoding transcripts (especially lncRNAs and circRNAs) exert important regulatory functions in PH and emerge as potential disease biomarkers and therapeutic targets. Recent technological advancements have established great momentum for discovery and functional characterization of ncRNAs, which include broad transcriptome sequencing such as bulk RNA-sequence, single-cell and spatial transcriptomics, and RNA-protein/RNA interactions. In this review, we summarize the current research on the classification, biogenesis, and the biological functions and molecular mechanisms of these noncoding RNAs (ncRNAs) involved in the pulmonary vascular remodeling in PH. Furthermore, we highlight the utility and challenges of using these ncRNAs as biomarkers and therapeutics in PH.
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Affiliation(s)
- Lin Deng
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China; (L.D.); (Z.W.)
| | - Xiaofeng Han
- Department of Diagnostic and Interventional Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China;
| | - Ziping Wang
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China; (L.D.); (Z.W.)
| | - Xiaowei Nie
- Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People’s Hospital (The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518055, China
- Correspondence: (X.N.); (J.B.)
| | - Jinsong Bian
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China; (L.D.); (Z.W.)
- Correspondence: (X.N.); (J.B.)
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9
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Zhao D, Wang C, Yan S, Chen R. Advances in the identification of long non-coding RNA binding proteins. Anal Biochem 2021; 639:114520. [PMID: 34896376 DOI: 10.1016/j.ab.2021.114520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/04/2021] [Accepted: 12/04/2021] [Indexed: 02/06/2023]
Abstract
Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nt without evident protein coding function. They play important regulatory roles in many biological processes, e.g., gene regulation, chromatin remodeling, and cell fate determination during development. Dysregulation of lncRNAs has been observed in various diseases including cancer. Interacting with proteins is a crucial way for lncRNAs to play their biological roles. Therefore, the characterization of lncRNA binding proteins is important to understand their functions and to delineate the underlying molecular mechanism. Large-scale studies based on mass spectrometry have characterized over a thousand new RNA binding proteins without known RNA-binding domains, thus revealing the complexity and diversity of RNA-protein interactions. In addition, several methods have been developed to identify the binding proteins for particular RNAs of interest. Here we review the progress of the RNA-centric methods for the identification of RNA-protein interactions, focusing on the studies involving lncRNAs, and discuss their strengths and limitations.
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Affiliation(s)
- Dongqing Zhao
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, 300072, China
| | - Chunqing Wang
- The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Shuai Yan
- Peking University First Hospital, Peking University Health Science Center, Beijing, 100191, China
| | - Ruibing Chen
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, 300072, China.
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Rincón-Riveros A, Morales D, Rodríguez JA, Villegas VE, López-Kleine L. Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions. Int J Mol Sci 2021; 22:11397. [PMID: 34768830 PMCID: PMC8583695 DOI: 10.3390/ijms222111397] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 12/16/2022] Open
Abstract
Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.
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Affiliation(s)
- Andrés Rincón-Riveros
- Bioinformatics and Systems Biology Group, Universidad Nacional de Colombia, Bogotá 111221, Colombia;
| | - Duvan Morales
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá 111221, Colombia;
| | - Josefa Antonia Rodríguez
- Grupo de Investigación en Biología del Cáncer, Instituto Nacional de Cancerología, Bogotá 111221, Colombia;
| | - Victoria E. Villegas
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá 111221, Colombia;
| | - Liliana López-Kleine
- Department of Statistics, Faculty of Science, Universidad Nacional de Colombia, Bogotá 111221, Colombia
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11
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Li L, Wei H, Zhang YW, Zhao S, Che G, Wang Y, Chen L. Differential expression of long non-coding RNAs as diagnostic markers for lung cancer and other malignant tumors. Aging (Albany NY) 2021; 13:23842-23867. [PMID: 34670194 PMCID: PMC8580341 DOI: 10.18632/aging.203523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 06/02/2021] [Indexed: 02/05/2023]
Abstract
Due to advances in chip and sequencing technology, several types and numbers of long non-coding RNAs (lncRNAs) have been identified. LncRNAs are defined as non-protein-coding RNA molecules longer than 200 nucleotides, and are now thought as a new frontier in the study of human malignant diseases including NSCLC. Diagnosis of numerous malignant tumors has been closely linked to the differential expression of certain lncRNAs. LncRNAs are involved in gene expression regulation at multiple levels of epigenetics, transcriptional regulation, and post-transcriptional regulation. Mutations, deletions, or abnormal expression levels lead to physiological abnormalities, disease occurrence and are closely associated with human tumor diseases. LncRNAs play a crucial role in cancerous processes as either oncogenes or tumor suppressor genes. The expression of lncRNAs can regulate tumor cell in the proliferation, migration, apoptosis, cycle, invasion, and metastasis. As such, lncRNAs are potential diagnostic and treatment targets for cancer. And that, tumor biomarkers need to be detectable in easily accessible body samples, should be characterized by high specificity and sufficient sensitivity. Herein, it is significant clinical importance to screen and supplement new biomarkers for early diagnosis of lung cancer. This study aimed at systematically describing lncRNAs from five aspects based on recent studies: concepts, classification, structure, molecular mechanism, signal pathway, as well as review lncRNA implications in malignant tumor.
