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      Cyclin dependent kinase inhibitor 3 ( CDKN3) upregulation is associated with unfavorable prognosis in clear cell renal cell carcinoma and shapes tumor immune microenvironment: A bioinformatics analysis

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          Abstract

          Cell cycle regulatory proteins plays a pivotal role in the development and progression of many human malignancies. Identification of their biological functions as well as their prognostic utility presents an active field of research. As a continuation of the ongoing efforts to elucidate the molecular characteristics of clear cell renal cell carcinoma (ccRCC); we present a comprehensive bioinformatics study targeting the prognostic and mechanistic role of cyclin-dependent kinase inhibitor 3 ( CDKN3) in ccRCC. The ccRCC cohort from the Cancer Genome Atlas Program was accessed through the UCSC Xena browser to obtain CDKN3 mRNA expression data and their corresponding clinicopathological variables. The independent prognostic signature of CDKN3 was evaluated using univariate and multivariate Cox logistic regression analysis. Gene set enrichment analysis and co-expression gene functional annotations were used to discern CDKN3-related altered molecular pathways. The tumor immune microenvironment was evaluated using TIMER 2.0 and gene expression profiling interactive analysis. CDKN3 upregulation is associated with shortened overall survival (hazard ratio [HR] = 2.325, 95% confident interval [CI]: 1.703–3.173, P < .0001) in the Cancer Genome Atlas Program ccRCC cohort. Univariate (HR: 0.426, 95% CI: 0.316–0.576, P < .001) and multivariate (HR: 0.560, 95% CI: 0.409–0.766, P < .001) Cox logistic regression analyses indicate that CDKN3 is an independent prognostic variable of the overall survival. High CDKN3 expression is associated with enrichment within the following pathways including allograph rejection, epithelial–mesenchymal transition, mitotic spindle, inflammatory response, IL-6/JAK/STAT3 signaling, spermatogenesis, TNF-α signaling via NF-kB pathway, complement activation, KRAS signaling, and INF-γ signaling. CDKN3 is also associated with significant infiltration of a wide spectrum of immune cells and correlates remarkably with immune-related genes. CDKN3 is a poor prognostic biomarker in ccRCC that alters many molecular pathways and impacts the tumor immune microenvironment.

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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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            STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

            Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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              GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

              Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
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                Author and article information

                Contributors
                Journal
                Medicine (Baltimore)
                Medicine (Baltimore)
                MD
                Medicine
                Lippincott Williams & Wilkins (Hagerstown, MD )
                0025-7974
                1536-5964
                08 September 2023
                08 September 2023
                : 102
                : 36
                : e35004
                Affiliations
                [a ] Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan
                [b ] Department of Medical Laboratory Sciences, Jordan University of Science and Technology, Irbid, Jordan
                [c ] Department of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
                [d ] College of Pharmacy, QU Health, Qatar University, Doha, Qatar.
                Author notes
                [* ] Correspondence: Feras Q. Alali, College of Pharmacy, QU Health, Qatar University, Doha, Qatar (e-mail: feras.alali@ 123456qu.edu.qa ).
                Author information
                https://orcid.org/0000-0003-1311-806X
                Article
                00055
                10.1097/MD.0000000000035004
                10489202
                37682177
                19286b62-a3bd-44e9-a1cd-13d64682cf9c
                Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 June 2023
                : 06 August 2023
                : 08 August 2023
                Categories
                5700
                Research Article
                Observational Study
                Custom metadata
                TRUE

                bioinformatics,ccrcc,cdkn3,clear cell renal cell carcinoma,cyclin-dependent kinase inhibitor 3,tcga

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