Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset

Clinical and Translational Oncology Pub Date : 2024-04-03 DOI:10.1007/s12094-024-03474-9

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Abstract

Background

Brain metastasis (BM) is common in lung adenocarcinoma (LUAD) and has a poor prognosis, necessitating predictive biomarkers. MicroRNAs (MiRNAs) promote cancer cell growth, infiltration, and metastasis. However, the relationship between the miRNA expression profiles and BM occurrence in patients with LUAD remains unclear.

Methods

We conducted an analysis to identify miRNAs in tissue samples that exhibited different expression levels between patients with and without BM. Using a machine learning approach, we confirmed whether the miRNA profile could be a predictive tool for BM. We performed pathway analysis of miRNA target genes using a matched mRNA dataset.

Results

We selected 25 miRNAs that consistently exhibited differential expression between the two groups of 32 samples. The 25-miRNA profile demonstrated a strong predictive potential for BM in both Group 1 and Group 2 and the entire dataset (area under the curve [AUC] = 0.918, accuracy = 0.875 in Group 1; AUC = 0.867, accuracy = 0.781 in Group 2; and AUC = 0.908, accuracy = 0.875 in the entire group). Patients predicted to have BM, based on the 25-miRNA profile, had lower survival rates. Target gene analysis of miRNAs suggested that BM could be induced through the ErbB signaling pathway, proteoglycans in cancer, and the focal adhesion pathway. Furthermore, patients predicted to have BM based on the 25-miRNA profile exhibited higher expression of the epithelial-mesenchymal transition signature, TWIST, and vimentin than those not predicted to have BM. Specifically, there was a correlation between EGFR mRNA levels and BM.

Conclusions

This 25-miRNA profile may serve as a biomarker for predicting BM in patients with LUAD.

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利用mRNA数据集的miRNA图谱和靶基因通路分析，以机器学习驱动预测肺腺癌的脑转移

摘要背景脑转移（BM）在肺腺癌（LUAD）中很常见，且预后较差，因此需要预测性生物标志物。微小RNA（MiRNA）可促进癌细胞的生长、浸润和转移。然而，LUAD 患者的 miRNA 表达谱与 BM 发生率之间的关系仍不清楚。方法我们进行了一项分析，以确定组织样本中的 miRNA，这些 miRNA 在有 BM 和无 BM 患者之间表现出不同的表达水平。利用机器学习方法，我们确认了 miRNA 图谱是否可作为预测骨髓瘤的工具。我们利用匹配的 mRNA 数据集对 miRNA 靶基因进行了通路分析。结果我们选取了两组 32 个样本中始终表现出差异表达的 25 个 miRNA。在第一组、第二组和整个数据集中，25 个 miRNA 图谱都显示出对 BM 有很强的预测潜力（第一组的曲线下面积 [AUC] = 0.918，准确率 = 0.875；第二组的曲线下面积 [AUC] = 0.867，准确率 = 0.781；整个数据集的曲线下面积 [AUC] = 0.908，准确率 = 0.875）。根据 25 个 miRNA 图谱预测患有骨髓瘤的患者生存率较低。miRNA 的靶基因分析表明，BM 可通过 ErbB 信号通路、癌症中的蛋白多糖和病灶粘附通路诱导。此外，根据25个miRNA图谱预测患有BM的患者比未预测患有BM的患者表现出更高的上皮-间质转化特征、TWIST和波形蛋白表达量。特别是，表皮生长因子受体 mRNA 水平与 BM 之间存在相关性。结论该 25-miRNA 图谱可作为预测 LUAD 患者 BM 的生物标记物。

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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Clinical and Translational Oncology

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