Magic™ Anti-SPINK7 (C-terminal) polyclonal antibody

A short tandem repeat (STR) polymorphism in the 3UTR region of esophageal cancer-related gene 2 (ECRG2, also known as SPINK7) has been widely reported to be associated with the incidence and the prognosis of esophageal squamous cell carcinoma (ESCC). This study explores how the microRNA binding to the STR region affects ECRG2 expression in ESCC. Dual-luciferase reporter assays were used to verify the effects of the four microRNAs (miR-580, miR-1182, miR-1272, and miR-1322) predicted to bind the STR region of the ECRG2 3 untranslated region (UTR). The expression of identified effective microRNA was then analyzed in 44 paired ESCC and adjacent normal tissues and 402 case-controlled serum samples (divided into a discovery group and an independent validation group) by real-time RT-PCR assay. We found that only miR-1322 could significantly down-regulate the ECRG2 with TCA3 allele (P<0.01), but it could not down-regulate the ECRG2 with TCA4 allele significantly (P>0.05). MiR-1322 was also expressed significantly higher in ESCC tissue and serum samples than in controls (both P<0.01). Additionally, serum levels of miR-1322 yielded an under receiver operating characteristic (ROC) curve area of 0.847 (95% CI, 0.795-0.890) for discriminating ESCCs from healthy controls in the discovery group and a similar result was obtained in the validation group (under ROC area is 0.845; 95%CI, 0.780-0.897). We conclude that miR-1322 can regulate ECRG2 in an allele-specific manner and that serum levels of miR-1322 can serve as a potential diagnostic biomarker for patients with ESCC. (c) 2012 Wiley Periodicals, Inc.

We explored the potential of combining carcinoembryonic antigen (CEA) and salivary mRNAs for gastric cancer (GC) detection. This study included 2 phases of study: a biomarker discovery phase and an independent validation phase. In the discovery phase, we measured CEA levels in blood samples and expression level of messenger RNAs (SPINK7, PPL, SEMA4B, SMAD4) in saliva samples of 140 GC patients and 140 healthy controls. We evaluated the clinical performance of each biomarker and developed a predictive model using machine-learning algorithm to differentiate GC patients and healthy controls. Our biomarker panel successfully discriminated GC patients from healthy controls with both high sensitivity (0.94) and high specificity (0.91). We next applied our biomarker panel in the independent validation phase, in which we recruited a new patient cohort of 60 GC patients and 60 healthy controls. Using our biomarker panel, the GC patients were discriminated from healthy controls in the validation phase, with sensitivity of 0.92 and specificity of 0.87. A combination of blood CEA and salivary messenger RNA could be a promising approach to detect GC.