Clinical and Mechanistic Implications of R-Loops in Human Leukemias

Lee, Seo-Yun; Miller, Kyle M.; Kim, Jae‐Jin

doi:10.3390/ijms24065966

Cited by 4 publications

(1 citation statement)

References 121 publications

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“…Leukemia affects the generation of white blood cells essential for fighting infections and diseases [1]. The development is influenced by a complex combination of genetic, environmental, and lifestyle elements [2,3]. Despite connections to genetic mutations, chemical exposure, radiation, and viral infections, pinpointing its exact origins can be challenging.…”

Section: Introductionmentioning

confidence: 99%

Advancing Early Leukemia Diagnostics: A Comprehensive Study Incorporating Image Processing and Transfer Learning

Haque,

Al Sakib,

Hossain

et al. 2024

BioMedInformatics

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Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood smears. This practice yields invaluable insights into a patient’s health, enabling the identification of conditions of blood malignancies such as leukemia. Early identification of leukemia subtypes is paramount for tailoring appropriate therapeutic interventions and enhancing patient survival rates. However, traditional diagnostic techniques, which depend on visual assessment, are arbitrary, laborious, and prone to errors. The advent of ML technologies offers a promising avenue for more accurate and efficient leukemia classification. In this study, we introduced a novel approach to leukemia classification by integrating advanced image processing, diverse dataset utilization, and sophisticated feature extraction techniques, coupled with the development of TL models. Focused on improving accuracy of previous studies, our approach utilized Kaggle datasets for binary and multiclass classifications. Extensive image processing involved a novel LoGMH method, complemented by diverse augmentation techniques. Feature extraction employed DCNN, with subsequent utilization of extracted features to train various ML and TL models. Rigorous evaluation using traditional metrics revealed Inception-ResNet’s superior performance, surpassing other models with F1 scores of 96.07% and 95.89% for binary and multiclass classification, respectively. Our results notably surpass previous research, particularly in cases involving a higher number of classes. These findings promise to influence clinical decision support systems, guide future research, and potentially revolutionize cancer diagnostics beyond leukemia, impacting broader medical imaging and oncology domains.

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Section: Introductionmentioning

confidence: 99%

Advancing Early Leukemia Diagnostics: A Comprehensive Study Incorporating Image Processing and Transfer Learning

Haque,

Al Sakib,

Hossain

et al. 2024

BioMedInformatics

View full text Add to dashboard Cite

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DHX9-mediated pathway contributes to the malignant phenotype of myelodysplastic syndromes

et al. 2023

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PCAF promotes R-loop resolution via histone acetylation

Lee,

Choi

et al. 2024

Nucleic Acids Research

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R-loops cause genome instability, disrupting normal cellular functions. Histone acetylation, particularly by p300/CBP-associated factor (PCAF), is essential for maintaining genome stability and regulating cellular processes. Understanding how R-loop formation and resolution are regulated is important because dysregulation of these processes can lead to multiple diseases, including cancer. This study explores the role of PCAF in maintaining genome stability, specifically for R-loop resolution. We found that PCAF depletion promotes the generation of R-loop structures, especially during ongoing transcription, thereby compromising genome stability. Mechanistically, we found that PCAF facilitates histone H4K8 acetylation, leading to recruitment of the a double-strand break repair protein (MRE11) and exonuclease 1 (EXO1) to R-loop sites. These in turn recruit Fanconi anemia (FA) proteins, including FANCM and BLM, to resolve the R-loop structure. Our findings suggest that PCAF, histone acetylation, and FA proteins collaborate to resolve R-loops and ensure genome stability. This study therefore provides novel mechanistic insights into the dynamics of R-loops as well as the role of PCAF in preserving genome stability. These results may help develop therapeutic strategies to target diseases associated with genome instability.

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Clinical and Mechanistic Implications of R-Loops in Human Leukemias

Cited by 4 publications

References 121 publications

Advancing Early Leukemia Diagnostics: A Comprehensive Study Incorporating Image Processing and Transfer Learning

Advancing Early Leukemia Diagnostics: A Comprehensive Study Incorporating Image Processing and Transfer Learning

DHX9-mediated pathway contributes to the malignant phenotype of myelodysplastic syndromes

PCAF promotes R-loop resolution via histone acetylation

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