New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx

Abstract

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7.

Distinct diagnostic and prognostic values of γ‑aminobutyric acid type A receptor family genes in patients with colon adenocarcinoma

“…Genes with P-value<0.05 and |log 2 fold-change (FC)|>2 were deemed to be significantly different. These genes were regarded as differentially expressed genes (DEGs) (16). First, tumor tissues and adjacent normal tissues data were isolated and then the gene expression data were integrated with clinical information.…”

A Pan-Cancer assessment of alterations of the kinase domain of ULK1, an upstream regulator of autophagy

“…We also employed this dataset as an additional source of information for the TCGA datasets with less than five normal samples (see Table S1). We used the TCGAquery_Recount2 function of TCGAbiolinks [56] to query the GTEx and TCGA unified datasets. We carried out GC-content normalization and quantile filtering on the unified datasets, as described above.…”

“…We carried out GC-content normalization and quantile filtering on the unified datasets, as described above. Differential expression analyses have been carried out using limma-voom [57] as implemented within the TCGAanalyze_DEA function of TCGAbiolinks [56], along with edgeR to confirm the results, as we recently applied to another case study [58]. We included in the design matrix conditions (tumor vs normal) and the TSS (Tissue Source Site; the center where the samples are collected) or the Plates (where available) as source of batch-effects to assess the robustness of the estimate of changes in expression with respect to different correction factors.…”

IRF1 association with tumor immune microenvironment and use as a diagnostic biomarker for colorectal cancer recurrence

“…gov/geo/query/acc.cgi?acc=GSE71187), which included a Chinese cohort with the mRNA expression profiles of 52 human biopsy samples of CRC, and included overall survival time information (12). The DNA methylation profile, RNA-seq raw counts and clinical information of 234 patients with CRC was retrieved from The Cancer Genome Atlas (TCGA) data portal using R package TCGAbiolinks (13).…”