Dataset Information

Prognostic Biomarkers in Pancreatic Cancer: Avoiding Errata When Using the TCGA Dataset.

ABSTRACT: Data from the Cancer Genome Atlas (TCGA) are now easily accessible through web-based platforms with tools to assess the prognostic value of molecular alterations. Pancreatic tumors have heterogeneous biology and aggressiveness ranging from the deadly adenocarcinoma (PDAC) to the better prognosis, neuroendocrine tumors. We assessed the availability of the pancreatic cancer TCGA data (TCGA_PAAD) from several repositories and investigated the nature of each sample and how non-PDAC samples impact prognostic biomarker studies. While the clinical and genomic data (n = 185) were fairly consistent across all repositories, RNAseq profiles varied from 176 to 185. As a result, 35 RNAseq profiles (18.9%) corresponded to a normal, inflamed pancreas or non-PDAC neoplasms. This information was difficult to obtain. By considering gene expression data as continuous values, the expression of the 5312 and 4221 genes were significantly associated with the progression-free and overall survival respectively. Considering the cohort was not curated, only 4 and 14, respectively, had prognostic value in the PDAC-only cohort. Similarly, mutations in key genes or well-described miRNA lost their prognostic significance in the PDAC-only cohort. Therefore, we propose a web-based application to assess biomarkers in the curated TCGA_PAAD dataset. In conclusion, TCGA_PAAD curation is critical to avoid important biological and clinical biases from non-PDAC samples.

SUBMITTER: Nicolle R

PROVIDER: S-EPMC6357157 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prognostic Biomarkers in Pancreatic Cancer: Avoiding Errata When Using the TCGA Dataset.

Nicolle Remy R Raffenne Jerome J Paradis Valerie V Couvelard Anne A de Reynies Aurelien A Blum Yuna Y Cros Jerome J

Cancers 20190121 1

Data from the Cancer Genome Atlas (TCGA) are now easily accessible through web-based platforms with tools to assess the prognostic value of molecular alterations. Pancreatic tumors have heterogeneous biology and aggressiveness ranging from the deadly adenocarcinoma (PDAC) to the better prognosis, neuroendocrine tumors. We assessed the availability of the pancreatic cancer TCGA data (TCGA_PAAD) from several repositories and investigated the nature of each sample and how non-PDAC samples impact pr ...[more]

PMID: 30669703

Similar Datasets

Project description:The "bidirectional gene pair" indicates a particular head-to-head gene organization in which transcription start sites of two genes are located on opposite strands of genomic DNA within a region of one kb. Despite bidirectional gene pairs are well characterized, little is known about their expression profiles and regulation features in tumorigenesis. We used RNA-seq data from The Cancer Genome Atlas (TCGA) dataset for a systematic analysis of the expression profiles of bidirectional gene pairs in 13 cancer datasets. Gene pairs on the opposite strand with transcription end site distance within one kb or on the same strand with the distance of two genes between 1-10 kb and gene pairs comprising two randomly chosen genes were used as control gene pairs (CG1, CG2, and random). We identified and characterized up-/down-regulated genes by comparing the expression level between tumors and adjacent normal tissues in 13 TCGA datasets. There were no consistently significant difference in the percentage of up-/down-regulated genes between bidirectional and control/random genes in most of TCGA datasets. However, the percentage of bidirectional gene pairs comprising two up- or two down-regulated genes was significantly higher than gene pairs from CG1/2 in 12/11 analyzed TCGA datasets and the random gene pairs in all 13 TCGA datasets. Then we identified the methylation correlated bidirectional genes to explore the regulatory mechanism of bidirectional genes. Like the differentially expressed gene pairs, the bidirectional genes in a pair were significantly prone to be both hypo- or hyper-methylation correlated genes in 12/13 TCGA datasets when comparing to the CG2/random gene pairs despite no significant difference between the percentages of hypo-/hyper-methylation correlated genes in bidirectional and CG2/random genes in most of TCGA datasets. Finally, we explored the correlation between bidirectional genes and patient's survival, identifying prognostic bidirectional genes and prognostic bidirectional gene pairs in each TCGA dataset. Remarkably, we found a group of prognostic bidirectional gene pairs in which the combination of two protein coding genes with different expression level correlated with different survival prognosis in survival analysis for OS. The percentage of these gene pairs in bidirectional gene pair were significantly higher than the gene pairs in controls in COAD datasets and lower in none of 13 TCGA datasets.

Dataset Information

Prognostic Biomarkers in Pancreatic Cancer: Avoiding Errata When Using the TCGA Dataset.

Publications

Prognostic Biomarkers in Pancreatic Cancer: Avoiding Errata When Using the TCGA Dataset.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets