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      Decreased expression of TXNIP is associated with poor prognosis and immune infiltration in kidney renal clear cell carcinoma

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          Abstract

          The most prevalent and insidious type of kidney cancer is kidney clear cell carcinoma (KIRC). Thioredoxin-interacting protein ( TXNIP) encodes a thioredoxin-binding protein involved in cellular energy metabolism, redox homeostasis, apoptosis induction and inflammatory responses. However, the relationship between TXNIP, immune infiltration and its prognostic value in KIRC remains unclear. Thus, the present study evaluated the potential for TXNIP as a prognostic marker in patients with KIRC. Data from The Cancer Genome Atlas were used to assess relative mRNA expression levels of TXNIP in different types of cancer. The protein expression levels of TXNIP were evaluated using the Human Protein Atlas. Enrichment analysis of genes co-expressed with TXNIP was performed to assess relevant biological processes that TXNIP may be involved in. CIBERSORT was used to predict the infiltration of 21 tumor-infiltrating immune cells (TIICs). Univariate and multivariate Cox regression analyses were used to assess the relationship between TXNIP expression and prognosis. Single-cell RNA-sequencing datasets were used to evaluate the mRNA expression levels of TXNIP in certain immune cells in KIRC. The CellMiner database was used to analyze the relationship between TXNIP mRNA expression and drug sensitivity in KIRC. The results from the present study demonstrated that TXNIP expression was significantly decreased in KIRC tissue compared with that in normal tissue, as confirmed by western blotting and reverse transcription-quantitative PCR. In addition, downregulated TXNIP expression was significantly associated with poor prognosis, a high histological grade and an advanced stage. The Cell Counting Kit-8 assay demonstrated that TXNIP overexpression significantly suppressed tumor cell proliferation. Univariate and multivariate Cox regression analyses indicated that TXNIP served as a separate prognostic factor in KIRC. Moreover, TXNIP expression was significantly correlated with the accumulation of several TIICs and its overexpression significantly downregulated the mRNA expression levels of CD25 and cytotoxic T-lymphocyte-associated protein 4, immune cell surface markers in CD4 + T lymphocytes. In conclusion, TXNIP may be used as a possible biomarker to assess unfavorable prognostic outcomes and identify immunotherapy targets in KIRC.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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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              • Record: found
              • Abstract: found
              • Article: not found

              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

                Journal
                Oncol Lett
                Oncol Lett
                OL
                Oncology Letters
                D.A. Spandidos
                1792-1074
                1792-1082
                March 2024
                12 January 2024
                12 January 2024
                : 27
                : 3
                : 97
                Affiliations
                [1 ]School of Basic Medical Sciences, Youjiang Medical University for Nationalities, Baise, Guangxi 533000, P.R. China
                [2 ]The Key Laboratory of Molecular Pathology of Hepatobiliary Diseases of Guangxi, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi 533000, P.R. China
                [3 ]Scientific Experiment Center, Affiliated Southwest Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi 533000, P.R. China
                Author notes
                Correspondence to: Professor Xiaolei Li, Scientific Experiment Center, Affiliated Southwest Hospital of Youjiang Medical University for Nationalities, 8 Chengxiang Road, Baise, Guangxi 533000, P.R. China, E-mail: lilinfengrenji.com xiaoleili2004@ 123456163.com
                Professor Zhongshi Huang, School of Basic Medical Sciences, Youjiang Medical University for Nationalities, 98 Chengxiang Road, Baise, Guangxi 533000, P.R. China, E-mail: lilinfengrenji.com zhongshihuang@ 123456ymun.edu.cn
                Article
                OL-27-3-14230
                10.3892/ol.2024.14230
                10823309
                38288038
                a26c79ab-5586-4768-9336-699add94d37b
                Copyright: © Liu et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

                History
                : 07 August 2023
                : 16 November 2023
                Funding
                Funded by: Guangxi Zhuang Autonomous Region Health and Family Planning Commission
                Award ID: Z-L20221837
                Funding was received from the Guangxi Zhuang Autonomous Region Health and Family Planning Commission (grant. no. Z-L20221837).
                Categories
                Articles

                Oncology & Radiotherapy
                txnip,kirc,immune infiltration,prognosis,cd4+ t cell
                Oncology & Radiotherapy
                txnip, kirc, immune infiltration, prognosis, cd4+ t cell

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