Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.

Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust and targeted immune response. Recent advancements in bioinformatics and artificial intelligence (AI) have significantly enhanced the design, prediction, and optimization of mRNA vaccines. This paper reviews technologies that streamline mRNA vaccine development, from genomic sequencing to lipid nanoparticle (LNP) formulation.

Neoantigen mRNA vaccines and AA receptor antagonism: A strategy to enhance T cell immunity.

Although neo-antigen mRNA vaccines are promising for personalized cancer therapy, their effectiveness is often limited by the immunosuppressive tumor microenvironment (TME). The adenosine AA receptor (AAR) inhibits dendritic cell (DC) function and weakens antitumor T cell responses through hypoxia-driven mechanisms within the TME. This review explores a novel strategy combining neo-antigen mRNA vaccines with AAR antagonists (AARi).

Identifying candidate RNA-seq biomarkers for severity discrimination in chemical injuries: A machine learning and molecular dynamics approach.

Introduction: Biomarkers play a crucial role across various fields by providing insights into biological responses to interventions. High-throughput gene expression profiling technologies facilitate the discovery of data-driven biomarkers through extensive datasets. This study focuses on identifying biomarkers in gene expression data related to chemical injuries by mustard gas, covering a spectrum from healthy individuals to severe injuries.

Discovery of LINC01614 associated with the SPP1 gene in colorectal cancer.

Colorectal cancer (CRC) is a prevalent malignancy worldwide, driven by complex molecular mechanisms. This study aims to elucidate the role of lncRNAs within TGF-β pathway, a crucial signaling pathway in CRC progression, focusing specifically on their interaction with the SPP1 gene. We employed a multi-faceted approach, starting with comprehensive in silico analyses to identify candidate lncRNAs potentially involved in TGF-β pathway regulation.