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Ballarino M, Pepe G, Helmer-Citterich M, Palma A. Exploring the landscape of tools and resources for the analysis of long non-coding RNAs. Comput Struct Biotechnol J 2023; 21:4706-4716. [PMID: 37841333 PMCID: PMC10568309 DOI: 10.1016/j.csbj.2023.09.041] [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: 08/12/2023] [Revised: 09/28/2023] [Accepted: 09/28/2023] [Indexed: 10/17/2023] Open
Abstract
In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.
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Affiliation(s)
- Monica Ballarino
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00161 Rome, Italy
| | - Gerardo Pepe
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 1, 00133 Rome, Italy
| | - Manuela Helmer-Citterich
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 1, 00133 Rome, Italy
| | - Alessandro Palma
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00161 Rome, Italy
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2
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Marino GB, Wojciechowicz ML, Clarke DJB, Kuleshov MV, Xie Z, Jeon M, Lachmann A, Ma’ayan A. lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs. Database (Oxford) 2023; 2023:7069621. [PMID: 36869839 PMCID: PMC9985331 DOI: 10.1093/database/baad009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 01/25/2023] [Accepted: 02/11/2023] [Indexed: 03/05/2023]
Abstract
Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18 705 human and 11 274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at https://maayanlab.cloud/lncHUB2. Database URL: https://maayanlab.cloud/lncHUB2.
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Affiliation(s)
- Giacomo B Marino
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Megan L Wojciechowicz
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Maxim V Kuleshov
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Minji Jeon
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Avi Ma’ayan
- *Corresponding author: Tel: +001-212-241-1153; Fax: +001-212-849-2456;
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3
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Maghsoudi Z, Nguyen H, Tavakkoli A, Nguyen T. A comprehensive survey of the approaches for pathway analysis using multi-omics data integration. Brief Bioinform 2022; 23:6761962. [PMID: 36252928 PMCID: PMC9677478 DOI: 10.1093/bib/bbac435] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/26/2022] [Accepted: 09/08/2022] [Indexed: 02/07/2023] Open
Abstract
Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.
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Affiliation(s)
- Zeynab Maghsoudi
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Ha Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Alireza Tavakkoli
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Tin Nguyen
- Corresponding author: Tin Nguyen, Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA. Tel.: +1-775-784-6619;
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4
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Fisher T, Gluck A, Narayanan K, Kuroda M, Nachshon A, Hsu JC, Halfmann PJ, Yahalom-Ronen Y, Tamir H, Finkel Y, Schwartz M, Weiss S, Tseng CTK, Israely T, Paran N, Kawaoka Y, Makino S, Stern-Ginossar N. Parsing the role of NSP1 in SARS-CoV-2 infection. Cell Rep 2022; 39:110954. [PMID: 35671758 PMCID: PMC9133101 DOI: 10.1016/j.celrep.2022.110954] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 04/06/2022] [Accepted: 05/23/2022] [Indexed: 11/18/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leads to shutoff of protein synthesis, and nsp1, a central shutoff factor in coronaviruses, inhibits cellular mRNA translation. However, the diverse molecular mechanisms employed by nsp1 as well as its functional importance are unresolved. By overexpressing various nsp1 mutants and generating a SARS-CoV-2 mutant, we show that nsp1, through inhibition of translation and induction of mRNA degradation, targets translated cellular mRNA and is the main driver of host shutoff during infection. The propagation of nsp1 mutant virus is inhibited exclusively in cells with intact interferon (IFN) pathway as well as in vivo, in hamsters, and this attenuation is associated with stronger induction of type I IFN response. Therefore, although nsp1's shutoff activity is broad, it plays an essential role, specifically in counteracting the IFN response. Overall, our results reveal the multifaceted approach nsp1 uses to shut off cellular protein synthesis and uncover nsp1's explicit role in blocking the IFN response.
