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Cancers
Special Issues
Technological Development for Advances in Cancer Research and Precision Oncology
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Technological Development for Advances in Cancer Research and Precision Oncology

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Molecular Cancer Biology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5662

Special Issue Editors

Dr. Nelson Yee

E-Mail Website
Guest Editor
Penn State Cancer Institute, Penn State Health Milton S. Hershey Medical Center, 500 University Drive, Hershey, PA 17033-0850, USA
Interests: cancer biomarkers, cancer therapy, clinical investigation, machine learning, translational research
Special Issues, Collections and Topics in MDPI journals
Dr. Ilias Georgakopoulos-Soares

E-Mail Website
Co-Guest Editor
Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA 17033-0850, USA
Interests: bioinformatics; genomics; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Fenglong Ma

E-Mail Website
Co-Guest Editor
College of Information Sciences and Technology, Institute for Computational and Data Sciences, Pennsylvania State University, University Park, State College, PA 16802, USA
Interests: data mining; healthcare informatics; machine learning; natural language processing

Special Issue Information

Dear Colleagues,

Innovations in a wide variety of technologies have accelerated cancer research and transformed clinical oncology. Applications of machine learning and advanced bioinformatics have made progressive breakthroughs in high-dimensional data analytics for cancer prediction. Novel methodologies for the isolation and analysis of extracellular vesicles, cell-free DNA/RNA/protein, circulating tumor cells, and molecular typing have emerged for clinical utility in detection and monitoring of cancer. Advances in single-cell profiling, genome editing, three-dimensional printing, and organoid technology have enhanced our understanding of cancer biology and the development of diagnostic tools and therapeutics. Multi-platform molecular analyses along with the development of targeted agents have led to achievement of personalized cancer therapy. Molecular, cellular, and organismal engineering have had a major impact on basic and translational cancer research. Advances in nanotechnology, molecular probes, spectrometry, and cryo-electron microscopy, as well as applications of biosensors, robotics, and radiation sciences, have accelerated the progress against cancer.

In this Special Issue, we aim to showcase recent advances and innovations in technologies for cancer research and precision oncology. We invite the submission of manuscripts covering all cancer types, from basic laboratory research to translational and clinical research. Narrated reviews describing the history and significant contributions of technology in cancer research are also welcome.

