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Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer.
- Source :
-
Nature communications [Nat Commun] 2018 Sep 05; Vol. 9 (1), pp. 3598. Date of Electronic Publication: 2018 Sep 05. - Publication Year :
- 2018
-
Abstract
- Different regions of oral squamous cell carcinoma (OSCC) have particular histopathological and molecular characteristics limiting the standard tumor-node-metastasis prognosis classification. Therefore, defining biological signatures that allow assessing the prognostic outcomes for OSCC patients would be of great clinical significance. Using histopathology-guided discovery proteomics, we analyze neoplastic islands and stroma from the invasive tumor front (ITF) and inner tumor to identify differentially expressed proteins. Potential signature proteins are prioritized and further investigated by immunohistochemistry (IHC) and targeted proteomics. IHC indicates low expression of cystatin-B in neoplastic islands from the ITF as an independent marker for local recurrence. Targeted proteomics analysis of the prioritized proteins in saliva, combined with machine-learning methods, highlights a peptide-based signature as the most powerful predictor to distinguish patients with and without lymph node metastasis. In summary, we identify a robust signature, which may enhance prognostic decisions in OSCC and better guide treatment to reduce tumor recurrence or lymph node metastasis.
- Subjects :
- Carcinoma, Squamous Cell diagnosis
Carcinoma, Squamous Cell pathology
Clinical Decision-Making
Female
Follow-Up Studies
Humans
Immunohistochemistry
Lymphatic Metastasis
Machine Learning
Male
Middle Aged
Mouth Neoplasms diagnosis
Mouth Neoplasms pathology
Neoplasm Recurrence, Local prevention & control
Peptides analysis
Predictive Value of Tests
Prognosis
Retrospective Studies
Saliva chemistry
Survival Rate
Biomarkers, Tumor analysis
Carcinoma, Squamous Cell mortality
Mouth Neoplasms mortality
Neoplasm Recurrence, Local diagnosis
Proteomics methods
Subjects
Details
- Language :
- English
- ISSN :
- 2041-1723
- Volume :
- 9
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Nature communications
- Publication Type :
- Academic Journal
- Accession number :
- 30185791
- Full Text :
- https://doi.org/10.1038/s41467-018-05696-2