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Affiliation(s)
- Tal Fisher
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Avi Gluck
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Krishna Narayanan
- Department of Microbiology and Immunology, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA
| | - Makoto Kuroda
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin, Madison, WI 53711, USA
| | - Aharon Nachshon
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Jason C Hsu
- Department of Microbiology and Immunology, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA
| | - Peter J Halfmann
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin, Madison, WI 53711, USA
| | - Yfat Yahalom-Ronen
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona 74100, Israel
| | - Hadas Tamir
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona 74100, Israel
| | - Yaara Finkel
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Michal Schwartz
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Shay Weiss
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona 74100, Israel
| | - Chien-Te K Tseng
- Department of Microbiology and Immunology, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA; Institute for Human Infections and Immunity, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA
| | - Tomer Israely
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona 74100, Israel
| | - Nir Paran
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona 74100, Israel.
| | - Yoshihiro Kawaoka
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin, Madison, WI 53711, USA; Department of Virology, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan; The Research Center for Global Viral Diseases, National Center for Global Health and Medicine Research Institute, Tokyo 162-8655, Japan.
| | - Shinji Makino
- Department of Microbiology and Immunology, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA; Institute for Human Infections and Immunity, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA.
| | - Noam Stern-Ginossar
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel.
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Fisher T, Gluck A, Narayanan K, Kuroda M, Nachshon A, Hsu JC, Halfmann PJ, Yahalom-Ronen Y, Finkel Y, Schwartz M, Weiss S, Tseng CTK, Israely T, Paran N, Kawaoka Y, Makino S, Stern-Ginossar N. Parsing the role of NSP1 in SARS-CoV-2 infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.03.14.484208. [PMID: 35313595 PMCID: PMC8936099 DOI: 10.1101/2022.03.14.484208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 19 (COVID-19) pandemic. Despite its urgency, we still do not fully understand the molecular basis of SARS-CoV-2 pathogenesis and its ability to antagonize innate immune responses. SARS-CoV-2 leads to shutoff of cellular protein synthesis and over-expression of nsp1, a central shutoff factor in coronaviruses, inhibits cellular gene translation. However, the diverse molecular mechanisms nsp1 employs as well as its functional importance in infection are still unresolved. By overexpressing various nsp1 mutants and generating a SARS-CoV-2 mutant in which nsp1 does not bind ribosomes, we untangle the effects of nsp1. We uncover that nsp1, through inhibition of translation and induction of mRNA degradation, is the main driver of host shutoff during SARS-CoV-2 infection. Furthermore, we find the propagation of nsp1 mutant virus is inhibited specifically in cells with intact interferon (IFN) response as well as in-vivo , in infected hamsters, and this attenuation is associated with stronger induction of type I IFN response. This illustrates that nsp1 shutoff activity has an essential role mainly in counteracting the IFN response. Overall, our results reveal the multifaceted approach nsp1 uses to shut off cellular protein synthesis and uncover the central role it plays in SARS-CoV-2 pathogenesis, explicitly through blockage of the IFN response.
