Transcription regulation and candidate diagnostic markers of esophageal cancer

Philosophiae Doctor - PhDEsophageal cancer (EC) ranks among the ten most frequent cancers worldwide. Mortality rates associated with EC are very similar to the incidence rates due to the relatively late stage of diagnosis and the poor efficacy of treatment. The aim of this study was to enhance our insights of putative transcriptional circuitry of EC genes, thereby potentially positively impacting our knowledge of therapeutic targets, providing indications as to more appropriate lines of treatment, and additionally allowing for the determination of putative candidate diagnostic markers for the early stage detection of EC. This thesis reports on the development of a novel comprehensive database (Dragon Database of Genes Implicated in Esophageal Cancer, DDEC) as an integrated knowledge database aimed at representing a gateway to esophageal cancer related data. More importantly, it illustrates how the biocurated genes in the database may represent a reliable starting point for divulging transcriptional regulation, diagnostic markers and the biology related to esophageal cancer. DDEC contains known and novel information for 529 differentially expressed EC genes compiled using scientific publications from PubMed and is freely accessible for academic and non-profit users at http://apps.sanbi.ac.za/ddec/. The novel information provided to users of the DDEC is the lists of putative transcription factors that potentially control the 529 manually curated genes. The value of the information accessible through the database was further refined by providing precompiled text-mined and data-mined reports about each of these genes to allow for easy exploration of information about associations of EC-implicated genes with other human genes and proteins, metabolites and enzymes, toxins, chemicals with pharmacological effects, disease concepts and human anatomy. This feature has the capacity to display potential associations that are rarely reported and thus difficult to identify, and it enables the inspection of potentially new ‘association hypotheses’ generated based on the precompiled reports. This study further illustrates how the biocurated esophageal squamous cell carcinoma (ESCC) genes in the database may represent a reliable starting point for exploring beyond current knowledge of the transcriptional circuitry of estrogen related hormone therapy. The genes were used to develop a method that identified 44 combinations of transcription factors (TFs) that characterize the promoter sequence of estrogen responsive genes implicated in ESCC. These significantly over-represented combinations of TFs were then used to increase confidence in the 47 novel putative estrogen response genes that may be related to ESCC too. Coincidently, two of the novel putative estrogen response genes were verified by current (2009), experimental publications.South Afric

Screening extracts of indigenous South African plants for the presence of anti-cancer compounds

Magister Scientiae - MScEarly man dabbled with the use of plant extracts to cure ailments. This practice has been passed down from generation to generation and today more than 50% of the world'sdrugs are natural products or derivatives thereof. Scientists have thus established a branch of research called natural product research. This branch of research involves the identification and purification of secondary metabolites with a specific biological activity. The methodology involves the screening of plant products for a specific biological activity, purification of the biologically active natural product by separation technology and structure determination. The biologically active natural products is then further scrutinized to serve as a novel drug or lead compound for the development of a novel drug. This research exploited this research methodology.South Afric

Recently Confirmed Apoptosis-Inducing Lead Compounds Isolated from Marine Sponge of Potential Relevance in Cancer Treatment

Despite intense efforts to develop non-cytotoxic anticancer treatments, effective agents are still not available. Therefore, novel apoptosis-inducing drug leads that may be developed into effective targeted cancer therapies are of interest to the cancer research community. Targeted cancer therapies affect specific aberrant apoptotic pathways that characterize different cancer types and, for this reason, it is a more desirable type of therapy than chemotherapy or radiotherapy, as it is less harmful to normal cells. In this regard, marine sponge derived metabolites that induce apoptosis continue to be a promising source of new drug leads for cancer treatments. A PubMed query from 01/01/2005 to 31/01/2011 combined with hand-curation of the retrieved articles allowed for the identification of 39 recently confirmed apoptosis-inducing anticancer lead compounds isolated from the marine sponge that are selectively discussed in this review

A low-cost flow cytometric assay for the detection and quantification of apoptosis using an anionic halogenated fluorescein dye

