Proteomics Studies on Extracellular Vesicles Derived from Glioblastoma: Where Do We Stand? DOI Open Access
Patricia Giuliani,

Chiara Simone,

Giorgia Febo

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(18), P. 9778 - 9778

Published: Sept. 10, 2024

Like most tumors, glioblastoma multiforme (GBM), the deadliest brain tumor in human adulthood, releases extracellular vesicles (EVs). Their content, reflecting that of origin, can be donated to nearby and distant cells which, by acquiring it, become more aggressive. Therefore, study EV-transported molecules has very important. Particular attention been paid EV proteins uncover new GBM biomarkers potential druggable targets. Proteomic studies have mainly performed “bottom-up” mass spectrometry (MS) analysis EVs isolated different procedures from conditioned media cultured biological fluids patients. Although a great number dysregulated identified, translation these findings into clinics remains elusive, probably due multiple factors, including lack standardized for isolation/characterization their proteome. Thus, it is time change research strategies adopting, addition harmonized selection techniques, MS methods aimed at identifying selected tumoral protein mutations and/or isoforms post-translational modifications, which deeply influence behavior. Hopefully, data integrated with those other “omics” disciplines will lead discovery pathways novel therapies.

Language: Английский

Impact of Metabolites from Foodborne Pathogens on Cancer DOI Creative Commons
Alice Njolke Mafe, Dietrich Büsselberg

Foods, Journal Year: 2024, Volume and Issue: 13(23), P. 3886 - 3886

Published: Dec. 1, 2024

Foodborne pathogens are microorganisms that cause illness through contamination, presenting significant risks to public health and food safety. This review explores the metabolites produced by these pathogens, including toxins secondary metabolites, their implications for human health, particularly concerning cancer risk. We examine various such as Salmonella sp., Campylobacter Escherichia coli, Listeria monocytogenes, detailing specific of concern carcinogenic mechanisms. study discusses analytical techniques detecting chromatography, spectrometry, immunoassays, along with challenges associated detection. covers effective control strategies, processing techniques, sanitation practices, regulatory measures, emerging technologies in pathogen control. manuscript considers broader highlighting importance robust policies, awareness, education. identifies research gaps innovative approaches, recommending advancements detection methods, preventive policy improvements better manage foodborne metabolites.

Language: Английский

Citations

11
Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions DOI Creative Commons
David B. Olawade, Aanuoluwapo Clement David-Olawade, Temitope Adereni

et al.

Diseases, Journal Year: 2025, Volume and Issue: 13(1), P. 24 - 24

Published: Jan. 20, 2025

Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy radiation often result in significant side effects varied patient outcomes. Immunotherapy has emerged as promising alternative, harnessing the immune system to target cancer cells. However, complexity responses tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores role artificial intelligence [AI] enhancing efficacy immunotherapy, predicting responses, discovering novel therapeutic targets. Methods: A comprehensive literature was conducted, focusing on studies published between 2010 2024 that examined application AI immunotherapy. Databases such PubMed, Google Scholar, Web Science were utilized, articles selected based relevance topic. Results: significantly contributed identifying biomarkers predict immunotherapy by analyzing genomic, transcriptomic, proteomic data. It also optimizes combination therapies most effective treatment protocols. AI-driven predictive models help assess response guiding clinical decision-making minimizing effects. Additionally, facilitates discovery targets, neoantigens, enabling development personalized immunotherapies. Conclusions: holds immense potential transforming related data privacy, algorithm transparency, integration must be addressed. Overcoming these hurdles will likely make central component future offering more treatments.

Language: Английский

Citations

1
Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic Review DOI Open Access

Yousaku Ozaki,

P M Broughton,

Hamed Abdollahi

et al.

Published: June 11, 2024

Cancer is one of the leading causes death, making timely diagnosis and prognosis very important. Utilization AI (artificial intelligence) enables providers to organize process patient data in a way that can lead better overall outcomes. This review paper aims look at varying uses for clinical utility. PubMed EBSCO databases were utilized finding publications from January 1, 2013, December 22, 2023. Articles collected using key search terms such as “artificial intelligence” “machine learning.” Included collection studies application determining cancer multi-omics data, radiomics, pathomics, laboratory data. The resulting 89 categorized into eight sections based on type then further subdivided two subsections focusing prognosis, respectively. 8 integrated more than form omics, namely genomics, transcriptomics, epigenomics, proteomics. Incorporating alongside omics represents significant advancement. Given considerable potential this domain, ongoing prospective are essential enhance algorithm interpretability ensure safe integration.

