1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      m6A‐related long noncoding RNAs predict prognosis and indicate therapeutic response in endometrial carcinoma

      research-article

      Read this article at

      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

          Background

          N6‐methyladenosine (m6A) has been identified as the most common, abundant, and conserved internal transcriptional modification. Long noncoding RNAs (lncRNAs) are noncoding RNAs consisting of more than 200 nucleotides, and the expression of various lncRNAs may affect cancer prognosis. The impact of m6A‐associated lncRNAs on uterine corpus endometrial carcinoma (UCEC) prognosis is unknown.

          Methods

          In this study, UCEC prognosis‐related m6A lncRNAs were screened, bioinformatics analysis was performed, and experimental validation was conducted. Endometrial carcinoma (EC) and normal tissue samples were obtained from The Cancer Genome Atlas. The prognosis‐related m6A lncRNAs screened by the least absolute shrinkage and selection operator method were used for multivariate Cox proportional risk regression modeling. Principal component analysis and Gene Ontology, immune function difference, and drug sensitivity analyses of the prognostic models were performed. Prognostic analysis was conducted for m6A‐associated lncRNAs. The immune infiltration relationship of m6A‐associated lncRNAs in EC was identified using the ssGSEA immune infiltration algorithm. A competing endogenouse RNA network was constructed using the LncACTdb database. Finally, quantitative real‐time polymerase chain reaction (qRT‐PCR) assays were used to validate the differences in m6A‐related lncRNA expression in normal and EC cells.

          Results

          CDKN2B‐AS1 and MIR924HG were found to be risk factors for EC. RAB11B‐AS1 was a protective factor in EC patients. MIR924HG expression was upregulated in KLE and RL95‐2 endometrial cancer cell lines. Prognostic models involved RAB11B‐AS1, LINC01812, HM13‐IT1, TPM1‐AS, SLC16A1‐AS1, LINC01936, and CDKN2B‐AS1. The high‐risk group was more sensitive to five compounds (ABT.263, ABT.888, AP.24534, ATRA, and AZD.0530) than the low‐risk group.

          Conclusion

          These findings contribute to understanding of the function of m6A‐related lncRNAs in UCEC and provide promising therapeutic strategies for UCEC.

          Abstract

          Differential expression of m6A‐related lncRNA in normal and tumor tissues (by clinical stage and tumor grade) (ns, p ≥ 0.05; * p < 0.05; ** p < 0.01; *** p < 0.001).

          Related collections

          Most cited references49

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

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

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

            The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

              SUMMARY For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.
                Bookmark

                Author and article information

                Contributors
                doczhray@163.com
                daihaiyan0218@sina.cn
                Journal
                J Clin Lab Anal
                J Clin Lab Anal
                10.1002/(ISSN)1098-2825
                JCLA
                Journal of Clinical Laboratory Analysis
                John Wiley and Sons Inc. (Hoboken )
                0887-8013
                1098-2825
                16 December 2022
                January 2023
                : 37
                : 1 ( doiID: 10.1002/jcla.v37.1 )
                : e24813
                Affiliations
                [ 1 ] Department of Obstetrics and Gynecology Shanghai Pudong Hospital, Fudan University Pudong Medical Center Shanghai China
                Author notes
                [*] [* ] Correspondence

                Haiyan Dai and Hu Zhang, Department of Obstetrics and Gynecology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, 2800 Gongwei Road, Pudong, Shanghai 201399, China.

                Email: daihaiyan0218@ 123456sina.cn and doczhray@ 123456163.com

                Author information
                https://orcid.org/0000-0002-7165-7195
                https://orcid.org/0000-0002-8785-6279
                Article
                JCLA24813 JCLA-22-2972.R1
                10.1002/jcla.24813
                9833960
                36525280
                02b64989-2c44-4026-8751-c19d94a02f94
                © 2022 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 December 2022
                : 29 October 2022
                : 02 December 2022
                Page count
                Figures: 13, Tables: 0, Pages: 19, Words: 9239
                Funding
                Funded by: The Discipline Construction Promoting Project of Shanghai Pudong Hospital
                Award ID: Zdzk2020‐16
                Award ID: Zdzk2020‐18
                Funded by: Key Specialty Construction Project of Pudong Health and Family Planning Commission of Shanghai
                Award ID: PWZzk2022‐21
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                January 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.3 mode:remove_FC converted:11.01.2023

                Clinical chemistry
                bioinformatics analysis,endometrial cancer,immune infiltration,m6a‐associated lncrna,prognosis

                Comments

                Comment on this article