التفاصيل البيبلوغرافية
| العنوان: |
Improving the accuracy of pancreatic cancer clinical staging by exploitation of nanoparticle-blood interactions: A pilot study. |
| المؤلفون: |
Caputo, D., Cartillone, M., Cascone, C., Pozzi, D., Digiacomo, L., Palchetti, S., Caracciolo, G., Coppola, R. |
| المصدر: |
Pancreatology (Elsevier Science); Sep2018, Vol. 18 Issue 6, p661-665, 5p |
| مستخلص: |
Background Pancreatic ductal adenocarcinoma (PDAC) early diagnosis is crucial and new, cheap and user-friendly techniques for biomarker identification are needed. “Protein corona” (PC) is emerging a new bio-interface potentially useful in tumor early diagnosis. In a previous investigation, we showed that relevant differences between the protein patterns of PCs formed on lipid NPs after exposure to PDAC and non-cancer plasma samples exist. To extend that research, We performed this pilot study to investigate the effect of PDAC tumor size and distant metastases on PC composition. Methods Twenty PDACs were clinically staged according to the UICC TNM staging system 8 t h Edition. Collected plasma samples were let to interact with lipid NPs; resulting PCs were characterized by SDS-PAGE. To properly evaluate changes in the PC, the protein intensity profiles were reduced to four regions of molecular weight: < 25 kDa, 25–50 kDa, 50–120 kDa, > 120 kDa. Results Data analysis allowed to distinguish T1-T2 cases from T3 and above all from metastatic ones (p < 0.05). Discrimination power was particularly due to a subset of plasma proteins with molecular weight comprised between 25-50 kDa and 50–120 kDa. Conclusions PC composition is critically influenced by tumor size and presence of distant metastases in PDAC. If our findings will be further confirmed, we envision that future developments of cheap and user-friendly PC-based tools will allow to improve the accuracy of PDAC clinical staging, identifying among resectable PDACs with potentially better prognosis (i.e. T1 and T2) those at higher risk of occult distant metastases. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
