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<p>Ovarian cancer is a deadly and complex disease associated with poor survival rates in women. Attempts to treat ovarian cancer and existing chemotherapies cause serious side effects, develop drug resistance to treatment, and are insufficient for complete recovery. Therefore, advanced approach is needed to increase the specific immune response by targeting cancer cells that could mediate long-term immunity. In this study, a multi-epitope mRNA vaccine was designed by determining T cell and MHC-binding antigenic epitopes of overexpressed cell surface proteins in ovarian cancer using immunoinformatics methods. For the design of mRNA vaccine, identified epitopes, 5′ cap, UTR regions, Kozak sequence, poly(A) tail, linkers and GM-CSF as genetic adjuvant were constructed into the mRNA construct. The antigenicity, allergenicity, toxicity, solubility, and physicochemical properties of the designed vaccine were predicted using bioinformatics tools. Secondary structure prediction and tertiary structure modeling of the vaccine candidate were carried out. Molecular docking was performed to evaluate the binding affinity and stability of MHC receptors and translated protein from mRNA vaccine. Finally, in silico simulation of the immune response after the vaccine immunization was conducted. The results showed that this novel vaccine is highly antigenic, non-toxic, non-allergenic, and has a good stability profile. In addition, immune response simulations have revealed that the vaccine has the potential to induce a strong immune response by increasing T cells and antigen-presenting cell populations for 30 days. Collectively, the proposed immunoinformatics workflow reflects a systems biology-focused perspective, integrating epitope selection using omics data and immune response predictions across multiple biological layers. Thus, an interaction network approach focused on the integration of molecular and immunological layers for translational interpretation has resulted in a novel vaccine design. The mRNA vaccine is a promising vaccine candidate against ovarian cancer but needs to be extensively investigated in future preclinical and clinical studies.</p>

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A novel design of an mRNA vaccine against ovarian cancer using immunoinformatics-driven methodology: an in silico framework

  • 期刊论文

  • 发表日期：

    2026年3月7日

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  • Rabia Yilmaz Ozturk,
  • Selcen Ari Yuka,
  • Rabia Cakir

摘要

Ovarian cancer is a deadly and complex disease associated with poor survival rates in women. Attempts to treat ovarian cancer and existing chemotherapies cause serious side effects, develop drug resistance to treatment, and are insufficient for complete recovery. Therefore, advanced approach is needed to increase the specific immune response by targeting cancer cells that could mediate long-term immunity. In this study, a multi-epitope mRNA vaccine was designed by determining T cell and MHC-binding antigenic epitopes of overexpressed cell surface proteins in ovarian cancer using immunoinformatics methods. For the design of mRNA vaccine, identified epitopes, 5′ cap, UTR regions, Kozak sequence, poly(A) tail, linkers and GM-CSF as genetic adjuvant were constructed into the mRNA construct. The antigenicity, allergenicity, toxicity, solubility, and physicochemical properties of the designed vaccine were predicted using bioinformatics tools. Secondary structure prediction and tertiary structure modeling of the vaccine candidate were carried out. Molecular docking was performed to evaluate the binding affinity and stability of MHC receptors and translated protein from mRNA vaccine. Finally, in silico simulation of the immune response after the vaccine immunization was conducted. The results showed that this novel vaccine is highly antigenic, non-toxic, non-allergenic, and has a good stability profile. In addition, immune response simulations have revealed that the vaccine has the potential to induce a strong immune response by increasing T cells and antigen-presenting cell populations for 30 days. Collectively, the proposed immunoinformatics workflow reflects a systems biology-focused perspective, integrating epitope selection using omics data and immune response predictions across multiple biological layers. Thus, an interaction network approach focused on the integration of molecular and immunological layers for translational interpretation has resulted in a novel vaccine design. The mRNA vaccine is a promising vaccine candidate against ovarian cancer but needs to be extensively investigated in future preclinical and clinical studies.