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Affiliation(s)
- Li Li
- College of Nursing and Health, Henan University, Kaifeng, Henan 475004, China.,Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Haitao Wei
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.,Department of Thoracic Surgery, Huaihe Hospital, Henan University, Kaifeng, Henan 475001, China
| | - Yi Wei Zhang
- College of Nursing and Health, Henan University, Kaifeng, Henan 475004, China
| | - Shizhe Zhao
- Basic Medical College of Henan University, Kaifeng, Henan 475004, China
| | - Guowei Che
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yun Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Longqi Chen
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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12
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Wozniak M, Czyz M. The Functional Role of Long Non-Coding RNAs in Melanoma. Cancers (Basel) 2021; 13:cancers13194848. [PMID: 34638331 PMCID: PMC8508152 DOI: 10.3390/cancers13194848] [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: 08/23/2021] [Revised: 09/23/2021] [Accepted: 09/25/2021] [Indexed: 12/15/2022] Open
Abstract
Melanoma is the most lethal skin cancer, with increasing incidence worldwide. The molecular events that drive melanoma development and progression have been extensively studied, resulting in significant improvements in diagnostics and therapeutic approaches. However, a high drug resistance to targeted therapies and adverse effects of immunotherapies are still a major challenge in melanoma treatment. Therefore, the elucidation of molecular mechanisms of melanomagenesis and cancer response to treatment is of great importance. Recently, many studies have revealed the close association of long noncoding RNAs (lncRNAs) with the development of many cancers, including melanoma. These RNA molecules are able to regulate a plethora of crucial cellular processes including proliferation, differentiation, migration, invasion and apoptosis through diverse mechanisms, and even slight dysregulation of their expression may lead to tumorigenesis. lncRNAs are able to bind to protein complexes, DNA and RNAs, affecting their stability, activity, and localization. They can also regulate gene expression in the nucleus. Several functions of lncRNAs are context-dependent. This review summarizes current knowledge regarding the involvement of lncRNAs in melanoma. Their possible role as prognostic markers of melanoma response to treatment and in resistance to therapy is also discussed.
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13
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Capturing Endogenous Long Noncoding RNAs and Their Binding Proteins Using Chromatin Isolation by RNA Purification. Methods Mol Biol 2021. [PMID: 34417745 DOI: 10.1007/978-1-0716-1697-0_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Long noncoding RNAs (lncRNAs) exert their functions through binding to other RNA, genomic DNA, or proteins. The identification of proteins that associate with the lncRNA of interest sheds light on the molecular basis of its biological functions. This can be achieved by tagging the lncRNA with chemically modified ribonucleotides, or by using in vitro transcribed lncRNA to retrieve proteins from cell lysates. Alternatively, endogenous lncRNAs can be pulled down from cells or tissues with biotinylated antisense DNA oligonucleotides, which may overcome the potential pitfalls of using tagged lncRNAs, such as artifacts caused by tagging or non-physiological interactions. Here we describe the detailed protocol for chromatin isolation by RNA purification (ChIRP) from mammalian cell lines and mouse tissues, which captures endogenous lncRNAs and enables subsequent identification of their physiologically relevant binding partners.
