Funnel plot for the assessment of potential bias in s-p53 antibody assays.

<p>The funnel graph plots the DOR (diagnostic odds ratio) against the 1/root (effective sample size). The dotted line is the regression line. The result of the test for publication bias showed publication bias (p<0.001).</p

Assessment of the Potential Diagnostic Value of Serum p53 Antibody for Cancer: A Meta-Analysis

<div><p>Background</p><p>Mutant p53 protein over-expression has been reported to induce serum antibodies against p53. We assessed the diagnostic precision of serum p53 (s-p53) antibodies for diagnosis of cancer patients and compared the positive rates of the s-p53 antibody in different types of cancers.</p><p>Methods</p><p>We systematically searched PubMed and Embase, through May 31, 2012. Studies were assessed for quality using QUADAS (quality assessment of studies of diagnostic accuracy). The positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were pooled separately and compared with overall accuracy measures using diagnostic odds ratios (DORs) and Area under the curve(AUC). Meta regression and subgroup analyses were done, and heterogeneity and publication bias were assessed.</p><p>Results</p><p>Of 1089 studies initially identified, 100 eligible studies with 23 different types of tumor met the inclusion criteria for the meta-analysis (cases = 15953, controls = 8694). However, we could conduct independent meta analysis on only 13 of 36 types of tumors. Approximately 56% (56/100) of the included studies were of high quality (QUADAS score≥8). The summary estimates for quantitative analysis of serum p53 antibody in the diagnosis of cancers were: PLR 5.75 (95% CI: 4.60–7.19), NLR 0.81 (95%CI: 0.79–0.83) and DOR 7.56 (95% CI: 6.02–9.50). However, for the 13 types of cancers on which meta-analysis was conducted, the ranges for PLR (2.33–11.05), NLR (0.74–0.97), DOR (2.86–13.80), AUC(0.29–0.81), and positive rate (4.47%–28.36%) indicated significant heterogeneity. We found that breast, colorectal, esophageal, gastric, hepatic, lymphoma, lung and ovarian cancer had relatively reasonable diagnostic accuracy. The remaining results of the five types of cancers suggested that s-p53 antibody had limited value.</p><p>Conclusions</p><p>The current evidence suggests that s-p53 antibody has potential diagnostic value for cancer, especially for breast, colorectal, esophageal, gastric, hepatic, lymphoma, lung and ovarian cancer. The results showed that s-p53 antibody had high correlation with cancers.</p></div

Possible sources of heterogeneity of sub-group analysis.

<p>Note: QUADAS: quality assessment of studies of diagnostic accuracy, PLR: positive likelihood ratio, NLR: negative likelihood ratio, DOR: diagnostic odds ratio. PLR (95% CI)*, DOR (95% CI)* and NLR (95% CI)* were calculated using a random effect model.</p

Pooled diagnostic accuracy of s-p53 antibody for detection of 13 types of cancer.

<p>Note: PLR: positive likelihood ratio, NLR: negative likelihood ratio, DOR: diagnostic odds ratio, AUC: the area under the SROC curve; PLR (95% CI)*, DOR (95% CI)* and NLR (95% CI)* were calculated using a random effect model.</p

Flow chart of study selection by using electronic database.

<p>Flow chart of study selection by using electronic database.</p

A schematic diagram of this systems biology-based analysis for HCC marker identification.

<p>First, HCC significant molecules which were differentially expressed or had genetic variations in HCC tissues relative to their corresponding normal tissues were collected from five existing HCC related databases (OncoDB.HCC, <a href="http://hcc.net" target="_blank">HCC.net</a>, dbHCCvar, EHCO and Liverome). Second, their protein-protein interaction networks were constructed and the hub proteins were chosen according to node degree. Third, a list of candidate HCC markers were identified by calculating four topological features of the network ('Degree', 'Betweenness', ‘Closeness’ and 'K-coreness' ). After that, the key PPIs of candidate HCC markers were selected by K-core analysis and edge-betweenness algorithm. The K-core of a graph is defined as the largest subgraph where every node has at least k links. For each choice of k, we determine the k-cores by iteratively pruning all nodes with degree lower than k and their incident links. The edge-betweenness algorithm is a top-down, divisive method for grouping network components into modules. Edge-betweenness centrality is the frequency of an edge that places on the shortest paths between all pairs of vertices. The edges with highest betweenness values are most likely to lie between sub-graphs. Here, we chose PPI (between RL30_HUMAN and GRB2_HUMAN) with highest edge-betweenness as the most important PPI in HCC network because it may connect different cores with the shortest paths. Finally, the clinical significance of RL30_HUMAN (RPL30) and GRB2_HUMAN (GRB2) was validated using a large cohort of patients with HCC.</p

Disease-free survival and overall survival curves for two groups defined by low and high expression of GRB2 (A and B) and GAB1 (C and D), and for four groups defined by combined expression of GRB2 and GAB1 (E and F), in patients with HCC.

<p>The patients with high GRB2 and GAB1 expression had a significantly shorter 5-year overall and disease-free survival rate than those with low GRB2 and GAB1 expression (both P=0.008). In addition, the results by pairwise comparisons showed that the statistically significant difference of overall and disease-free survival existed between GRB2-high/GAB1-high patients and any of other three groups (both P<0.001). In all four groups, GRB2-high/GAB1-high patients had the poorest prognosis.</p

Top three significantly enriched gene ontology (GO) molecular functions (A) and KEGG pathways (B) involved by candidate hepatocellular carcinoma (HCC) markers.

<p>The functional distribution of candidate HCC markers was obtained from GO enrichment analysis. Candidate HCC markers more frequently had protein serine/threonine kinase and pyrophosphatase activities, and also were involved in dynein binding process. Since the pathway information is important for understanding gene and protein functions, we also analyzed the enriched KEGG biological pathways among these candidate HCC markers, which were most commonly implicated in cancer related pathways, such as non-small cell lung cancer, pancreatic cancer and glioma. '*'P<0.05,'**'P<0.01.</p

Co-expression network of 331 candidate HCC markers constructed using the K-core analysis.

<p>Nodes in different cores are marked with various colors. Blue edges refer to the shortest paths through the interaction between GRB2 and GAB1 for the connection of different cores.</p

Representative immunohistochemical images of GRB2 (A and B), GAB1 (D and E) and p-ERK1 (G and H) expression in HCC and adjacent non-neoplastic liver tissues (Original magnification×400).

<p>Statistical analyses of IRS for GRB2 (C), GAB1 (F) and p-ERK1 (I) immunostainings in HCC and adjacent non-neoplastic liver tissues. GRB2 positive staining was localized in the cell nucleus and cytoplasm, while GAB1 positive staining was localized in the cytoplasm of tumor cells in HCC tissues. Compared with the adjacent nonneoplastic tissues, the expression levels of GRB2 (IRS for HCC vs. nonneoplastic liver: 6.32±1.50 vs. 2.67±0.32, P<0.001) and GAB1 (IRS for HCC vs. nonneoplastic liver: 5.72±0.95 vs. 1.75±0.48, P<0.001) proteins were all significantly increased in HCC tissues. More interestingly, in all four groups according to the combined expression of GRB2 and GAB1, GRB2-high/GAB1-high patients expressed the highest level of p-ERK1 protein (all P=0.01).</p