4th Department of Medicine-Department of Gastroenterology and Hepatology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
3rd Department of Medicine-Department of Endocrinology and Metabolism, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
Neoplasma. 2022 Mar;69(2):474-483. doi: 10.4149/neo_2022_210730N1075. Epub 2022 Feb 10.
To identify non-invasive biomarkers of non-metastatic pancreatic cancer (PC), the blood from 186 patients (PC n=28; DM-diabetes mellitus n=60; ChP-chronic pancreatitis n=47; healthy controls n=51) was analyzed for 58 candidate biomarkers. Their effectiveness to identify PC was compared with CA19-9. Panel defined by Random-forest (RF) analysis (CA19-9, AAT, IGFBP2, albumin, ALP, Reg3A, HSP27) outperforms CA19-9 in discrimination of PC from DM (AUC 0.92 vs. 0.82). Panel (S100A11, CA72-4, AAT, CA19-9, CB, MMP-7, S100P-s, Reg3A) is better in discrimination PC from ChP than CA19-9 (AUC 0.90 vs. 0.75). Panel (MMP-7, Reg3A, sICAM1, OPG, CB, ferritin) is better in discrimination PC from healthy controls than CA19-9 (AUC 0.89 vs. 0.78). Panel (CA19-9, S100P-pl, AAT, albumin, adiponectin, IGF-1, MMP7, S100A11) identifies PC among other groups better than CA19-9 (AUC 0.91 vs. 0.80). Panel defined by logistic regression analysis (prealbumin, IGFBP-2, DJ-1, MIC-1, CA72-4) discriminates PC from DM worse than CA19-9 (AUC 0.80 vs. 0.82). Panel (IGF-1, S100A11, Reg1alfa) outperforms CA19-9 in discrimination PC from ChP (AUC 0.76 vs. 0.75). Panel (IGF-2, S100A11, Reg3A) outperforms CA19-9 in discrimination PC from healthy controls (AUC 0.95 vs. 0.78). Panel (albumin, AAT, S100P-serum, CRP, CA19-9, TFF1, MMP-7) outperforms CA19-9 in identification PC among other groups (AUC 0.89 vs. 0.8). The combination of biomarkers identifies PC better than CA19-9 in most cases. S100A11, Reg3A, DJ-1 were to our knowledge identified for the first time as possible serum biomarkers of PC.
为了鉴定非转移性胰腺癌（PC）的非侵入性生物标志物，对 186 名患者（PC n=28；DM-糖尿病 n=60；ChP-慢性胰腺炎 n=47；健康对照组 n=51）的血液进行了 58 种候选生物标志物分析。将它们识别 PC 的有效性与 CA19-9 进行了比较。随机森林（RF）分析定义的面板（CA19-9、AAT、IGFBP2、白蛋白、ALP、Reg3A、HSP27）在区分 PC 与 DM 方面优于 CA19-9（AUC 0.92 对 0.82）。与 CA19-9 相比，面板（S100A11、CA72-4、AAT、CA19-9、CB、MMP-7、S100P-s、Reg3A）在区分 PC 与 ChP 方面更好（AUC 0.90 对 0.75）。与 CA19-9 相比，面板（MMP-7、Reg3A、sICAM1、OPG、CB、铁蛋白）在区分 PC 与健康对照组方面更好（AUC 0.89 对 0.78）。与 CA19-9 相比，面板（CA19-9、S100P-pl、AAT、白蛋白、脂联素、IGF-1、MMP7、S100A11）在其他组中识别 PC 优于 CA19-9（AUC 0.91 对 0.80）。由逻辑回归分析定义的面板（前白蛋白、IGFBP-2、DJ-1、MIC-1、CA72-4）在区分 PC 与 DM 方面劣于 CA19-9（AUC 0.80 对 0.82）。与 CA19-9 相比，面板（IGF-1、S100A11、Reg1alfa）在区分 PC 与 ChP 方面表现更好（AUC 0.76 对 0.75）。与 CA19-9 相比，面板（IGF-2、S100A11、Reg3A）在区分 PC 与健康对照组方面表现更好（AUC 0.95 对 0.78）。与 CA19-9 相比，面板（白蛋白、AAT、S100P 血清、CRP、CA19-9、TFF1、MMP-7）在其他组中识别 PC 的性能更好（AUC 0.89 对 0.8）。在大多数情况下，与 CA19-9 相比，生物标志物的组合能更好地识别 PC。S100A11、Reg3A、DJ-1 是我们首次发现的 PC 可能的血清生物标志物。
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