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Luo HL, Luo T, Liu JJ, Wu FX, Bai T, Ou C, Chen J, Li LQ, Zhong JH. Macrophage polarization-associated lnc-Ma301 interacts with caprin-1 to inhibit hepatocellular carcinoma metastasis through the Akt/Erk1 pathway. Cancer Cell Int 2021; 21:422. [PMID: 34376192 PMCID: PMC8353734 DOI: 10.1186/s12935-021-02133-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/03/2021] [Indexed: 01/27/2023] Open
Abstract
Background Epithelial–mesenchymal transition (EMT) promotes migration, invasion, and metastasis of hepatocellular carcinoma (HCC) cells. The molecular mechanisms behind EMT and metastasis in HCC remain unclear. Methods Microarray analysis was used to identify lncRNAs expression during polarization of U937 macrophages from M2 to M1 phenotype. The expression of the identified lncRNA was compared between clinical samples of HCC tissues or adjacent normal tissues, as well as between HCC and normal liver cell lines. lnc-Ma301 was overexpressed or knocked-down in HCC cell lines, and the effects were assessed in vitro and in vivo. Interactions among lnc-Ma301 and its potential downstream targets caprin-1 were investigated in HCC cell lines. Effects of lnc-Ma301 over- and underexpression on the Akt/Erk1 signaling pathways were examined. Results Microarray analyses identified lnc-Ma301 as one of the most overexpressed long non-coding RNAs during polarization of U937 macrophages from M2 to M1 phenotype. Lnc-Ma301 showed lower expression in HCC tissues than in adjacent normal tissues, and lower expression was associated with worse prognosis. Activation of lnc-Ma301 inhibited cell proliferation, migration and EMT in HCC cell cultures, and it inhibited lung metastasis of HCC tumors in mice. Mechanistic studies suggested that lnc-Ma301 interacts with caprin-1 to inhibit HCC metastasis and EMT through Akt/Erk1 pathway. Conclusions Lnc-Ma301 may help regulate onset and metastasis of HCC. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-02133-1.
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Affiliation(s)
- Hong-Lin Luo
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, He Di Rd 71, Nanning, 530021, People's Republic of China.,Key Laboratory of High-Incidence Tumor Early Prevention and Treatment, Ministry of Education, Nanning, 530021, People's Republic of China
| | - Tao Luo
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, He Di Rd 71, Nanning, 530021, People's Republic of China
| | - Jun-Jie Liu
- Department of Ultrasound, Guangxi Medical University Cancer Hospital, Nanning, 530021, People's Republic of China
| | - Fei-Xiang Wu
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, He Di Rd 71, Nanning, 530021, People's Republic of China
| | - Tao Bai
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, He Di Rd 71, Nanning, 530021, People's Republic of China
| | - Chao Ou
- Department of Clinical Laboratory Medicine, Guangxi Medical University Cancer Hospital, Nanning, 530021, People's Republic of China
| | - Jie Chen
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, He Di Rd 71, Nanning, 530021, People's Republic of China
| | - Le-Qun Li
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, He Di Rd 71, Nanning, 530021, People's Republic of China. .,Key Laboratory of High-Incidence Tumor Early Prevention and Treatment, Ministry of Education, Nanning, 530021, People's Republic of China.
| | - Jian-Hong Zhong
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, He Di Rd 71, Nanning, 530021, People's Republic of China.
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15
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Liu B, Zhu X, Zhang L, Liang Z, Li Z. Combined embedding model for MiRNA-disease association prediction. BMC Bioinformatics 2021; 22:161. [PMID: 33765909 PMCID: PMC7995599 DOI: 10.1186/s12859-021-04092-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 03/19/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cumulative evidence from biological experiments has confirmed that miRNAs have significant roles to diagnose and treat complex diseases. However, traditional medical experiments have limitations in time-consuming and high cost so that they fail to find the unconfirmed miRNA and disease interactions. Thus, discovering potential miRNA-disease associations will make a contribution to the decrease of the pathogenesis of diseases and benefit disease therapy. Although, existing methods using different computational algorithms have favorable performances to search for the potential miRNA-disease interactions. We still need to do some work to improve experimental results. RESULTS We present a novel combined embedding model to predict MiRNA-disease associations (CEMDA) in this article. The combined embedding information of miRNA and disease is composed of pair embedding and node embedding. Compared with the previous heterogeneous network methods that are merely node-centric to simply compute the similarity of miRNA and disease, our method fuses pair embedding to pay more attention to capturing the features behind the relative information, which models the fine-grained pairwise relationship better than the previous case when each node only has a single embedding. First, we construct the heterogeneous network from supported miRNA-disease pairs, disease semantic similarity and miRNA functional similarity. Given by the above heterogeneous network, we find all the associated context paths of each confirmed miRNA and disease. Meta-paths are linked by nodes and then input to the gate recurrent unit (GRU) to directly learn more accurate similarity measures between miRNA and disease. Here, the multi-head attention mechanism is used to weight the hidden state of each meta-path, and the similarity information transmission mechanism in a meta-path of miRNA and disease is obtained through multiple network layers. Second, pair embedding of miRNA and disease is fed to the multi-layer perceptron (MLP), which focuses on more important segments in pairwise relationship. Finally, we combine meta-path based node embedding and pair embedding with the cost function to learn and predict miRNA-disease association. The source code and data sets that verify the results of our research are shown at https://github.com/liubailong/CEMDA . CONCLUSIONS The performance of CEMDA in the leave-one-out cross validation and fivefold cross validation are 93.16% and 92.03%, respectively. It denotes that compared with other methods, CEMDA accomplishes superior performance. Three cases with lung cancers, breast cancers, prostate cancers and pancreatic cancers show that 48,50,50 and 50 out of the top 50 miRNAs, which are confirmed in HDMM V2.0. Thus, this further identifies the feasibility and effectiveness of our method.
