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Metastasis is the primary cause of an unfavourable prognosis in patients with malignant cancer. Over the last decade, the role of proteinases in the tumour microenvironment has attracted increasing attention. As a sensor of proteinases, proteinase-activated receptor 2 (PAR2 ) plays crucial roles in the metastatic progression of cervical cancer. In the present study, the expression of PAR2 in multiple types of cancer was analysed by Gene Expression Profiling Interactive Analysis (GEPIA). Kaplan-Meier plotter was used to calculate the correlation between survival and the levels of PAR2 , Grb-associated binding protein 2(Gab2) and miR-125b. Immunohistochemistry (IHC) was performed to examine PAR2 expression in a tissue microarray (TMA) of CESCs. Empower Stats was used to assess the predictive value of PAR2 in the metastatic potential of CESC. We found that PAR2 up-regulation was observed in multiple types of cancer. Moreover, PAR2 expression was positively correlated with the clinicopathologic characteristics of CESC. miR-125b and its target Gab2, which are strongly associated with PAR2 -induced cell migration, are well-characterized as predictors of the prognostic value of CESC. Most importantly, the Cancer Genome Atlas (TCGA) data set analysis showed that the area under the curve (AUC) of the PAR2 model was significantly greater than that of the traditional model (0.833 vs 0.790, P < .05), demonstrating the predictive value of PAR2 in CESC metastasis. Our results suggest that PAR2 may serve as a prognostic factor for metastasis in CESC patients.

Research Summary This study investigates incumbent responses to a main rival's exit. We argue that long‐time rivals have developed an equilibrium by offering a mix of overlapping and unique products and by choosing geographic proximity to each other. A rival's exit, however, disrupts this equilibrium and motivates surviving firms to expand in both product and geographic spaces to seek a new equilibrium. Using data from all U.S. Best Buy stores before and after the exit of Circuit City, we find that Best Buy uses product variety expansion as its major response in markets where Circuit City was colocated, but it more often responds by opening new stores in non‐colocated markets. Regardless of preexisting market structures, the magnitude of product variety expansion decreases with the opening of new stores. Managerial Summary How do surviving firms respond to a major rival's exit? By studying Best Buy's responses to Circuit City's withdrawal, we find the survivor expands in both product space (increasing product variety) and geographic space (opening new stores), due to two motives. First, the survivor strives to fill in “holes” left in the market. Second, the survivor experiences uncertainty in the postexit world wherein its reference point is gone, threat of potential entry looms, and it lacks information about new entrants. Thus, it must deter potential entry ex ante by preempting many prime product and geographic locations. Best Buy also responds according to preexisting market structures, primarily through product variety expansion in markets wherein Circuit City was colocated and through opening new stores in non‐colocated markets.

Hepcidin is crucial in regulating iron metabolism, and increased serum levels were strongly linked with poor outcomes in various malignancies. Thus, we investigated if genetic variants in the BMP/Smad4/Hamp hepcidin‐regulating pathway were associated with outcomes in patients receiving definitive radiotherapy for NSCLC. Subjects were 664 NSCLC patients who received ≥60 Gy radiotherapy for NSCLC retrospectively identified from a single‐institution database. Potentially, functional and tagging single nucleotide polymorphisms (SNPs) of BMP2 (rs170986, rs1979855, rs1980499, rs235768, and rs3178250), BMP4 (rs17563, rs4898820, and rs762642), Smad4 (rs12456284), and Hamp (rs1882694, rs10402233, rs10421768, and rs12971321) were genotyped by TaqMan real‐time polymerase chain reaction. Cox proportional hazard's analyses were used to assess potential influences of SNPs on overall survival (OS), local‐regional progression‐free survival (LRPFS), progression‐free survival (PFS), and distant metastasis‐free survival (DMFS). Nomogram of each endpoint model was developed using R project. The median patient age was 66 years. Most (488 [73.2%]) had stage III NSCLC. Age, disease stage, receipt of concurrent chemotherapy, and gross tumor volume were independent factors of OS. Hamp rs1882694 AC/CC genotypes were associated with poor OS, LRPFS, PFS, and DMFS in multivariate analyses. Besides, BMP2 rs1979855, rs3178250, and rs1980499 associated with PFS; Hamp rs10402233 and BMP2 rs1979855 associated with LRPFS; BMP2 rs3178250 associated with DMFS after adjustment for clinical factors. After adding SNPs to each model, all the likelihood ratios were increased; the nomograms were improved significantly to predict LRPFS (P

High throughput gene expression profiling has showed great promise in providing insight into molecular mechanisms. Metastasis‐related mRNAs may potentially enrich genes with the ability to predict cancer recurrence, therefore we attempted to build a recurrence‐associated gene signature to improve prognostic prediction of colorectal cancer (CRC). We identified 2848 differentially expressed mRNAs by analyzing CRC tissues with or without metastasis. For the selection of prognostic genes, a LASSO Cox regression model (least absolute shrinkage and selection operator method) was employed. Using this method, a 13‐mRNA signature was identified and then validated in two independent Gene Expression Omnibus cohorts. This classifier could successfully discriminate the high‐risk patients in discovery cohort [hazard ratio (HR) = 5.27, 95% confidence interval (CI) 2.30–12.08, P

Activity cliffs are defined as pairs or groups of structurally similar or analogous compounds that share the same specific activity but have large differences in potency. Although activity cliffs are mostly studied in medicinal chemistry at the level of molecular graphs, they can also be assessed by comparing compound binding modes. If such three-dimensional activity cliffs (3D-cliffs) are studied on the basis of X-ray complex structures, experimental ligand-target interaction details can be taken into account. Rapid growth in the number of 3D-cliffs that can be derived from X-ray complex structures has made it possible to identify targets for which a substantial body of 3D-cliff information is available. Activity cliffs are typically studied to identify structure-activity relationship determinants and aid in compound optimization. However, 3D-cliff information can also be used to search for interaction hot spots and key residues, as reported herein. For six of seven drug targets for which more than 20 3D-cliffs were available, series of 3D-cliffs were identified that were consistently involved in interactions with different hot spots. These 3D-cliffs often encoded chemical modifications resulting in interactions that were characteristic of highly potent compounds but absent in weakly potent ones, thus providing information for structure-based design.

