Dataset Information

Human oral cancer brush biopsy iTRAQ

ABSTRACT: iTRAQ-based comparison of proteins derived from oral cells collected by brush biopsy. Protein abundance levels compared between oral pre-malignant cells, oral cancer cells and healthy normal cells, all collected from human patients. Two separate iTRAQ labeled biological replicate analyses were conducted.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Dr Timothy Jon Griffin

PROVIDER: MSV000079994 | MassIVE | Fri Jul 22 16:09:00 BST 2016

SECONDARY ACCESSION(S): PXD000807

REPOSITORIES: MassIVE

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Publications

Quantitative proteomic analysis of oral brush biopsies identifies secretory leukocyte protease inhibitor as a promising, mechanism-based oral cancer biomarker.

Yang Ya Y Rhodus Nelson L NL Ondrey Frank G FG Wuertz Beverly R K BR Chen Xiaobing X Zhu Yaqin Y Griffin Timothy J TJ

PloS one 20140418 4

A decrease in the almost fifty percent mortality rate from oral cancer is needed urgently. Improvements in early diagnosis and more effective preventive treatments could affect such a decrease. Towards this end, we undertook for the first time an in-depth mass spectrometry-based quantitative shotgun proteomics study of non-invasively collected oral brush biopsies. Proteins isolated from brush biopsies from healthy normal tissue, oral premalignant lesion tissue (OPMLs), oral squamous cell carcino ...[more]

PMID: 24748380

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Project description:Analysis of various of up-regulated and down-regulated genes in normal oral mucosa and different stages of OSF. The report provides a data analysis methodology for identification of co-expressed gene patterns, as emerging clusters, in global transcriptome of oral mucosal pre-malignant and malignant conditions in comparison to their normal counterparts. Microarray based study of global gene expression is often used to extract molecular signatures underlying cancer progression. Such endeavors endorse self organizing map, a type of artificial neural network to analyze high dimensional pre-processed transcriptome data to segregate hotspot genes in component plane for disease subtypes. This report provides a data analysis methodology for identification of co-expressed gene patterns, as emerging clusters, in global transcriptome of oral mucosal pre-malignant and malignant conditions in comparison to their normal counterparts. Four exclusive cluster patterns, each involving 100 − 300 genes, were identified from component planes for oral study groups. Gene expression associated with each pattern belonged to 32 biological processes. The transcription and RNA processing, and angiogenesis were dominant in normal oral condition, while immunity, cellular differentiation and migration, neuron signalling, connective tissue organization, tumor suppression, and enzyme regulation showed predominance in oral squamous cell carcinoma. In dysplasia skeletal and ligament development processes were dominant while in non-dysplastic sub-mucous fibrosis cytoskeleton reorganization and neuron signalling were pre-dominant. In dysplasia an intermediate pattern with nine different dominant functional processes was identified. This analysis demonstrated utility of self organizing map to capture dominant enriched patterns as visual plots and revealed six common biological processes like transcription and RNA processing, cytoskeleton reorganization, angiogenesis, immunity, neuron signalling, and connective tissue remodeling in the pathogenesis of oral cancer. In fact it could provide an intuitive understanding of molecular course in carcinogenesis and may contribute for combinatorial biomarker discovery.

Dataset Information

Human oral cancer brush biopsy iTRAQ

Publications

Quantitative proteomic analysis of oral brush biopsies identifies secretory leukocyte protease inhibitor as a promising, mechanism-based oral cancer biomarker.

Similar Datasets

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