A proteomic classifier panel for early screening of colorectal cancer: a case control study.

BACKGROUND: Diagnosis of colorectal cancer (CRC) during early stages can greatly improve patient outcome. Although technical advances in the field of genomics and proteomics have identified a number of candidate biomarkers for non-invasive screening and diagnosis, developing more sensitive and specific methods with improved cost-effectiveness and patient compliance has tremendous potential to help combat the disease. METHODS: We enrolled three cohorts of 479 subjects, including 226 CRC cases, 197 healthy controls, and 56 advanced precancerous lesions (APC). In the discovery cohort, we used quantitative mass spectrometry to measure the expression profile of plasma proteins and applied machine-learning to select candidate proteins. We then developed a targeted mass spectrometry assay to measure plasma concentrations of seven proteins and a logistic regression classifier to distinguish CRC from healthy subjects. The classifier was further validated using two independent cohorts. RESULTS: The seven-protein panel consisted of leucine rich alpha-2-glycoprotein 1 (LRG1), complement C9 (C9), insulin-like growth factor binding protein 2 (IGFBP2), carnosine dipeptidase 1 (CNDP1), inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3), serpin family A member 1 (SERPINA1), and alpha-1-acid glycoprotein 1 (ORM1). The panel classified CRC and healthy subjects with high accuracy, since the area under curve (AUC) of the training and testing cohort reached 0.954 and 0.958. The AUC of the two independent validation cohorts was 0.905 and 0.909. In one validation cohort, the panel had an overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 89.9%, 81.8%, 89.2%, and 82.9%, respectively. In another blinded validation cohort, the panel classified CRC from healthy subjects with a sensitivity of 81.5%, specificity of 97.9%, and overall accuracy of 92.0%. Finally, the panel was able to detect APC with a sensitivity of 49%. CONCLUSIONS: This seven-protein classifier is a clear improvement compared to previously published blood-based protein biomarkers for detecting early-stage CRC, and is of translational potential to develop into a clinically useful assay.

Physicochemical properties of esterified/crosslinked quinoa starches and their influence on bread quality.

BACKGROUND: Starch is the main component of quinoa seeds. However, quinoa starch has poor solubility in cold water and poor mechanical resistance and is easily aged, which limit its application. Therefore, modification of its structure to improve its functional properties is necessary. RESULTS: This research used acetic anhydride and sodium trimetaphosphate to modify the structure of starch molecules and investigated their influence on bread quality. The results showed that both esterification and crosslinking prevented the aggregation behavior of starch molecules. Moreover, they both decreased the gelatinization enthalpy change and relative crystallinity of the starch. Compared with native starch, modification significantly decreased the gelatinization temperature from 57.01 to 52.01 °C and the esterified starch exhibited the lowest enthalpy change with a 44.2% decrease. Modified starch increased the specific volume and decreased the hardness and chewiness of bread. Modification did not influence the moisture content in bread but impacted the water retention capacity, depending on the degree of modification. Low and medium degrees of modification improved the water retention capacity during storage. By contrast, a high degree of modification (10 g kg-1 crosslinking agent) decreased the water retention capacity. The dually modified quinoa starch (esterified and crosslinked) showed no influence on the textural properties of bread. CONCLUSION: This study demonstrated that both esterification and crosslinking significantly improved the functional properties of quinoa starch. Crosslinked or esterified quinoa starches have the potential to improve the textural properties of bakery products. © 2024 Society of Chemical Industry.