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Affiliation(s)
- Bailong Liu
- Engineering Research Center of Mine Digitalization of Ministry of Education, China University of Mining and Technology, Xuzhou, China
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
| | - Xiaoyan Zhu
- Engineering Research Center of Mine Digitalization of Ministry of Education, China University of Mining and Technology, Xuzhou, China
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
| | - Lei Zhang
- Engineering Research Center of Mine Digitalization of Ministry of Education, China University of Mining and Technology, Xuzhou, China.
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.
| | - Zhizheng Liang
- Engineering Research Center of Mine Digitalization of Ministry of Education, China University of Mining and Technology, Xuzhou, China
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
| | - Zhengwei Li
- Engineering Research Center of Mine Digitalization of Ministry of Education, China University of Mining and Technology, Xuzhou, China.
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.
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16
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Xu D, Xu H, Zhang Y, Wang M, Chen W, Gao R. MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities. J Transl Med 2021; 19:66. [PMID: 33579301 PMCID: PMC7881563 DOI: 10.1186/s12967-021-02732-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 02/01/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Microbes are closely related to human health and diseases. Identification of disease-related microbes is of great significance for revealing the pathological mechanism of human diseases and understanding the interaction mechanisms between microbes and humans, which is also useful for the prevention, diagnosis and treatment of human diseases. Considering the known disease-related microbes are still insufficient, it is necessary to develop effective computational methods and reduce the time and cost of biological experiments. METHODS In this work, we developed a novel computational method called MDAKRLS to discover potential microbe-disease associations (MDAs) based on the Kronecker regularized least squares. Specifically, we introduced the Hamming interaction profile similarity to measure the similarities of microbes and diseases besides Gaussian interaction profile kernel similarity. In addition, we introduced the Kronecker product to construct two kinds of Kronecker similarities between microbe-disease pairs. Then, we designed the Kronecker regularized least squares with different Kronecker similarities to obtain prediction scores, respectively, and calculated the final prediction scores by integrating the contributions of different similarities. RESULTS The AUCs value of global leave-one-out cross-validation and 5-fold cross-validation achieved by MDAKRLS were 0.9327 and 0.9023 ± 0.0015, which were significantly higher than five state-of-the-art methods used for comparison. Comparison results demonstrate that MDAKRLS has faster computing speed under two kinds of frameworks. In addition, case studies of inflammatory bowel disease (IBD) and asthma further showed 19 (IBD), 19 (asthma) of the top 20 prediction disease-related microbes could be verified by previously published biological or medical literature. CONCLUSIONS All the evaluation results adequately demonstrated that MDAKRLS has an effective and reliable prediction performance. It may be a useful tool to seek disease-related new microbes and help biomedical researchers to carry out follow-up studies.