Language: Английский

Citations

5
Precision Targeting in Metastatic Prostate Cancer: Molecular Insights to Therapeutic Frontiers DOI Creative Commons
Whi‐An Kwon, Jae Young Joung

Biomolecules, Journal Year: 2025, Volume and Issue: 15(5), P. 625 - 625

Published: April 27, 2025

Metastatic prostate cancer (mPCa) remains a significant cause of cancer-related mortality in men. Advances molecular profiling have demonstrated that the androgen receptor (AR) axis, DNA damage repair pathways, and PI3K/AKT/mTOR pathway are critical drivers disease progression therapeutic resistance. Despite established benefits hormone therapy, chemotherapy, bone-targeting agents, mPCa commonly becomes treatment-resistant. Recent breakthroughs highlighted importance identifying actionable genetic alterations, such as BRCA2 or ATM defects, render tumors sensitive to poly-ADP ribose polymerase (PARP) inhibitors. Parallel efforts refined imaging—particularly prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography—to detect localize metastatic lesions with high sensitivity, thereby guiding patient selection for PSMA-targeted radioligand therapies. Multi-omics innovations, including liquid biopsy technologies, enable real-time tracking emergent AR splice variants reversion mutations, supporting adaptive therapy paradigms. Nonetheless, complexity necessitates combination strategies, pairing inhibition PI3K/AKT blockade PARP inhibitors, inhibit tumor plasticity. Immuno-oncological approaches remain challenging unselected patients; however, subsets mismatch deficiency neuroendocrine phenotypes may benefit from immune checkpoint targeted epigenetic interventions. We present these pivotal advances, discuss how biomarker-guided integrative treatments can improve management.

Language: Английский

Citations

0
Artificial Intelligence–Driven Computational Approaches in the Development of Anticancer Drugs DOI Open Access
Pankaj Garg, G. D. Singhal, Prakash Kulkarni

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(22), P. 3884 - 3884

Published: Nov. 20, 2024

The integration of AI has revolutionized cancer drug development, transforming the landscape discovery through sophisticated computational techniques. AI-powered models and algorithms have enhanced computer-aided design (CADD), offering unprecedented precision in identifying potential anticancer compounds. Traditionally, been a complex, resource-intensive process, but introduces new opportunities to accelerate discovery, reduce costs, optimize efficiency. This manuscript delves into transformative applications AI-driven methodologies predicting developing drugs, critically evaluating their reshape future therapeutics while addressing challenges limitations.

Language: Английский

Citations

3
Proteomics Studies on Extracellular Vesicles Derived from Glioblastoma: Where Do We Stand? DOI Open Access
Patricia Giuliani,

Chiara Simone,

Giorgia Febo

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(18), P. 9778 - 9778

Published: Sept. 10, 2024

Like most tumors, glioblastoma multiforme (GBM), the deadliest brain tumor in human adulthood, releases extracellular vesicles (EVs). Their content, reflecting that of origin, can be donated to nearby and distant cells which, by acquiring it, become more aggressive. Therefore, study EV-transported molecules has very important. Particular attention been paid EV proteins uncover new GBM biomarkers potential druggable targets. Proteomic studies have mainly performed “bottom-up” mass spectrometry (MS) analysis EVs isolated different procedures from conditioned media cultured biological fluids patients. Although a great number dysregulated identified, translation these findings into clinics remains elusive, probably due multiple factors, including lack standardized for isolation/characterization their proteome. Thus, it is time change research strategies adopting, addition harmonized selection techniques, MS methods aimed at identifying selected tumoral protein mutations and/or isoforms post-translational modifications, which deeply influence behavior. Hopefully, data integrated with those other “omics” disciplines will lead discovery pathways novel therapies.

Language: Английский

Citations

0