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      Melanoma Stem Cell-Like Phenotype and Significant Suppression of Immune Response within a Tumor Are Regulated by TRIM28 Protein

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          Abstract

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          A growing body of evidence indicates that stem cell-associated molecular features, collectively known as stemness, are biologically important in cancer development and progression, and negatively associate with anticancer immunity. The aim of our study was to investigate the association between TRIM28 level and melanoma stemness accompanied by low antitumor immune response. Furthermore, we aimed to evaluate potential value for TRIM28 in predicting stem-like melanoma phenotype. Our results indicate that TRIM28 might facilitate the “stemness high/immune low” melanoma phenotype by attenuating interferon signaling leading to a worse prognosis for melanoma patients. TRIM28 emerged as a regulator Interferon Regulatory Factor family of transcription factors’ expression, mediating epigenetic repression of IRF family members in “stemness high/immune low” melanomas.

          Abstract

          TRIM28 emerged as a guard of the intrinsic “state of cell differentiation”, facilitating self-renewal of pluripotent stem cells. Recent reports imply TRIM28 engagement in cancer stem cell (CSC) maintenance, although the exact mechanism remains unresolved. TRIM28 high expression is associated with worse melanoma patient outcomes. Here, we investigated the association between TRIM28 level and melanoma stemness, and aligned it with the antitumor immune response to find the mechanism of “stemness high/immune low” melanoma phenotype acquisition. Based on the SKCM TCGA data, the TRIM28 expression profile, clinicopathological features, expression of correlated genes, and the level of stemness and immune scores were analyzed in patient samples. The biological function for differentially expressed genes was annotated with GSEA. Results were validated with additional datasets from R2: Genomics Analysis and Visualization Platform and in vitro with a panel of seven melanoma cell lines. All statistical analyses were accomplished using GraphPad Prism 8. TRIM28 HIGH-expressing melanoma patients are characterized by worse outcomes and significantly different gene expression profiles than the TRIM28 NORM cohort. TRIM28 high level related to higher melanoma stemness as measured with several distinct scores and TRIM28 HIGH-expressing melanoma cell lines possess the greater potential of melanosphere formation. Moreover, TRIM28 HIGH melanoma tumors were significantly depleted with infiltrating immune cells, especially cytotoxic T cells, helper T cells, and B cells. Furthermore, TRIM28 emerged as a good predictor of “stemness high/immune low” melanoma phenotype. Our data indicate that TRIM28 might facilitate this phenotype by direct repression of interferon signaling. TRIM28 emerged as a direct link between stem cell-like phenotype and attenuated antitumor immune response in melanoma, although further studies are needed to evaluate the direct mechanism of TRIM28-mediated stem-like phenotype acquisition.

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          Most cited references55

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            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.
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              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.
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                Author and article information

                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                15 October 2020
                October 2020
                : 12
                : 10
                : 2998
                Affiliations
                [1 ]Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 15 Garbary St., 61-866 Poznan, Poland; 74684@ 123456student.ump.edu.pl (A.M.J.); nikola.wlodarczyk95@ 123456wp.pl (N.A.W.)
                [2 ]Department of Diagnostics and Cancer Immunology, Greater Poland Cancer Centre, 15 Garbary St., 61-866 Poznan, Poland
                Author notes
                [†]

                These authors contributed equally.

                Author information
                https://orcid.org/0000-0003-2400-1174
                https://orcid.org/0000-0001-5574-6609
                Article
                cancers-12-02998
                10.3390/cancers12102998
                7650661
                33076560
                b16df230-2f9d-4fd5-9e50-de26dc7e144d
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 August 2020
                : 13 October 2020
                Categories
                Article

                trim28,kap1,cancer stemness,immune cell infiltration,melanoma,tcga

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