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Agrawal A, Vindal V. Competing endogenous RNAs in head and neck squamous cell carcinoma: a review. Brief Funct Genomics 2024; 23:335-348. [PMID: 37941447 DOI: 10.1093/bfgp/elad049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 11/10/2023] Open
Abstract
Our understanding of RNA biology has evolved with recent advances in research from it being a non-functional product to molecules of the genome with specific regulatory functions. Competitive endogenous RNA (ceRNA), which has gained prominence over time as an essential part of post-transcriptional regulatory mechanism, is one such example. The ceRNA biology hypothesis states that coding RNA and non-coding RNA co-regulate each other using microRNA (miRNA) response elements. The ceRNA components include long non-coding RNAs, pseudogene and circular RNAs that exert their effect by interacting with miRNA and regulate the expression level of its target genes. Emerging evidence has revealed that the dysregulation of the ceRNA network is attributed to the pathogenesis of various cancers, including the head and neck squamous cell carcinoma (HNSCC). This is the most prevalent cancer developed from the mucosal epithelium in the lip, oral cavity, larynx and pharynx. Although many efforts have been made to comprehend the cause and subsequent treatment of HNSCC, the morbidity and mortality rate remains high. Hence, there is an urgent need to understand the holistic progression of HNSCC, mediated by ceRNA, that can have immense relevance in identifying novel biomarkers with a defined therapeutic intervention. In this review, we have made an effort to highlight the ceRNA biology hypothesis with a focus on its involvement in the progression of HNSCC. For the identification of such ceRNAs, we have additionally highlighted a number of databases and tools.
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Affiliation(s)
- Avantika Agrawal
- Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana 500046, India
| | - Vaibhav Vindal
- Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana 500046, India
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Daniel Thomas S, Vijayakumar K, John L, Krishnan D, Rehman N, Revikumar A, Kandel Codi JA, Prasad TSK, S S V, Raju R. Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:213-233. [PMID: 38752932 DOI: 10.1089/omi.2024.0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.
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Affiliation(s)
- Sonet Daniel Thomas
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Krithika Vijayakumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Deepak Krishnan
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Niyas Rehman
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Amjesh Revikumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Kerala Genome Data Centre, Kerala Development and Innovation Strategic Council, Thiruvananthapuram, Kerala, India
| | - Jalaluddin Akbar Kandel Codi
- Department of Surgical Oncology, Yenepoya Medical College, Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | | | - Vinodchandra S S
- Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Rajesh Raju
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
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Petti M, Farina L. Network medicine for patients' stratification: From single-layer to multi-omics. WIREs Mech Dis 2023; 15:e1623. [PMID: 37323106 DOI: 10.1002/wsbm.1623] [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: 10/14/2022] [Revised: 03/08/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Precision medicine research increasingly relies on the integrated analysis of multiple types of omics. In the era of big data, the large availability of different health-related information represents a great, but at the same time untapped, chance with a potentially fundamental role in the prevention, diagnosis and prognosis of diseases. Computational methods are needed to combine this data to create a comprehensive view of a given disease. Network science can model biomedical data in terms of relationships among molecular players of different nature and has been successfully proposed as a new paradigm for studying human diseases. Patient stratification is an open challenge aimed at identifying subtypes with different disease manifestations, severity, and expected survival time. Several stratification approaches based on high-throughput gene expression measurements have been successfully applied. However, few attempts have been proposed to exploit the integration of various genotypic and phenotypic data to discover novel sub-types or improve the detection of known groupings. This article is categorized under: Cancer > Biomedical Engineering Cancer > Computational Models Cancer > Genetics/Genomics/Epigenetics.
