Protein deep sequencing applied to biobank samples from patients with pancreatic cancer
| Autor: | Charlotte Welinder, Monika Bauden, Agata Sasor, Daniel Ansari, György Marko-Varga, Bodil Andersson, Roland Andersson, Joanne B. Connolly |
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| Rok vydání: | 2014 |
| Předmět: |
Male
Proteomics Cancer Research medicine.medical_specialty Pathology Pancreatic disease Cell Biology medicine.disease_cause Deep sequencing Pancreatic tumor Internal medicine Pancreatic cancer Biomarkers Tumor medicine Humans Pancreas Aged Biological Specimen Banks Hematology Computational Biology Blood Proteins General Medicine Middle Aged Prognosis medicine.disease Pancreatic Neoplasms medicine.anatomical_structure Oncology Case-Control Studies Spectrometry Mass Matrix-Assisted Laser Desorption-Ionization Cancer research Female Carcinogenesis |
| Zdroj: | Journal of Cancer Research and Clinical Oncology. 141:369-380 |
| ISSN: | 1432-1335 0171-5216 |
| DOI: | 10.1007/s00432-014-1817-x |
| Popis: | Pancreatic cancer is commonly detected at advanced stages when the tumor is no longer amenable to surgical resection. Therefore, finding biomarkers for early stage disease is urgent. Here, we show that high-definition mass spectrometry (HDMSE) can be used to identify serum protein alterations associated with early stage pancreatic cancer. We analyzed serum samples from patients with resectable pancreatic cancer, benign pancreatic disease, and healthy controls. The SYNAPT G2-Si platform was used in a data-independent manner coupled with ion mobility. The dilution of the samples with yeast alcohol dehydrogenase tryptic digest of known concentration allowed the estimated amounts of each identified protein to be calculated (Silva et al. in Anal Chem 77:2187–2200, 2005; Silva et al. in Mol Cell Proteomics 5:144–156, 2006). A global protein expression comparison of the three study groups was made using label-free quantification and bioinformatic analyses. Two-way unsupervised hierarchical clustering revealed 134 proteins that successfully classified pancreatic cancer patients from the controls, and identified 40 proteins that showed a significant up-regulation in the pancreatic cancer group. This discrimination reliability was further confirmed by principal component analysis. The differentially expressed candidates were aligned with protein network analyses and linked to biological pathways related to pancreatic tumorigenesis. Pancreatic disease link associations could be made for BAZ2A, CDK13, DAPK1, DST, EXOSC3, INHBE, KAT2B, KIF20B, SMC1B, and SPAG5, by pathway network linkages to p53, the most frequently altered tumor suppressor in pancreatic cancer. These pancreatic cancer study candidates may provide new avenues of research for a noninvasive blood-based diagnosis for pancreatic tumor stratification. |
| Databáze: | OpenAIRE |
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