Molecular changes related to migration.

<p>Heatmap for genes differentially expressed after MPA treatment for 0h, 12h, 24h, 48h and 72h. Each sample is represented in a column and each gene is represented in a row. Increased expression is indicated as red and decreased expression is indicated as green. Representative genes are shown on the right panel and gene clusters are indicated on the left. </p

Confirmation of gene expression differences by real-time RT-PCR.

<p>Box blots are shown for each gene. Relative expression levels are shown in log 2 scale. FC (fold change) for each time point (12h, 24h and 48h) is compared to untreated controls (0h) with respective p-values.</p

A network illustrating the connectedness of the genes (>2-fold differences) involved in cell migration.

<p>A network illustrating the connectedness of the genes (>2-fold differences) involved in cell migration.</p

MPA modulates the migration and invasion abilities of AGS cells but not Hs746T cells.

<p><b>A</b>: Images of migrating cells after treatment with vehicle (DMSO) or different concentration of MPA for 24h. The mean relative migrating cells are shown at the bottom. <b>B</b>: Images of invading cells after treatment with vehicle (DMSO) or different concentration of MPA for 24h. The mean relative invading cells are shown at the bottom. *p < 0.05 compared to DMSO control.</p

MMP1 protein levels in the cell culture medium.

<p><b>A</b>: AGS cells. <b>B</b>: Hs746T cells. </p

Inflammatory Serum Proteins Are Severely Altered in Metastatic Gastric Adenocarcinoma Patients from the Chinese Population

<div><p>Background</p><p>Inflammation is one of the major hallmarks of cancer. This study was designed to profile a panel of inflammatory mediators in gastric adenocarcinoma (GA) and to identify their potential differences separately in metastatic and non-metastatic patient subgroups.</p><p>Methods</p><p>Serum samples from 216 GA patients and 333 healthy controls from China were analyzed for six proteins using the Luminex multiplex assay.</p><p>Results</p><p>The serum levels for all the six proteins were significantly elevated in metastatic GA compared to non-metastatic GA. Two acute phase proteins (SAA and CRP) and a CXC chemokine (GRO) were significantly elevated in metastatic GA (p <0.01) but smaller changes were observed in non-metastatic GA compared to healthy controls. OPN is moderately increased in non-metastatic GA (2.05-fold) and more severely elevated in metastatic GA (3.34-fold). Surprisingly, soluble VCAM1 and AGP were significantly lower in both non-metastatic and metastatic GA patients compared to controls. Several individual proteins were shown to possess moderate diagnostic value for non-metastatic GA (AUC = 0.786, 0.833, 0.823 for OPN, sVCAM1 and AGP, respectively) and metastatic GA (AUC = 0.931, 0.720, 0.834 and 0.737 for OPN, sVCAM1, SAA and CRP, respectively). However, protein combinations further improve the diagnostic potential for both non-metastatic GA (best AUC = 0.946) and metastatic GA (best AUC = 0.963). The protein combination with best AUC value for both comparisons is OPN+sVCAM1+AGP+SAA.</p><p>Conclusions</p><p>These results suggest that several serum proteins are directly related to the severity of gastric cancer. Overall, stronger associations are observed with metastatic than non-metastatic GA as the protein changes are greater with the metastatic status. A combination of these serum proteins may serve as non-invasive markers to assess the severity status and stage of gastric cancer.</p></div

The utility of protein combinations to distinguish GA and associated distant metastasis.

<p>For multi-protein models, linear discriminate analysis was performed using 3 protein and 4 protein combinations. The diagnostic performance of each model was evaluated using leave one out cross validation method. The utility of serum proteins as cancer biomarkers was evaluated using the area-under-curve (AUC) of the ROC curves for different models. Several protein combinations possess excellent diagnostic value (AUC >0.9) in distinguishing both non-metastatic and metastatic samples from healthy controls.</p

Logistic Regression Analysis using serum protein concentration, before and after adjusting for covariates.

<p>Logistic Regression Analysis using serum protein concentration, before and after adjusting for covariates.</p

Boxplots representing the serum protein levels in patient subgroups and healthy controls.

<p>Subgroups are defined based on the presence of cancer (A), metastasis status (B), and cancer stage (C). H: Healthy controls, GA: Gastric Adenocarcinoma, NM: patients with no metastasis, M: patients with distant metastasis. The pairwise correlations between all six proteins are shown in H, NM, and M subgroups (D). Correlations between individual protein levels were computed using Pearson correlation coefficient. Clustering and visualization of correlation matrix was performed using hierarchical clustering method and heatmap. Overall strong positive correlations were observed in both the GA patients with distant metastasis and without distant metastasis as compared to healthy controls.</p

Risk of gastric adenocarcinoma and metastasis with protein alterations.

<p>H: Healthy controls, NM: patients with no metastasis, M: patients with distant metastasis. Three comparisons were made separately: H <i>vs</i> NM (A, B), H <i>vs</i> M (C, D), and NM <i>vs</i> M (E, F). Subjects were divided into five quintiles based on individual protein levels. The 1st quintile was used as reference and odds ratios of having disease was calculated for upper four quintiles (A, C, E). The chi-squared test for trend in proportions was used to calculate the p-value of overall trend. For a combination of proteins (B, D, F) the combined risk score of each subject was calculated by simply adding risk score from multiple proteins and odds ratios were computed.</p