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Affiliation(s)
- Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
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Nukala SB, Jousma J, Cho Y, Lee WH, Ong SG. Long non-coding RNAs and microRNAs as crucial regulators in cardio-oncology. Cell Biosci 2022; 12:24. [PMID: 35246252 PMCID: PMC8895873 DOI: 10.1186/s13578-022-00757-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 02/10/2022] [Indexed: 12/23/2022] Open
Abstract
Cancer is one of the leading causes of morbidity and mortality worldwide. Significant improvements in the modern era of anticancer therapeutic strategies have increased the survival rate of cancer patients. Unfortunately, cancer survivors have an increased risk of cardiovascular diseases, which is believed to result from anticancer therapies. The emergence of cardiovascular diseases among cancer survivors has served as the basis for establishing a novel field termed cardio-oncology. Cardio-oncology primarily focuses on investigating the underlying molecular mechanisms by which anticancer treatments lead to cardiovascular dysfunction and the development of novel cardioprotective strategies to counteract cardiotoxic effects of cancer therapies. Advances in genome biology have revealed that most of the genome is transcribed into non-coding RNAs (ncRNAs), which are recognized as being instrumental in cancer, cardiovascular health, and disease. Emerging studies have demonstrated that alterations of these ncRNAs have pathophysiological roles in multiple diseases in humans. As it relates to cardio-oncology, though, there is limited knowledge of the role of ncRNAs. In the present review, we summarize the up-to-date knowledge regarding the roles of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in cancer therapy-induced cardiotoxicities. Moreover, we also discuss prospective therapeutic strategies and the translational relevance of these ncRNAs.
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Affiliation(s)
- Sarath Babu Nukala
- Department of Pharmacology & Regenerative Medicine, The University of Illinois College of Medicine, 909 S Wolcott Ave, COMRB 4100, Chicago, IL, 60612, USA
| | - Jordan Jousma
- Department of Pharmacology & Regenerative Medicine, The University of Illinois College of Medicine, 909 S Wolcott Ave, COMRB 4100, Chicago, IL, 60612, USA
| | - Yoonje Cho
- Department of Pharmacology & Regenerative Medicine, The University of Illinois College of Medicine, 909 S Wolcott Ave, COMRB 4100, Chicago, IL, 60612, USA
| | - Won Hee Lee
- Department of Basic Medical Sciences, University of Arizona College of Medicine, ABC-1 Building, 425 North 5th Street, Phoenix, AZ, 85004, USA.
| | - Sang-Ging Ong
- Department of Pharmacology & Regenerative Medicine, The University of Illinois College of Medicine, 909 S Wolcott Ave, COMRB 4100, Chicago, IL, 60612, USA.
- Division of Cardiology, Department of Medicine, The University of Illinois College of Medicine, 909 S Wolcott Ave, COMRB 4100, Chicago, IL, 60612, USA.
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Saha B, Chhatriya B, Pramanick S, Goswami S. Bioinformatic Analysis and Integration of Transcriptome and Proteome Results Identify Key Coding and Noncoding Genes Predicting Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas. BIOMED RESEARCH INTERNATIONAL 2021; 2021:1056622. [PMID: 34790815 PMCID: PMC8592698 DOI: 10.1155/2021/1056622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 10/07/2021] [Accepted: 10/21/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions of pancreatic ductal adenocarcinoma (PDAC). IPMNs are generally associated with high risk of developing malignancy and therefore need to be diagnosed and assessed accurately, once detected. Existing diagnostic methods are inadequate, and identification of efficient biomarker capable of detecting high-risk IPMNs is necessitated. Moreover, the mechanism of development of malignancy in IPMNs is also elusive. METHODS Gene expression meta-analysis conducted using 12 low-risk IPMN and 23 high-risk IPMN tissue samples. We have also listed all the altered miRNAs and long noncoding RNAs (lncRNAs), identified their target genes, and performed pathway analysis. We further enlisted cyst fluid proteins detected to be altered in high-risk or malignant IPMNs and compared them with fraction of differentially expressed genes secreted into cyst fluid. RESULTS Our meta-analysis identified 270 upregulated and 161 downregulated genes characteristically altered in high-risk IPMNs. We further identified 61 miRNAs and 14 lncRNAs and their target genes and key pathways contributing towards understanding of the gene regulation during the progression of the disease. Most importantly, we have detected 12 genes altered significantly both in cystic lesions and cyst fluid. CONCLUSION Our study reports, for the first time, a meta-analysis identifying key changes in gene expression between low-risk and high-risk IPMNs and also explains the regulatory aspect through construction of a miRNA-lncRNA-mRNA interaction network. The 12-gene-signature could function as potential biomarker in cyst fluid for detection of IPMN with a high risk of developing malignancy.
