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      Deciphering the heterogeneity of epithelial cells in pancreatic ductal adenocarcinoma: implications for metastasis and immune evasion

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          Abstract

          Objective

          This study examines the cellular heterogeneity of epithelial cells within pancreatic ductal adenocarcinoma (PDAC) and their contributions to tumor progression, metastasis, and immunosuppressive interactions using single-cell RNA sequencing.

          Methods

          Single-cell RNA-sequencing data from two datasets (GSE154778 and GSE158356) were integrated using the Harmony algorithm, followed by quality control, clustering, and differential gene expression analysis. Distinct subpopulations of epithelial cells were identified, and their gene expression profiles were analyzed. To assess the malignancy of these subpopulations, single-cell copy number variation (CNV) analysis and trajectory analysis were conducted. Additionally, intercellular communication was examined using the CellChat platform.

          Results

          The analysis revealed pronounced heterogeneity among PDAC epithelial cells, with specific subpopulations exhibiting distinct roles in tumor proliferation, extracellular matrix remodeling, and metastatic dissemination. Subpopulations 4 and 6 were characterized by increased CNV levels and a more malignant phenotype, suggesting an enhanced capacity for metastasis. Single-cell trajectory analysis, along with CellChat, mapped the temporal evolution of epithelial cells, identifying key regulatory genes such as DCBLD2 and JUN. A prognostic model incorporating five key genes, including KLF6, was developed and demonstrated strong predictive accuracy for patient outcomes. Notably, KLF6 emerged as a critical prognostic marker associated with immune modulation, particularly through interactions with M2 macrophages.

          Conclusion

          The study highlights the pronounced heterogeneity of epithelial cells in PDAC and their distinct contributions to tumor progression, metastasis, and immune modulation. Through single-cell transcriptomic and CNV analyses, we identified epithelial subpopulations with varying malignant potentials and distinct interactions with the tumor microenvironment. Among these, KLF6 emerged as a key regulator associated with immune modulation and metastasis. Our findings emphasize the significance of epithelial cell heterogeneity in shaping pancreatic cancer progression. These insights provide a foundation for future investigations into novel prognostic markers and therapeutic strategies.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12957-025-03793-3.

          Related collections

          Most cited references61

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          Robust enumeration of cell subsets from tissue expression profiles

          We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
            • Record: found
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            Is Open Access

            Cancer statistics, 2023

            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 using incidence data collected by central cancer registries and mortality data collected by the National Center for Health Statistics. In 2023, 1,958,310 new cancer cases and 609,820 cancer deaths are projected to occur in the United States. Cancer incidence increased for prostate cancer by 3% annually from 2014 through 2019 after two decades of decline, translating to an additional 99,000 new cases; otherwise, however, incidence trends were more favorable in men compared to women. For example, lung cancer in women decreased at one half the pace of men (1.1% vs. 2.6% annually) from 2015 through 2019, and breast and uterine corpus cancers continued to increase, as did liver cancer and melanoma, both of which stabilized in men aged 50 years and older and declined in younger men. However, a 65% drop in cervical cancer incidence during 2012 through 2019 among women in their early 20s, the first cohort to receive the human papillomavirus vaccine, foreshadows steep reductions in the burden of human papillomavirus-associated cancers, the majority of which occur in women. Despite the pandemic, and in contrast with other leading causes of death, the cancer death rate continued to decline from 2019 to 2020 (by 1.5%), contributing to a 33% overall reduction since 1991 and an estimated 3.8 million deaths averted. This progress increasingly reflects advances in treatment, which are particularly evident in the rapid declines in mortality (approximately 2% annually during 2016 through 2020) for leukemia, melanoma, and kidney cancer, despite stable/increasing incidence, and accelerated declines for lung cancer. In summary, although cancer mortality rates continue to decline, future progress may be attenuated by rising incidence for breast, prostate, and uterine corpus cancers, which also happen to have the largest racial disparities in mortality.
              • Record: found
              • Abstract: found
              • Article: found
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              Inference and analysis of cell-cell communication using CellChat

              Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.

                Author and article information

                Contributors
                linbinllb@126.com
                Journal
                World J Surg Oncol
                World J Surg Oncol
                World Journal of Surgical Oncology
                BioMed Central (London )
                1477-7819
                16 April 2025
                16 April 2025
                2025
                : 23
                : 144
                Affiliations
                [1 ]Department of Hepatopancreatobiliary Medical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, ( https://ror.org/050s6ns64) Fuzhou, 350014 China
                [2 ]Department of Orthodontics, Fujian Medical University Union Hospital, ( https://ror.org/055gkcy74) No. 29 of Xinquan Road, Gulou District, Fuzhou, 350001 China
                Article
                3793
                10.1186/s12957-025-03793-3
                12004766
                40240899
                0623d5fa-4634-4f12-a078-6d6b55f1550a
                © The Author(s) 2025

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 5 February 2025
                : 29 March 2025
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2025

                Surgery
                copy number variation (cnv),epithelial cell heterogeneity,immune modulation,metastasis,pancreatic cancer,single-cell rna sequencing,tumor microenvironment

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