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Affiliation(s)
- Tal Fisher
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
- T. Fisher, A. Gluck, K. Narayanan, and K. Makoto contributed equally to the studies
| | - Avi Gluck
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
- T. Fisher, A. Gluck, K. Narayanan, and K. Makoto contributed equally to the studies
| | - Krishna Narayanan
- Department of Microbiology and Immunology, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA
- T. Fisher, A. Gluck, K. Narayanan, and K. Makoto contributed equally to the studies
| | - Makoto Kuroda
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin, Madison, WI 53711, USA
- T. Fisher, A. Gluck, K. Narayanan, and K. Makoto contributed equally to the studies
| | - Aharon Nachshon
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Jason C. Hsu
- Department of Microbiology and Immunology, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA
| | - Peter J. Halfmann
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin, Madison, WI 53711, USA
| | - Yfat Yahalom-Ronen
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona 74100, Israel
| | - Yaara Finkel
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Michal Schwartz
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Shay Weiss
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona 74100, Israel
| | - Chien-Te K. Tseng
- Department of Microbiology and Immunology, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA
- Institute for Human Infections and Immunity, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA
| | - Tomer Israely
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona 74100, Israel
| | - Nir Paran
- Department of Infectious Diseases, Israel Institute for Biological Research, Ness Ziona 74100, Israel
| | - Yoshihiro Kawaoka
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin, Madison, WI 53711, USA
- Department of Virology, Institute of Medical Science, University of Tokyo, 108-8639 Tokyo, Japan
- The Research Center for Global Viral Diseases, National Center for Global Health and Medicine Research Institute, 162-8655 Tokyo, Japan
| | - Shinji Makino
- Department of Microbiology and Immunology, The University of Texas Medical Branch, Galveston, TX 77555-1019, USA
- Department of Virology, Institute of Medical Science, University of Tokyo, 108-8639 Tokyo, Japan
| | - Noam Stern-Ginossar
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
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6
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Tyagi S, Chan EC, Barker D, McElduff P, Taylor KA, Riveros C, Singh E, Smith R. Transcriptomic analysis reveals myometrial topologically associated domains linked to onset of human term labor. Mol Hum Reprod 2022; 28:6527642. [PMID: 35150271 PMCID: PMC8903000 DOI: 10.1093/molehr/gaac003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Changes in cell phenotype are thought to occur through the expression of groups of co-regulated genes within topologically associated domains (TADs). In this paper we allocate genes expressed within the myometrium of the human uterus during the onset of term labor into TADs. Transformation of the myometrial cells of the uterus into a contractile phenotype during term human labor is the result of a complex interaction of different epigenomic and genomic layers. Recent work suggests that the transcription factor RELA lies at the top of this regulatory network. Using deep RNA sequencing (RNAseq) analysis of myometrial samples (n = 16) obtained at term from women undergoing Caesarean section prior to or after the onset of labor we have identified evidence for how other gene expression regulatory elements interact with transcription factors in the labor phenotype transition. Gene set enrichment analysis of our RNAseq data identified three modules of enriched genes (M1, M2 and M3), which in gene ontology studies are linked to matrix degradation, smooth muscle and immune gene signatures, respectively. These genes were predominantly located within chromosomal TADs suggesting co-regulation of expression. Our transcriptomic analysis also identified significant differences in the expression of long non-coding RNAs (lncRNA), microRNAs (miRNA) and transcription factors that were predicted to target genes within the TADs. Additionally, network analysis revealed 15 new lncRNA (MCM3AP-AS1, TUG1, MIR29B2CHG, HCG18, LINC00963, KCNQ1OT1, NEAT1, HELLPAR, SNHG16, NUTM2B-AS1, MALAT1, PSMA3-AS1, GABPB1-AS1, NORAD, NKILA) and four miRNA (mir-145, mir-223, mir-let-7a, mir-132) as top gene hubs with three transcription factors (NFKB1, RELA, ESR1) as master regulators. Together, these factors are likely to be involved in co-regulatory networks driving a myometrial transformation to generate an estrogen sensitive phenotype. We conclude that lncRNA and miRNA targeting the estrogen receptor 1 and nuclear factor kappa B pathways play a key role in the initiation of human labor. For the first time we perform an integrative analysis to present a multi-level genomic signature made of mRNA, ncRNA and transcription factors in the myometrium for spontaneous term labor.
