RESUMEN
Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers in India. Despite improvements in treatment strategy, the survival rates of HNSCC patients remain poor. Thus, it is necessary to identify biomarkers that can be used for early detection of disease. In this study, we employed iTRAQ-based quantitative mass spectrometry analysis to identify dysregulated proteins from a panel of head and neck squamous cell carcinoma (HNSCC) cell lines. We identified 2468 proteins, of which 496 proteins were found to be dysregulated in at least two out of three HNSCC cell lines compared to immortalized normal oral keratinocytes. We detected increased expression of replication protein A1 (RPA1) and heat shock protein family H (Hsp110) member 1 (HSPH1), in HNSCC cell lines compared to control. The differentially expressed proteins were further validated using parallel reaction monitoring (PRM) and western blot analysis in HNSCC cell lines. Immunohistochemistry-based validation using HNSCC tissue microarrays revealed overexpression of RPA1 and HSPH1 in 15.7% and 32.2% of the tested cases, respectively. Our study illustrates quantitative proteomics as a robust approach for identification of potential HNSCC biomarkers. The proteomic data has been submitted to ProteomeXchange Consortium (http://www.proteomecentral.proteomexchange.org) via the PRIDE public data repository accessible using the data identifier - PXD009241.
RESUMEN
Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted "noncoding RNAs" to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.