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Affiliation(s)
- Barsha Saha
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | | | | | - Srikanta Goswami
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
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Liu SJ, Li ZQ, Wang XY, Liu F, Xiao ZM, Zhang DC. lncRNA UCA1 induced by SP1 and SP3 forms a positive feedback loop to facilitate malignant phenotypes of colorectal cancer via targeting miR-495. Life Sci 2021; 277:119569. [PMID: 33961855 DOI: 10.1016/j.lfs.2021.119569] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 12/01/2022]
Abstract
AIMS Long noncoding RNA (LncRNA) urothelial cancer associated 1 (UCA1) was dysregulated in colorectal cancers (CRC) and promoted tumor progression of CRC. The aims of this study are to further investigate the underlying mechanism. MAIN METHODS Short hairpin RNAs (shRNAs) were applied for gene knockdown. microRNA mimic and pcDNA-UCA1 plasmids were transfected for miR-495 and UCA1 overexpression, respectively. MTT was applied to determine cell viability and sensitivity of 5-fluorouracil (FU). Transwell assays were performed to evaluate cell migration/invasion. Angiogenesis was evaluated by tube formation. Western blotting and quantitative PCR were utilized for protein and mRNA detection, respectively. The interaction of UCA1, miR-495 and SP1/SP3 were explored by dual-luciferase assay. RNA pulldown was adopted to determine the UCA1/miR-495 interaction. KEY FINDINGS UCA1 was significantly upregulated in CRC tissues. UCA1 enhanced cell proliferation, migration/invasion, angiogenesis, epithelial-mesenchymal transition, and resistance to 5-FU in CRC cell lines. MiR-495 was inversely correlated to the expression of UCA1. The results indicated that UCA1 sponged miR-495, leading to the disinhibition of SP1/SP3 expression. SP1/SP3 induced the expression of DNA methyltransferases and, in turn, contributed to UCA1 mediated tumor-promoting actions. Reduction of SP1/SP3 exerted anti-cancer effects, which can be reversed by forced expression of UCA1. SIGNIFICANCE UCA1-miR-495-SP1/SP3 axis is dysregulated in CRC and contributed to malignant phenotypes of CRC. UCA1-SP1/SP3 may form a positive feedback loop in CRC.
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Affiliation(s)
- Shao-Jun Liu
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, PR China; Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410013, Hunan Province, PR China
| | - Zhao-Qi Li
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, PR China; Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410013, Hunan Province, PR China
| | - Xiao-Yan Wang
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, PR China; Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410013, Hunan Province, PR China
| | - Fen Liu
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, PR China; Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410013, Hunan Province, PR China
| | - Zhi-Ming Xiao
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, PR China; Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410013, Hunan Province, PR China
| | - De-Cai Zhang
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, PR China; Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410013, Hunan Province, PR China.
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7
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Ye J, Li J, Zhao P. Roles of ncRNAs as ceRNAs in Gastric Cancer. Genes (Basel) 2021; 12:genes12071036. [PMID: 34356052 PMCID: PMC8305186 DOI: 10.3390/genes12071036] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/28/2021] [Accepted: 06/28/2021] [Indexed: 01/19/2023] Open
Abstract
Although ignored in the past, with the recent deepening of research, significant progress has been made in the field of non-coding RNAs (ncRNAs). Accumulating evidence has revealed that microRNA (miRNA) response elements regulate RNA. Long ncRNAs, circular RNAs, pseudogenes, miRNAs, and messenger RNAs (mRNAs) form a competitive endogenous RNA (ceRNA) network that plays an essential role in cancer and cardiovascular, neurodegenerative, and autoimmune diseases. Gastric cancer (GC) is one of the most common cancers, with a high degree of malignancy. Considerable progress has been made in understanding the molecular mechanism and treatment of GC, but GC’s mortality rate is still high. Studies have shown a complex ceRNA crosstalk mechanism in GC. lncRNAs, circRNAs, and pseudogenes can interact with miRNAs to affect mRNA transcription. The study of the involvement of ceRNA in GC could improve our understanding of GC and lead to the identification of potential effective therapeutic targets. The research strategy for ceRNA is mainly to screen the different miRNAs, lncRNAs, circRNAs, pseudogenes, and mRNAs in each sample through microarray or sequencing technology, predict the ceRNA regulatory network, and, finally, conduct functional research on ceRNA. In this review, we briefly discuss the proposal and development of the ceRNA hypothesis and the biological function and principle of ceRNAs in GC, and briefly introduce the role of ncRNAs in the GC’s ceRNA network.