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Affiliation(s)
- Sonika Tyagi
- Central Clinical School, Monash University and the Alfred Hospital, Melbourne, VIC, Australia
| | - Eng-Cheng Chan
- Mothers and Babies Research Centre, HMRI University of Newcastle, NSW, Australia
| | | | | | - Kelly A Taylor
- Mothers and Babies Research Centre, HMRI University of Newcastle, NSW, Australia
| | | | - Esha Singh
- Department of Biotechnology and Biochemical Engineering, Indian Institute of Technology, New Delhi, India
| | - Roger Smith
- Mothers and Babies Research Centre, HMRI University of Newcastle, NSW, Australia.,University of Newcastle, Newcastle, NSW, Australia
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Chen S, Ben X, Guo L, Li X. Identification of lncRNAs based on different patterns of immune infiltration in gastric cancer. J Gastrointest Oncol 2022; 13:102-116. [PMID: 35284124 PMCID: PMC8899746 DOI: 10.21037/jgo-21-833] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 01/04/2022] [Indexed: 12/09/2023] Open
Abstract
BACKGROUND Gastric cancer is one of the most common malignant tumors in the world, which brings great challenges to people's life and health. The purpose of this study was to investigate immune related-lncRNAs and identify new biomarkers for the prognosis of gastric cancer (GC). METHODS We downloaded data from The Cancer Genome Atlas (TCGA) and used R software to determine the ESTIMATEScore, ImmuneScore, and StromalScore of each tumor sample. We performed prognostic analysis and identified the differentially expressed lnRNAs, which were then used to construct a prognostic model. Among the 44 hub genes in the competitive endogenous RNA (ceRNA) network, 3 differentially expressed genes were verified by qPCR. RESULTS Based on the degree of immune infiltration, cluster A had a higher ESTIMATEScore, ImmuneScore, and StromalScore and higher expression levels of PD-L1 (CD274) and CTLA4 than cluster B. Univariate Cox analysis was conducted for these differential lncRNAs, and 57 lncRNAs were found to have prognostic value (P<0.05). gene cluster A had a worse prognosis than gene cluster B (P=0.021). Then, a prognostic model was constructed. The low-risk group had a significantly higher survival rate. Finally, the qPCR results showed that the expression levels of BMPER, PRUNE2, and RBPMS2 were low in GC cell lines. CONCLUSIONS We identified a risk score of 19 lncRNAs as a prognostic marker of GC. There was a relationship between these 19 prognostic-related lncRNAs and the subtypes of infiltrating immune cells. An approach for predicting the prognosis of GC was therefore provided in this study.
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Affiliation(s)
- Shujia Chen
- Department of Gastroenterology, the First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Xinyu Ben
- Key Laboratory of Brain Science Research and Transformation in Tropical Environment of Hainan Province & Laboratory of Neurology, the First Affiliated Hospital, Hainan Medical University, Haikou, China
| | - Lianyi Guo
- Department of Gastroenterology, the First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Xiaofei Li
- Department of Gastroenterology, the First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
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8
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Yan Y, Ren L, Liu Y, Liu L. Development and Validation of Genome Instability-Associated lncRNAs to Predict Prognosis and Immunotherapy of Patients With Hepatocellular Carcinoma. Front Genet 2022; 12:763281. [PMID: 35154241 PMCID: PMC8832282 DOI: 10.3389/fgene.2021.763281] [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: 10/15/2021] [Accepted: 12/21/2021] [Indexed: 12/16/2022] Open
Abstract
The pathophysiology of hepatocellular carcinoma (HCC) is prevalently related to genomic instability. However, research on the association of extensive genome instability lncRNA (GILnc) with the prognosis and immunotherapy of HCC remains scarce. We placed the top 25% of somatic mutations into the genetically unstable group and placed the bottom 25% of somatic mutations into the genetically stable group, and then to identify different expression of GILnc between the two groups. Then, LASSO was used to identify the most powerful prognostic GILnc, and a risk score for each patient was calculated according to the formula. Based on a computational frame, 245 different GILncs in HCC were identified. An eight GILnc model was successfully established to predict overall survival in HCC patients based on LASSO, then we divided HCC patients into high-risk and low-risk groups, and a significantly shorter overall survival in the high-risk group was observed compared to those in the low-risk group, and this was validated in GSE76427 and Tongji cohorts. GSEA revealed that the high-risk group was more likely to be enriched in cancer-specific pathways. Besides, the GILnc signature has greater prognostic significance than TP53 mutation status alone, and it is capable of identifying intermediate subtype groups existing with partial TP53 functionality in TP53 wild-type patients. Importantly, the high-risk group was associated with the therapeutic efficacy of PD-L1 blockade, suggesting that the development of potential drugs targeting these GILnc could aid the clinical benefits of immunotherapy. Finally, the GILnc signature model is better than the prediction performance of two recently published lncRNA signatures. In summary, we applied bioinformatics approaches to suggest that an eight GILnc model could serve as prognostic biomarkers to provide a novel direction to explore the pathogenesis of HCC.
