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<p>In this study, we investigated the properties of exosomal miRNAs to identify potential biomarkers for liquid biopsy. We collected 956 exosomal and 956 non-exosomal miRNA sequences from RNALocate and miRBase to develop predictive models. Our initial analysis reveals that specific nucleotides are preferred at certain positions in miRNAs associated with exosomes. We employed an alignment-based approach, artificial intelligence (AI) models, and ensemble methods for predicting exosomal miRNAs. For the alignment-based approach, we used a motif-based method with MERCI and a similarity-based method with BLAST, achieving high precision but low coverage of about 29%. The AI models, developed using machine learning, deep learning techniques, and pretrained language models, achieved a maximum AUC of 0.707 and an MCC of 0.268 on an independent dataset. Finally, our ensemble method, combining alignment-based and AI-based models, reached a maximum AUC of 0.73 and an MCC of 0.352 on an independent dataset. We have developed a web server, EmiRPred, to assist the scientific community in predicting and designing exosomal miRNAs and identifying associated motifs (<a href="https://webs.iiitd.edu.in/raghava/emirpred/">https://webs.iiitd.edu.in/raghava/emirpred/</a>).</p>

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Prediction of exosomal miRNA-based biomarkers for liquid biopsy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月25日

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  • Akanksha Arora,
  • Gajendra Pal Singh Raghava

摘要

In this study, we investigated the properties of exosomal miRNAs to identify potential biomarkers for liquid biopsy. We collected 956 exosomal and 956 non-exosomal miRNA sequences from RNALocate and miRBase to develop predictive models. Our initial analysis reveals that specific nucleotides are preferred at certain positions in miRNAs associated with exosomes. We employed an alignment-based approach, artificial intelligence (AI) models, and ensemble methods for predicting exosomal miRNAs. For the alignment-based approach, we used a motif-based method with MERCI and a similarity-based method with BLAST, achieving high precision but low coverage of about 29%. The AI models, developed using machine learning, deep learning techniques, and pretrained language models, achieved a maximum AUC of 0.707 and an MCC of 0.268 on an independent dataset. Finally, our ensemble method, combining alignment-based and AI-based models, reached a maximum AUC of 0.73 and an MCC of 0.352 on an independent dataset. We have developed a web server, EmiRPred, to assist the scientific community in predicting and designing exosomal miRNAs and identifying associated motifs (https://webs.iiitd.edu.in/raghava/emirpred/).