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Affiliation(s)
- Da Xu
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, China
| | - Hanxiao Xu
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, China
| | - Yusen Zhang
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, China.
| | - Mingyi Wang
- Department of Central Lab, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong, China.
| | - Wei Chen
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, China
| | - Rui Gao
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China
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Shaw D, Chen H, Xie M, Jiang T. DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms. BMC Bioinformatics 2021; 22:24. [PMID: 33461501 PMCID: PMC7814738 DOI: 10.1186/s12859-020-03914-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 11/30/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs) regulate diverse biological processes via interactions with proteins. Since the experimental methods to identify these interactions are expensive and time-consuming, many computational methods have been proposed. Although these computational methods have achieved promising prediction performance, they neglect the fact that a gene may encode multiple protein isoforms and different isoforms of the same gene may interact differently with the same lncRNA. RESULTS In this study, we propose a novel method, DeepLPI, for predicting the interactions between lncRNAs and protein isoforms. Our method uses sequence and structure data to extract intrinsic features and expression data to extract topological features. To combine these different data, we adopt a hybrid framework by integrating a multimodal deep learning neural network and a conditional random field. To overcome the lack of known interactions between lncRNAs and protein isoforms, we apply a multiple instance learning (MIL) approach. In our experiment concerning the human lncRNA-protein interactions in the NPInter v3.0 database, DeepLPI improved the prediction performance by 4.7% in term of AUC and 5.9% in term of AUPRC over the state-of-the-art methods. Our further correlation analyses between interactive lncRNAs and protein isoforms also illustrated that their co-expression information helped predict the interactions. Finally, we give some examples where DeepLPI was able to outperform the other methods in predicting mouse lncRNA-protein interactions and novel human lncRNA-protein interactions. CONCLUSION Our results demonstrated that the use of isoforms and MIL contributed significantly to the improvement of performance in predicting lncRNA and protein interactions. We believe that such an approach would find more applications in predicting other functional roles of RNAs and proteins.
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Affiliation(s)
- Dipan Shaw
- Department of Computer Science and Engineering, University of California, Riverside, CA 92521 USA
| | - Hao Chen
- Department of Computer Science and Engineering, University of California, Riverside, CA 92521 USA
| | - Minzhu Xie
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Tao Jiang
- Department of Computer Science and Engineering, University of California, Riverside, CA 92521 USA
- Bioinformatics Division, BNRIST/Department of Computer Science and Technology, Tsinghua University, Beijing, China
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Suzuki S, Yuan H, Hirata-Tsuchiya S, Yoshida K, Sato A, Nemoto E, Shiba H, Yamada S. DMP-1 promoter-associated antisense strand non-coding RNA, panRNA-DMP-1, physically associates with EGFR to repress EGF-induced squamous cell carcinoma migration. Mol Cell Biochem 2021; 476:1673-1690. [PMID: 33420898 DOI: 10.1007/s11010-020-04046-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 12/26/2020] [Indexed: 02/06/2023]
Abstract
Accumulating evidence suggests that specific non-coding RNAs exist in many types of malignant tissues, and are involved in cancer invasion and metastasis. However, little is known about the precise roles of non-coding RNAs in squamous cell carcinoma (SQCC) invasion and migration. Recently, the dentin matrix protein-1 (DMP-1) gene locus was identified as a transcriptionally active site in squamous cell carcinoma (SQCC) tissue and cells. However, it is unclear whether RNA associated with cell migration exist at the DMP-1 gene locus in SQCC cells. We identified a novel promoter-associated non-coding RNA in the antisense strand of DMP-1 gene locus, promoter-associated non-coding RNA (panRNA)-DMP-1, by the RACE method in SQCC cells and tissues, and characterized the functions of panRNA-DMP-1 in EGF-driven SQCC cell migration. The inhibition of endogenous panRNA-DMP-1 expression by specific siRNAs and exogenous over-expression of panRNA-DMP-1 resulted in increased and suppressed cellular migration toward EGF in SQCC cells, respectively, and nuclear expression of panRNA-DMP-1 was induced by EGF stimulation. Mechanistically, suppression of panRNA-DMP-1 expression increased EGFR nuclear localization upon EGF treatment and nuclear panRNA-DMP-1 physically interacted with EGFR, which was confirmed by RNA immunoprecipitation assay using a bacteriophage-delivered PP7 RNA labeling system. Furthermore, co-immunoprecipitation assay revealed that suppression of panRNA-DMP-1 stabilized EGFR interaction with STAT3, a known co-transcription factors of EGFR, to induce migratory properties in many cancer cells. Based on these findings, panRNA-DMP-1 is an EGFR-associating RNA that inhibits the EGF-induced migratory properties of SQCC possibly by regulating EGFR nuclear localization and EGFR binding to STAT3.