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Affiliation(s)
- Yifeng Yan
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Forensic Medicine, Wannan Medical College, Wuhu, China
| | - Liang Ren
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Liu
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liang Liu
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Liang Liu, ,
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9
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Yi C, Zhang X, Chen X, Huang B, Song J, Ma M, Yuan X, Zhang C. A novel 8-genome instability-associated lncRNAs signature predicting prognosis and drug sensitivity in gastric cancer. Int J Immunopathol Pharmacol 2022; 36:1-15. [PMID: 35696730 PMCID: PMC9203952 DOI: 10.1177/03946320221103195] [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] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Genome instability lncRNA (GILnc) is prevalently related with gastric cancer (GC) pathophysiology. However, the study on the relationship GILnc and prognosis and drug sensitivity of GC remains scarce. METHOD We extracted expression data of 375 GC patients from TCGA cohort and 205 GC patients from GSE26942 cohort. Then, lncRNA was separated from expression data, and systematically characterized the 8 marker lncRNAs using the LASSO method. Next, we constructed a GILnc model (GILnc score) to quantify the GILnc index of each GC patient. Finally, we analyzed the relationship between GILnc score and clinical traits including survival outcomes, TP53, and drug sensitivity of GC. RESULTS Based on a computational frame, 205 GILncs in GC has been identified. Then, a 8 GILncs was successfully established to predict overall survival in GC patients based on LASSO analysis, divided GC samples into high GILnc score and low GILnc score groups with significantly different outcome and was validated in multiple independent patient cohorts. Furthermore, GILnc model is better than the prediction performance of two recently published lncRNA signatures, and the high GILnc score group was more sensitive to mitomycin. Besides, the GILnc score has greater prognostic significance than TP53 mutation status alone and is capable of identifying intermediate subtype group existing with partial TP53 functionality in TP53 wild-type patients. Finally, GILnc signature as verified in GSE26942. CONCLUSION We applied bioinformatics approaches to suggest that a 8 GILnc signature could serve as prognostic biomarkers, and provide a novel direction to explore the pathogenesis of GC.
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Affiliation(s)
- Changhong Yi
- Department of Interventional, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Xiulan Zhang
- Department of Nuclear Medicine, The First People’s Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China
| | - Xia Chen
- Department of Oncology, Jingzhou Central Hospital, The Second Clinical Medical College, Yangtze University, China
| | - Birun Huang
- Department of Vascular Surgery, The First People’s Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Jing Song
- Department of Nursing, Hubei College of Chinese Medicine, Jingzhou, People's Republic of China
| | - Minghui Ma
- Department of Gastrointestinal Surgery, Maoming People’s Hospital, Maoming, China
| | - Xiaolu Yuan
- Department of Gastrointestinal Surgery, Maoming People’s Hospital, Maoming, China
| | - Chaohao Zhang
- Department of Gastrointestinal Surgery, Maoming People’s Hospital, Maoming, China
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10
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Keihani S, Kluever V, Fornasiero EF. Brain Long Noncoding RNAs: Multitask Regulators of Neuronal Differentiation and Function. Molecules 2021; 26:molecules26133951. [PMID: 34203457 PMCID: PMC8272081 DOI: 10.3390/molecules26133951] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/21/2021] [Accepted: 06/24/2021] [Indexed: 02/07/2023] Open
Abstract
The extraordinary cellular diversity and the complex connections established within different cells types render the nervous system of vertebrates one of the most sophisticated tissues found in living organisms. Such complexity is ensured by numerous regulatory mechanisms that provide tight spatiotemporal control, robustness and reliability. While the unusual abundance of long noncoding RNAs (lncRNAs) in nervous tissues was traditionally puzzling, it is becoming clear that these molecules have genuine regulatory functions in the brain and they are essential for neuronal physiology. The canonical view of RNA as predominantly a 'coding molecule' has been largely surpassed, together with the conception that lncRNAs only represent 'waste material' produced by cells as a side effect of pervasive transcription. Here we review a growing body of evidence showing that lncRNAs play key roles in several regulatory mechanisms of neurons and other brain cells. In particular, neuronal lncRNAs are crucial for orchestrating neurogenesis, for tuning neuronal differentiation and for the exact calibration of neuronal excitability. Moreover, their diversity and the association to neurodegenerative diseases render them particularly interesting as putative biomarkers for brain disease. Overall, we foresee that in the future a more systematic scrutiny of lncRNA functions will be instrumental for an exhaustive understanding of neuronal pathophysiology.
