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      Hypoxia-induced PLOD2 promotes clear cell renal cell carcinoma progression via modulating EGFR-dependent AKT pathway activation

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          Abstract

          Clear cell renal cell carcinoma (ccRCC) is a type of kidney cancer that is both common and aggressive, with a rising incidence in recent decades. Hypoxia is a key factor that plays a vital role in the tumorigenesis and metastasis of malignancy. However, the precise mechanisms of hypoxia driving ccRCC progression were not totally uncovered. Our study found that hypoxia level was elevated in ccRCC and might be an independent risk factor of prognosis in ccRCC patients. We identified a key protein PLOD2 was induced under hypoxic conditions and strongly associated with poor prognosis in ccRCC patients. When PLOD2 was depleted, the proliferation and migration of ccRCC cells were reduced in vitro and in vivo, while overexpression of PLOD2 had the opposite effect. Mechanically, the study further revealed that PLOD2 was transcriptionally activated by HIF1A, which binds to a specific promoter region of the PLOD2 gene. PLOD2 was also shown to interact with EGFR, leading to the phosphorylation of the receptor. Furthermore, PLOD2 was responsible for binding to the extracellular domain of EGFR, which ultimately activated the AKT signaling pathway, thus promoting the malignant progression of ccRCC. Treatment with the PLOD2 inhibitor Minoxidil significantly suppressed ccRCC progression by inactivating the EGFR/AKT signaling axis. In summary, the findings of this study shed light on the molecular mechanisms behind PLOD2 expression in ccRCC and suggest that it may serve as a potential predictor and therapeutic target for the clinical prognosis and treatment of ccRCC.

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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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            Hallmarks of Cancer: The Next Generation

            The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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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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                Author and article information

                Contributors
                mengzhe@whu.edu.cn
                luoywen@whu.edu.cn
                chenliang2014@whu.edu.cn
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                27 November 2023
                27 November 2023
                November 2023
                : 14
                : 11
                : 774
                Affiliations
                [1 ]Department of Urology, Zhongnan Hospital of Wuhan University, ( https://ror.org/01v5mqw79) Wuhan, China
                [2 ]Department of Biological Repositories, Zhongnan Hospital of Wuhan University, ( https://ror.org/01v5mqw79) Wuhan, China
                [3 ]Tumor Precision Diagnosis and Treatment Technology and Translational Medicine, Hubei Engineering Research Center, Zhongnan Hospital of Wuhan University, ( https://ror.org/01v5mqw79) Wuhan, China
                [4 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Department of Urology, Beijing Friendship Hospital, , Capital Medical University, ; Beijing, China
                [5 ]GRID grid.413247.7, ISNI 0000 0004 1808 0969, Human Genetics Resource Preservation Center of Hubei Province, ; Wuhan, China
                [6 ]Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, ( https://ror.org/01v5mqw79) Wuhan, China
                Author information
                http://orcid.org/0000-0001-7757-7615
                http://orcid.org/0000-0002-6550-7043
                http://orcid.org/0000-0002-8724-6550
                http://orcid.org/0000-0002-0579-1361
                Article
                6298
                10.1038/s41419-023-06298-7
                10679098
                38008826
                6a31f9cb-cb9d-4798-aedb-2e081640d31b
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 May 2023
                : 5 November 2023
                : 13 November 2023
                Funding
                Funded by: the Excellent Doctoral Program of Zhongnan Hospital of Wuhan University (ZNYB2020006), Science and Technology Innovation Cultivation Fund of Zhongnan Hospital of Wuhan University (CXYP2022011)
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 81902598
                Award ID: 82001000
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100002858, China Postdoctoral Science Foundation;
                Award ID: 2020M670071ZX
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003819, Natural Science Foundation of Hubei Province (Hubei Provincial Natural Science Foundation);
                Award ID: 2021CFB074
                Award Recipient :
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                © Associazione Differenziamento e Morte Cellulare ADMC 2023

                Cell biology
                renal cell carcinoma,growth factor signalling
                Cell biology
                renal cell carcinoma, growth factor signalling

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