This report describes an integrated study on identification of potential markers for gastric cancer in patients’ cancer tissues and sera based on: (i) genome-scale transcriptomic analyses of 80 paired gastric cancer/reference tissues and (ii) computational prediction of blood-secretory proteins supported by experimental validation. Our findings show that: (i) 715 and 150 genes exhibit significantly differential expressions in all cancers and early-stage cancers versus reference tissues, respectively; and a substantial percentage of the alteration is found to be influenced by age and/or by gender; (ii) 21 co-expressed gene clusters have been identified, some of which are specific to certain subtypes or stages of the cancer; (iii) the top-ranked gene signatures give better than 94% classification accuracy between cancer and the reference tissues, some of which are gender-specific; and (iv) 136 of the differentially expressed genes were predicted to have their proteins secreted into blood, 81 of which were detected experimentally in the sera of 13 validation samples and 29 found to have differential abundances in the sera of cancer patients versus controls. Overall, the novel information obtained in this study has led to identification of promising diagnostic markers for gastric cancer and can benefit further analyses of the key (early) abnormalities during its development.

Receptor−ligand interactions (RLIs) that play pivotal roles in living organisms are often depicted with the classic keys-and-locks model. Nevertheless, RLIs on the cell surface are generally highly complex and nonlinear, partially due to the noncontinuous and dynamic distribution of receptors on extracellular membranes. Here, we develop a tetrahedral DNA framework (TDF)-programmed approach to topologically engineer RLIs on the cell membrane, which enables active recruitment-binding of clustered receptors for high-affinity capture of circulating tumor cells (CTCs). The four vertices of a TDF afford orthogonal anchoring of ligands with spatial organization, based on which we synthesized n-simplexes harboring 1−3 aptamers targeting epithelial cell adhesion molecule (EpCAM) that are overexpressed on the membrane of tumor cells. The 2-simplex with three aptamers not only shows increased binding affinity (∼19-fold) but prevents endocytosis by cells. By using 2-simplex as the capture probe, we demonstrate the high-efficiency CTC capture, which is challenged in real clinical breast cancer patient samples. This TDFprogrammed platform thus provides a powerful means for studying RLIs in physiological settings and for cancer diagnosis.