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Clinical Epigenetics Jun 2024Gastrointestinal malignancies encompass a diverse group of cancers that pose significant challenges to global health. The major histocompatibility complex (MHC) plays a... (Review)
Review
BACKGROUND
Gastrointestinal malignancies encompass a diverse group of cancers that pose significant challenges to global health. The major histocompatibility complex (MHC) plays a pivotal role in immune surveillance, orchestrating the recognition and elimination of tumor cells by the immune system. However, the intricate regulation of MHC gene expression is susceptible to dynamic epigenetic modification, which can influence functionality and pathological outcomes.
MAIN BODY
By understanding the epigenetic alterations that drive MHC downregulation, insights are gained into the molecular mechanisms underlying immune escape, tumor progression, and immunotherapy resistance. This systematic review examines the current literature on epigenetic mechanisms that contribute to MHC deregulation in esophageal, gastric, pancreatic, hepatic and colorectal malignancies. Potential clinical implications are discussed of targeting aberrant epigenetic modifications to restore MHC expression and 0 the effectiveness of immunotherapeutic interventions.
CONCLUSION
The integration of epigenetic-targeted therapies with immunotherapies holds great potential for improving clinical outcomes in patients with gastrointestinal malignancies and represents a compelling avenue for future research and therapeutic development.
Topics: Humans; Gastrointestinal Neoplasms; Epigenesis, Genetic; Major Histocompatibility Complex; Gene Expression Regulation, Neoplastic; Immunotherapy; DNA Methylation; Tumor Escape
PubMed: 38915093
DOI: 10.1186/s13148-024-01698-8 -
Cancer Genetics Apr 2024Early detection of breast cancer would help alleviate the burden of treatment for early-stage breast cancer and help patient prognosis. There is currently no established...
BACKGROUND
Early detection of breast cancer would help alleviate the burden of treatment for early-stage breast cancer and help patient prognosis. There is currently no established gene panel that utilizes the potential of DNA methylation as a molecular signature for the early detection of breast cancer. This systematic review aims to identify the optimal methylation biomarkers for a non-invasive liquid biopsy assay and the gaps in knowledge regarding biomarkers for early detection of breast cancer.
METHODS
Following the PRISMA-ScR method, Pubmed and Google Scholar was searched for publications related to methylation biomarkers in breast cancer over a five-year period. Eligible publications were mined for key data fields such as study aims, cohort demographics, types of breast cancer studied, technologies used, and outcomes. Data was analyzed to address the objectives of the review.
RESULTS
Literature search identified 112 studies of which based on eligibility criteria, 13 studies were included. 28 potential methylation gene targets were identified, of which 23 were methylated at the promoter region, 1 was methylated in the body of the gene and 4 were methylated at yet to be identified locations.
CONCLUSIONS
Our evaluation shows that at minimum APC, RASSFI, and FOXA1 genes would be a promising set of genes to start with for the early detection of breast cancer, based on the sensitivity and specificity outlined in the studies. Prospective studies are needed to optimize biomarkers for broader impact in early detection of breast cancer.
Topics: Female; Humans; Biomarkers, Tumor; Breast Neoplasms; DNA Methylation; Early Detection of Cancer; Prognosis; Sensitivity and Specificity
PubMed: 38134587
DOI: 10.1016/j.cancergen.2023.12.003 -
Journal of Cancer Research and Clinical... Jan 2024Accurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive... (Review)
Review
BACKGROUND
Accurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive biomarker associated with improved overall survival (OS). In response to the clinical need, recent studies have focused on the development of non-invasive artificial intelligence (AI)-based methods for MGMT estimation. In this systematic review, we not only delve into the technical aspects of these AI-driven MGMT estimation methods but also emphasize their profound clinical implications. Specifically, we explore the potential impact of accurate non-invasive MGMT estimation on GBM patient care and treatment decisions.
METHODS
Employing a PRISMA search strategy, we identified 33 relevant studies from reputable databases, including PubMed, ScienceDirect, Google Scholar, and IEEE Explore. These studies were comprehensively assessed using 21 diverse attributes, encompassing factors such as types of imaging modalities, machine learning (ML) methods, and cohort sizes, with clear rationales for attribute scoring. Subsequently, we ranked these studies and established a cutoff value to categorize them into low-bias and high-bias groups.
RESULTS
By analyzing the 'cumulative plot of mean score' and the 'frequency plot curve' of the studies, we determined a cutoff value of 6.00. A higher mean score indicated a lower risk of bias, with studies scoring above the cutoff mark categorized as low-bias (73%), while 27% fell into the high-bias category.
CONCLUSION
Our findings underscore the immense potential of AI-based machine learning (ML) and deep learning (DL) methods in non-invasively determining MGMT promoter methylation status. Importantly, the clinical significance of these AI-driven advancements lies in their capacity to transform GBM patient care by providing accurate and timely information for treatment decisions. However, the translation of these technical advancements into clinical practice presents challenges, including the need for large multi-institutional cohorts and the integration of diverse data types. Addressing these challenges will be critical in realizing the full potential of AI in improving the reliability and accessibility of MGMT estimation while lowering the risk of bias in clinical decision-making.
Topics: Humans; Glioblastoma; Artificial Intelligence; Reproducibility of Results; DNA Methylation; Brain Neoplasms; DNA Modification Methylases; DNA Repair Enzymes; DNA; Tumor Suppressor Proteins
PubMed: 38291266
DOI: 10.1007/s00432-023-05566-5