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.

Profiling lipid mediators in serum from children with H1N1 influenza.

Influenza A virus subtype H1N1 can cause severe acute respiratory distress syndrome and death in young children and elderly individuals. H1N1 initiates inflammatory responses that aim to contain and eliminate microbial invaders. Various lipid mediators (LMs) are biosynthesized and play a critical role in fighting viruses during inflammation; thus, by profiling the LMs in patients, researchers can obtain mechanistic insights into diseases, such as the pathways disrupted. To date, the relationship between molecular alterations in LMs and the pathogenesis of H1N1 influenza in children is poorly understood. Here, we employed a targeted liquid chromatography coupled with tandem mass spectrometry (LC‒MS/MS) to profile LMs in serum from children with H1N1 influenza (H1N1 children) and recovered children. We found that 22 LM species were altered in H1N1 children with mild symptoms. Analysis of the LM profiles of recovered children revealed a decrease in the levels of thromboxane B2 (TxB2) and thromboxane B3 (TxB3) and an increase in the levels of other 8 altered LM species associated with H1N1 influenza, including cytochrome P450 (CYP) enzyme-derived dihydroxyeicosatrienoic acids (DiHETrEs) and hydroxyeicosatetraenoic acids (HETEs) from arachidonic acid (AA), and epoxyoctadecamonoenoic acids (EpOMEs) from linoleic acid (LA). Taken together, the results of this study revealed that serum LMs change dynamically in H1N1 children with mild symptoms. The dramatically altered LMs in H1N1 children could serve as a basis for potential therapeutics or adjuvants against H1N1 influenza.