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      Identifying the prognosis implication, immunotherapy response prediction value, and potential targeted compound inhibitors of integrin subunit α3 (ITGA3) in human cancers

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          Abstract

          The integrin subunit α3 (ITGA3) is a member of the integrin alpha chain protein family, which could promote progression, metastasis, and invasion in some cancers. Still, its function in the tumor microenvironment (TME), cancer prognosis, and immunotherapy remains unclear. A multifaceted analysis of ITGA3 in pan-cancer utilizing various databases and online web tools revealed ITGA3 was aberrantly expressed in tumor tissues and upregulated in most cancers, which may be related to ITGA3 genomic alterations and methylation modification. In addition, ITGA3 was significantly correlated with the poor or better prognosis of cancer patients, immune-related pathways in hallmark, immune infiltration, and immune checkpoints, revealing a biological function of ITGA3 in the tumor progression, tumor microenvironment, and tumor immunity. We also found that ITGA3 could predict the response to tumor immunotherapy based on cytokine-treated samples and immunotherapy cohorts. ITGA3 may participate in shaping and regulating the tumor microenvironment to affect the tumor immune response, which was a promising immunotherapy response predictive biomarker and potential therapeutic target to work synergistically with cancer immunotherapy to boost the response and efficacy. Finally, potential targeted compound inhibitors and sensitive drugs were screened using databases ConnectivityMap (CMap) and CellMiner, and AutoDock Tools was used for molecular docking.

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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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            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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              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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                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                06 January 2024
                30 January 2024
                06 January 2024
                : 10
                : 2
                : e24236
                Affiliations
                [a ]Department of Neurosurgery, The 2 nd Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, PR China
                [b ]Institute of Neuroscience, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, PR China
                [c ]Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang, PR China
                [d ]JXHC Key Laboratory of Neurological Medicine, Jiangxi, 330006, Nanchang, PR China
                [e ]HuanKui Academy, Jiangxi Medical College, Nanchang University, Nanchang 330031, PR China
                [f ]Queen Mary School, Jiangxi Medical College, Nanchang University, Nanchang, PR China
                [g ]The Second Clinical Medical College, Jiangxi Medical College, Nanchang University, Nanchang 330031, PR China
                Author notes
                []Corresponding author. Department of Neurosurgery, The 2 nd Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, PR China. kaihuang@ 123456ncu.edu.cn
                [1]

                These authors contributed equally to this work.

                Article
                S2405-8440(24)00267-6 e24236
                10.1016/j.heliyon.2024.e24236
                10825359
                38293430
                dd0a128d-560e-4c04-a44c-aada7ad3071a
                © 2024 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 29 May 2023
                : 30 November 2023
                : 4 January 2024
                Categories
                Research Article

                integrin subunit α3 (itga3),pan-cancer,biomarker,tumor microenvironment,immunotherapy,molecular docking

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