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Affiliation(s)
- Junhong Ye
- State Key Laboratory of Silkworm Genome Biology, Biological Science Research Center, Southwest University, Chongqing 400716, China;
| | - Jifu Li
- College of Sericulture, Textile and Biomass Sciences, Southwest University, Chongqing 400716, China;
| | - Ping Zhao
- State Key Laboratory of Silkworm Genome Biology, Biological Science Research Center, Southwest University, Chongqing 400716, China;
- Correspondence: ; Tel.: +86-23-6825-0885
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8
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Chowdhary A, Satagopam V, Schneider R. Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer. Front Genet 2021; 12:649619. [PMID: 34276764 PMCID: PMC8281131 DOI: 10.3389/fgene.2021.649619] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/20/2021] [Indexed: 01/09/2023] Open
Abstract
Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).
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Affiliation(s)
- Anshika Chowdhary
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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9
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Tian L, Wang SL. Exploring miRNA Sponge Networks of Breast Cancer by Combining miRNA-disease-lncRNA and miRNA-target Networks. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200711171530] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Recently, ample researches show that microRNAs (miRNAs) not only
interact with coding genes but interact with a pool of different RNAs. Those RNAs are called
miRNA sponges, including long non-coding RNAs (lncRNAs), circular RNA, pseudogenes and
various messenger RNAs. Understanding regulatory networks of miRNA sponges can better help
researchers to study the mechanisms of breast cancers.
Objective:
We develop a new method to explore miRNA sponge networks of breast cancer by combining miRNAdisease-lncRNA and miRNA-target networks (MSNMDL).
Method:
Firstly, MSNMDL infers miRNA-lncRNA functional similarity networks from miRNAdisease-
lncRNA networks. Secondly, MSNMDL forms lncRNA-target networks by using lncRNA
to replace the role of matched miRNA in miRNA-target networks according to the lncRNA-miRNA
pair of miRNA-lncRNA functional similarity networks. And MSNMDL only retains the genes of
breast cancer in lncRNA-target networks to construct candidate miRNA sponge networks. Thirdly,
MSNMDL merges these candidate miRNA sponge networks with other miRNA sponge interactions
and then selects top-hub lncRNA and its interactions to construct miRNA sponge networks.
Results:
MSNMDL is superior to other methods in terms of biological significance and its identified modules might
act as module signatures for prognostication of breast cancer.
Conclusion:
MiRNA sponge networks identified by MSNMDL are biologically significant and are
closely associated with breast cancer, which makes MSNMDL a promising way for researchers to
study the pathogenesis of breast cancer.
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Affiliation(s)
- Lei Tian
- School of Information Science and Engineering, Hunan University, Changsha, China
| | - Shu-Lin Wang
- School of Information Science and Engineering, Hunan University, Changsha, China
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10
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Zhang J, Liu L, Xu T, Zhang W, Zhao C, Li S, Li J, Rao N, Le TD. miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data. RNA Biol 2021; 18:2308-2320. [PMID: 33822666 DOI: 10.1080/15476286.2021.1905341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, miRSM, for facilitating the procedure of inferring and analysing miRNA sponge modules. miRSM provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. miRSM will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.
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Affiliation(s)
- Junpeng Zhang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.,School of Engineering, Dali University, Dali, Yunnan, China
| | - Lin Liu
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
| | - Taosheng Xu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Wu Zhang
- School of Agriculture and Biological Sciences, Dali University, Dali, Yunnan, China
| | - Chunwen Zhao
- School of Engineering, Dali University, Dali, Yunnan, China
| | - Sijing Li
- School of Engineering, Dali University, Dali, Yunnan, China
| | - Jiuyong Li
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
| | - Nini Rao
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Thuc Duy Le
- UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia
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CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network. BMC Bioinformatics 2021; 22:75. [PMID: 33602117 PMCID: PMC7890952 DOI: 10.1186/s12859-021-04012-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 02/08/2021] [Indexed: 01/01/2023] Open
Abstract
Background The competing endogenous RNA (ceRNA) regulation is a newly discovered post-transcriptional regulation mechanism and plays significant roles in physiological and pathological progress. CeRNA networks provide global views to help understand the regulation of ceRNAs. CeRNA networks have been widely used to detect survival biomarkers, select candidate regulators of disease genes, and predict long noncoding RNA functions. However, there is no software platform to provide overall functions from the construction to analysis of ceRNA networks. Results To fill this gap, we introduce CeNet Omnibus, an R/Shiny application, which provides a unified framework for the construction and analysis of ceRNA network. CeNet Omnibus enables users to select multiple measurements, such as Pearson correlation coefficient (PCC), mutual information (MI), and liquid association (LA), to identify ceRNA pairs and construct ceRNA networks. Furthermore, CeNet Omnibus provides a one-stop solution to analyze the topological properties of ceRNA networks, detect modules, and perform gene enrichment analysis and survival analysis. CeNet Omnibus intends to cover comprehensiveness, high efficiency, high expandability, and user customizability, and it also offers a web-based user-friendly interface to users to obtain the output intuitionally. Conclusion CeNet Omnibus is a comprehensive platform for the construction and analysis of ceRNA networks. It is highly customizable and outputs the results in intuitive and interactive. We expect that CeNet Omnibus will assist researchers to understand the property of ceRNA networks and associated biological phenomena. CeNet Omnibus is an R/Shiny application based on the Shiny framework developed by RStudio. The R package and detailed tutorial are available on our GitHub page with the URL https://github.com/GaoLabXDU/CeNetOmnibus.
