METHODS: Colon tissues (normal and cancerous) were homogenized and the proteins were extracted using three protein extraction buffers. The extraction buffers were used in an orderly sequence of increasing extraction strength for proteins with hydrophobic properties. The protein extracts were separated using the SDS-PAGE method and the images were captured and analyzed using Quantity One software. The target protein bands were subjected to in-gel digestion with trypsin and finally analyzed using an ESI-ion trap mass spectrometer.

RESULTS: A total of 50 differentially expressed proteins in colonic cancerous and normal tissues were identified.

METHODS: The study was divided into two phases: (I) Marker discovery by miRNA microarray using paired cancer tissues (n = 30) and blood samples (CRC, n = 42; control, n = 18). (II) Marker validation by stem-loop reverse transcription real time PCR using an independent set of paired cancer tissues (n = 30) and blood samples (CRC, n = 70; control, n = 32). Correlation analysis was determined by Pearson's test. Logistic regression and receiver operating characteristics curve analyses were applied to obtain diagnostic utility of the miRNAs.

RESULTS: Seven miRNAs (miR-150, miR-193a-3p, miR-23a, miR-23b, miR-338-5p, miR-342-3p and miR-483-3p) have been found to be differentially expressed in both tissue and blood samples. Significant positive correlations were observed in the tissue and blood levels of miR-193a-3p, miR-23a and miR-338-5p. Moreover, increased expressions of these miRNAs were detected in the more advanced stages. MiR-193a-3p, miR-23a and miR-338-5p were demonstrated as a classifier for CRC detection, yielding a receiver operating characteristic curve area of 0.887 (80.0% sensitivity, 84.4% specificity and 83.3% accuracy).

AIM OF THE STUDY: This study aimed to investigate the effect of ionic liquid-Graviola fruit pulp extract (IL-GPE) on the metabolomics behavior of colon cancer (HT29) by using an untargeted GC-TOFMS-based metabolic profiling.

MATERIALS AND METHODS: Multivariate data analysis was used to determine the metabolic profiling, and the ingenuity pathway analysis (IPA) was used to predict the altered canonical pathways after treating the HT29 cells with crude IL-GPE and Taxol (positive control).

RESULTS: The principal components analysis (PCA) identified 44 metabolites with the most reliable factor loading, and the cluster analysis (CA) separated three groups of metabolites: metabolites specific to the non-treated HT29 cells, metabolites specific to the treated HT29 cells with the crude IL-GPE and metabolites specific to Taxol treatment. Pathway analysis of metabolomic profiles revealed an alteration of many metabolic pathways, including amino acid metabolism, aerobic glycolysis, urea cycle and ketone bodies metabolism that contribute to energy metabolism and cancer cell proliferation.

MATERIALS AND METHODS: Seventy-two male Sprague-Dawley rats were divided into 6 equal groups with 12 rats in each group. For cancer induction two intraperitoneal injections of azoxymethane (AOM) were given at 15 mg/kg bodyweight over a 2-weeks period. During the post initiation phase, two different concentrations of PA, 0.2% (w/v) and 0.5% (w/v) were administered in the diet.

RESULTS: Results of β-catenin, COX-2 expressions and cell proliferation of Ki-67 showed a significant contribution in colonic cancer progression. For β-catenin and COX-2 expression, there was a significant difference between groups at p<0.05. With Ki-67, there was a statistically significant lowering the proliferating index as compared to AOM alone (p<0.05). A significant positive correlation (p=0.01) was noted between COX-2 expression and proliferation. Total β-catenin also demonstrated a significant positive linear relationship with total COX-2 (p=0.044).