Table 3.
Proteomic based studies
| Ref. | Type of marker | Markers | Sample | Study group | Analytical methods | Statistical methods | Performance |
| 54 | D | Among 2393 unique proteins, 104 proteins significantly changed in cancer | T | 5 patients; matched pairs of tumor and non-tumor pancreas | Tissues treated to obtain cytosol, membrane, nucleus and cytoskeletonfractions. Fractions separated and digested underwent LC-MS/MS | PLGEM | 104 proteins significantly changed in cancer. Among these, 4 proteins validated that were up-regulated in cancer: biglycan (BGN), Pigment Epithelium-derived Factor (PEDF) Thrombospondin-2 (THBS-2) and TGF-β induced protein ig-h3 precursor (βIGH3) |
| 57 | D | Serum MALDI-TOF features | S | 15 healthy (H), 24 cancer (Ca), 11 chronic pancreatitis (CP) samples | MALDI-TOF | Nonparametric | 8 serum features: Ca samples differentiated from H (SN = 88%, SP = 93%), Ca from CP (SN = 88%, SP = 30%), and Ca from both H and CP combined (SN = 88%, SP = 66%). 9 features obtained from urine: differentiated Ca from both H and CP combined (SN = 90%, SP = 90%) |
| 59 | D | Serum SELDI-TOF features | S | 96 serum samples from patients undergoing cancer surgery compared with sera from 96 controls | SELDI-TOF | pairwise statistics, MDS, hierarchical analysis Mann-Whitney U test, CART | Data analysis identified 24 differentially expressed protein peaks, 21 of which under-expressed in cancer samples. The best single marker predicts 92% of controls and 89% of cancer samples. Multivariate analysis: best model (3 markers) with SN = 100% and SP = 98% for the training data and SN = 83% and SP = 77% for test data. Apolipoprotein A-II, transthyretin and apolipoprotein A-I identified as markers and decreased at least 2 fold in cancer sera |
| 60 | D | Serum SELDI-TOF features | S | 57 PC samples were compared to 59 controls | SELDI-TOF | Multivariate decorrelation filtering | Improved classification performances when the presented strategy is compared to standard univariate feature selection strategies |
| 61 | D | Proteins | S | Sera from patients diagnosed with PC compared with age- and sex-matched normal subjects | Protein microarrays | Rank-based non-parametric statistical testing | A serum diagnosis of PC was predicted with 86.7% accuracy, with a sensitivity and specificity of 93.3% and 80%. Candidate autoantibody biomarkers studied for their classification power using an independent sample set of 238 sera. Phosphoglycerate kinase-1 and histone H4 noted to elicit a significant differential humoral response in cancer sera compared with age- and sex-matched sera from normal patients and patients with chronic pancreatitis and diabetes |
| 62 | D | Proteins | PDAC cell lines | 435 spots identified from 18 samples from 2 cell lines (Paca44 and T3M4) of control and drug-treated PDAC cells | 2D-PAGE | PCA, SIMCA, Ranking-PCA | Samples were all perfectly classified. Significant proteins were further identified by MS analysis |
| 63 | D | Proteins regulating the conversion of quiescent to activated PaSC cells | rat PaSC cell line | - | SDS-PAGE and GeLC-MS/MS | QSPEC | Qualitative and quantitative proteomic analysis revealed several hundred proteins as differentially abundant between the two cell states. Proteins of greater abundance in activated PaSC: isoforms of actin and ribosomal proteins. Proteins more abundant in non-proliferating PaSC: signaling proteins MAP kinase 3 and Ras-related proteins |
Type of marker: P: Prognostic/predictive; D: Diagnostic; Sample: S: Serum; P: Plasma; T: Tissue.