Special Issue "Associations Between Non-Coding RNA and Diseases"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: closed (22 November 2019).

Special Issue Editor

Prof. Yan Guo
Website
Guest Editor
Department of Internal Medicine, University of New Mexico, Albuquerque, NM, 87131, USA.
Interests: bioinformatics; ncRNA; machine learning; NGS analysis

Special Issue Information

Dear colleagues,

Non-coding RNA has attracted enormous interest in biomedical research during recent years. More and more researchers are starting to realize that the cure to some diseases may lie outside coding regions. The National Human Genome Research Institute (NHGRI) launched the ENCODE consortium to study the non-coding regulatory genome, and the consortium claims that 80% of the human genome is associated with some kind of biochemical function through the regulation of the expression of coding genes. Nonetheless, current high throughput technology such as RNA-seq provides us with the opportunity to study non-coding RNA at an unprecedented level. We invite investigators to contribute original research articles, as well as review articles, that will stimulate the continuing efforts in the research of non-coding RNAs. Manuscripts submitted to this Special Issue are guaranteed to have a quick and fair review process. Potential topics for the Special Issue include but are not limited to long non-coding RNA, small RNA, miRNA, tRNA, snoRNAs, pseudo genes, circular RNA, bioinformatics/biostatistics tools related to non-coding RNA, potential functional research of non-coding RNA, and disease associations of non-coding RNA.

Prof. Yan Guo
Guest Editor

Manuscript Submission Information

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Keywords

  • Non-coding RNA
  • lncRNA
  • miRNA
  • small RNA
  • circular RNA
  • Disease association
  • Functional genomics

Published Papers (4 papers)