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Abstract
Systematics is described for annotation of variations in RNA molecules. The conceptual framework is part of Variation Ontology (VariO) and facilitates depiction of types of variations, their functional and structural effects and other consequences in any RNA molecule in any organism. There are more than 150 RNA related VariO terms in seven levels, which can be further combined to generate even more complicated and detailed annotations. The terms are described together with examples, usually for variations and effects in human and in diseases. RNA variation type has two subcategories: variation classification and origin with subterms. Altogether six terms are available for function description. Several terms are available for affected RNA properties. The ontology contains also terms for structural description for affected RNA type, post-transcriptional RNA modifications, secondary and tertiary structure effects and RNA sugar variations. Together with the DNA and protein concepts and annotations, RNA terms allow comprehensive description of variations of genetic and non-genetic origin at all possible levels. The VariO annotations are readable both for humans and computer programs for advanced data integration and mining.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, Sweden
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13
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LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer. PLoS Comput Biol 2020; 16:e1007851. [PMID: 32324747 PMCID: PMC7200020 DOI: 10.1371/journal.pcbi.1007851] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 05/05/2020] [Accepted: 04/06/2020] [Indexed: 12/12/2022] Open
Abstract
Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline’s performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data. Previous studies have revealed that long non-coding RNAs (lncRNAs), as microRNA (miRNA) sponges or competing endogenous RNAs (ceRNAs), can regulate the expression levels of messenger RNAs (mRNAs) by decreasing the amount of miRNAs interacting with mRNAs. In this work, we hypothesize that the “tug-of-war” between RNA transcripts for attracting miRNAs is across groups or modules. Based on the hypothesis, we propose a framework called LMSM, to identify LncRNA related MiRNA Sponge Modules. Based on the two miRNA sponge modular competition principles, significant sharing of miRNAs and high canonical correlation between the sponge lncRNAs and mRNAs, LMSM is also capable of predicting miRNA targets. LMSM not only extends the ceRNA hypothesis, but also provides a novel way to investigate the biological functions and modular mechanism of lncRNAs in breast cancer.
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Wen X, Gao L, Hu Y. LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation. Front Genet 2020; 11:235. [PMID: 32256525 PMCID: PMC7093494 DOI: 10.3389/fgene.2020.00235] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 02/27/2020] [Indexed: 12/14/2022] Open
Abstract
Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer.
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Affiliation(s)
- Xiao Wen
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Yuxuan Hu
- School of Computer Science and Technology, Xidian University, Xi'an, China
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Sahlu BW, Zhao S, Wang X, Umer S, Zou H, Huang J, Zhu H. Long noncoding RNAs: new insights in modulating mammalian spermatogenesis. J Anim Sci Biotechnol 2020; 11:16. [PMID: 32128162 PMCID: PMC7047388 DOI: 10.1186/s40104-019-0424-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/30/2019] [Indexed: 12/12/2022] Open
Abstract
Spermatogenesis is a complex differentiating developmental process in which undifferentiated spermatogonial germ cells differentiate into spermatocytes, spermatids, and finally, to mature spermatozoa. This multistage developmental process of spermatogenesis involves the expression of many male germ cell-specific long noncoding RNAs (lncRNAs) and highly regulated and specific gene expression. LncRNAs are a recently discovered large class of noncoding cellular transcripts that are still relatively unexplored. Only a few of them have post-meiotic; however, lncRNAs are involved in many cellular biological processes. The expression of lncRNAs is biologically relevant in the highly dynamic and complex program of spermatogenesis and has become a research focus in recent genome studies. This review considers the important roles and novel regulatory functions whereby lncRNAs modulate mammalian spermatogenesis.
