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      Prognostic value and immune infiltration of novel signatures in clear cell renal cell carcinoma microenvironment

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          Abstract

          Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in clear cell renal cell carcinoma (ccRCC). This study categorized ccRCC cases into high and low score groups based on their immune/stromal scores generated by the ESTIMATE algorithm, and identified an association between these scores and prognosis. Differentially expressed tumor environment (TME)-related genes extracted from common upregulated components in immune and stromal scores were described using functional annotations and protein–protein interaction (PPI) networks. Most PPIs were selected for further prognostic investigation. Many additional previously neglected signatures, including AGPAT9, AQP7, HMGCS2, KLF15, MLXIPL, PPARGC1A, exhibited significant prognostic potential. In addition, multivariate Cox analysis indicated that MIXIPL and PPARGC1A were the most significant prognostic signatures, and were closely related to immune infiltration in TCGA cohort. External prognostic validation of MIXIPL and PPARGC1A was undertaken in 380 ccRCC cases from a real-world cohort. These findings indicate the relevance of monitoring and manipulation of the microenvironment for ccRCC prognosis and precision immunotherapy.

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          Most cited references24

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            The KEGG database.

            KEGG (http://www.genome.ad.jp/kegg/) is a suite of databases and associated software for understanding and simulating higher-order functional behaviours of the cell or the organism from its genome information. First, KEGG computerizes data and knowledge on protein interaction networks (PATHWAY database) and chemical reactions (LIGAND database) that are responsible for various cellular processes. Second, KEGG attempts to reconstruct protein interaction networks for all organisms whose genomes are completely sequenced (GENES and SSDB databases). Third, KEGG can be utilized as reference knowledge for functional genomics (EXPRESSION database) and proteomics (BRITE database) experiments. I will review the current status of KEGG and report on new developments in graph representation and graph computations.
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              The metabolic co-regulator PGC1α suppresses prostate cancer metastasis

              Cellular transformation and cancer progression is accompanied by changes in the metabolic landscape. Master co-regulators of metabolism orchestrate the modulation of multiple metabolic pathways through transcriptional programs, and hence constitute a probabilistically parsimonious mechanism for general metabolic rewiring. Here we show that the transcriptional co-activator PGC1α suppresses prostate cancer progression and metastasis. A metabolic co-regulator data mining analysis unveiled that PGC1α is down-regulated in prostate cancer and associated to disease progression. Using genetically engineered mouse models and xenografts, we demonstrated that PGC1α opposes prostate cancer progression and metastasis. Mechanistically, the use of integrative metabolomics and transcriptomics revealed that PGC1α activates an Oestrogen-related receptor alpha (ERRα)-dependent transcriptional program to elicit a catabolic state and metastasis suppression. Importantly, a signature based on the PGC1α-ERRα pathway exhibited prognostic potential in prostate cancer, thus uncovering the relevance of monitoring and manipulating this pathway for prostate cancer stratification and treatment.
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                Author and article information

                Journal
                Aging (Albany NY)
                Aging (Albany NY)
                Aging
                Aging (Albany NY)
                Impact Journals
                1945-4589
                15 September 2019
                07 September 2019
                : 11
                : 17
                : 6999-7020
                Affiliations
                [1 ]Department of Urology, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China
                [2 ]Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
                [3 ]Department of Ophthalmology, The First Affiliated Hospital of Soochow University, Suzhou 215000, P.R. China
                Author notes
                [*]

                Equal contribution

                Correspondence to: Ding-Wei Ye; email: dwyeli@163.com
                Correspondence to: Hai-Liang Zhang; email: zhanghl918@163.com
                Correspondence to: Yuan-Yuan Qu; email: quyy1987@163.com
                Article
                102233 102233
                10.18632/aging.102233
                6756904
                31493764
                a2ceb656-ed9f-416d-ae1a-4281263a24bb
                Copyright © 2019 Xu et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 08 August 2019
                : 19 August 2019
                Categories
                Research Paper

                Cell biology
                clear cell renal cell carcinoma,tumor microenvironment,estimate algorithm,immune signature,prognosis

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