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Vancura A, Lanzós A, Bosch-Guiteras N, Esteban MT, Gutierrez AH, Haefliger S, Johnson R. Cancer LncRNA Census 2 (CLC2): an enhanced resource reveals clinical features of cancer lncRNAs. NAR Cancer 2021; 3:zcab013. [PMID: 34316704 PMCID: PMC8210278 DOI: 10.1093/narcan/zcab013] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 03/12/2021] [Accepted: 03/17/2021] [Indexed: 01/28/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) play key roles in cancer and are at the vanguard of precision therapeutic development. These efforts depend on large and high-confidence collections of cancer lncRNAs. Here, we present the Cancer LncRNA Census 2 (CLC2). With 492 cancer lncRNAs, CLC2 is 4-fold greater in size than its predecessor, without compromising on strict criteria of confident functional/genetic roles and inclusion in the GENCODE annotation scheme. This increase was enabled by leveraging high-throughput transposon insertional mutagenesis screening data, yielding 92 novel cancer lncRNAs. CLC2 makes a valuable addition to existing collections: it is amongst the largest, contains numerous unique genes (not found in other databases) and carries functional labels (oncogene/tumour suppressor). Analysis of this dataset reveals that cancer lncRNAs are impacted by germline variants, somatic mutations and changes in expression consistent with inferred disease functions. Furthermore, we show how clinical/genomic features can be used to vet prospective gene sets from high-throughput sources. The combination of size and quality makes CLC2 a foundation for precision medicine, demonstrating cancer lncRNAs’ evolutionary and clinical significance.
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Affiliation(s)
- Adrienne Vancura
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Andrés Lanzós
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Núria Bosch-Guiteras
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Mònica Torres Esteban
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Alejandro H Gutierrez
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Simon Haefliger
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
| | - Rory Johnson
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
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Chen J, Zhang J, Gao Y, Li Y, Feng C, Song C, Ning Z, Zhou X, Zhao J, Feng M, Zhang Y, Wei L, Pan Q, Jiang Y, Qian F, Han J, Yang Y, Wang Q, Li C. LncSEA: a platform for long non-coding RNA related sets and enrichment analysis. Nucleic Acids Res 2021; 49:D969-D980. [PMID: 33045741 PMCID: PMC7778898 DOI: 10.1093/nar/gkaa806] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/03/2020] [Accepted: 09/30/2020] [Indexed: 02/01/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and various biological functions. Establishing a comprehensive collection of human lncRNA sets is urgent work at present. Using reference lncRNA sets, enrichment analyses will be useful for analyzing lncRNA lists of interest submitted by users. Therefore, we developed a human lncRNA sets database, called LncSEA, which aimed to document a large number of available resources for human lncRNA sets and provide annotation and enrichment analyses for lncRNAs. LncSEA supports >40 000 lncRNA reference sets across 18 categories and 66 sub-categories, and covers over 50 000 lncRNAs. We not only collected lncRNA sets based on downstream regulatory data sources, but also identified a large number of lncRNA sets regulated by upstream transcription factors (TFs) and DNA regulatory elements by integrating TF ChIP-seq, DNase-seq, ATAC-seq and H3K27ac ChIP-seq data. Importantly, LncSEA provides annotation and enrichment analyses of lncRNA sets associated with upstream regulators and downstream targets. In summary, LncSEA is a powerful platform that provides a variety of types of lncRNA sets for users, and supports lncRNA annotations and enrichment analyses. The LncSEA database is freely accessible at http://bio.liclab.net/LncSEA/index.php.
