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      Upregulation of SLC12A3 and SLC12A9 Mediated by the HCP5/miR-140-5p Axis Confers Aggressiveness and Unfavorable Prognosis in Uveal Melanoma

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          Abstract

          <p class="first" id="d2658709e166">Perturbation of solute carriers (SLCs) has been implicated in metabolic disorders and cancer, highlighting the potential for drug discovery and therapeutic opportunities. However, there is relatively little exploration of the clinical relevance and potential molecular mechanisms underlying the role of the SLC12 family in uveal melanoma (UVM). Here, we performed an integrative multiomics analysis of the SLC12 family in multicenter UVM datasets and found that high expression of SLC12A3 and SLC12A9 was associated with unfavorable prognosis. Moreover, SLC12A3 and SLC12A9 were highly expressed in UVM in vivo. We experimentally characterized the roles of these proteins in tumorigenesis in vitro and explored their association with the prognosis of UVM. Lastly, we identified the HCP5-miR-140-5p axis as a potential noncoding RNA pathway upstream of SLC12A3 and SLC12A9, which was associated with immunomodulation and may represent a novel predictor for clinical prognosis and responsiveness to checkpoint blockade immunotherapy. These findings may facilitate a better understanding of the SLCome and guide future rationalized development of SLC-targeted therapy and drug discovery for UVM. </p>

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            Is Open Access

            Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

            The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
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              Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

              Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9 , demonstrating utility for immunotherapy research.
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                Author and article information

                Contributors
                Journal
                Laboratory Investigation
                Laboratory Investigation
                Elsevier BV
                00236837
                March 2023
                March 2023
                : 103
                : 3
                : 100022
                Article
                10.1016/j.labinv.2022.100022
                36925204
                53d4dcbc-8935-4c08-aaf5-c973c96bb742
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

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