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Affiliation(s)
- Bahlibi Weldegebriall Sahlu
- 1Embryo Biotechnology and Reproduction Laboratory, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193 People's Republic of China.,Tigray Agricultural Research Institute, Mekelle Agricultural Research Center, Mekelle, Ethiopia
| | - Shanjiang Zhao
- 1Embryo Biotechnology and Reproduction Laboratory, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193 People's Republic of China
| | - Xiuge Wang
- 3Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, 250131 People's Republic of China
| | - Saqib Umer
- 1Embryo Biotechnology and Reproduction Laboratory, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193 People's Republic of China
| | - Huiying Zou
- 1Embryo Biotechnology and Reproduction Laboratory, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193 People's Republic of China
| | - Jinming Huang
- 3Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, 250131 People's Republic of China
| | - Huabin Zhu
- 1Embryo Biotechnology and Reproduction Laboratory, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193 People's Republic of China
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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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Zhang J, Liu L, Xu T, Xie Y, Zhao C, Li J, Le TD. miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules. BMC Bioinformatics 2019; 20:235. [PMID: 31077152 PMCID: PMC6509829 DOI: 10.1186/s12859-019-2861-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Accepted: 04/29/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND A microRNA (miRNA) sponge is an RNA molecule with multiple tandem miRNA response elements that can sequester miRNAs from their target mRNAs. Despite growing appreciation of the importance of miRNA sponges, our knowledge of their complex functions remains limited. Moreover, there is still a lack of miRNA sponge research tools that help researchers to quickly compare their proposed methods with other methods, apply existing methods to new datasets, or select appropriate methods for assisting in subsequent experimental design. RESULTS To fill the gap, we present an R/Bioconductor package, miRspongeR, for simplifying the procedure of identifying and analyzing miRNA sponge interaction networks and modules. It provides seven popular methods and an integrative method to identify miRNA sponge interactions. Moreover, it supports the validation of miRNA sponge interactions and the identification of miRNA sponge modules, as well as functional enrichment and survival analysis of miRNA sponge modules. CONCLUSIONS This package enables researchers to quickly evaluate their new methods, apply existing methods to new datasets, and consequently speed up miRNA sponge research.
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Affiliation(s)
- Junpeng Zhang
- School of Engineering, Dali University, Dali, 671003, Yunnan, China.
| | - Lin Liu
- School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia
| | - Taosheng Xu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Yong Xie
- School of Engineering, Dali University, Dali, 671003, Yunnan, China
| | - Chunwen Zhao
- School of Engineering, Dali University, Dali, 671003, Yunnan, China
| | - Jiuyong Li
- School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia
| | - Thuc Duy Le
- School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.
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Evolutionary Patterns of Non-Coding RNA in Cardiovascular Biology. Noncoding RNA 2019; 5:ncrna5010015. [PMID: 30709035 PMCID: PMC6468844 DOI: 10.3390/ncrna5010015] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 01/26/2019] [Accepted: 01/29/2019] [Indexed: 12/15/2022] Open
Abstract
Cardiovascular diseases (CVDs) affect the heart and the vascular system with a high prevalence and place a huge burden on society as well as the healthcare system. These complex diseases are often the result of multiple genetic and environmental risk factors and pose a great challenge to understanding their etiology and consequences. With the advent of next generation sequencing, many non-coding RNA transcripts, especially long non-coding RNAs (lncRNAs), have been linked to the pathogenesis of CVD. Despite increasing evidence, the proper functional characterization of most of these molecules is still lacking. The exploration of conservation of sequences across related species has been used to functionally annotate protein coding genes. In contrast, the rapid evolutionary turnover and weak sequence conservation of lncRNAs make it difficult to characterize functional homologs for these sequences. Recent studies have tried to explore other dimensions of interspecies conservation to elucidate the functional role of these novel transcripts. In this review, we summarize various methodologies adopted to explore the evolutionary conservation of cardiovascular non-coding RNAs at sequence, secondary structure, syntenic, and expression level.
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