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Affiliation(s)
- Jiaxin Chen
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yu Gao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yanyu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chao Song
- Department of Pharmacology, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Ziyu Ning
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Xinyuan Zhou
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jianmei Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Minghong Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yuexin Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Ling Wei
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Qi Pan
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yong Jiang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Fengcui Qian
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yongsan Yang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Qiuyu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chunquan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
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Gnodi E, Mancuso C, Elli L, Ballarini E, Meneveri R, Beaulieu JF, Barisani D. Gliadin, through the Activation of Innate Immunity, Triggers lncRNA NEAT1 Expression in Celiac Disease Duodenal Mucosa. Int J Mol Sci 2021; 22:ijms22031289. [PMID: 33525473 PMCID: PMC7865487 DOI: 10.3390/ijms22031289] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/20/2021] [Accepted: 01/25/2021] [Indexed: 12/14/2022] Open
Abstract
Celiac disease (CD) is an autoimmune enteropathy arising in genetically predisposed subjects exposed to gluten, which activates both innate and adaptive immunity. Although the pathogenesis is common to all patients, the clinical spectrum is quite variable, and differences could be explained by gene expression variations. Among the factors able to affect gene expression, there are lncRNAs. We evaluated the expression profile of 87 lncRNAs in CD vs. healthy control (HC) intestinal biopsies by RT-qPCR array. Nuclear enriched abundant transcript 1 (NEAT1) and taurine upregulated gene 1 (TUG1) were detected as downregulated in CD patients at diagnosis, but their expression increased in biopsies of patients on a gluten-free diet (GFD) exposed to gluten. The increase in NEAT1 expression after gluten exposure was mediated by IL-15 and STAT3 activation and binding to the NEAT1 promoter, as demonstrated by gel shift assay. NEAT1 is localized in the nucleus and can regulate gene expression by sequestering transcription factors, and it has been implicated in immune regulation and control of cell proliferation. The demonstration of its regulation by gluten thus also supports the role of lncRNAs in CD and prompts further research on these RNAs as gene expression regulators.
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Affiliation(s)
- Elisa Gnodi
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (E.G.); (C.M.); (E.B.); (R.M.)
| | - Clara Mancuso
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (E.G.); (C.M.); (E.B.); (R.M.)
| | - Luca Elli
- Centre for the Prevention and Diagnosis of Celiac Disease, Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Elisa Ballarini
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (E.G.); (C.M.); (E.B.); (R.M.)
| | - Raffaella Meneveri
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (E.G.); (C.M.); (E.B.); (R.M.)
| | - Jean François Beaulieu
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke and Research Center of the Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada;
| | - Donatella Barisani
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; (E.G.); (C.M.); (E.B.); (R.M.)
- Correspondence: ; Tel.: +39-0264488304
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Seifuddin F, Pirooznia M. Bioinformatics Approaches for Functional Prediction of Long Noncoding RNAs. Methods Mol Biol 2021; 2254:1-13. [PMID: 33326066 DOI: 10.1007/978-1-0716-1158-6_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
There is accumulating evidence that long noncoding RNAs (lncRNAs) play crucial roles in biological processes and diseases. In recent years, computational models have been widely used to predict potential lncRNA-disease relations. In this chapter, we systematically describe various computational algorithms and prediction tools that have been developed to elucidate the roles of lncRNAs in diseases, coding potential/functional characterization, or ascertaining their involvement in critical biological processes as well as provide a comprehensive summary of these applications.
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Affiliation(s)
- Fayaz Seifuddin
- Bioinformatics and Computational Biology, National Heart, Lung, and Blood Institute National Institutes of Health, Bethesda, MD, USA
| | - Mehdi Pirooznia
- Bioinformatics and Computational Biology, National Heart, Lung, and Blood Institute National Institutes of Health, Bethesda, MD, USA.
