28
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Gut-derived lipopolysaccharide remodels tumoral microenvironment and synergizes with PD-L1 checkpoint blockade via TLR4/MyD88/AKT/NF-κB pathway in pancreatic cancer

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Lipopolysaccharide (LPS) as an important inflammatory mediator activates the innate/adaptive immune system. The existence of LPS in pancreatic ductal adenocarcinoma (PDAC) has been reported, however, its biological function in PDAC remains unclear. Here, we demonstrated that circulating and tumoral LPS was significantly increased by intestinal leakage in the orthotopic murine PDAC model, and LPS administration promoted T cell infiltration but exhaustion paradoxically in the subcutaneous murine PDAC model. By bioinformatic analysis, Toll-like receptor 4 (TLR4), LPS receptor, was further found to enrich in immune tolerance signaling in PDAC tissues. Then, a significant positive correlation was found between TLR4 and programmed death ligand-1 (PD-L1) in clinical PDAC tissues, as well as serum LPS and tumoral PD-L1. Meanwhile, LPS stimulation in vitro and in vivo obviously upregulated tumor PD-L1 expression, and effectively promoted cancer cells resistance to T cell cytotoxicity. Mechanistically, the activation of TLR4/MyD88/AKT/NF-κB cascade was found to participate in LPS mediated PD-L1 transcription via binding to its promoter regions, which was enhanced by crosstalk between NF-κB and AKT pathways. Finally, PD-L1 blockade could significantly reverse LPS-induced immune escape, and synergized with LPS treatment. Taken together, LPS can remodel tumor microenvironment, and synergize with PD-L1 blockade to suppress tumor growth, which may be a promising comprehensive strategy for PDAC.

          Related collections

          Most cited references46

          • Record: found
          • Abstract: found
          • Article: not found

          Cancer Statistics, 2021

          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. Incidence data (through 2017) 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 2018) were collected by the National Center for Health Statistics. In 2021, 1,898,160 new cancer cases and 608,570 cancer deaths are projected to occur in the United States. After increasing for most of the 20th century, the cancer death rate has fallen continuously from its peak in 1991 through 2018, for a total decline of 31%, because of reductions in smoking and improvements in early detection and treatment. This translates to 3.2 million fewer cancer deaths than would have occurred if peak rates had persisted. Long-term declines in mortality for the 4 leading cancers have halted for prostate cancer and slowed for breast and colorectal cancers, but accelerated for lung cancer, which accounted for almost one-half of the total mortality decline from 2014 to 2018. The pace of the annual decline in lung cancer mortality doubled from 3.1% during 2009 through 2013 to 5.5% during 2014 through 2018 in men, from 1.8% to 4.4% in women, and from 2.4% to 5% overall. This trend coincides with steady declines in incidence (2.2%-2.3%) but rapid gains in survival specifically for nonsmall cell lung cancer (NSCLC). For example, NSCLC 2-year relative survival increased from 34% for persons diagnosed during 2009 through 2010 to 42% during 2015 through 2016, including absolute increases of 5% to 6% for every stage of diagnosis; survival for small cell lung cancer remained at 14% to 15%. Improved treatment accelerated progress against lung cancer and drove a record drop in overall cancer mortality, despite slowing momentum for other common cancers.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

            The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

              Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
                Bookmark

                Author and article information

                Contributors
                lou.wenhui@zs-hospital.sh.cn
                wu.wenchuan@zs-hospital.sh.cn
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                30 October 2021
                30 October 2021
                November 2021
                : 12
                : 11
                : 1033
                Affiliations
                [1 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of General Surgery, Zhongshan Hospital, , Fudan University, ; Shanghai, 200032 China
                [2 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Cancer Center, Zhongshan Hospital, , Fudan University, ; Shanghai, 200032 China
                Author information
                http://orcid.org/0000-0002-6622-4301
                http://orcid.org/0000-0002-3123-493X
                http://orcid.org/0000-0001-5820-6843
                http://orcid.org/0000-0002-8408-885X
                Article
                4293
                10.1038/s41419-021-04293-4
                8557215
                34718325
                58a66324-79d9-44f5-9862-4218f66b2400
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 May 2021
                : 5 September 2021
                : 24 September 2021
                Funding
                Funded by: Excellent Backbone Program of Zhongshan Hospital, Fudan University (2017ZSGG04)
                Funded by: Shanghai Sailing Program (21YF1407100) China Postdoctoral Science Foundation (2021M690037) National Natural Science Foundation of China;82103409)
                Funded by: National Natural Science Foundation of China (No. 81773068), Clinical Science and Technology Innovation Project of the Shanghai Shenkang Hospital Development Center (SHDC2020CR2017B), and National Key RD Program (2019YFC1315902).
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Cell biology
                cancer microenvironment,cancer immunotherapy
                Cell biology
                cancer microenvironment, cancer immunotherapy

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content197

                Cited by20

                Most referenced authors1,358