鉴定 miR-20a 作为结直肠癌诊断和预后生物标记物:MicroRNA 测序和机器学习分析。

Hamid Jamialahmadi, Alireza Asadnia, Ghazaleh Khalili-Tanha, Reza Mohit, Hanieh Azari, Majid Khazaei, Mina Maftooh, Mohammadreza Nassiri, Seyed Mahdi Hassanian, Majid Ghayour-Mobarhan, Gordon A Ferns, Elham Nazari, Amir Avan
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引用次数: 0

摘要

导言:miRNA是许多细胞信号通路的关键调控因子,在各种恶性肿瘤中的差异表达已被研究,并可能在包括结直肠癌(CRC)在内的癌症进展中发挥重要作用:本研究利用机器学习和基因相互作用研究工具来探讨 miRNAs 在 CRC 中的预后和诊断价值。研究人员从 TCGA 数据库中获取了 353 个 CRC 样本和正常组织的整合分析数据,并使用 R 软件包对这些数据进行了进一步分析,从而确定了递延表达的 miRNAs(DEMs)。此外,机器学习和卡普兰-梅耶尔生存分析有助于更好地明确 miRNA 的重要预后价值。然后,结合在线数据库评估了靶基因之间的相互作用、其分子通路以及 DEMs 之间的相关性:结果:研究结果表明,miR-19b和miR-20a具有重要的预后作用,与CRC的进展相关。ROC曲线分析发现,miR-20a单独或与其他miRNA(包括hsa-mir-21和hsa-mir-542)结合,都是CRC的诊断生物标志物。此外,研究还发现 NTRK2、CDC42、表皮生长因子受体、AGO2、PRKCA、HSP90AA1、TLR4、IGF1、ESR1、SMAD2、SMAD4 和 NEDD4L 等 12 个基因是这些 miRNA 的最高得分靶标。通路分析确定了与关键相互作用基因(即表皮生长因子受体、CDC42 和 HSP90AA1)相关的两个酪氨酸激酶和 MAPK 信号通路:为了更好地界定这些 miRNAs 的作用,还编制了包括 lncRNAs 在内的 ceRNA 网络。总之,R数据分析与机器学习的结合为解决miRNA与其靶标之间复杂的相互作用提供了一种稳健的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.

Introduction: The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC).

Method: The present study used machine learning and gene interaction study tools to explore the prognostic and diagnostic value of miRNAs in CRC. Integrative analysis of 353 CRC samples and normal tissue data was obtained from the TCGA database and further analyzed by R packages to define the deferentially expressed miRNAs (DEMs). Furthermore, machine learning and Kaplan Meier survival analysis helped better specify the significant prognostic value of miRNAs. A combination of online databases was then used to evaluate the interactions between target genes, their molecular pathways, and the correlation between the DEMs.

Result: The results indicated that miR-19b and miR-20a have a significant prognostic role and are associated with CRC progression. The ROC curve analysis discovered that miR-20a alone and combined with other miRNAs, including hsa-mir-21 and hsa-mir-542, are diagnostic biomarkers in CRC. In addition, 12 genes, including NTRK2, CDC42, EGFR, AGO2, PRKCA, HSP90AA1, TLR4, IGF1, ESR1, SMAD2, SMAD4, and NEDD4L, were found to be the highest score targets for these miRNAs. Pathway analysis identified the two correlated tyrosine kinase and MAPK signaling pathways with the key interaction genes, i.e., EGFR, CDC42, and HSP90AA1.

Conclusion: To better define the role of these miRNAs, the ceRNA network, including lncRNAs, was also prepared. In conclusion, the combination of R data analysis and machine learning provides a robust approach to resolving complicated interactions between miRNAs and their targets.

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