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Jia K, Gao Y, Shi J, Zhou Y, Zhou Y, Cui Q. Annotation and curation of the causality information in LncRNADisease. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5706766. [PMID: 31942978 PMCID: PMC6964212 DOI: 10.1093/database/baz150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 11/14/2019] [Accepted: 12/12/2019] [Indexed: 12/12/2022]
Abstract
Disease causative non-coding RNAs (ncRNAs) are of great importance in understanding a disease, for they directly contribute to the development or progress of a disease. Identifying the causative ncRNAs can provide vital implications for biomedical researches. In this work, we updated the long non-coding RNA disease database (LncRNADisease) with long non-coding RNA (lncRNA) causality information with manual annotations of the causal associations between lncRNAs/circular RNAs (circRNAs) and diseases by reviewing related publications. Of the total 11 568 experimental associations, 2297 out of 10 564 lncRNA-disease associations and 198 out of 1004 circRNA-disease associations were identified to be causal, whereas 635 lncRNAs and 126 circRNAs were identified to be causative for the development or progress of at least one disease. The updated information and functions of the database can offer great help to future researches involving lncRNA/circRNA-disease relationship. The latest LncRNADisease database is available at http://www.rnanut.net/lncrnadisease.
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Affiliation(s)
- Kaiwen Jia
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
| | - Yuanxu Gao
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
| | - Jiangcheng Shi
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
| | - Yuan Zhou
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
| | - Yong Zhou
- Sanbo Brain Institute, Sanbo Brain Hospital, Capital Medical University, 50 Yikesong Rd, Beijing, 100093, China
| | - Qinghua Cui
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
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Target Enrichment Enables the Discovery of lncRNAs with Somatic Mutations or Altered Expression in Paraffin-Embedded Colorectal Cancer Samples. Cancers (Basel) 2020; 12:cancers12102844. [PMID: 33019720 PMCID: PMC7650602 DOI: 10.3390/cancers12102844] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/20/2020] [Accepted: 09/23/2020] [Indexed: 12/25/2022] Open
Abstract
Simple Summary Alterations in long noncoding RNAs and their mutations have been increasingly recognized in tumorogenesis and cancer progression awakening especial interest as potential novel cancer biomarkers and therapeutic targets. The use of adjuvant chemotherapy in stage II colorectal cancer patients is challenging, and new biomarkers are required to identify patients with high probability of relapse. We focused on translational potential of non-coding RNAs in colorectal cancer. In this study, we aim to validate a new tool which couples target enrichment and RNAseq for transcriptomics studies of lncRNAs in formalin-fixed paraffin embedded (FFPE) tissue samples. Our results show that this new approach efficiently detects lncRNAs and differences in their expression between healthy and tumor FFPE tissues, as well as somatic mutations in expressed lncRNAs, identifying novel lncRNAs as potential candidates for colorectal cancer. This new approach could represent a promising avenue that would reduce costs and enable more efficient translational research. Abstract Long non-coding RNAs (lncRNAs) play important roles in cancer and are potential new biomarkers or targets for therapy. However, given the low and tissue-specific expression of lncRNAs, linking these molecules to particular cancer types and processes through transcriptional profiling is challenging. Formalin-fixed, paraffin-embedded (FFPE) tissues are abundant resources for research but are prone to nucleic acid degradation, thereby complicating the study of lncRNAs. Here, we designed and validated a probe-based enrichment strategy to efficiently profile lncRNA expression in FFPE samples, and we applied it for the detection of lncRNAs associated with colorectal cancer (CRC). Our approach efficiently enriched targeted lncRNAs from FFPE samples, while preserving their relative abundance, and enabled the detection of tumor-specific mutations. We identified 379 lncRNAs differentially expressed between CRC tumors and matched healthy tissues and found tumor-specific lncRNA variants. Our results show that numerous lncRNAs are differentially expressed and/or accumulate variants in CRC tumors, thereby suggesting a role in CRC progression. More generally, our approach unlocks the study of lncRNAs in FFPE samples, thus enabling the retrospective use of abundant, well documented material available in hospital biobanks.
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