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Medical Journal of Islamic Republic of Iran، جلد ۳۹، شماره ۱، صفحات ۱۸-۲۶
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عنوان انگلیسی Identification of Important Diagnostic Genes in the Uterine Using Bioinformatics and Machine Learning
چکیده انگلیسی مقاله
    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.

کلیدواژه‌های انگلیسی مقاله Uterine Corpus Endometrial Carcinoma, Deep learning, Biomarker, Bioinformatic Analysis, UCEC
نویسندگان مقاله | Hossein Valizadeh Laktarashi
Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran


| Milad Rahimi
Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran


| Kimia Abrishamifar
School of Health Management and Information Science, Shiraz University of Medical Sciences, Shiraz, Iran


| Ali Mahmoudabadi
Department of Medical Genetics, Afzalipoor Faculty of Medicine, Kerman University of Medical Sciences, Kerman, Iran


| Elham Nazari
Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
نشانی اینترنتی http://mjiri.iums.ac.ir/browse.php?a_code=A-10-9085-1&slc_lang=en&sid=1
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کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده Human Genetics
نوع مقاله منتشر شده Original Research
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