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Research

Open AccessArticle
Long Noncoding RNA from PVT1 Exon 9 Is Overexpressed in Prostate Cancer and Induces Malignant Transformation and Castration Resistance in Prostate Epithelial Cells
Genes 2019, 10(12), 964; https://doi.org/10.3390/genes10120964 - 22 Nov 2019
Cited by 5
Abstract
Prostate cancer (PCa) is the most common non-cutaneous cancer and second leading cause of cancer-related death for men in the United States. The nonprotein coding gene locus plasmacytoma variant translocation 1 (PVT1) is located at 8q24 and is dysregulated in different cancers. PVT1 [...] Read more.
Prostate cancer (PCa) is the most common non-cutaneous cancer and second leading cause of cancer-related death for men in the United States. The nonprotein coding gene locus plasmacytoma variant translocation 1 (PVT1) is located at 8q24 and is dysregulated in different cancers. PVT1 gives rise to several alternatively spliced transcripts and microRNAs. There are at least twelve exons of PVT1, which make separate transcripts, and likely have different functions. Here, we demonstrate that PVT1 exon 9 is significantly overexpressed in PCa tissues in comparison to normal prostate tissues. Both transient and stable overexpression of PVT1 exon 9 significantly induced greater prostate epithelial cell migration, as well as increased proliferation and corresponding proliferating cell nuclear antigen (PCNA) expression. Notably, implantation into mice of a non-tumorigenic prostate epithelial cell line stably overexpressing PVT1 exon 9 resulted in the formation of malignant tumors. Furthermore, PVT1 exon 9 overexpression significantly induced castration resistance. Consequently, PVT1 exon 9 expression is important for PCa initiation and progression, and holds promise as a therapeutic target in PCa. Full article
(This article belongs to the Special Issue Associations Between Non-Coding RNA and Diseases)
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Open AccessArticle
Expression of ZNF695 Transcript Variants in Childhood B-Cell Acute Lymphoblastic Leukemia
Genes 2019, 10(9), 716; https://doi.org/10.3390/genes10090716 - 16 Sep 2019
Cited by 2
Abstract
B-cell acute lymphoblastic leukemia is the most commonly diagnosed childhood malignancy worldwide; more than 50% of these cases are diagnosed in Mexico. Although the five-year survival rate is >80%, 30% of patients experience relapse with poor prognosis. Cancer-associated gene expression profiles have been [...] Read more.
B-cell acute lymphoblastic leukemia is the most commonly diagnosed childhood malignancy worldwide; more than 50% of these cases are diagnosed in Mexico. Although the five-year survival rate is >80%, 30% of patients experience relapse with poor prognosis. Cancer-associated gene expression profiles have been identified in several malignancies, and some transcripts have been used to predict disease prognosis. The human transcriptome is incompletely elucidated; moreover, more than 80% of transcripts can be processed via alternative splicing (AS), which increases transcript and protein diversity. The human transcriptome is divided; coding RNA accounts for ~2%, and the remaining 98% is noncoding RNA. Noncoding RNA can undergo AS, promoting the diversity of noncoding transcripts. We designed specific primers to amplify previously reported alternative transcript variants of ZNF695 and showed that six ZNF695 transcript variants are co-expressed in cancer cell lines. The amplicons were sequenced and identified. Additionally, we analyzed the expression of these six transcript variants in bone marrow from B-cell acute lymphoblastic leukemia patients and observed that ZNF695 transcript variants one and three were the predominant variants expressed in leukemia. Moreover, our results showed the co-expression of coding and long noncoding RNA. Finally, we observed that long noncoding RNA ZNF695 expression predicted survival rates. Full article
(This article belongs to the Special Issue Associations Between Non-Coding RNA and Diseases)
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Open AccessArticle
Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information
Genes 2019, 10(9), 685; https://doi.org/10.3390/genes10090685 - 06 Sep 2019
Cited by 1
Abstract
Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize [...] Read more.
Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize the projections of miRNAs and diseases in a low-dimensional space. Thus, it is necessary to develop a method that can utilize the effective information in the low-dimensional space to predict potential disease-related miRNA candidates. We proposed a method based on non-negative matrix factorization, named DMAPred, to predict potential miRNA-disease associations. DMAPred exploits the similarities and associations of diseases and miRNAs, and it integrates local topological information of the miRNA network. The likelihood that a miRNA is associated with a disease also depends on their projections in low-dimensional space. Therefore, we project miRNAs and diseases into low-dimensional feature space to yield their low-dimensional and dense feature representations. Moreover, the sparse characteristic of miRNA-disease associations was introduced to make our predictive model more credible. DMAPred achieved superior performance for 15 well-characterized diseases with AUCs (area under the receiver operating characteristic curve) ranging from 0.860 to 0.973 and AUPRs (area under the precision-recall curve) ranging from 0.118 to 0.761. In addition, case studies on breast, prostatic, and lung neoplasms demonstrated the ability of DMAPred to discover potential disease-related miRNAs. Full article
(This article belongs to the Special Issue Associations Between Non-Coding RNA and Diseases)
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Open AccessArticle
A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma
Genes 2019, 10(6), 414; https://doi.org/10.3390/genes10060414 - 29 May 2019
Cited by 10
Abstract
Osteosarcoma is a common malignancy with high mortality and poor prognosis due to lack of predictive markers. Increasing evidence has demonstrated that pseudogenes, a type of non-coding gene, play an important role in tumorigenesis. The aim of this study was to identify a [...] Read more.
Osteosarcoma is a common malignancy with high mortality and poor prognosis due to lack of predictive markers. Increasing evidence has demonstrated that pseudogenes, a type of non-coding gene, play an important role in tumorigenesis. The aim of this study was to identify a prognostic pseudogene signature of osteosarcoma by machine learning. A sample of 94 osteosarcoma patients’ RNA-Seq data with clinical follow-up information was involved in the study. The survival-related pseudogenes were screened and related signature model was constructed by cox-regression analysis (univariate, lasso, and multivariate). The predictive value of the signature was further validated in different subgroups. The putative biological functions were determined by co-expression analysis. In total, 125 survival-related pseudogenes were identified and a four-pseudogene (RPL11-551L14.1, HR: 0.65 (95% CI: 0.44–0.95); RPL7AP28, HR: 0.32 (95% CI: 0.14–0.76); RP4-706A16.3, HR: 1.89 (95% CI: 1.35–2.65); RP11-326A19.5, HR: 0.52(95% CI: 0.37–0.74)) signature effectively distinguished the high- and low-risk patients, and predicted prognosis with high sensitivity and specificity (AUC: 0.878). Furthermore, the signature was applicable to patients of different genders, ages, and metastatic status. Co-expression analysis revealed the four pseudogenes are involved in regulating malignant phenotype, immune, and DNA/RNA editing. This four-pseudogene signature is not only a promising predictor of prognosis and survival, but also a potential marker for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance. Full article
(This article belongs to the Special Issue Associations Between Non-Coding RNA and Diseases)
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