Sets of scaffolds with conserved molecular topology are abundant among drugs and bioactive compounds. Core structure topology is one of the determinants of biological activity. Heteroatom replacements and/or bond order variation render topologically equivalent scaffolds chemically distinct and also contribute to differences in the biological activity of compounds containing these scaffolds. Relationships between core structure topology, chemical modifications, and observed activity profiles are difficult to analyze. A computational method is introduced to consistently assess chemical transformations that distinguish scaffolds with conserved topology. The methodology is applied to quantify chemical differences in conserved topological environments and systematically relate chemical changes in topologically equivalent scaffolds to associated activity profiles.

Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets.

Matching molecular series (MMS) have originally been introduced as an extension of the matched molecular pair (MMP) concept to facilitate the design of substructure-based structure-activity relationship (SAR) networks. An MMP is defined as a pair of compounds that only differ by a structural change at a single site. In addition, an MMS is defined as an MMP-based series of compounds that have a conserved structural core and are distinguished by modifications at a single site. Systematic generation of MMS from specifically active compounds generalizes the search for series of structural analogs. Potency-ordered MMS provide series associated with SAR information. We have systematically extracted MMS from publicly available compounds with well-defined activity measurements and generated a large database with approx. 40 000 single- and 13 600 multi-target series, which provide a rich source of SAR information. As an application, we introduce MMP-based mapping of screening hits to MMS to search for initial SAR information and determine all SAR environments available for such hits. The MMS database is made freely available to the scientific community.

Gastric cancer (GC) is one of the most common cancers in the world. The incidence and mortality rate of GC vary among different countries. It is suggested that GC is the result of the interaction between Helicobacter pylori (H. pylori) infection and the genetic and environmental factors in the host. H. pylori infection is the trigger of intestinal gastric adenocarcinoma. The incidence of GC is highest in East Asia and East Europe, but much lower in Africa; however, H. pylori infection is commonly seen in Africa, which is known as the African enigma. The 5-year survival of early GC is far better than that of advanced GC. A high detection rate of early GC could help us to conquer GC. A decreasing trend of GC incidence has been witnessed worldwide. With the improvement of living conditions and the achievements of scientific research, it seems possible that there will be a further reduction in the incidence of GC in the new century.

The real-time, personalized and highly sensitive early-stage diagnosis of disease remains an important challenge in modern medicine. With the ability to interact with matter at the nanoscale, the development of nanotechnology architectures and materials could potentially extend subcellular and molecular detection beyond the limits of conventional diagnostic modalities. At the very least, nanotechnology should be able to dramatically accelerate biomarker discovery, as well as facilitate disease monitoring, especially of maladies presenting a high degree of molecular and compositional heterogeneity. This article gives an overview of several of the most promising nanodevices and nanomaterials along with their applications in clinical practice. Significant work to adapt nanoscale materials and devices to clinical applications involving large interdisciplinary collaborations is already underway with the potential for nanotechnology to become an important enabling diagnostic technology.

The exploration of structure-activity relationships (SARs) in chemical lead optimization is mostly focused on activity against single targets. Because many active compounds have the potential to act against multiple targets, achieving a sufficient degree of target selectivity often becomes a major issue during optimization. Herein we report a data analysis approach to explore compound selectivity in a systematic and quantitative manner. Sets of compounds that are active against multiple targets provide a basis for exploring structure-selectivity relationships (SSRs). Compound similarity and selectivity data are analyzed with the aid of network-like similarity graphs (NSGs), which organize molecular networks on the basis of similarity relationships and SAR index (SARI) values. For this purpose, the SARI framework has been adapted to quantify SSRs. Using sets of compounds with differential activity against four cathepsin thiol proteases, we show that SSRs can be quantitatively described and categorized. Furthermore, local SSR environments are identified, the analysis of which provides insight into compound selectivity determinants at the molecular level. These environments often contain "selectivity cliffs" formed by pairs or groups of similar compounds with significantly different selectivity. Moreover, key compounds are identified that determine characteristic features of single-target SARs and dual-target SSRs. The comparison of compounds involved in the formation of selectivity cliffs often reveals chemical modifications that render compounds target selective.

An inexpensive and effective iron/copper catalytic system is used in the cross-coupling of benzoxazoles and benzothiazoles with arylboronic acids.

High-sulfonated melamine–formaldehyde (HSMF) resins were prepared with 1.0–2.0 of sulfite/melamine (S/M) molar ratio. The factors affecting the preparation and the properties of the resin were studied. Chemical analysis indicated that there are 0.98–1.96 sulfonate groups per unit of the polymeric chain. The viscosity of HSMF resins is lower than that of sulfonated melamine–formaldehyde (SMF) resins, and the HSMF resins are more effective superplasticizers at small dosages of admixture. © 1995 John Wiley & Sons, Inc.