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Affiliation(s)
- Shigeki Suzuki
- Department of Periodontology and Endodontology, Tohoku University Graduate School of Dentistry, 4-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
- Department of Biological Endodontics, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan.
| | - Hang Yuan
- Department of Periodontology and Endodontology, Tohoku University Graduate School of Dentistry, 4-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
| | - Shizu Hirata-Tsuchiya
- Department of Biological Endodontics, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan
| | - Kazuma Yoshida
- Department of Biological Endodontics, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan
| | - Akiko Sato
- Department of Periodontology and Endodontology, Tohoku University Graduate School of Dentistry, 4-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
| | - Eiji Nemoto
- Department of Periodontology and Endodontology, Tohoku University Graduate School of Dentistry, 4-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
| | - Hideki Shiba
- Department of Biological Endodontics, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan
| | - Satoru Yamada
- Department of Periodontology and Endodontology, Tohoku University Graduate School of Dentistry, 4-1, Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
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Dashti S, Taheri M, Ghafouri-Fard S. An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer. Sci Rep 2020; 10:18770. [PMID: 33128008 PMCID: PMC7603345 DOI: 10.1038/s41598-020-76024-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 10/22/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein-protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA-mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan-Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan-Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA-lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.
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Affiliation(s)
- Sepideh Dashti
- Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Taheri
- Urogenital Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soudeh Ghafouri-Fard
- Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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20
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Chen Q, Yang H, Zhu X, Xiong S, Chi H, Xu W. Integrative Analysis of the Doxorubicin-Associated LncRNA-mRNA Network Identifies Chemoresistance-Associated lnc-TRDMT1-5 as a Biomarker of Breast Cancer Progression. Front Genet 2020; 11:566. [PMID: 32547604 PMCID: PMC7272716 DOI: 10.3389/fgene.2020.00566] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 05/11/2020] [Indexed: 12/18/2022] Open
Abstract
Increasing evidence has revealed close relationships between long non-coding RNAs (lncRNAs) and chemoresistance in multiple types of tumors; however, functional lncRNAs in breast cancer (BC) have not been completely identified. In this study, we aimed to identify novel lncRNAs that might play critical roles in doxorubicn resistance, which could reveal potential biomarkers of BC. Using a BC dataset (GSE81971), we identified 452 lncRNAs that were upregulated and 659 that were downregulated; furthermore, there were 1896 differentially expressed mRNAs, of which 1137 were upregulated and 758 were downregulated in MCF-7/ADR cells compared with the expression in MCF-7 cells. We constructed an lncRNA–mRNA network by integrating probe reannotation and regulatory interactions. To elucidate the key lncRNAs in BC, we further analyzed dysregulated lncRNA–mRNA crosstalk, and six candidate lncRNAs (lnc-TRDMT1-5, ZNF667-AS1, lnc-MPPE1-13, DSCAM-AS1:5, DSCAM-AS1:2, and lnc-CFI-3) were identified. Notably, the expression level of lnc-TRDMT1-5 was significantly upregulated in resistant cells compared with sensitive cells, and its levels were increased in BC tissues compared with adjacent tissues. Levels were positively associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) expression levels. High expression of lnc-TRDMT1-5 predicted poor prognosis in ER-positve and HER2-positive BC patients, especially in patients with chemoresistance. Bioinformatic and functional analysis revealed that lnc-TRDMT1-5 was involved in many crucial pathways in cancer, such as the PI3K/AKT and Wnt signaling pathways. Subcellular localization predicted that lnc-TRDMT1-5 was located in the cytoplasm, and the lncRNA–miRNA–mRNA network showed that lnc-TRDMT1-5 might serve as a regulator in BC. Here, our results demonstrated a dysregulated lncRNA–mRNA network that might provide new treatment strategies for chemoresistant BC, and the results identified a new lncRNA, lnc-TRDMT1-5, with oncogenic and prognostic functions in human BC.
