Multiple myeloma (MM) is the second most frequent haematological malignancy in the world although the related pathogenesis remains unclear. The study of how gene expression profiling (GEP) is correlated with patients’ survival could be important for understanding the initiation and progression of MM. In order to aid researchers in identifying new prognostic RNA biomarkers as targets for functional cell-based studies, the use of appropriate bioinformatic tools for integrative analysis is required. In this context, TCGABiolinks package represents a valid tool for integrative analysis of MM data if its functions are properly adapted for handling MMRF data.

This paper aims to extend largely our previous work [1] in which we introduced some bridging functions to make TCGABiolinks package able to deal with Multiple Myeloma Research Foundation (MMRF) CoMMpass study data available at the NCI's Genomic Data Commons (GDC) Data Portal.

Here we present an integrative analysis workflow based on the usage of a novel R-package, called MMRFBiolinks, that collects the set of the previously mentioned bridging functions besides of extending them.

Our workflow leads towards a comparative analysis of MMRF data stored at GDC Data Portal that allows to carry out a Kaplan Meier (KM) Survival Analysis and an enrichment analysis for a differential gene expression (DGE) gene set.

Furthermore, it leads towards an integrative analysis of MMRF Research Gateway (MMRF-RG) data. In order to show the potential of our workflow, we present two case studies. The former deals with RNA-Seq data of MM Bone Marrow sample types available at GDC Data Portal. The latter deals with MMRF-RG data for analyzing the correlation between canonical variants in a gene set obtained from the case study 1 and the treatment outcome as well as the treatment class.

尽管相关的发病机制仍不清楚，但多发性骨髓瘤（MM）是世界上第二常见的血液系统恶性肿瘤。研究基因表达谱（GEP）与患者生存率之间的关系可能对理解MM的发生和发展具有重要意义。为了帮助研究人员确定新的预后性RNA生物标记物作为基于功能细胞的研究的靶标，需要使用适当的生物信息学工具进行整合分析。在这种情况下，如果TCGABiolinks程序包的功能适当地适用于处理MMRF数据，则它是用于MM数据集成分析的有效工具。

本文旨在扩展我们先前的工作[1]，其中我们引入了一些桥接功能，以使TCGABiolinks程序包能够处理可在NCI的基因组数据共享（GDC）数据门户上获得的多发性骨髓瘤研究基金会（MMRF）CoMMpass研究数据。

在这里，我们基于一个称为MMRFBiolinks的新型R包的使用，提出了一个综合分析工作流，该R-package收集了前面提到的桥接功能集，并对其进行了扩展。

我们的工作流程导致对存储在GDC数据门户中的MMRF数据进行比较分析，从而可以执行Kaplan Meier（KM）生存分析和差异基因表达（DGE）基因集的富集分析。

此外，它还可以对MMRF研究网关（MMRF-RG）数据进行综合分析。为了展示我们工作流程的潜力，我们提出了两个案例研究。前者处理可从GDC数据门户网站获得的MM骨髓样本类型的RNA-Seq数据。后者处理MMRF-RG数据，以分析从案例研究1获得的基因组中的典型变异与治疗结果以及治疗类别之间的相关性。