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      Systematic Characterization of DNA Methyltransferases Family in Tumor Progression and Antitumor Immunity


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          Objective: DNA methylation is an essential epigenetic marker governed by DNA methyltransferases (DNMTs), which can influence cancer onset and progression. However, few studies have provided an integrated analysis of the relevance of DNMT family genes to cell stemness, the tumor microenvironment (TME), and immunotherapy biomarkers across diverse cancers. Methods: This study investigated the impact of five DNMTs on transcriptional profiles, prognosis, and their association with Ki67 expression, epithelial–mesenchymal transition signatures, stemness scores, the TME, and immunological markers across 31 cancer types from recognized public databases. Results: The results indicated that DNMT1/DNMT3B/DNMT3A expression increased, whereas TRDMT1/DNMT3L expression decreased in most cancer types. DNMT family genes were identified as prognostic risk factors for numerous cancers, as well as being prominently associated with immune, stromal, and ESTIMATE scores, as well as with immune-infiltrating cell levels. Expression of the well-known immune checkpoints, PDCD1 and CILA4, was noticeably related to DNMT1/DNMT3A/DNMT3B expression. Finally, we validated the role of DNMT1 in MCF-7 and HepG2-C3A cell lines through its knockdown, whereafter a decrease in cell proliferation and migration ability in vitro was observed. Conclusion: Our study comprehensively expounded that DNMT family genes not only behave as promising prognostic factors but also have the potential to serve as therapeutic targets in cancer immunotherapy for various types of cancer.

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          The Tumor Microenvironment Innately Modulates Cancer Progression

          Cancer development and progression occurs in concert with alterations in the surrounding stroma. Cancer cells can functionally sculpt their microenvironment through the secretion of various cytokines, chemokines, and other factors. This results in a reprogramming of the surrounding cells, enabling them to play a determinative role in tumor survival and progression. Immune cells are important constituents of the tumor stroma and critically take part in this process. Growing evidence suggests that the innate immune cells (macrophages, neutrophils, dendritic cells, innate lymphoid cells, myeloid-derived suppressor cells, and NK cells) as well as adaptive immune cells (T cells and B cells) contribute to tumor progression when present in the tumor microenvironment (TME). Crosstalk between cancer cells and the proximal immune cells ultimately results in an environment that fosters tumor growth and metastasis. Understanding the nature of this dialog will allow for improved therapeutics that simultaneously target multiple components of the TME, increasing the likelihood of favorable patient outcomes.
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            Functions of DNA methylation: islands, start sites, gene bodies and beyond.

            DNA methylation is frequently described as a 'silencing' epigenetic mark, and indeed this function of 5-methylcytosine was originally proposed in the 1970s. Now, thanks to improved genome-scale mapping of methylation, we can evaluate DNA methylation in different genomic contexts: transcriptional start sites with or without CpG islands, in gene bodies, at regulatory elements and at repeat sequences. The emerging picture is that the function of DNA methylation seems to vary with context, and the relationship between DNA methylation and transcription is more nuanced than we realized at first. Improving our understanding of the functions of DNA methylation is necessary for interpreting changes in this mark that are observed in diseases such as cancer.
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              Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

              Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.

                Author and article information

                Technol Cancer Res Treat
                Technol Cancer Res Treat
                Technology in Cancer Research & Treatment
                SAGE Publications (Sage CA: Los Angeles, CA )
                7 June 2024
                : 23
                : 15330338241260658
                [1 ]Department of Pharmacy, Department of Oncology, Ringgold 89667, universityFudan University Shanghai Cancer Center; , Shanghai Medical College, Fudan University, Shanghai, China
                [2 ]Tongji University Cancer Center, Shanghai Tenth People's Hospital of Tongji University, Ringgold 481875, universitySchool of Medicine, Tongji University; , Shanghai, China
                [3 ]Department of Hepatobiliary and Pancreatic Surgery, Ringgold 66324, universityShanghai East Hospital; , School of Medicine, Tongji University, Shanghai, China
                Author notes

                Fengru Huang and Xinyi Wu contributed equally to this work.

                [*]Wencong Ma, Department of Hepatobiliary and Pancreatic Surgery Shanghai East Hospital, School of Medicine Tongji University, NO. 150, Jimo Road, Pudong New District, Shanghai 200120, China. Email: emailwithjob@ 123456126.com .
                [*]Jiyong Liu, Department of Pharmacy, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, NO. 4333, Kangxin Road, pudong new district, Shanghai 200032, China. Email: liujiyong@ 123456fudan.edu.cn .
                Author information
                © 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 page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                : 22 February 2024
                : 7 May 2024
                : 20 May 2024
                Funded by: Key Discipline Construction Project of Pudong Health Bureau of Shanghai;
                Award ID: PWRd2022-05
                Original Research Article
                Custom metadata
                January-December 2024

                dnmt family,prognosis,tumor microenvironment,immunomodulators,various cancers


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