Identification of tumor-specific neoantigens and immune clusters of hepatocellular carcinoma for mRNA vaccine development

An integrated bioinformatics and machine learning approach to identifying biomarkers connecting parkinson's disease with purine metabolism-related genes

BACKGROUND

Parkinson's disease (PD), a prevalent neurodegenerative disorder in the aging population, poses significant challenges in unraveling its pathogenesis and progression. A key area of investigation is the disruption of oncological metabolic networks in PD, where diseased cells display distinct metabolic profiles compared to healthy counterparts. Of particular interest are Purine Metabolism Genes (PMGs), which play a pivotal role in nucleic acid synthesis.

METHODS

In this study, bioinformatics analyses were employed to identify and validate PMGs associated with PD. A set of 20 candidate PMGs underwent differential expression analysis. GSEA and GSVA were conducted to explore the biological roles and pathways of these PMGs. Lasso regression and SVM-RFE methods were applied to identify hub genes and assess the diagnostic efficacy of the nine PMGs in distinguishing PD. The correlation between these hub PMGs and clinical characteristics was also explored. Validation of the expression levels of the nine identified PMGs was performed using the GSE6613 and GSE7621 datasets.

RESULTS

The study identified nine PMGs related to PD: NME7, PKM, RRM2, POLR3 C, POLA1, PDE6 C, PDE9 A, PDE11 A, and AMPD1. Biological function analysis highlighted their involvement in processes like neutrophil activation and immune response. The diagnostic potential of these nine PMGs in differentiating PD was found to be substantial.

CONCLUSIONS

This investigation successfully identified nine PMGs associated with PD, providing valuable insights into potential novel biomarkers for this condition. These findings contribute to a deeper understanding of PD's pathogenesis and may aid in monitoring its progression, offering a new perspective in the study of neurodegenerative diseases.

Advancements and challenges in personalized neoantigen-based cancer vaccines

Advancements in personalized neoantigen-based cancer vaccines are ushering in a new era in oncology, targeting unique genetic alterations within tumors to enhance treatment precision and efficacy. Neoantigens, specific to cancer cells and absent in normal tissues, are at the heart of these vaccines, promising to direct the immune system specifically against the tumor, thereby maximizing therapeutic efficacy while minimizing side effects. The identification of neoantigens through genomic and proteomic technologies is central to developing these vaccines, allowing for the precise mapping of a tumor's mutational landscape. Despite advancements, accurately predicting which neoantigens will elicit strong immune responses remains challenging due to tumor variability and the complexity of immune system interactions. This necessitates further refinement of bioinformatics tools and predictive models. Moreover, the efficacy of these vaccines heavily depends on innovative delivery methods that enhance neoantigen presentation to the immune system. Techniques like encapsulating neoantigens in lipid nanoparticles and using viral vectors are critical for improving vaccine stability and delivery. Additionally, these vaccines contribute towards achieving Sustainable Development Goal 3.8, promoting universal health coverage by advancing access to safe and effective cancer treatments. This review delves into the potential of neoantigen-based vaccines to transform cancer treatment, examining both revolutionary advancements and the ongoing challenges they face.

Unraveling neoantigen-associated genes in bladder cancer: An in-depth analysis employing 101 machine learning algorithms

The therapeutic outcomes for bladder cancer (BLCA) remain suboptimal. Concurrently, there is a growing appreciation for the role of neoantigens in tumors. In this study, we explored the mechanisms underlying the involvement of neoantigen-associated genes in BLCA and their impact on prognosis. Our analysis incorporated both single-cell sequencing and bulk sequencing data sourced from publicly available databases. By employing a comprehensive set of 10 machine learning algorithms, we generated 101 algorithm combinations. The optimal combination, determined based on consistency indices, was utilized to construct a prognostic model comprising nine genes (CAPG, ACTA2, PDIA6, AKNA, PTMS, SNAP23, ID2, CD3G, SP140). Subsequently, we validated this model in an independent cohort, demonstrating its robust testing efficacy. Moreover, we explored the correlations between various clinical traits, model scores, and genes. Leveraging extensive public data resources, we conducted a drug sensitivity analysis to provide insights for targeted drug screening. Additionally, consensus clustering analysis and immune infiltration analysis were performed on bulk sequencing datasets and immunotherapy cohorts. These analyses yield valuable insights into the role of neoantigens in BLCA, guiding future research endeavors.

