Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.0
4.5
Journals
Cancers
Special Issues
Computational Research in Cancer Neuroscience
share announcement

Computational Research in Cancer Neuroscience

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1665

Special Issue Editor

Dr. Shaolei Teng

E-Mail Website
Guest Editor
Department of Biology, Howard University, Washington, DC 20059, USA
Interests: bioinformatics; mental disorders; next-generation sequencing; machine learning; protein structure modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The nervous system is fundamental in regulating tissue development, homeostasis, and regeneration across the body. Recent discoveries have revealed its significant involvement in cancer initiation and progression, with neural–cancer interactions driving tumor growth, invasion, and metastasis. This new field of cancer neuroscience uncovers how neurons and cancer cells communicate, such as through synaptic connections and paracrine signaling. With the advent of machine learning, large language models, omics data analysis, and protein structure modeling, computational approaches offer new insights into these dynamic processes.

This Special Issue welcomes original research and reviews focused on computational modeling of neural–cancer communication, neural influence on tumor microenvironments, predictive analytics for cancer therapies, and computational approaches to protein structure modeling in cancer neuroscience. Collaborative studies integrating neurobiology, oncology, and data science are highly encouraged. We aim to cover foundational discoveries and translational applications that advance our understanding of this new cross-disciplinary field.

Dr. Shaolei Teng
Guest Editor

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

  • machine learning
  • large language models
  • protein structure modeling
  • omics data analysis
  • neural–cancer communication
  • tumor microenvironments
  • personalized medicine
  • targeted therapies
  • cancer neuroscience

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 5140 KiB  
Article
Aptamer’s Structure Optimization for Better Diagnosis and Treatment of Glial Tumors
by Anastasia A. Koshmanova, Polina V. Artyushenko, Irina A. Shchugoreva, Victoriya D. Fedotovskaya, Natalia A. Luzan, Olga S. Kolovskaya, Galina S. Zamay, Kirill A. Lukyanenko, Dmitriy V. Veprintsev, Elena D. Khilazheva, Tatiana N. Zamay, Daria A. Ivanova, Maria R. Kastyuk, Ivan N. Lapin, Valery A. Svetlichnyi, Felix N. Tomilin, Nikita A. Shved, Valeriia S. Gulaia, Vadim V. Kumeiko, Maxim V. Berezovski and Anna S. Kichkailoadd Show full author list remove Hide full author list
Cancers 2024, 16(23), 4111; https://doi.org/10.3390/cancers16234111 - 8 Dec 2024
Viewed by 1283
Abstract
Background: Oncological diseases are a major focus in medicine, with millions diagnosed each year, leading researchers to seek new diagnostic and treatment methods. One promising avenue is the development of targeted therapies and rapid diagnostic tests using recognition molecules. The pharmaceutical industry is [...] Read more.
Background: Oncological diseases are a major focus in medicine, with millions diagnosed each year, leading researchers to seek new diagnostic and treatment methods. One promising avenue is the development of targeted therapies and rapid diagnostic tests using recognition molecules. The pharmaceutical industry is increasingly exploring nucleic acid-based therapeutics. However, producing long oligonucleotides, especially aptamers, poses significant production challenges. Objectives: This study aims to demonstrate the efficacy of using molecular modeling, supported by experimental procedures, for altering aptamer nucleotide sequences while maintaining their binding capabilities. The focus is on reducing production costs and enhancing binding dynamics by removing nonfunctional regions and minimizing nonspecific binding. Methods: A molecular modeling approach was employed to elucidate the structure of a DNA aptamer, Gli-55, facilitating the truncation of nonessential regions in the Gli-55 aptamer, which selectively binds to glioblastoma (GBM). This process aimed to produce a truncated aptamer, Gli-35, capable of forming similar structural elements to the original sequence with reduced nonspecific binding. The efficiency of the truncation was proved by flow cytometry, fluorescence polarization (FP), and confocal microscopy. Results: The molecular design indicated that the new truncated Gli-35 aptamer retained the structural integrity of Gli-55. In vitro studies showed that Gli-35 had a binding affinity comparable to the initial long aptamer while the selectivity increased. Gli-35 internalized inside the cell faster than Gli-55 and crossed the blood–brain barrier (BBB), as demonstrated in an in vitro model. Conclusions: The success of this truncation approach suggests its potential applicability in scenarios where molecular target information is limited. The study highlights a strategic and resource-efficient methodology for aptamer development. By employing molecular modeling and truncation, researchers can reduce production costs and avoid trial and error in sequence selection. This approach is promising for enhancing the efficiency of therapeutic agent development, particularly in cases lacking detailed molecular target insights. Full article
(This article belongs to the Special Issue Computational Research in Cancer Neuroscience)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop