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      “Crosstalk between non-coding RNAs and transcription factor LRF in non-small cell lung cancer”

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          Abstract

          Epigenetic approaches in direct correlation with assessment of critical genetic mutations in non-small cell lung cancer (NSCLC) are currently very intensive, as the epigenetic components underlying NSCLC development and progression have attained high recognition. In this level of research, established human NSCLC cell lines as well as experimental animals are widely used to detect novel biomarkers and pharmacological targets to treat NSCLC. The epigenetic background holds a great potential for the identification of epi-biomarkers for treatment response however, it is highly complex and requires precise definition as these phenomena are variable between NSCLC subtypes and systems origin.

          We engaged an in-depth characterization of non-coding (nc)RNAs prevalent in human KRAS-mutant NSCLC cell lines A549 and H460 and mouse KRAS-mutant NSCLC tissue by Next Generation Sequencing (NGS) and quantitative Real Time PCRs (qPCRs). Also, the transcription factor (TF) LRF, a known epigenetic silencer, was examined as a modulator of non-coding RNAs expression. Finally, interacting networks underlying epigenetic variations in NSCLC subtypes were created. Data derived from our study highlights the divergent epigenetic profiles of NSCLC of human and mouse origin, as well as the significant contribution of 12qf1: 109,709,060–109,747,960 mouse chromosomal region to micro-RNA upregulated species. Furthermore, the novel epigenetic miR-148b-3p/lnc PVT1/ZBTB7A axis was identified, which differentiates human cell line of lung adenocarcinoma from large cell lung carcinoma, two characteristic NSCLC subtypes.

          The detailed recording of epigenetic events in NSCLC and combinational studies including networking between ncRNAs and TFs validate the identification of significant epigenetic features, prevailing in NSCLC subtypes and among experimental models. Our results enrich knowledge in the field and empower research on the epigenetic prognostic biomarkers of the disease progression, NSCLC subtypes discrimination and advancement to patient-tailored treatments.

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          Most cited references74

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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                Author and article information

                Contributors
                Journal
                Noncoding RNA Res
                Noncoding RNA Res
                Non-coding RNA Research
                KeAi Publishing
                2468-0540
                23 March 2024
                September 2024
                23 March 2024
                : 9
                : 3
                : 759-771
                Affiliations
                [a ]Biology Laboratory, School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
                [b ]Department of Physiology, Faculty of Medicine, University of Patras, Rio, 26504, Greece
                Author notes
                [* ]Corresponding author. sgourou@ 123456eap.gr
                Article
                S2468-0540(24)00059-3
                10.1016/j.ncrna.2024.03.009
                10990748
                38577020
                9a8d014d-efcd-4062-8fdb-b0a61ce0a2b2
                © 2024 The Authors

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

                History
                : 22 December 2023
                : 23 February 2024
                : 20 March 2024
                Categories
                Original Research Article

                non-small cell lung cancer,epigenetic deregulation,micro-rnas,long non-coding rnas,transcription factor lrf,epigenetic networks

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