Nanomaterial Delivery Vehicles for the Development of Neoantigen Tumor Vaccines for Personalized Treatment

Tumor vaccines have been considered a promising therapeutic approach for treating cancer in recent years. With the development of sequencing technologies, tumor vaccines based on neoantigens or genomes specifically expressed in tumor cells, mainly in the form of peptides, nucleic acids, and dendritic cells, are beginning to receive widespread attention. Therefore, in this review, we have introduced different forms of neoantigen vaccines and discussed the development of these vaccines in treating cancer. Furthermore, neoantigen vaccines are influenced by factors such as antigen stability, weak immunogenicity, and biosafety in addition to sequencing technology. Hence, the biological nanomaterials, polymeric nanomaterials, inorganic nanomaterials, etc., used as vaccine carriers are principally summarized here, which may contribute to the design of neoantigen vaccines for improved stability and better efficacy.

Molecular profiling in the management of hepatocellular carcinoma

PURPOSE OF REVIEW

The purpose of this review is to both summarize the current knowledge of hepatocellular carcinoma molecular biology and to suggest a framework in which to prospectively translate this knowledge into patient care. This is timely as recent guidelines recommend increased use of these technologies to advance personalized liver cancer care.

RECENT FINDINGS

The main themes covered here address germline and somatic genetic alterations recently discovered in hepatocellular carcinoma, largely owing to next generation sequencing technologies, and nascent efforts to translate these into contemporary practice.

SUMMARY

Early efforts of translating molecular profiling to hepatocellular carcinoma care demonstrate a growing number of potentially actionable alterations. Still lacking are a consensus on what biomarkers and technologies to adopt, at what scale and cost, and how to integrate them most effectively into care.

Sensitizing the Efficiency of ICIs by Neoantigen mRNA Vaccines for HCC Treatment

This study builds upon the groundbreaking mRNA vaccine Nobel Prize win in 2023 for COVID-19 prevention, paving the way for next-generation mRNA cancer vaccines to revolutionize immunotherapy. Despite the existing challenges, such as the presence of a suppressive tumor microenvironment and the identification of cancer-associated antigens, recent results from the KEYNOTE-942 trial have successfully demonstrated the effectiveness of mRNA-based cancer treatments, providing clinical evidence for the first time. This trial aimed to evaluate the efficacy and safety of combining immune checkpoint inhibitors with mRNA-based therapies in treating cancer. This advancement undeniably represents new hope for hepatocellular carcinoma (HCC) patients. However, progress in this field remains limited. In this article, we summarized the current state of applying immune checkpoint inhibitors (ICIs) combined with neoantigen mRNA vaccines. Additionally, we discussed potential targets for designing novel mRNA vaccines and potential mRNA vaccine delivery vehicles. The objective of this article is to inspire enthusiasm for the exploration of innovative therapeutic strategies that combine ICIs with neoantigen mRNA vaccines for HCC treatment and HCC prevention.

mRNA vaccine in gastrointestinal tumors: Immunomodulatory effects and immunotherapy

Gastrointestinal tumors remain a significant healthcare burden worldwide, necessitating the development of innovative therapeutic strategies. mRNA vaccines have emerged as a promising approach in cancer immunotherapy, harnessing the immune system's potential to recognize and eliminate tumor cells. mRNA vaccines offer several advantages, including their ability to elicit both innate and adaptive immune responses, ease of production, and adaptability to different tumor types. In the context of gastrointestinal tumors, mRNA vaccines hold great potential as a therapeutic strategy. In this review, we will delve into the immunomodulatory mechanisms and immunotherapy strategies of mRNA vaccines in gastrointestinal tumors. Additionally, we will discuss the challenges and ongoing research efforts in optimizing mRNA vaccine development, delivery, and stability. By understanding the potential of mRNA vaccines in addressing the unmet medical need of gastrointestinal tumors, we aim to pave the way for improved treatment strategies and better patient outcomes.