Deciphering the c-MYC miRNA Regulatory Network through Machine Learning

  • سال انتشار: 1402
  • محل انتشار: دوازدهمین همایش ملی و سومین همایش بین المللی بیوانفورماتیک
  • کد COI اختصاصی: IBIS12_067
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 137
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نویسندگان

Ali Malmir

Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

Maryam Hassanlou

Farzanegan campus, semnan University, Semnan, Iran

Mahsa Mohammad Amoli

Endocrinology and Metabolism Research Institute (EMRI), Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

Majid Sadeghizadeh

Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

چکیده

Introduction: The oncogene c-MYC plays a pivotal role in cancer development andprogression[۱], yet its intricate control network, particularly involving microRNAs (miRNAs), remainselusive[۲]. This study delves into c-MYC's potential to encode miRNA regulatory information usingmachine learning, aiming to decipher its miRNA regulatory landscape.Materials and Methods: We compiled diverse datasets encompassing c-MYC expression, miRNAexpression, and predicted miRNA target sites. Using feature engineering techniques, we extractedsequence and positional features surrounding potential miRNA target sites within the c-MYC gene.Subsequently, we employed different machine learning algorithms, including random forests andsupport vector machines, to classify true miRNA target sites based on these features. We evaluatedmodel performance using cross-validation and compared results across algorithms.Results: Our models achieved high accuracy in identifying true miRNA target sites within the c-MYCgene, exceeding ۸۵% in most cases. By analyzing the learned feature weights, we uncovered keysequence and positional motifs crucial for miRNA recognition and binding. Interestingly, we identifiedclusters of enriched motifs within the c-MYC gene, suggesting the presence of miRNA regulatoryhotspots. Furthermore, we mapped these hotspots to specific functional domains within c-MYC,revealing a potential link between miRNA regulation and c-MYC function.Conclusions: Our study demonstrates the feasibility of using machine learning to decode miRNAregulatory networks encoded within genes. By applying this approach to c-MYC, we unveiled acomplex miRNA regulatory landscape with hotspots likely linked to specific c-MYC functions. Thisunderstanding paves the way for further investigations into miRNA-mediated control of c-MYC incancer. Future research will focus on validating predicted target sites experimentally and elucidatingthe functional consequences of this newly discovered miRNA regulatory network in cancer biology.

کلیدواژه ها

c-MYC; microRNA; miRNA regulatory network; machine learning; target site prediction; cancer

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