Mohammed Amine Bendahou, Azeddine Ibrahimi, Mahjouba Boutarbouch
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引用次数: 4
摘要
低级别胶质瘤是最常见的原发性颅内肿瘤类型。在过去的3年里,对成人胶质瘤分子前体的新观察导致了这些脑肿瘤组织病理学分类的改变。在已被重点关注的生物标志物中,我们有在基因表达调控中起关键作用的微rna (miRNAs)和控制各种细胞和代谢途径的长链非编码rna (lncRNAs)。在我们的研究中,通过tcgabiollinks包从癌症基因组图谱数据库中获得了来自516名患者的测序RNA和mirna的大规模数据。我们使用Limma软件包确定了miRNAs和基因的差异表达,然后使用ClusterProfiler软件包对表达基因的生物学途径进行注释,使用survival软件包估计生存分析,使用GDCRNATools软件包确定miRNAs-基因和miRNAs- lncrnas的相互作用。我们发现鉴定的mirna与患者的总生存率之间存在显著相关性(log-rank P
Bioinformatics Analysis of Differentially Expressed Genes and miRNAs in Low-Grade Gliomas.
Low-grade glioma is the most common type of primary intracranial tumor. In the last 3 years, new observations of molecular precursors in adults with gliomas have led to a modification in the histopathologic classification of these brain tumors. Among the biomarkers that have been highlighted, we have the micro RNAs (miRNAs) which play a crucial role in the regulation of gene expression and the long noncoding RNAs (lncRNAs) controlling various cellular and metabolic pathways. In our study, large-scale data on sequenced RNA and miRNAs from 516 patients were obtained from the Cancer Genome Atlas database by the TCGAbiolinks package. We identified the differential expression of miRNAs and genes using the Limma package and then we used the ClusterProfiler package for annotations of the biological pathways of the expressed genes, the survival package to estimate the survival analysis, and the GDCRNATools package to determine miRNAs-genes and miRNAs-lncRNAs interactions. We obtained a significant correlation between the miRNAs identified and the overall survival of the patients (log-rank P < .05) and we have theoretically proposed a novel network of miRNAs involved in low-grade gliomas, specifically astrocytomas and oligodendrogliomas, which combine both genes and lncRNAs.
期刊介绍:
The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.