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      High Expression of ZNF208 Predicts Better Prognosis and Suppresses the Tumorigenesis of Breast Cancer

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          Abstract

          Background:

          Breast cancer (BRCA), the hormone related malignant tumor, is well-known for poor prognosis. ZNF208 mainly acts as a transcription factor in various tumors, and the single nucleotide polymorphisms (SNPs) of ZNF208 are related to telomere length. Nevertheless, its role in breast tumorigenesis is largely unknown.

          Methods:

          We systematically investigated the gene expression, prognostic value, and promoter methylation of ZNF208 in BRCA with Gene Expression Profiling Interactive Analysis (GEPIA) and DNA Methylation Interactive Visualization Database (DNMIVD). Meanwhile, we clarified the association of ZNF208 with tumor-infiltrating immune cells (TICs) from Tumor Immune Estimation Resource (TIMER). Furthermore, we determined the biological process and functional enrichment from Cancer single-cell state atlas (CancerSEA). Finally, we verified our results with prognostic analysis and immunohistochemistry (IHC) assay.

          Results:

          We discovered that ZNF208 was downregulated in breast cancer, and low expression of ZNF208 predicted worse prognosis of BRCA patients. The promoter methylation level of ZNF208 was obviously increased, and ZNF208 was associated with TlCs in BRCA. In addition, ZNF208 could inhibit the metastasis and invasion biological processes, and regulate the MAPK and RAS signaling pathways in BRCA.

          Conclusion:

          Our findings illustrate that ZNF208 can function as a tumor suppressor and predict prognosis of breast cancer.

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          Most cited references47

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          Cancer statistics, 2022

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes. Incidence data (through 2018) were collected by the Surveillance, Epidemiology, and End Results program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2019) were collected by the National Center for Health Statistics. In 2022, 1,918,030 new cancer cases and 609,360 cancer deaths are projected to occur in the United States, including approximately 350 deaths per day from lung cancer, the leading cause of cancer death. Incidence during 2014 through 2018 continued a slow increase for female breast cancer (by 0.5% annually) and remained stable for prostate cancer, despite a 4% to 6% annual increase for advanced disease since 2011. Consequently, the proportion of prostate cancer diagnosed at a distant stage increased from 3.9% to 8.2% over the past decade. In contrast, lung cancer incidence continued to decline steeply for advanced disease while rates for localized-stage increased suddenly by 4.5% annually, contributing to gains both in the proportion of localized-stage diagnoses (from 17% in 2004 to 28% in 2018) and 3-year relative survival (from 21% to 31%). Mortality patterns reflect incidence trends, with declines accelerating for lung cancer, slowing for breast cancer, and stabilizing for prostate cancer. In summary, progress has stagnated for breast and prostate cancers but strengthened for lung cancer, coinciding with changes in medical practice related to cancer screening and/or treatment. More targeted cancer control interventions and investment in improved early detection and treatment would facilitate reductions in cancer mortality.
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            UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses1

            Genomics data from The Cancer Genome Atlas (TCGA) project has led to the comprehensive molecular characterization of multiple cancer types. The large sample numbers in TCGA offer an excellent opportunity to address questions associated with tumo heterogeneity. Exploration of the data by cancer researchers and clinicians is imperative to unearth novel therapeutic/diagnostic biomarkers. Various computational tools have been developed to aid researchers in carrying out specific TCGA data analyses; however there is need for resources to facilitate the study of gene expression variations and survival associations across tumors. Here, we report UALCAN, an easy to use, interactive web-portal to perform to in-depth analyses of TCGA gene expression data. UALCAN uses TCGA level 3 RNA-seq and clinical data from 31 cancer types. The portal's user-friendly features allow to perform: 1) analyze relative expression of a query gene(s) across tumor and normal samples, as well as in various tumor sub-groups based on individual cancer stages, tumor grade, race, body weight or other clinicopathologic features, 2) estimate the effect of gene expression level and clinicopathologic features on patient survival; and 3) identify the top over- and under-expressed (up and down-regulated) genes in individual cancer types. This resource serves as a platform for in silico validation of target genes and for identifying tumor sub-group specific candidate biomarkers. Thus, UALCAN web-portal could be extremely helpful in accelerating cancer research. UALCAN is publicly available at http://ualcan.path.uab.edu.
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              TIMER2.0 for analysis of tumor-infiltrating immune cells

              Abstract Tumor progression and the efficacy of immunotherapy are strongly influenced by the composition and abundance of immune cells in the tumor microenvironment. Due to the limitations of direct measurement methods, computational algorithms are often used to infer immune cell composition from bulk tumor transcriptome profiles. These estimated tumor immune infiltrate populations have been associated with genomic and transcriptomic changes in the tumors, providing insight into tumor–immune interactions. However, such investigations on large-scale public data remain challenging. To lower the barriers for the analysis of complex tumor–immune interactions, we significantly improved our previous web platform TIMER. Instead of just using one algorithm, TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms. TIMER2.0 provides four modules for investigating the associations between immune infiltrates and genetic or clinical features, and four modules for exploring cancer-related associations in the TCGA cohorts. Each module can generate a functional heatmap table, enabling the user to easily identify significant associations in multiple cancer types simultaneously. Overall, the TIMER2.0 web server provides comprehensive analysis and visualization functions of tumor infiltrating immune cells.
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                Author and article information

                Journal
                Clin Med Insights Oncol
                Clin Med Insights Oncol
                ONC
                sponc
                Clinical Medicine Insights. Oncology
                SAGE Publications (Sage UK: London, England )
                1179-5549
                29 November 2024
                2024
                : 18
                : 11795549241301341
                Affiliations
                [1 ]Department of Basic Medicine, Shaanxi University of Chinese Medicine, Xianyang, China
                [2 ]Department of Clinical Medicine, Yan’an University School of Medicine, Yan’an, China
                [3 ]Department of Pathology, The Second Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
                Author notes
                [*]Jing Wei, Department of Basic Medicine, Shaanxi University of Chinese Medicine, Xianyang 712046, China. Email: wj361874220@ 123456sina.com
                Author information
                https://orcid.org/0009-0008-5216-8899
                Article
                10.1177_11795549241301341
                10.1177/11795549241301341
                11607762
                39618843
                78a43340-5154-481f-be40-d7d07799272d
                © The Author(s) 2024

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 23 May 2024
                : 30 October 2024
                Funding
                Funded by: the Key Research and Development Projects of Shaanxi Province, ;
                Award ID: No. 2023-YBSF-556
                Funded by: the National Natural Science Foundation of China, ;
                Award ID: No. 82003806
                Categories
                Emerging Next Generation Biomarkers in Cancer
                Original Research Article
                Custom metadata
                January-December 2024
                ts1

                Oncology & Radiotherapy
                brca,znf208,prognosis,immune infiltration,tumor suppressor
                Oncology & Radiotherapy
                brca, znf208, prognosis, immune infiltration, tumor suppressor

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