Biomarkers for pancreatic cancer: Recent achievements in proteomics and genomics through classical and multivariate statistical methods

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.