Medical journal of the Islamic Republic of Iran · 2025
Identification of Important Diagnostic Genes in the Uterine Using Bioinformatics and Machine Learning.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Valizadeh Laktarashi Hossein, Rahimi Milad, Abrishamifar Kimia, Mahmoudabadi Ali, Nazari Elham
Аннотация
BACKGROUND: Uterine corpus endometrial cancer (UCEC) is known as the sixth most common cancer in the world. Advances in bioinformatics and deep learning have provided the 2 tools for screening large-scale genomic data and discovering potential biomarkers indicative of disease states. This study aimed to investigate the identification of important genes for diagnosis and prognosis in the uterus using bioinformatics and machine learning algorithms.
METHODS: RNA expression profiles of UECE patients were analyzed to identify differentially expressed genes (DEGs) using deep learning techniques. Prognostic biomarkers were assessed through survival curve analysis utilizing COMBIO-ROC. Additionally, molecular pathways, protein-protein interaction (PPI) networks, co-expression patterns of DEGs, and their associations with clinical data were thoroughly examined. Ultimately, diagnostic markers were determined through deep learning-based analyses.
RESULTS: According to our findings, MEX3B, CTRP2 (C1QTNF2), and AASS are new biomarkers for UCEC. The evaluation metrics demonstrate the deep learning model's (DNN) efficacy, with a minimal mean squared error (MSE) of 5.1096067E-5 and a root mean squared error (RMSE) of 0.007, indicative of accurate predictions. The R-squared value of 0.99 underscores the model's ability to explain a substantial portion of the variance in the data. Thus, the model achieves a perfect area under the curve (AUC) of 1, signifying exceptional discrimination ability, and an accuracy rate of 97%.
CONCLUSION: The GDCA database and deep learning algorithms identified 3 significant genes -MEX3B, CTRP2 (C1QTNF2), and AASS-as potential diagnosis biomarkers of UCEC. Thus, identifying new UCEC biomarkers has promise for effective care, improved prognosis, and early diagnosis.
METHODS: RNA expression profiles of UECE patients were analyzed to identify differentially expressed genes (DEGs) using deep learning techniques. Prognostic biomarkers were assessed through survival curve analysis utilizing COMBIO-ROC. Additionally, molecular pathways, protein-protein interaction (PPI) networks, co-expression patterns of DEGs, and their associations with clinical data were thoroughly examined. Ultimately, diagnostic markers were determined through deep learning-based analyses.
RESULTS: According to our findings, MEX3B, CTRP2 (C1QTNF2), and AASS are new biomarkers for UCEC. The evaluation metrics demonstrate the deep learning model's (DNN) efficacy, with a minimal mean squared error (MSE) of 5.1096067E-5 and a root mean squared error (RMSE) of 0.007, indicative of accurate predictions. The R-squared value of 0.99 underscores the model's ability to explain a substantial portion of the variance in the data. Thus, the model achieves a perfect area under the curve (AUC) of 1, signifying exceptional discrimination ability, and an accuracy rate of 97%.
CONCLUSION: The GDCA database and deep learning algorithms identified 3 significant genes -MEX3B, CTRP2 (C1QTNF2), and AASS-as potential diagnosis biomarkers of UCEC. Thus, identifying new UCEC biomarkers has promise for effective care, improved prognosis, and early diagnosis.