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通过生物信息学和机器学习评估长链非编码RNA作为转移性三阴性乳腺癌诊断潜在生物标志物的研究

Evaluation of lncRNAs as Potential Biomarkers for Diagnosis of Metastatic Triple-Negative Breast Cancer through Bioinformatics and Machine Learning.

作者信息

Soleimani Shiva, Pouresmaeili Farkhondeh, Salahshoori Far Iman

机构信息

Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Men's Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

出版信息

Iran J Biotechnol. 2024 Jul 1;22(3):e3853. doi: 10.30498/ijb.2024.432171.3853. eCollection 2024 Jul.

DOI:10.30498/ijb.2024.432171.3853
PMID:39737204
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11682528/
Abstract

BACKGROUND

Triple-negative breast cancer (TNBC) is highly invasive and metastatic to the lymph nodes. Therefore, it is an urgent priority to distinguish novel biomarkers and molecular mechanisms of lymph node metastasis as the first step to the disease investigation. Long non-coding RNAs (lncRNAs) have widely been explored in cancer tumorigenesis, progression, and invasion.

OBJECTIVES

This study aimed to identify and evaluate lncRNAs in the signaling pathway of gene in both metastatic and non-metastatic TNBC samples. The potential of lncRNAs in prognosis and diagnosis of the disease was also assessed using bioinformatics analysis, machine learning, and quantitative real-time PCR.

MATERIALS AND METHODS

Using machine learning algorithms, we analyzed the available BC data from the Cancer Genome Atlas Network (TCGA) and identified three potential lncRNAs, gastric adenocarcinoma-associated, positive CD44 regulator, long intergenic noncoding RNA (), , and antisense RNA 1 () that could successfully distinguish between metastatic and non-metastatic TNBC.

RESULTS

The results showed the upregulation of lncRNA in metastatic BC tissues, compared to non-metastatic (P<0.01) and normal samples, though and were downregulated in metastatic TNBC samples (P<0.01).

CONCLUSION

Given the aberrant expression of candidate lncRNAs and the underlying mechanisms, the above-mentioned RNAs could act as novel diagnostic and prognostic biomarkers in metastatic BC.

摘要

背景

三阴性乳腺癌（TNBC）具有高度侵袭性且易发生淋巴结转移。因此，作为疾病研究的第一步，区分淋巴结转移的新型生物标志物和分子机制是当务之急。长链非编码RNA（lncRNAs）已在癌症的肿瘤发生、进展和侵袭过程中得到广泛研究。

目的

本研究旨在鉴定和评估转移性和非转移性TNBC样本中基因信号通路中的lncRNAs。还使用生物信息学分析、机器学习和定量实时PCR评估了lncRNAs在该疾病预后和诊断中的潜力。

材料与方法

我们使用机器学习算法分析了来自癌症基因组图谱网络（TCGA）的可用乳腺癌数据，并鉴定出三种潜在的lncRNAs，即胃腺癌相关的、阳性CD44调节因子、长链基因间非编码RNA（）、和反义RNA 1（），它们能够成功区分转移性和非转移性TNBC。

结果

结果显示，与非转移性（P<0.01）和正常样本相比，转移性乳腺癌组织中lncRNA上调，而在转移性TNBC样本中，和下调（P<0.01）。

结论

鉴于候选lncRNAs的异常表达及其潜在机制，上述RNA可作为转移性乳腺癌新的诊断和预后生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/9e7c3e58c20c/IJB-22-e3853-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/ee93fc39804f/IJB-22-e3853-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/596013c4dde8/IJB-22-e3853-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/a87728b9e526/IJB-22-e3853-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/fa4bc887c902/IJB-22-e3853-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/52d28044f886/IJB-22-e3853-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/9e7c3e58c20c/IJB-22-e3853-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/ee93fc39804f/IJB-22-e3853-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/596013c4dde8/IJB-22-e3853-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/a87728b9e526/IJB-22-e3853-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/fa4bc887c902/IJB-22-e3853-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/52d28044f886/IJB-22-e3853-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e9a/11682528/9e7c3e58c20c/IJB-22-e3853-g006.jpg

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