Proteomics

Dataset Information

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DIA-MS analysis of 21 breast cancer tissues

ABSTRACT: In recent years genomic and proteomic technologies have been employed in a combined effort to extrapolate key clinical and biological information of complex diseases such as cancer. Most integrative studies employ DNA and/or RNA sequencing technologies coupled to mass spectrometry-derived information to achieve deep information extraction levels, resulting in massive experimental efforts. In this context the employment of data independent acquisition (DIA) methods, which generally do not rely on fractionation, has seldom been tested. In this study, we evaluated the ability of DIA and data dependent acquisition (DDA) MS in determining key biological features of a set of 21 breast cancer tissues for whose RNA-sequencing data was also collected. We evaluated how proteomic data layers matched RNA analysis-derived genomic information, their degree of consensus, and their discrepancies.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Breast Epithelium, Epithelial Cell, Breast Cancer Cell

DISEASE(S): Breast Cancer

SUBMITTER: Tommaso De Marchi  

LAB HEAD: Emma Niméus

PROVIDER: PXD018830 | Pride | 2021-04-26

REPOSITORIES: Pride

Json Xml

Dataset's files

Source:
Action DRS
2017-06-12.uniprot.hsapiens.reviewed.DECOYed.fasta Fasta
2017-06-12.uniprot.hsapiens.reviewed.fasta Fasta
2019-05-20.Subtypes.Merged.GTL.old.DIA.Scale.FDR.0.10.tr.3.to.100.lenient.Unimod.DECOY.PQP Other
DIA_RAW_search_feature_alignment_matrix.tsv Tabular
PRIDE_sample_INFO.xlsx Xlsx
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Publications

Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance.

De Marchi Tommaso T   Pyl Paul Theodor PT   Sjöström Martin M   Klasson Stina S   Sartor Hanna H   Tran Lena L   Pekar Gyula G   Malmström Johan J   Malmström Lars L   Niméus Emma E  

Journal of proteome research 20210415 5

Proteogenomic approaches have enabled the generat̲ion of novel information levels when compared to single omics studies although burdened by extensive experimental efforts. Here, we improved a data-independent acquisition mass spectrometry proteogenomic workflow to reveal distinct molecular features related to mammographic appearances in breast cancer. Our results reveal splicing processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and prot  ...[more]

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