For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
3
views
68
references
 Top references
cited by
13
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      135
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Identification of lncRNA-mRNA Regulatory Module to Explore the Pathogenesis and Prognosis of Melanoma

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Skin cutaneous melanoma (SKCM) is an aggressive form of skin cancer that results in high mortality rate worldwide. It is vital to discover effective prognostic biomarkers and therapeutic targets for the treatment of melanoma. Long non-coding RNA (lncRNA) has been verified to play an essential role in the regulation of gene expression in diseases and tumors. Therefore, it is significant to explore the function of lncRNAs in the development and progression of SKCM. In this paper, a set of differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were first screened out using 471 cutaneous melanoma samples and 813 normal skin samples. Gene Ontology and KEGG pathway enrichment analysis were performed to obtain the significant function annotations and pathways of DEmRNAs. We also ran survival analysis on both DElncRNAs and DEmRNAs to identify prognostic-related lncRNAs and mRNAs. Next, a set of hub genes derived from protein-protein interaction (PPI) network analysis and lncRNA target genes screened from starbase-ENCORI database were integrated to construct a lncRNA-mRNA regulatory module, which includes 6 lncRNAs 4 target mRNAs. We further checked the capacity of these lncRNA and mRNA in the diagnosis of melanoma, and found that single lncRNA can effectively distinguish tumor and normal tissue. Moreover, we ran CMap analysis to select a list of small molecule drugs for SKCM, such as EGFR inhibitor AG-490, growth factor receptor inhibitor GW-441756 and apoptosis stimulant betulinic-acid, which have shown therapeutic effect in the treatment of melanoma.

          Related collections

          Most cited references68

          • Record: found
          • Abstract: found
          • Article: not found

          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)
          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
          Article 0 comments Cited 11595 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            limma powers differential expression analyses for RNA-sequencing and microarray studies

            Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)
            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
            Article 0 comments Cited 11488 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found
              Is Open Access

              The Genotype-Tissue Expression (GTEx) project.

              John M. Lonsdale, Jeffrey Thomas, Mike Salvatore … (2014)
              Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
              Article 0 comments Cited 2165 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Cell Dev Biol
                Journal ID (iso-abbrev): Front Cell Dev Biol
                Journal ID (publisher-id): Front. Cell Dev. Biol.
                Title: Frontiers in Cell and Developmental Biology
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 2296-634X
                Publication date (Electronic): 17 December 2020
                Publication date Collection: 2020
                Volume: 8
                Electronic Location Identifier: 615671
                Affiliations
                [1] 1Department of Radiotherapy, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University , Changzhou, China
                [2] 2Department of Dermatology, Graduate School of Dalian Medical University , Dalian, China
                [3] 3Aliyun School of Big Data, Changzhou University , Changzhou, China
                [4] 4Department of Oncology, Affiliated 3201 Hospital of Xi'an Jiaotong University , Hanzhong, China
                [5] 5Department of Oncology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University , Changzhou, China
                Author notes

                Edited by: Liang Cheng, Harbin Medical University, China

                Reviewed by: Jiajie Peng, Northwestern Polytechnical University, China; Jingpu Zhang, Henan University of Urban Construction, China

                *Correspondence: Hua Jiang lygcancer@ 123456126.com

                This article was submitted to Molecular Medicine, a section of the journal Frontiers in Cell and Developmental Biology

                Article
                DOI: 10.3389/fcell.2020.615671
                PMC ID: 7773644
                PubMed ID: 33392203
                SO-VID: 8621a76a-a793-4664-800a-dc5122a7dd99
                Copyright © Copyright © 2020 Zhang, Liu, Zhang, Li, Fan, Jiang and Luo.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 09 October 2020
                Date accepted : 24 November 2020
                Page count
                Figures: 7, Tables: 3, Equations: 0, References: 68, Pages: 14, Words: 8094
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 62072058
                Award ID: 81773224
                Award ID: 82073339
                Categories
                Subject: Cell and Developmental Biology
                Subject: Original Research

                Keywords: skin cutaneous melanoma,long non-coding rna,differential expression,gene regulation,messenger rna (mrna),survival analysis,prognostic biomarker
                Data availability:
                Keywords: skin cutaneous melanoma, long non-coding rna, differential expression, gene regulation, messenger rna (mrna), survival analysis, prognostic biomarker

                Comments

                Comment on this article

                scite_

                Similar content135

                See all similar

                Cited by13

                See all cited by

                Most referenced authors2,325

                See all reference authors