We describe here a technical improvement of an established colorimetric method used to detect and measure the occurrence of apoptosis in mammalian cells during in vitro cell culture. This assay uses an anionic halogenated fluorescein dye that is taken up by apoptotic cells at the stage of phosphatidylserine externalization. We demonstrate that apoptotic cells stained with this dye can be detected by flow cytometric analysis. Furthermore, we show that the modified method compares well with the standard annexin-V–based apoptosis assay and that it is significantly more cost-effective than the annexin-V assay

Network analysis of microRNAs and their regulation in human ovarian cancer

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are small non-coding RNA molecules that repress the translation of messenger RNAs (mRNAs) or degrade mRNAs. These functions of miRNAs allow them to control key cellular processes such as development, differentiation and apoptosis, and they have also been implicated in several cancers such as leukaemia, lung, pancreatic and ovarian cancer (OC). Unfortunately, the specific machinery of miRNA regulation, involving transcription factors (TFs) and transcription co-factors (TcoFs), is not well understood. In the present study we focus on computationally deciphering the underlying network of miRNAs, their targets, and their control mechanisms that have an influence on OC development.</p> <p>Results</p> <p>We analysed experimentally verified data from multiple sources that describe miRNA influence on diseases, miRNA targeting of mRNAs, and on protein-protein interactions, and combined this data with <it>ab initio </it>transcription factor binding site predictions within miRNA promoter regions. From these analyses, we derived a network that describes the influence of miRNAs and their regulation in human OC. We developed a methodology to analyse the network in order to find the nodes that have the largest potential of influencing the network's behaviour (network hubs). We further show the potentially most influential miRNAs, TFs and TcoFs, showing subnetworks illustrating the involved mechanisms as well as regulatory miRNA network motifs in OC. We find an enrichment of miRNA targeted OC genes in the highly relevant pathways cell cycle regulation and apoptosis.</p> <p>Conclusions</p> <p>We combined several sources of interaction and association data to analyse and place miRNAs within regulatory pathways that influence human OC. These results represent the first comprehensive miRNA regulatory network analysis for human OC. This suggests that miRNAs and their regulation may play a major role in OC and that further directed research in this area is of utmost importance to enhance our understanding of the molecular mechanisms underlying human cancer development and OC in particular.</p

Diabetic cardiomyopathy: The role of microRNAs and long non-coding RNAs

Diabetes mellitus (DM) is on the rise, necessitating the development of novel therapeutic and preventive strategies to mitigate the disease’s debilitating effects. Diabetic cardiomyopathy (DCMP) is among the leading causes of morbidity and mortality in diabetic patients globally. DCMP manifests as cardiomyocyte hypertrophy, apoptosis, and myocardial interstitial fibrosis before progressing to heart failure. Evidence suggests that non-coding RNAs, such as long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), regulate diabetic cardiomyopathy-related processes such as insulin resistance, cardiomyocyte apoptosis and inflammation, emphasizing their heart-protective effects. This paper reviewed the literature data from animal and human studies on the non-trivial roles of miRNAs and lncRNAs in the context of DCMP in diabetes and demonstrated their future potential in DCMP treatment in diabetic patients

New insights on the cardiovascular effects of IGF-1

Cardiovascular (CV) disorders are steadily increasing, making them the world’s most prevalent health issue. New research highlights the importance of insulin-like growth factor 1 (IGF-1) for maintaining CV healthMethodsWe searched PubMed and MEDLINE for English and non-English articles with English abstracts published between 1957 (when the first report on IGF-1 identification was published) and 2022. The top search terms were: IGF-1, cardiovascular disease, IGF-1 receptors, IGF-1 and microRNAs, therapeutic interventions with IGF-1, IGF-1 and diabetes, IGF-1 and cardiovascular disease. The search retrieved original peer-reviewed articles, which were further analyzed, focusing on the role of IGF-1 in pathophysiological conditions. We specifically focused on including the most recent findings published in the past five years.ResultsIGF-1, an anabolic growth factor, regulates cell division, proliferation, and survival. In addition to its well-known growth-promoting and metabolic effects, there is mounting evidence that IGF-1 plays a specialized role in the complex activities that underpin CV function. IGF-1 promotes cardiac development and improves cardiac output, stroke volume, contractility, and ejection fraction. Furthermore, IGF-1 mediates many growth hormones (GH) actions. IGF-1 stimulates contractility and tissue remodeling in humans to improve heart function after myocardial infarction. IGF-1 also improves the lipid profile, lowers insulin levels, increases insulin sensitivity, and promotes glucose metabolism. These findings point to the intriguing medicinal potential of IGF-1. Human studies associate low serum levels of free or total IGF-1 with an increased risk of CV and cerebrovascular illness. Extensive human trials are being conducted to investigate the therapeutic efficacy and outcomes of IGF-1-related therapy.DiscussionWe anticipate the development of novel IGF-1-related therapy with minimal side effects. This review discusses recent findings on the role of IGF-1 in the cardiovascular (CVD) system, including both normal and pathological conditions. We also discuss progress in therapeutic interventions aimed at targeting the IGF axis and provide insights into the epigenetic regulation of IGF-1 mediated by microRNAs

