Label-based comparative proteomics of oral mucosal tissue to understand progression of precancerous lesions to oral squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39483176", "kind": "science-record", "revision": 4, "content_hash": "sha256:47197ebbdcbc49c03671a77f7a0bb41f0e02da0baa18d68797a911309bb9a1e3", "created_at": "2026-11-01T20:29:44Z", "updated_at": "2026-11-01T22:55:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Label-based comparative proteomics of oral mucosal tissue to understand progression of precancerous lesions to oral squamous cell carcinoma.", "doi": "10.1016/j.bbrep.2024.101842", "pmid": "39483176", "authors": ["Vipra Sharma", "Sundararajan Baskar Singh", "Sabyasachi Bandyopadhyay", "Kapil Sikka", "Aanchal Kakkar", "Gururao Hariprasad"], "journal": "Biochemistry and biophysics reports", "year": 2024, "abstract": "Introduction: Oral squamous cell carcinomas typically arise from precancerous lesions such as leukoplakia and erythroplakia. These lesions exhibit a range of histological changes from hyperplasia to dysplasia and carcinoma in situ, during their transformation to malignancy. The molecular mechanisms driving this multistage transition remain incompletely understood. To bridge this knowledge gap, our current study utilizes label based comparative proteomics to compare protein expression profiles across different histopathological grades of leukoplakia, erythroplakia, and oral squamous cell carcinoma samples, aiming to elucidate the molecular changes underlying lesion evolution.\nMethodology: An 8-plex iTRAQ proteomics of 4 biological replicates from 8 clinical phenotypes of leukoplakia and erythroplakia, with hyperplasia, mild dysplasia, moderate dysplasia; along with phenotypes of well differentiated squamous cell carcinoma and moderately differentiated squamous cell carcinoma was carried out using the Orbitrap Fusion Lumos mass spectrometer. Raw files were processed with Maxquant, and statistical analysis across groups was conducted using MetaboAnalyst. Statistical tools such as ANOVA, PLS-DA VIP scoring, and correlation analysis were employed to identify differentially expressed proteins that had a linear expression variation across phenotypes of hyperplasia to cancer. Validation was done using Bioinformatic tools such as ClueGO + Cluepedia plugin in Cytoscape to extract functional annotations from gene ontology and pathway databases.\nResults and discussion: A total of 2685 protein groups and 12,397 unique peptides were identified, and 61 proteins consistently exhibited valid reporter ion corrected intensities across all samples. Of these, 6 proteins showed linear varying expression across the analysed sample phenotypes. Collagen type VI alpha 2 chain (COL6A2), Fibrinogen β chain (FGB), and Vimentin (VIM) were found to have increased linear expression across pre-cancer phenotypes of leukoplakia to cancer, while Annexin A7 (ANXA7) was seen to be having a linear decreasing expression. Collagen type VI alpha 2 chain (COL6A2) and Annexin A2 (ANXA2) had increased linear expression across precancer phenotypes of erythroplakia to cancer. The mass spectrometry proteomics data have been deposited to the ProteomeXchanger Consortium via the PRIDE partner repository with the data set identifier PXD054190. These differentially expressed proteins mediate cancer progression mainly through extracellular exosome; collagen-containing extracellular matrix, hemostasis, platelet aggregation, and cell adhesion molecule binding.\nConclusion: Label-based proteomics is an ideal platform to study oral cancer progression. The differentially expressed proteins provide insights into the molecular mechanisms underlying the progression of oral premalignant lesions to malignant phenotypes. The study has translational value for early detection, risk stratification, and potential therapeutic targeting of oral premalignant lesions and in its prevention to malignant forms.", "claims": [{"id": "claim-method", "statement": "Univariate statistical analysis was performed by one-way ANOVA followed by Tukey's multiple comparison test at a cut-off p-value <0.05. Multivariate Partial Least Square Discriminant Analysis (OPLS-DA) was applied to discriminate the different histological grades. Proteins having VIP scores greater than 1.5 were considered significant for the final model. Pattern hunter feature was used to perform correlation analysis against a predefined pattern to identify protein expression changes during disease progression - from hyperplasia to dysplasia to neoplasia - ranked by Pearson correlation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39483176", "locator": "sections[10] \"Quantification and downstream analyses\" paragraph 4", "quote": "Univariate statistical analysis was performed by one-way ANOVA followed by Tukey's multiple comparison test at a cut-off p-value <0.05. Multivariate Partial Least Square Discriminant Analysis (OPLS-DA) was applied to discriminate the different histological grades. Proteins having VIP scores greater than 1.5 were considered significant for the final model. 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