Machine learning and WGCNA reveal the PVT1/miR-143–3p/CDK1 ceRNA axis as a key regulator in NSCLC

IF 2.2 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY

Biochemistry and Biophysics Reports Pub Date : 2025-09-30 DOI:10.1016/j.bbrep.2025.102292

Arash Safarzadeh, Setareh Ataei, Arezou Sayad, Soudeh Ghafouri-Fard

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引用次数: 0

Abstract

Machine learning has provided novel tools for analysis of multi-omics data for subgroups recognition in cancer to reach a clinically meaningful classification of cancer and identification of potential biomarkers. In this work, we retrieved mRNA, lncRNA, miRNA and protein expression data of non-small cell lung cancer (NSCLC) samples and used different machine learning methods for biomarker selection, diagnostic validation, construction of competing endogenous RNA network, identification of the hub axes and drug prediction. Integration of multi-omics data and machine learning resulted in identification of CDK1, TOP2A, AURKA, TPX2, BUB1B, and CENPF as key biomarkers in NSCLC. We also identified the PVT1/miR-143–3p/CDK1 axis and its associated transcription factors (FOXC1, YY1, and GATA2) as a potential regulatory network for additional investigations. These findings increase the understanding of NSCLC molecular processes and provide a foundation for developing targeted therapies and diagnostic tools.

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机器学习和WGCNA揭示PVT1/ miR-143-3p /CDK1 ceRNA轴是NSCLC的关键调节因子

机器学习为癌症亚群识别的多组学数据分析提供了新的工具，从而达到有临床意义的癌症分类和潜在生物标志物的鉴定。在这项工作中，我们检索了非小细胞肺癌（NSCLC）样本的mRNA、lncRNA、miRNA和蛋白质表达数据，并使用不同的机器学习方法进行生物标志物的选择、诊断验证、竞争内源RNA网络的构建、枢纽轴的鉴定和药物预测。整合多组学数据和机器学习，发现CDK1、TOP2A、AURKA、TPX2、BUB1B和CENPF是NSCLC的关键生物标志物。我们还确定了PVT1/ miR-143-3p /CDK1轴及其相关转录因子（FOXC1， YY1和GATA2）作为潜在的调控网络进行进一步研究。这些发现增加了对NSCLC分子过程的理解，并为开发靶向治疗和诊断工具提供了基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biochemistry and Biophysics Reports Biochemistry, Genetics and Molecular Biology-Biophysics

CiteScore

4.60

自引率

0.00%

发文量

191

审稿时长

59 days

期刊介绍： Open access, online only, peer-reviewed international journal in the Life Sciences, established in 2014 Biochemistry and Biophysics Reports (BB Reports) publishes original research in all aspects of Biochemistry, Biophysics and related areas like Molecular and Cell Biology. BB Reports welcomes solid though more preliminary, descriptive and small scale results if they have the potential to stimulate and/or contribute to future research, leading to new insights or hypothesis. Primary criteria for acceptance is that the work is original, scientifically and technically sound and provides valuable knowledge to life sciences research. We strongly believe all results deserve to be published and documented for the advancement of science. BB Reports specifically appreciates receiving reports on: Negative results, Replication studies, Reanalysis of previous datasets.