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利用机器学习探索各种 ATLL 癌症亚型的 mRNAs 和 miRNA 分类器。

Exploration of mRNAs and miRNA classifiers for various ATLL cancer subtypes using machine learning.

机构信息

Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran.

Department of Physics, University of Isfahan, Hezar Jarib, Isfahan, 81746, Iran.

出版信息

BMC Cancer. 2022 Apr 21;22(1):433. doi: 10.1186/s12885-022-09540-1.

DOI:10.1186/s12885-022-09540-1
PMID:35449091
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9026691/
Abstract

BACKGROUND

Adult T-cell Leukemia/Lymphoma (ATLL) is a cancer disease that is developed due to the infection by human T-cell leukemia virus type 1. It can be classified into four main subtypes including, acute, chronic, smoldering, and lymphoma. Despite the clinical manifestations, there are no reliable diagnostic biomarkers for the classification of these subtypes.

METHODS

Herein, we employed a machine learning approach, namely, Support Vector Machine-Recursive Feature Elimination with Cross-Validation (SVM-RFECV) to classify the different ATLL subtypes from Asymptomatic Carriers (ACs). The expression values of multiple mRNAs and miRNAs were used as the features. Afterward, the reliable miRNA-mRNA interactions for each subtype were identified through exploring the experimentally validated-target genes of miRNAs.

RESULTS

The results revealed that miR-21 and its interactions with DAAM1 and E2F2 in acute, SMAD7 in chronic, MYEF2 and PARP1 in smoldering subtypes could significantly classify the diverse subtypes.

CONCLUSIONS

Considering the high accuracy of the constructed model, the identified mRNAs and miRNA are proposed as the potential therapeutic targets and the prognostic biomarkers for various ATLL subtypes.

摘要

背景

成人 T 细胞白血病/淋巴瘤(ATLL)是一种癌症疾病,是由于人类 T 细胞白血病病毒 1 的感染而发展起来的。它可以分为四个主要亚型,包括急性、慢性、亚临床和淋巴瘤。尽管有临床表现,但目前还没有可靠的诊断生物标志物来对这些亚型进行分类。

方法

在这里,我们采用了一种机器学习方法,即支持向量机-递归特征消除与交叉验证(SVM-RFECV),从无症状携带者(ACs)中对不同的 ATLL 亚型进行分类。多个 mRNA 和 miRNA 的表达值被用作特征。然后,通过探索 miRNA 的实验验证靶基因,确定每个亚型的可靠 miRNA-mRNA 相互作用。

结果

结果表明,miR-21 及其与急性的 DAAM1 和 E2F2、慢性的 SMAD7、亚临床的 MYEF2 和 PARP1 的相互作用,可以显著区分不同的亚型。

结论

考虑到所构建模型的高精度,所鉴定的 mRNA 和 miRNA 被提议为各种 ATLL 亚型的潜在治疗靶点和预后生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c31/9026691/e0f2f51fbc62/12885_2022_9540_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c31/9026691/079ba16914ed/12885_2022_9540_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c31/9026691/0ee3b91027e4/12885_2022_9540_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c31/9026691/f5b3cde1fc18/12885_2022_9540_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c31/9026691/e0f2f51fbc62/12885_2022_9540_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c31/9026691/079ba16914ed/12885_2022_9540_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c31/9026691/0ee3b91027e4/12885_2022_9540_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c31/9026691/f5b3cde1fc18/12885_2022_9540_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c31/9026691/e0f2f51fbc62/12885_2022_9540_Fig4_HTML.jpg

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