一种将细胞外 miRNA 与 mRNA 整合进行癌症研究的深度学习方法。

A deep learning method to integrate extracelluar miRNA with mRNA for cancer studies.

机构信息

Department of Computer Science, University of Central Florida, 4000 Central Florida BLVD, Orlando, FL, 32816, United States.

Burnett School of Biomedical Sciences, University of Central Florida, 4000 Central Florida BLVD, Orlando, FL, 32816, United States.

出版信息

Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae653.

DOI:10.1093/bioinformatics/btae653

Abstract

MOTIVATION

Extracellular miRNAs (exmiRs) and intracellular mRNAs both can serve as promising biomarkers and therapeutic targets for various diseases. However, exmiR expression data is often noisy, and obtaining intracellular mRNA expression data usually involves intrusive procedures. To gain valuable insights into disease mechanisms, it is thus essential to improve the quality of exmiR expression data and develop noninvasive methods for assessing intracellular mRNA expression.

RESULTS

We developed CrossPred, a deep-learning multi-encoder model for the cross-prediction of exmiRs and mRNAs. Utilizing contrastive learning, we created a shared embedding space to integrate exmiRs and mRNAs. This shared embedding was then used to predict intracellular mRNA expression from noisy exmiR data and to predict exmiR expression from intracellular mRNA data. We evaluated CrossPred on three types of cancers and assessed its effectiveness in predicting the expression levels of exmiRs and mRNAs. CrossPred outperformed the baseline encoder-decoder model, exmiR or mRNA-based models, and variational autoencoder models. Moreover, the integration of exmiR and mRNA data uncovered important exmiRs and mRNAs associated with cancer. Our study offers new insights into the bidirectional relationship between mRNAs and exmiRs.

AVAILABILITY AND IMPLEMENTATION

The datasets and tool are available at https://doi.org/10.5281/zenodo.13891508.

摘要

动机

细胞外 miRNAs(exmiRs) 和细胞内 mRNAs 均可作为各种疾病有前途的生物标志物和治疗靶点。然而，exmiR 表达数据通常存在噪声，并且获得细胞内 mRNA 表达数据通常涉及侵入性程序。因此，深入了解疾病机制的关键是提高 exmiR 表达数据的质量，并开发用于评估细胞内 mRNA 表达的非侵入性方法。

结果

我们开发了 CrossPred，这是一种用于 exmiRs 和 mRNAs 交叉预测的深度学习多编码器模型。我们利用对比学习创建了一个共享嵌入空间来整合 exmiRs 和 mRNAs。然后，该共享嵌入用于从嘈杂的 exmiR 数据预测细胞内 mRNA 表达，并从细胞内 mRNA 数据预测 exmiR 表达。我们在三种癌症类型上评估了 CrossPred，并评估了其预测 exmiRs 和 mRNAs 表达水平的有效性。CrossPred 优于基线编码器-解码器模型、exmiR 或 mRNA 为基础的模型以及变分自动编码器模型。此外，exmiR 和 mRNA 数据的整合揭示了与癌症相关的重要 exmiRs 和 mRNAs。我们的研究为 mRNAs 和 exmiRs 之间的双向关系提供了新的见解。