ClassifieR 2.0:将基于基因表达的交互式分层方法扩展到前列腺癌和高级别浆液性卵巢癌。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Aideen McCabe, Gerard P Quinn, Suneil Jain, Micheál Ó Dálaigh, Kellie Dean, Ross G Murphy, Simon S McDade
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引用次数: 0

摘要

背景:转录剖析方法的进步使人们能够在传统的基于组织的癌症分类中发现分子亚型。这些分子亚型具有改善患者预后的潜力,可指导治疗决策,揭示生理差异和靶向途径。将转录组数据分层到分子亚组的计算方法越来越多。然而,将样本归入这些亚型和其他转录推断预测需要耗费大量时间,而且需要大量生物信息学专业知识。为了满足这一需求,我们最近报道了 "ClassifieR",这是一种灵活、交互式的云应用程序,用于结直肠癌和乳腺癌转录组的功能注释。在此,我们报告了 "ClassifieR 2.0",它引入了用于前列腺癌和高级别浆液性卵巢癌(HGSOC)分子亚型分析的附加模块:结果:ClassifieR 2.0引入了ClassifieRp和ClassifieRov,这是两个专门设计的模块,用于应对前列腺癌和高级别浆液性卵巢癌分子分类的挑战。ClassifieRp 包括 sigInfer,这是我们开发的一种方法,用于从公开的基因列表或任何用户上传的基因列表中推断商业化的前列腺预后基因表达特征。ClassifieRov 利用 HGSOC 的共识分子亚型方法,包括共识OV 等工具,对卵巢癌进行精确分层。这两个模块都包含原始 ClassifieR 框架中的功能,用于估计细胞组成、预测转录因子(TF)活性和单样本基因组富集分析(ssGSEA):ClassifieR 2.0 将前列腺癌和 HGSOC 的分子亚型分析与常用的样本注释工具结合在一个用户友好的平台上,让没有接受过生物信息学培训的科学家也能探索前列腺癌和 HGSOC 的转录数据,而不需要丰富的生物信息学知识或手动数据处理来操作各种软件包。我们在 ClassifieRp 中采用的 sigInfer 方法可以推断出前列腺癌的商用基因特征,而 ClassifieRov 则纳入了 HGSOC 的共识分子亚型。总之,ClassifieR 2.0 的目标是让更多的研究人员能够进行分子亚型分析。这对于进一步了解这些癌症的分子异质性和制定个性化治疗策略至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ClassifieR 2.0: expanding interactive gene expression-based stratification to prostate and high-grade serous ovarian cancer.

Background: Advances in transcriptional profiling methods have enabled the discovery of molecular subtypes within and across traditional tissue-based cancer classifications. Such molecular subgroups hold potential for improving patient outcomes by guiding treatment decisions and revealing physiological distinctions and targetable pathways. Computational methods for stratifying transcriptomic data into molecular subgroups are increasingly abundant. However, assigning samples to these subtypes and other transcriptionally inferred predictions is time-consuming and requires significant bioinformatics expertise. To address this need, we recently reported "ClassifieR," a flexible, interactive cloud application for the functional annotation of colorectal and breast cancer transcriptomes. Here, we report "ClassifieR 2.0" which introduces additional modules for the molecular subtyping of prostate and high-grade serous ovarian cancer (HGSOC).

Results: ClassifieR 2.0 introduces ClassifieRp and ClassifieRov, two specialised modules specifically designed to address the challenges of prostate and HGSOC molecular classification. ClassifieRp includes sigInfer, a method we developed to infer commercial prognostic prostate gene expression signatures from publicly available gene-lists or indeed any user-uploaded gene-list. ClassifieRov utilizes consensus molecular subtyping methods for HGSOC, including tools like consensusOV, for accurate ovarian cancer stratification. Both modules include functionalities present in the original ClassifieR framework for estimating cellular composition, predicting transcription factor (TF) activity and single sample gene set enrichment analysis (ssGSEA).

Conclusions: ClassifieR 2.0 combines molecular subtyping of prostate cancer and HGSOC with commonly used sample annotation tools in a single, user-friendly platform, allowing scientists without bioinformatics training to explore prostate and HGSOC transcriptional data without the need for extensive bioinformatics knowledge or manual data handling to operate various packages. Our sigInfer method within ClassifieRp enables the inference of commercially available gene signatures for prostate cancer, while ClassifieRov incorporates consensus molecular subtyping for HGSOC. Overall, ClassifieR 2.0 aims to make molecular subtyping more accessible to the wider research community. This is crucial for increased understanding of the molecular heterogeneity of these cancers and developing personalised treatment strategies.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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