使用稀疏方法揭示神经胶质瘤异质性的基于转录组学的特征。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2023-09-26 DOI:10.1186/s13040-023-00341-1
Sofia Martins, Roberta Coletti, Marta B Lopes
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引用次数: 0

摘要

胶质瘤是原发性恶性脑肿瘤,生存率低,对现有治疗方法的耐药性高。提高对神经胶质瘤的分子理解并揭示肿瘤发展和进展的新生物标志物可能有助于找到这种类型癌症的新靶向治疗方法。癌症基因组图谱(TCGA)等公共数据库为癌症组织的分子信息提供了宝贵的来源。机器学习工具在处理高维组学数据并从中提取相关信息方面表现出了良好的前景,应用于TCGA神经胶质瘤患者的RNA测序数据,以确定不同类型神经胶质瘤(胶质母细胞瘤、星形细胞瘤和少突胶质瘤)之间共享和不同的基因网络,并揭示新的患者群体和群体分离背后的相关基因。结果表明,与胶质母细胞瘤相比,星形细胞瘤和少突胶质瘤有更多的相似性,突出了胶质母细胞癌与其他胶质瘤亚型之间的分子差异。在对我们分析的相关基因进行全面的文献检索后,我们确定了神经胶质瘤生物标志物的潜在候选者。鼓励对这些基因进行进一步的分子验证,以了解它们在诊断和新疗法设计中的潜在作用。
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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Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods.

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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