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

      RNA-binding protein MEX3A controls G1/S transition via regulating the RB/E2F pathway in clear cell renal cell 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

          MEX3A is an RNA-binding protein that mediates mRNA decay through binding to 3′ untranslated regions. However, its role and mechanism in clear cell renal cell carcinoma remain unknown. In this study, we found that MEX3A expression was transcriptionally activated by ETS1 and upregulated in clear cell renal cell carcinoma. Silencing MEX3A markedly reduced clear cell renal cell carcinoma cell proliferation in vitro and in vivo. Inhibiting MEX3A induced G1/S cell-cycle arrest. Gene set enrichment analysis revealed that E2F targets are the central downstream pathways of MEX3A. To identify MEX3A targets, systematic screening using enhanced cross-linking and immunoprecipitation sequencing, and RNA-immunoprecipitation sequencing assays were performed. A network of 4,000 genes was identified as potential targets of MEX3A. Gene ontology analysis of upregulated genes bound by MEX3A indicated that negative regulation of the cell proliferation pathway was highly enriched. Further assays indicated that MEX3A bound to the CDKN2B 3′ untranslated region, promoting its mRNA degradation. This leads to decreased levels of CDKN2B and an uncontrolled cell cycle in clear cell renal cell carcinoma, which was confirmed by rescue experiments. Our findings revealed that MEX3A acts as a post-transcriptional regulator of abnormal cell-cycle progression in clear cell renal cell carcinoma.

          Graphical abstract

          Abstract

          We described the comprehensive downstream targets of MEX3A using RIP-seq and eCLIP-seq. The data revealed that oncogene MEX3A was transcriptionally activated and mainly regulated G1/S transition in ccRCC, thus providing a novel molecular basis for clinical diagnosis and treatment.

          Related collections

          Most cited references51

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

          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Cancer Statistics, 2021

            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2017) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2018) were collected by the National Center for Health Statistics. In 2021, 1,898,160 new cancer cases and 608,570 cancer deaths are projected to occur in the United States. After increasing for most of the 20th century, the cancer death rate has fallen continuously from its peak in 1991 through 2018, for a total decline of 31%, because of reductions in smoking and improvements in early detection and treatment. This translates to 3.2 million fewer cancer deaths than would have occurred if peak rates had persisted. Long-term declines in mortality for the 4 leading cancers have halted for prostate cancer and slowed for breast and colorectal cancers, but accelerated for lung cancer, which accounted for almost one-half of the total mortality decline from 2014 to 2018. The pace of the annual decline in lung cancer mortality doubled from 3.1% during 2009 through 2013 to 5.5% during 2014 through 2018 in men, from 1.8% to 4.4% in women, and from 2.4% to 5% overall. This trend coincides with steady declines in incidence (2.2%-2.3%) but rapid gains in survival specifically for nonsmall cell lung cancer (NSCLC). For example, NSCLC 2-year relative survival increased from 34% for persons diagnosed during 2009 through 2010 to 42% during 2015 through 2016, including absolute increases of 5% to 6% for every stage of diagnosis; survival for small cell lung cancer remained at 14% to 15%. Improved treatment accelerated progress against lung cancer and drove a record drop in overall cancer mortality, despite slowing momentum for other common cancers.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

              Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
                Bookmark

                Author and article information

                Contributors
                Journal
                Mol Ther Nucleic Acids
                Mol Ther Nucleic Acids
                Molecular Therapy. Nucleic Acids
                American Society of Gene & Cell Therapy
                2162-2531
                02 December 2021
                08 March 2022
                02 December 2021
                : 27
                : 241-255
                Affiliations
                [1 ]Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
                [2 ]Department of Urology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
                [3 ]ShiLong Hospital (Research Center for Pneumoconiosis Prevention and Treatment), National Center For Occupational Safety and Health, NHC, Beijing 100021, China
                Author notes
                []Corresponding author Lehang Lin, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510120, China. linlh7@ 123456mail2.sysu.edu.cn
                [∗∗ ]Corresponding author Xingang Bi, Department of Urology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China. bixingang@ 123456csco.org.cn
                [∗∗∗ ]Corresponding author Dong Yin, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510120, China. yind3@ 123456mail.sysu.edu.cn
                [4]

                These authors contributed equally

                Article
                S2162-2531(21)00301-2
                10.1016/j.omtn.2021.11.026
                8703191
                34976441
                de6c4bc0-5e9c-40d2-8588-ba2cde503da4
                © 2021 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 7 July 2021
                : 29 November 2021
                Categories
                Original Article

                Molecular medicine
                mex3a,cdkn2b,eclip-seq,rip-seq,g1/s arrest,rb/e2f,clear cell renal cell carcinoma
                Molecular medicine
                mex3a, cdkn2b, eclip-seq, rip-seq, g1/s arrest, rb/e2f, clear cell renal cell carcinoma

                Comments

                Comment on this article