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      DDX41 regulates the expression and alternative splicing of genes involved in tumorigenesis and immune response

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          Abstract

          DEAD-box helicase 41 (DDX41) is an RNA helicase and accumulating evidence has suggested that DDX41 is involved in pre-mRNA splicing during tumor development. However, the role of DDX41 in tumorigenesis remains unclear. In order to determine the function of DDX41, the human DDX41 gene was cloned and overexpressed in HeLa cells. The present study demonstrated that DDX41 overexpression inhibited proliferation and promoted apoptosis in HeLa cells. RNA-sequencing analysis of the transcriptomes in overexpressed and normal control samples. DDX41 regulated 959 differentially expressed genes compared with control cells. Expression levels of certain oncogenes were also regulated by DDX41. DDX41 selectively regulated the alternative splicing of genes in cancer-associated pathways including the EGFR and FGFR signaling pathways. DDX41 selectively upregulated the expression levels of five antigen processing and presentation genes ( HSPA1A, HSPA1B, HSPA6, HLA-DMB and HLA-G) and downregulated other immune-response genes in HeLa cells. Additionally, DDX41-regulated oncogenes and antigen processing and presentation genes were associated with patient survival rates. Moreover, DDX41 expression was associated with immune infiltration in cervical and endocervical squamous cancer. The present findings showed that DDX41 regulated the cancer cell transcriptome at both the transcriptional and alternative splicing levels. The DDX41 regulatory network predicted the biological function of DDX41 in suppressing tumor cell growth and regulating cancer immunity, which may be important for developing anticancer therapeutics.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

              TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.
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                Author and article information

                Journal
                Oncol Rep
                Oncol Rep
                Oncology Reports
                D.A. Spandidos
                1021-335X
                1791-2431
                March 2021
                25 January 2021
                25 January 2021
                : 45
                : 3
                : 1213-1225
                Affiliations
                [1 ]Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P.R. China
                [2 ]Department of Otolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, P.R. China
                [3 ]Laboratory for Genome Regulation and Human Health, ABLife Inc., Optics Valley International Biomedical Park, Wuhan, Hubei 430075, P.R. China
                [4 ]Center for Genome Analysis, ABLife Inc., Optics Valley International Biomedical Park, Wuhan, Hubei 430075, P.R. China
                Author notes
                Correspondence to: Professor Xianglin Yuan, Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Building 3, Qiaokou, Wuhan, Hubei 430030, P.R. China, E-mail: yuan100452@ 123456126.com
                Article
                or-45-03-1213
                10.3892/or.2021.7951
                7859996
                33650667
                2b4b91a1-e17e-4e97-91e9-b2ee12048441
                Copyright: © Qin et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

                History
                : 30 April 2020
                : 30 November 2020
                Funding
                Funded by: Health Commission of Hubei Province Scientific Research Project
                Award ID: WJ2019M118
                Funded by: ABLife
                Award ID: ABL-7702157
                The present study was supported by the Health Commission of Hubei Province Scientific Research Project (grant no. WJ2019M118) and ABLife (grant no. ABL-7702157).
                Categories
                Articles

                dead-box helicase 41,rna-sequencing,gene expression,alternative splicing

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