An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer

BACKGROUND: The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations. RESULTS: In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network. CONCLUSION: Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression

Hepatitis D double reflex testing of all hepatitis B carriers in low-HBV- and high-HBV/HDV-prevalence countries

Hepatitis D virus (HDV) infection occurs as a coinfection with hepatitis B and increases the risk of hepatocellular carcinoma, decompensated cirrhosis, and mortality compared to hepatitis B virus (HBV) monoinfection. Reliable estimates of the prevalence of HDV infection and disease burden are essential to formulate strategies to find coinfected individuals more effectively and efficiently. The global prevalence of HBV infections was estimated to be 262,240,000 in 2021. Only 1,994,000 of the HBV infections were newly diagnosed in 2021, with more than half of the new diagnoses made in China. Our initial estimates indicated a much lower prevalence of HDV antibody (anti-HDV) and HDV RNA positivity than previously reported in published studies. Accurate estimates of HDV prevalence are needed. The most effective method to generate estimates of the prevalence of anti-HDV and HDV RNA positivity and to find undiagnosed individuals at the national level is to implement double reflex testing. This requires anti-HDV testing of all hepatitis B surface antigen-positive individuals and HDV RNA testing of all anti-HDV-positive individuals. This strategy is manageable for healthcare systems since the number of newly diagnosed HBV cases is low. At the global level, a comprehensive HDV screening strategy would require only 1,994,000 HDV antibody tests and less than 89,000 HDV PCR tests. Double reflex testing is the preferred strategy in countries with a low prevalence of HBV and those with a high prevalence of both HBV and HDV. For example, in the European Union and North America only 35,000 and 22,000 cases, respectively, will require anti-HDV testing annually

Transitive functional annotation by shortestpath analysis of gene expression data

Current methods for the functional analysis of microarray gene expression data make the implicit assumption that genes with similar expression profiles have similar functions in cells. However, among genes involved in the same biological pathway, not all gene pairs show high expression similarity. Here, we propose that transitive expression similarity among genes can be used as an important attribute to link genes of the same biological pathway. Based on large-scale yeast microarray expression data, we use the shortestpath analysis to identify transitive genes between two given genes from the same biological process. We find that not only functionally related genes with correlated expression profiles are identified but also those without. In the latter case, we compare our method to hierarchical clustering, and show that our method can reveal functional relationships among genes in a more precise manner. Finally, we show that our method can be used to reliably predict the function of unknown genes from known genes lying on the same shortest path. We assigned functions for 146 yeast genes that are considered as unknown by the Saccharomyces Genome Database and by the Yeast Proteome Database. These genes constitute around 5% of the unknown yeast ORFome

Regioselective Piperidine-Catalyzed Tandem Imination–Isocyanate Annulation to Fused Tricyclic Triazines

A novel tandem imination–isocyanate-mediated annulation was explored. Ionic liquid-immobilized 2-aminobenzimidazoles react sequentially with aldehydes and isocyanates to give highly functionalized benzimidazole-embedded triazines. The second-stage transformation revealed that the formation of triazinone functionality is entirely regioselective to allow rapid assembly of biologically interesting tricyclic skeletons. In conjunction with the application of microwave irradiation and IL support, this method provides an efficient route to access substituted benzoimidazotriazines

Pain behavior mediates the relationship between perceived injustice and opioid prescription for chronic pain: a collaborative health outcomes information registry study

Background and purpose: Perceived injustice has been defined as an appraisal regarding the severity and irreparability of loss associated with pain, blame and a sense of unfairness. Recent findings have identified perceived injustice as an important risk factor for pain-related outcomes. Studies suggest that perceived injustice is associated with opioid prescription in patients with pain conditions. However, the mechanisms by which perceived injustice is linked to opioid prescription are not well understood. The primary objective of this study was to examine the potential mediating roles of pain intensity, depressive symptoms and pain behavior in the association between perceived injustice and opioid prescription among patients with chronic pain. Methods: This cross-sectional study used a sample of 344 patients with chronic pain being treated at a tertiary pain treatment center. Participants completed measures of perceived injustice, pain intensity, depressive symptoms, pain behavior and opioid prescription. Bootstrapped multiple mediation analyses were used to examine the mediating role of patients’ pain intensity, depressive symptoms and pain behavior in the association between perceived injustice and opioid prescription. Results: Consistent with previous research, we found a significant association between perceived injustice and opioid prescription. Interestingly, results revealed that pain behavior was the only variable that mediated the association between perceived injustice and opioid prescription. Conclusion: This study was the first to examine the mechanisms by which perceived injustice is associated with opioid prescription in patients with chronic pain. We found that pain behavior, rather than pain intensity and depressive symptoms, mediated the association between perceived injustice and opioid prescription. Future research in this area should employ a longitudinal research design in order to arrive at clearer causal conclusions about the relationships between pain behavior, perceived injustice and opioid prescription