Building the process-drug–side effect network to discover the relationship between biological Processes and side effects

<p>Abstract</p> <p>Background</p> <p>Side effects are unwanted responses to drug treatment and are important resources for human phenotype information. The recent development of a database on side effects, the side effect resource (SIDER), is a first step in documenting the relationship between drugs and their side effects. It is, however, insufficient to simply find the association of drugs with biological processes; that relationship is crucial because drugs that influence biological processes can have an impact on phenotype. Therefore, knowing which processes respond to drugs that influence the phenotype will enable more effective and systematic study of the effect of drugs on phenotype. To the best of our knowledge, the relationship between biological processes and side effects of drugs has not yet been systematically researched.</p> <p>Methods</p> <p>We propose 3 steps for systematically searching relationships between drugs and biological processes: enrichment scores (ES) calculations, t-score calculation, and threshold-based filtering. Subsequently, the side effect-related biological processes are found by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in 2 ways: first, by discerning the number of biological processes discovered by our method that co-occur with Gene Ontology (GO) terms in relation to effects extracted from PubMed records using a text-mining technique and second, determining whether there is improvement in performance by limiting response processes by drugs sharing the same side effect to frequent ones alone.</p> <p>Results</p> <p>The multi-level network (the process-drug-side effect network) was built by merging the drug-biological process network and the drug-side effect network. We generated a network of 74 drugs-168 side effects-2209 biological process relation resources. The preliminary results showed that the process-drug-side effect network was able to find meaningful relationships between biological processes and side effects in an efficient manner.</p> <p>Conclusions</p> <p>We propose a novel process-drug-side effect network for discovering the relationship between biological processes and side effects. By exploring the relationship between drugs and phenotypes through a multi-level network, the mechanisms underlying the effect of specific drugs on the human body may be understood.</p

POMC: The Physiological Power of Hormone Processing.

Pro-opiomelanocortin (POMC) is the archetypal polypeptide precursor of hormones and neuropeptides. In this review, we examine the variability in the individual peptides produced in different tissues and the impact of the simultaneous presence of their precursors or fragments. We also discuss the problems inherent in accurately measuring which of the precursors and their derived peptides are present in biological samples. We address how not being able to measure all the combinations of precursors and fragments quantitatively has affected our understanding of the pathophysiology associated with POMC processing. To understand how different ratios of peptides arise, we describe the role of the pro-hormone convertases (PCs) and their tissue specificities and consider the cellular processing pathways which enable regulated secretion of different peptides that play crucial roles in integrating a range of vital physiological functions. In the pituitary, correct processing of POMC peptides is essential to maintain the hypothalamic-pituitary-adrenal axis, and this processing can be disrupted in POMC-expressing tumors. In hypothalamic neurons expressing POMC, abnormalities in processing critically impact on the regulation of appetite, energy homeostasis, and body composition. More work is needed to understand whether expression of the POMC gene in a tissue equates to release of bioactive peptides. We suggest that this comprehensive view of POMC processing, with a focus on gaining a better understanding of the combination of peptides produced and their relative bioactivity, is a necessity for all involved in studying this fascinating physiological regulatory phenomenon

Gene Expression Profiles in Stage I Uterine Serous Carcinoma in Comparison to Grade 3 and Grade 1 Stage I Endometrioid Adenocarcinoma

Endometrial cancer is the most common gynecologic malignancy in the developed countries. Clinical studies have shown that early stage uterine serous carcinoma (USC) has outcomes similar to early stage high grade endometrioid adenocarcinoma (EAC-G3) than to early stage low grade endometrioid adenocarcinoma (EAC-G1). However, little is known about the origin of these different clinical outcomes. This study applied the whole genome expression profiling to explore the expression difference of stage I USC (n = 11) relative to stage I EAC-G3 (n = 11) and stage I EAC-G1 (n = 11), respectively.We found that the expression difference between USC and EAC-G3, as measured by the number of differentially expressed genes (DEGs), is consistently less than that found between USC and EAC-G1. Pathway enrichment analyses suggested that DEGs specific to USC vs. EAC-G3 are enriched for genes involved in signaling transduction, while DEGs specific to USC vs. EAC-G1 are enriched for genes involved in cell cycle. Gene expression differences for selected DEGs are confirmed by quantitative RT-PCR with a high validation rate.This data, although preliminary, indicates that stage I USC is genetically similar to stage I EAC-G3 compared to stage I EAC-G1. DEGs identified from this study might provide an insight in to the potential mechanisms that influence the clinical outcome differences between endometrial cancer subtypes. They might also have potential prognostic and therapeutic impacts on patients diagnosed with uterine cancer

Microarray Analysis Reveals Distinct Gene Expression Profiles Among Different Tumor Histology, Stage and Disease Outcomes in Endometrial Adenocarcinoma

Endometrial cancer is the most common gynecologic malignancy in developed countries and little is known about the underlying mechanism of stage and disease outcomes. The goal of this study was to identify differentially expressed genes (DEG) between late vs. early stage endometrioid adenocarcinoma (EAC) and uterine serous carcinoma (USC), as well as between disease outcomes in each of the two histological subtypes.Gene expression profiles of 20 cancer samples were analyzed (EAC = 10, USC = 10) using the human genome wide illumina bead microarrays. There was little overlap in the DEG sets between late vs. early stages in EAC and USC, and there was an insignificant overlap in DEG sets between good and poor prognosis in EAC and USC. Remarkably, there was no overlap between the stage-derived DEGs and the prognosis-derived DEGs for each of the two histological subtypes. Further functional annotation of differentially expressed genes showed that the composition of enriched function terms were different among different DEG sets. Gene expression differences for selected genes of various stages and outcomes were confirmed by qRT-PCR with a high validation rate.This data, although preliminary, suggests that there might be involvement of distinct groups of genes in tumor progression (late vs. early stage) in each of the EAC and USC. It also suggests that these genes are different from those involved in tumor outcome (good vs. poor prognosis). These involved genes, once clinically verified, may be important for predicting tumor progression and tumor outcome