MicroRNA networks linked with BRCA1/2, PTEN, and common genes for Alzheimer's disease and breast cancer share highly enriched pathways that may unravel targets for the AD/BC comorbidity treatment

MicroRNAs (miRNAs) are involved in the regulation of various cellular processes including pathological conditions. MiRNA networks have been extensively researched in age-related degenerative diseases, such as cancer, Alzheimer’s disease (AD), and heart failure. Thus, miRNA has been studied from different approaches, in vivo, in vitro, and in silico including miRNA networks. Networks linking diverse biomedical entities unveil information not readily observable by other means. This work focuses on biological networks related to Breast cancer susceptibility 1 (BRCA1) in AD and breast cancer (BC). Using various bioinformatics approaches, we identified subnetworks common to AD and BC that suggest they are linked. According to our results, miR-107 was identified as a potentially good candidate for both AD and BC treatment (targeting BRCA1/2 and PTEN in both diseases), accompanied by miR-146a and miR-17. The analysis also confirmed the involvement of the miR-17-92 cluster, and miR-124-3p, and highlighted the importance of poorly researched miRNAs such as mir-6785 mir6127, mir-6870, or miR-8485. After filtering the in silico analysis results, we found 49 miRNA molecules that modulate the expression of at least five genes common to both BC and AD. Those 49 miRNAs regulate the expression of 122 genes in AD and 93 genes in BC, from which 26 genes are common genes for AD and BC involved in neuron differentiation and genesis, cell differentiation and migration, regulation of cell cycle, and cancer development. Additionally, the highly enriched pathway was associated with diabetic complications, pointing out possible interplay among molecules underlying BC, AD, and diabetes patholog

Type 2 Diabetes Mellitus and its comorbidity, Alzheimer’s disease: Identifying critical microRNA using machine learning

MicroRNAs (miRNAs) are critical regulators of gene expression in healthy and diseased states, and numerous studies have established their tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this regard, we computationally identify novel top-ranked hub miRNAs that might be involved in T2D. We accomplish this via two strategies: 1) by ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and 2) using only the common DEGs between T2D and its comorbidity, Alzheimer’s disease (AD) to predict and rank miRNA. Then classifier models are built using the DEGs targeted by each miRNA as features. Here, we show the T2D DEGs targeted by hsa-mir-1-3p, hsa-mir-16-5p, hsa-mir-124-3p, hsa-mir-34a-5p, hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-129-2-3p, and hsa-mir-146a-5p are capable of distinguishing T2D samples from the controls, which serves as a measure of confidence in the miRNAs’ potential role in T2D progression. Moreover, for the second strategy, we show other critical miRNAs can be made apparent through the disease’s comorbidities, and in this case, overall, the hsa-mir-103a-3p models work well for all the datasets, especially in T2D, while the hsa-mir-124-3p models achieved the best scores for the AD datasets. To the best of our knowledge, this is the first study that used predicted miRNAs to determine the features that can separate the diseased samples (T2D or AD) from the normal ones, instead of using conventional non-biology-based feature selection methods

DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.

Drug-target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug-target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the novel predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool