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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
Published on: August 19, 2025
Data from quantitative proteomic analysis of lung adenocarcinoma and squamous cell carcinoma primary tissues using
Aafaque Ahmad Khan1,2, Kiran Kumar Mangalaparthi1,3, Jayshree Advani1,4
1Institute of Bioinformatics, 7th floor, Discoverer building, International Tech Park, Bangalore 560066, India.
Abstract:
Lung cancer is the leading cause of preventable death globally and is broadly classified into adenocarcinoma and squamous cell carcinoma. In this study, we carried out mass spectrometry based quantitative proteomic analysis of lung adenocarcinoma and squamous cell carcinoma primary tissue by employing the isobaric tags for relative and absolute quantitation (iTRAQ) approach. Proteomic data analyzed using SEQUEST algorithm resulted in identification of 25,998 peptides corresponding to 4342 proteins of which 610 proteins were differentially expressed (≥ 2-fold) between adenocarcinoma and squamous cell carcinoma. These differentially expressed proteins were further classified by gene ontology for their localization and biological processes. Pathway analysis of differentially expressed proteins revealed distinct alterations in networks and pathways in both adenocarcinoma and squamous cell carcinoma. We identified a subset of proteins that show inverse expression pattern between lung adenocarcinoma and squamous cell carcinoma. Such proteins may serve as potential markers to distinguish between the two subtypes. Mass spectrometric data generated in this study was submitted to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD008700.
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