福尔马林固定石蜡包埋格式的原发性脑癌转录组学和表观基因组学数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-15 DOI:10.1038/s41597-025-04597-6
Anabel García-Heredia, Luna Guerra-Núñez, Paula Martín-Climent, Estefanía Rojas, Raúl López-Domínguez, Clara Alcántara-Domínguez, Cristina Alenda, Luis M Valor
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引用次数: 0

摘要

基于公共组学的数据集在脑癌研究中至关重要,因为它允许提出和验证胶质瘤的生物标志物和治疗靶点,特别是在最普遍和侵袭性的胶质母细胞瘤中。利用下一代测序和DNA甲基化分析的最新进展,我们已经创建了大约150个福尔马林固定石蜡包埋(FFPE)肿瘤的数据集。这些数据集首次实现了综合转录和表观遗传学研究,考虑了医院中最广泛的存档格式的降解和固定衍生的化学变化,并提供了一个来自当前公共数据库的独立队列,用于进一步验证假定的新型生物标志物。除了最广为人知的胶质母细胞瘤、星形细胞瘤和少突胶质胶质瘤外,为了进行比较,我们还包括了一些在脑癌研究中经常被忽视的罕见肿瘤。总之,我们提供了一个有价值的工具来探索神经胶质瘤和神经胶质细胞肿瘤研究中的基因表达和DNA甲基化模式。
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Transcriptomics and epigenomics datasets of primary brain cancers in formalin-fixed paraffin embedded format.

The access of public omics-based datasets is of paramount importance in brain cancer research as allows the proposal and validation of both biomarkers and therapeutic targets in gliomas, especially in the most prevalent and aggressive glioblastomas. Taking profit of current advances in next generation sequencing and DNA methylation profiling, we have created datasets from approximately 150 formalin-fixed paraffin embedded (FFPE) tumours. These datasets enable for the first time integrative transcriptional and epigenetics studies in a context that consider the degradation and fixation-derived chemical alterations of the most extended archiving format in hospitals, and provide an independent cohort from current public databases for further validation of putative novel biomarkers. Alongside with the most profusely known glioblastomas, astrocytomas and oligodendrogliomas, we have also included for comparison purposes few examples of rare tumours that are often neglected in brain cancer research. Taken together, we provide a valuable tool to explore combined gene expression and DNA methylation patterns in the study of gliomas and glioneuronal tumours.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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