title: Integrated quantitative proteomic and transcriptomic analysis of lung tumor and control tissue: a lung cancer showcase.
abstract: 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.
authors.affiliation: Institute for Immunology, University Medical Center of The Johannes Gutenberg University of Mainz, Mainz, Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Tenzer
authors.affiliation: Department of Human Genetics, Saarland University, Homburg, Germany.
authors.forename: Petra
authors.initials: P
authors.lastname: Leidinger
authors.affiliation: Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.
authors.forename: Christina
authors.initials: C
authors.lastname: Backes
authors.affiliation: SHG Clinics, Völklingen, Germany.
authors.forename: Hanno
authors.initials: H
authors.lastname: Huwer
authors.affiliation: Software Engineering and Bioinformatics, Johannes Gutenberg University of Mainz, Mainz, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Hildebrandt
authors.affiliation: Center for Bioinformatics, Saarland University, Saarbrücken, Germany.
authors.forename: Hans-Peter
authors.initials: HP
authors.lastname: Lenhof
authors.affiliation: Kiel University, Kiel, Germany.
authors.forename: Tanja
authors.initials: T
authors.lastname: Wesse
authors.affiliation: Kiel University, Kiel, Germany.
authors.forename: Andre
authors.initials: A
authors.lastname: Franke
authors.affiliation: Department of Human Genetics, Saarland University, Homburg, Germany.
authors.forename: Eckart
authors.initials: E
authors.lastname: Meese
authors.affiliation: Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Keller
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics / Q000378:metabolism* / Q000473:pathology; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D002294:Carcinoma, Squamous Cell / Q000235:genetics / Q000378:metabolism* / Q000473:pathology; D016022:Case-Control Studies; D005500:Follow-Up Studies; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics / Q000378:metabolism* / Q000473:pathology; D009367:Neoplasm Staging; D011379:Prognosis; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods; D059467:Transcriptome
chemical_list: D014408:Biomarkers, Tumor; D020543:Proteome
keywords: adenocarcinoma; lung tumors; mass spectrometry; proteomics analysis; transcriptomics