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Abstract
We performed a pilot proteogenomic study to compare lung adenocarcinoma to lung squamous cell carcinoma using quantitative proteomics (6-plex TMT) combined with a customized Affymetrix GeneChip. Using MaxQuant software, we identified 51,001 unique peptides that mapped to 7,241 unique proteins and from these identified 6,373 genes with matching protein expression for further analysis. We found a minor correlation between gene expression and protein expression; both datasets were able to independently recapitulate known differences between the adenocarcinoma and squamous cell carcinoma subtypes. We found 565 proteins and 629 genes to be differentially expressed between adenocarcinoma and squamous cell carcinoma, with 113 of these consistently differentially expressed at both the gene and protein levels. We then compared our results to published adenocarcinoma versus squamous cell carcinoma proteomic data that we also processed with MaxQuant. We selected two proteins consistently overexpressed in squamous cell carcinoma in all studies, MCT1 (SLC16A1) and GLUT1 (SLC2A1), for further investigation. We found differential expression of these same proteins at the gene level in our study as well as in other public gene expression datasets. These findings combined with survival analysis of public datasets suggest that MCT1 and GLUT1 may be potential prognostic markers in adenocarcinoma and druggable targets in squamous cell carcinoma. Data are available via ProteomeXchange with identifier PXD002622.
Highlights
Recent developments in liquid chromatography and high-resolution mass spectrometry (MS) have allowed for highly precise, comprehensive characterization of proteomes [1]
We found a minor correlation between gene expression and protein expression; both datasets were able to independently recapitulate known differences between the adenocarcinoma and squamous cell carcinoma subtypes
Quantitative proteomic analysis was performed on three lung ADC and three lung squamous cell carcinoma (SCC) tumor samples
Summary
Recent developments in liquid chromatography and high-resolution mass spectrometry (MS) have allowed for highly precise, comprehensive characterization of proteomes [1]. Sample throughput is an issue in MS-based discovery proteomics because the necessary biological and technical replicates generate dozens of samples These numbers can expand significantly when analyzing large groups of patient samples in order to develop specific biomarkers or molecular classification schemes, and these numbers can expand even further if samples are fractionated using chromatographic methods in order to increase the depth of proteome coverage. One solution to this increasing sample number problem is to utilize multiplexing methods with isobaric chemical labeling reagents [e.g., tandem mass tag (TMT) or isobaric tags for relative and absolute quantitation (iTRAQ)]. These methods provide a means for parallelization by running many samples simultaneously in a single mass spectrometer [10]
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