Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 4 de 4
Filtrar
Más filtros




Base de datos
Intervalo de año de publicación
1.
Adv Protein Chem Struct Biol ; 142: 25-43, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-39059987

RESUMEN

Breast cancer (BC) is the most common cancer among women and a major cause of death from cancer. The role of estrogen and progestins, including synthetic hormones like R5020, in the development of BC has been highlighted in numerous studies. In our study, we employed machine learning and advanced bioinformatics to identify genes that could serve as diagnostic markers for BC. We thoroughly analyzed the transcriptomic data of two BC cell lines, T47D and UDC4, and performed differential gene expression analysis. We also conducted functional enrichment analysis to understand the biological functions influenced by these genes. Our study identified several diagnostic genes strongly associated with BC, including MIR6728, ENO1-IT1, ENO1-AS1, RNU6-304P, HMGN2P17, RP3-477M7.5, RP3-477M7.6, and CA6. The genes MIR6728, ENO1-IT1, ENO1-AS1, and HMGN2P17 are involved in cancer control, glycolysis, and DNA-related processes, while CA6 is associated with apoptosis and cancer development. These genes could potentially serve as predictors for BC, paving the way for more precise diagnostic methods and personalized treatment plans. This research enhances our understanding of BC and offers promising avenues for improving patient care in the future.


Asunto(s)
Neoplasias de la Mama , Estrógenos , Progestinas , Humanos , Neoplasias de la Mama/genética , Neoplasias de la Mama/diagnóstico , Neoplasias de la Mama/metabolismo , Neoplasias de la Mama/patología , Femenino , Estrógenos/metabolismo , Genómica , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismo
2.
Neurosci Lett ; 828: 137764, 2024 Apr 01.
Artículo en Inglés | MEDLINE | ID: mdl-38582325

RESUMEN

BACKGROUND: Ataxia Telangiectasia (AT) is a genetic disorder characterized by compromised DNA repair, cerebellar degeneration, and immune dysfunction. Understanding the molecular mechanisms driving AT pathology is crucial for developing targeted therapies. METHODS: In this study, we conducted a comprehensive analysis to elucidate the molecular mechanisms underlying AT pathology. Using publicly available RNA-seq datasets comparing control and AT samples, we employed in silico transcriptomics to identify potential genes and pathways. We performed differential gene expression analysis with DESeq2 to reveal dysregulated genes associated with AT. Additionally, we constructed a Protein-Protein Interaction (PPI) network to explore the interactions between proteins implicated in AT. RESULTS: The network analysis identified hub genes, including TYROBP and PCP2, crucial in immune regulation and cerebellar function, respectively. Furthermore, pathway enrichment analysis unveiled dysregulated pathways linked to AT pathology, providing insights into disease progression. CONCLUSION: Our integrated approach offers a holistic understanding of the complex molecular landscape of AT and identifies potential targets for therapeutic intervention. By combining transcriptomic analysis with network-based methods, we provide valuable insights into the underlying mechanisms of AT pathogenesis.


Asunto(s)
Ataxia Telangiectasia , Enfermedades Cerebelosas , Humanos , Enfermedades Neuroinflamatorias , Mapas de Interacción de Proteínas , Perfilación de la Expresión Génica/métodos , Biología Computacional/métodos
3.
Life Sci ; 337: 122360, 2024 Jan 15.
Artículo en Inglés | MEDLINE | ID: mdl-38135117

RESUMEN

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.


Asunto(s)
Neoplasias de la Mama Triple Negativas , Vacunas , Humanos , Neoplasias de la Mama Triple Negativas/tratamiento farmacológico , Multiómica , Inteligencia Artificial , Epítopos , Vacunas/uso terapéutico , Antígenos de Neoplasias
4.
Int J Biol Macromol ; 258(Pt 1): 128753, 2024 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-38104690

RESUMEN

Viruses transmitted by arthropods, such as Dengue, Zika, and Chikungunya, represent substantial worldwide health threats, particularly in countries like India. The lack of approved vaccines and effective antiviral therapies calls for developing innovative strategies to tackle these arboviruses. In this study, we employed immunoinformatics methodologies, incorporating reverse vaccinology, to design a multivalent vaccine targeting the predominant arboviruses. Epitopes of B and T cells were recognized within the non-structural proteins of Dengue, Zika, and Chikungunya viruses. The predicted epitopes were enhanced with adjuvants ß-defensin and RS-09 to boost the vaccine's immunogenicity. Sixteen distinct vaccine candidates were constructed, each incorporating epitopes from all three viruses. FUVAC-11 emerged as the most promising vaccine candidate through molecular docking and molecular dynamics simulations, demonstrating favorable binding interactions and stability. Its effectiveness was further evaluated using computational immunological studies confirming strong immune responses. The in silico cloning performed using the pET-28a(+) plasmid facilitates the future experimental implementation of this vaccine candidate, paving the way for potential advancements in combating these significant arboviral threats. However, further in vitro and in vivo studies are warranted to confirm the results obtained in this computational study, which highlights the effectiveness of immunoinformatics and reverse vaccinology in creating vaccines against major Arboviruses, offering a promising model for developing vaccines for other vector-borne diseases and enhancing global health security.


Asunto(s)
Arbovirus , Fiebre Chikungunya , Dengue , Vacunas , Infección por el Virus Zika , Virus Zika , Humanos , Simulación del Acoplamiento Molecular , Fiebre Chikungunya/prevención & control , Vacunas Combinadas , Vacunología/métodos , Epítopos de Linfocito T/química , Biología Computacional/métodos , Epítopos de Linfocito B , Vacunas de Subunidad
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA