Research Papers:

Integrated quantitative proteomic and transcriptomic analysis of lung tumor and control tissue: a lung cancer showcase

Stefan Tenzer, Petra Leidinger, Christina Backes, Hanno Huwer, Andreas Hildebrandt, Hans-Peter Lenhof, Tanja Wesse, Andre Franke, Eckart Meese and Andreas Keller _

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Oncotarget. 2016; 7:14857-14870. https://doi.org/10.18632/oncotarget.7562

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Stefan Tenzer1,*, Petra Leidinger2,*, Christina Backes3,*, Hanno Huwer4, Andreas Hildebrandt5, Hans-Peter Lenhof6, Tanja Wesse7, Andre Franke7, Eckart Meese2,*, Andreas Keller3

1Institute for Immunology, University Medical Center of the Johannes Gutenberg University of Mainz, Mainz, Germany

2Department of Human Genetics, Saarland University, Homburg, Germany

3Clinical Bioinformatics, Saarland University, Saarbrücken, Germany

4SHG Clinics, Völklingen, Germany

5Software Engineering and Bioinformatics, Johannes Gutenberg University of Mainz, Mainz, Germany

6Center for Bioinformatics, Saarland University, Saarbrücken, Germany

7Kiel University, Kiel, Germany

*These authors contributed equally to this work

Correspondence to:

Andreas Keller, e-mail: [email protected]

Keywords: mass spectrometry, proteomics analysis, transcriptomics, lung tumors, adenocarcinoma

Received: July 07, 2015     Accepted: January 01, 2016     Published: February 22, 2016


Proteomics analysis of paired cancer and control tissue can be applied to investigate pathological processes in tumors. Advancements in data-independent acquisition mass spectrometry allow for highly reproducible quantitative analysis of complex proteomic patterns. Optimized sample preparation workflows enable integrative multi-omics studies from the same tissue specimens.

We performed ion mobility enhanced, data-independent acquisition MS to characterize the proteome of 21 lung tumor tissues including adenocarcinoma and squamous cell carcinoma (SCC) as compared to control lung tissues of the same patient each. Transcriptomic data were generated for the same specimens. The quantitative proteomic patterns and mRNA abundances were subsequently analyzed using systems biology approaches.

We report a significantly (p = 0.0001) larger repertoire of proteins in cancer tissues. 12 proteins were higher in all tumor tissues as compared to matching control tissues. Three proteins, CAV1, CAV2, and RAGE, were vice versa higher in all controls. We also identified characteristic SCC and adenocarcinoma protein patterns. Principal Component Analysis provided evidence that not only cancer from control tissue but also tissue from adenocarcinoma and SCC can be differentiated. Transcriptomic levels of key proteins measured from the same matched tissue samples correlated with the observed protein patterns.

The applied study set-up with paired lung tissue specimens of which different omics are measured, is generally suited for an integrated multi-omics analysis.

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