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pmid:39654143

TREMSUCS-TCGA - an integrated workflow for the identification of biomarkers for treatment success.

Gabor Balogh · Natasha Jorge · Célia Dupain · Maud Kamal · Nicolas Servant · Christophe Le Tourneau · Peter F Stadler · Stephan H Bernhart

Journal of integrative bioinformatics · 2024 doi:10.1515/jib-2024-0031

Paper

complete

Experiment

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Data

partial

Analysis

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Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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paper object · 10 fields
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title
TREMSUCS-TCGA - an integrated workflow for the identification of biomarkers for treatment success.
doi
10.1515/jib-2024-0031
pmid
39654143
authors array · 8 items
[0]
Gabor Balogh
[1]
Natasha Jorge
[2]
Célia Dupain
[3]
Maud Kamal
[4]
Nicolas Servant
[5]
Christophe Le Tourneau
[6]
Peter F Stadler
[7]
Stephan H Bernhart
journal
Journal of integrative bioinformatics
year
2024
abstract
Many publicly available databases provide disease related data, that makes it possible to link genomic data to medical and meta-data. The cancer genome atlas (TCGA), for example, compiles tens of thousand of datasets covering a wide array of cancer types. Here we introduce an interactive and highly automatized TCGA-based workflow that links and analyses epigenomic and transcriptomic data with treatment and survival data in order to identify possible biomarkers that indicate treatment success. TREMSUCS-TCGA is flexible with respect to type of cancer and treatment and provides standard methods for differential expression analysis or DMR detection. Furthermore, it makes it possible to examine several cancer types together in a pan-cancer type approach. Parallelisation and reproducibility of all steps is ensured with the workflowmanagement system Snakemake. TREMSUCS-TCGA produces a comprehensive single report file which holds all relevant results in descriptive and tabular form that can be explored in an interactive manner. As a showcase application we describe a comprehensive analysis of the available data for the combination of patients with squamous cell carcinomas of head and neck, cervix and lung treated with cisplatin, carboplatin and the combination of carboplatin and paclitaxel. The best ranked biomarker candidates are discussed in the light of the existing literature, indicating plausible causal relationships to the relevant cancer entities.
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For transcriptomic analysis, we employ DESeq2 [25], a tool for differential gene expression analysis based on the negative binomial distribution. For methylation data, we invoke metilene [26] which uses a binary segmentation algorithm combined with a two-dimensional statistical test for DMR detection.
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sections[5] "Overview of the workflow" paragraph 6
quote
For transcriptomic analysis, we employ DESeq2 [25], a tool for differential gene expression analysis based on the negative binomial distribution. For methylation data, we invoke metilene [26] which uses a binary segmentation algorithm combined with a two-dimensional statistical test for DMR detection.
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Data availability: The raw data can be obtained on request from the corresponding author.
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For transcriptomic analysis, we employ DESeq2 [25], a tool for differential gene expression analysis based on the negative binomial distribution. For methylation data, we invoke metilene [26] which uses a binary segmentation algorithm combined with a two-dimensional statistical test for DMR detection.
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