Dr. Nelson Yee
Dr. Ilias Georgakopoulos-Soares
Dr. Fenglong Ma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioengineering
  • bioinformatics
  • biosensor
  • extracellular vesicles
  • genome editing
  • machine learning
  • molecular profiling
  • nanoparticles
  • organoids
  • robotics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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16 pages, 4964 KiB  
Article
Innovative Cancer Immunotherapy with MAGE-A3 mRNA Cancer Vaccines
by Kangchan Choi, Hyorim Jeong, Do Hyun Lee, Ji Won Lee, Ju-Eun Hong, Jin Ee Baek and Yong Serk Park
Cancers 2024, 16(19), 3428; https://doi.org/10.3390/cancers16193428 - 9 Oct 2024
Cited by 1 | Viewed by 2437
Abstract
Cancer causes over 10 million deaths annually worldwide and remains a significant global health challenge. This study investigated advanced immunotherapy strategies, focusing on mRNA vaccines that target tumor-specific antigens to activate the immune system. We developed a novel mRNA vaccine using O,O′-dimyristyl-N-lysyl aspartate [...] Read more.
Cancer causes over 10 million deaths annually worldwide and remains a significant global health challenge. This study investigated advanced immunotherapy strategies, focusing on mRNA vaccines that target tumor-specific antigens to activate the immune system. We developed a novel mRNA vaccine using O,O′-dimyristyl-N-lysyl aspartate (DMKD) to improve stability and phosphatidylserine (PS) to enhance antigen presentation to immune cells. This vaccine, containing melanoma-associated antigen A3 (MAGE-A3) mRNA encapsulated within lipid nanoparticles (LNPs), was evaluated for its therapeutic potential against colorectal cancer. Our findings demonstrated that MAGE-A3 mRNA-containing DMKD-PS LNPs significantly reduced tumor size and weight, effectively combating metastatic cancer. The vaccine elicited a robust immune response, increasing specific immunoglobulin and cytokine levels without causing histotoxicity in major organs. These results confirm that the DMKD-PS-based MAGE-A3 mRNA vaccine holds promise for cancer prevention and treatment. Full article
(This article belongs to the Special Issue Technological Development for Advances in Cancer Research and Precision Oncology)
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14 pages, 1973 KiB  
Article
3D Chromatin Alteration by Disrupting β-Catenin/CBP Interaction Is Enriched with Insulin Signaling in Pancreatic Cancer
by Yufan Zhou, Zhijing He, Tian Li, Lavanya Choppavarapu, Xiaohui Hu, Ruifeng Cao, Gustavo W. Leone, Michael Kahn and Victor X. Jin
Cancers 2024, 16(12), 2202; https://doi.org/10.3390/cancers16122202 - 12 Jun 2024
Cited by 1 | Viewed by 1533
Abstract
The therapeutic potential of targeting the β-catenin/CBP interaction has been demonstrated in a variety of preclinical tumor models with a small molecule inhibitor, ICG-001, characterized as a β-catenin/CBP antagonist. Despite the high binding specificity of ICG-001 for the N-terminus of CBP, this β-catenin/CBP [...] Read more.
The therapeutic potential of targeting the β-catenin/CBP interaction has been demonstrated in a variety of preclinical tumor models with a small molecule inhibitor, ICG-001, characterized as a β-catenin/CBP antagonist. Despite the high binding specificity of ICG-001 for the N-terminus of CBP, this β-catenin/CBP antagonist exhibits pleiotropic effects. Our recent studies found global changes in three-dimensional (3D) chromatin architecture in response to disruption of the β-catenin/CBP interaction in pancreatic cancer cells. However, an understanding of how the functional crosstalk between the antagonist and the β-catenin/CBP interaction affects changes in 3D chromatin architecture and, thereby, gene expression and downstream effects remains to be elucidated. Here, we perform Hi-C analyses on canonical and patient-derived pancreatic cancer cells before and after treatment with ICG-001. In addition to global alteration of 3D chromatin domains, we unexpectedly identify insulin signaling genes enriched in the altered chromatin domains. We further demonstrate that the chromatin loops associated with insulin signaling genes are significantly weakened after ICG-001 treatment. We finally elicit the deletion of a looping of IRS1—a key insulin signaling gene—significantly impeding pancreatic cancer cell growth, indicating that looping-mediated insulin signaling might act as an oncogenic pathway to promote pancreatic cancer progression. Our work shows that targeting aberrant insulin chromatin looping in pancreatic cancer might provide a therapeutic benefit. Full article
(This article belongs to the Special Issue Technological Development for Advances in Cancer Research and Precision Oncology)
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Review

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19 pages, 2749 KiB  
Review
Prioritizing Context-Dependent Cancer Gene Signatures in Networks
by Enrico Capobianco, Thomas S. Lisse and Sandra Rieger
Cancers 2025, 17(1), 136; https://doi.org/10.3390/cancers17010136 - 3 Jan 2025
Viewed by 842
Abstract
There are numerous ways of portraying cancer complexity based on combining multiple types of data. A common approach involves developing signatures from gene expression profiles to highlight a few key reproducible features that provide insight into cancer risk, progression, or recurrence. Normally, a [...] Read more.
There are numerous ways of portraying cancer complexity based on combining multiple types of data. A common approach involves developing signatures from gene expression profiles to highlight a few key reproducible features that provide insight into cancer risk, progression, or recurrence. Normally, a selection of such features is made through relevance or significance, given a reference context. In the case of highly metastatic cancers, numerous gene signatures have been published with varying levels of validation. Then, integrating the signatures could potentially lead to a more comprehensive view of the connection between cancer and its phenotypes by covering annotations not fully explored in individual studies. This broader understanding of disease phenotypes would improve the predictive accuracy of statistical models used to identify meaningful associations. We present an example of this approach by reconciling a great number of published signatures into meta-signatures relevant to Osteosarcoma (OS) metastasis. We generate a well-annotated and interpretable interactome network from integrated OS gene expression signatures and identify key nodes that regulate essential aspects of metastasis. While the connected signatures link diverse prognostic measurements for OS, the proposed approach is applicable to any type of cancer. Full article
(This article belongs to the Special Issue Technological Development for Advances in Cancer Research and Precision Oncology)
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