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Affiliation(s)
- Qi Chen
- Department of Breast Diseases, Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, China.,School of Medicine, Jiangsu University, Zhenjiang, China
| | - Hui Yang
- Department of Breast Diseases, Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Xiaolan Zhu
- Central Laboratory, Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Shangwan Xiong
- Central Laboratory, Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Huamao Chi
- Department of Breast Diseases, Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Wenlin Xu
- Department of Breast Diseases, Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, China.,School of Medicine, Jiangsu University, Zhenjiang, China
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Computational Models in Non-Coding RNA and Human Disease. Int J Mol Sci 2020; 21:ijms21051557. [PMID: 32106478 PMCID: PMC7084754 DOI: 10.3390/ijms21051557] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 02/24/2020] [Indexed: 01/01/2023] Open
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Koutrouli M, Karatzas E, Paez-Espino D, Pavlopoulos GA. A Guide to Conquer the Biological Network Era Using Graph Theory. Front Bioeng Biotechnol 2020; 8:34. [PMID: 32083072 PMCID: PMC7004966 DOI: 10.3389/fbioe.2020.00034] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 01/15/2020] [Indexed: 12/24/2022] Open
Abstract
Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.
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Affiliation(s)
- Mikaela Koutrouli
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece.,Department of Informatics and Telecommunications, University of Athens, Athens, Greece
| | - David Paez-Espino
- Lawrence Berkeley National Laboratory, Department of Energy, Joint Genome Institute, Walnut Creek, CA, United States
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23
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Zimta AA, Tomuleasa C, Sahnoune I, Calin GA, Berindan-Neagoe I. Long Non-coding RNAs in Myeloid Malignancies. Front Oncol 2019; 9:1048. [PMID: 31681586 PMCID: PMC6813191 DOI: 10.3389/fonc.2019.01048] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 09/26/2019] [Indexed: 12/19/2022] Open
Abstract
Acute myeloid leukemia (AML) represents 80% of adult leukemias and 15-20% of childhood leukemias. AML are characterized by the presence of 20% blasts or more in the bone marrow, or defining cytogenetic abnormalities. Laboratory diagnoses of myelodysplastic syndromes (MDS) depend on morphological changes based on dysplasia in peripheral blood and bone marrow, including peripheral blood smears, bone marrow aspirate smears, and bone marrow biopsies. As leukemic cells are not functional, the patient develops anemia, neutropenia, and thrombocytopenia, leading to fatigue, recurrent infections, and hemorrhage. The genetic background and associated mutations in AML blasts determine the clinical course of the disease. Over the last decade, non-coding RNAs transcripts that do not codify for proteins but play a role in regulation of functions have been shown to have multiple applications in the diagnosis, prognosis and therapeutic approach of various types of cancers, including myeloid malignancies. After a comprehensive review of current literature, we found reports of multiple long non-coding RNAs (lncRNAs) that can differentiate between AML types and how their exogenous modulation can dramatically change the behavior of AML cells. These lncRNAs include: H19, LINC00877, RP11-84C10, CRINDE, RP11848P1.3, ZNF667-AS1, AC111000.4-202, SFMBT2, LINC02082-201, MEG3, AC009495.2, PVT1, HOTTIP, SNHG5, and CCAT1. In addition, by performing an analysis on available AML data in The Cancer Genome Atlas (TCGA), we found 10 lncRNAs with significantly differential expression between patients in favorable, intermediate/normal, or poor cytogenetic risk categories. These are: DANCR, PRDM16-DT, SNHG6, OIP5-AS1, SNHG16, JPX, FTX, KCNQ1OT1, TP73-AS1, and GAS5. The identification of a molecular signature based on lncRNAs has the potential for have deep clinical significance, as it could potentially help better define the evolution from low-grade MDS to high-grade MDS to AML, changing the course of therapy. This would allow clinicians to provide a more personalized, patient-tailored therapeutic approach, moving from transfusion-based therapy, as is the case for low-grade MDS, to the introduction of azacytidine-based chemotherapy or allogeneic stem cell transplantation, which is the current treatment for high-grade MDS.
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Affiliation(s)
- Alina-Andreea Zimta
- MedFuture - Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Ciprian Tomuleasa
- Department of Hematology, Research Center for Functional Genomics and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Hematology, Ion Chiricuta Clinical Cancer Center, Cluj-Napoca, Romania
| | - Iman Sahnoune
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - George A. Calin
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Center for RNA Interference and Non-Coding RNAs, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ioana Berindan-Neagoe
- MedFuture - Research Center for Advanced Medicine, Research Center for Functional Genomics and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Functional Genomics and Experimental Pathology, Ion Chiricuta Clinical Cancer Center, Cluj-Napoca, Romania
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