Deciphering the Role of Insulin-Like Growth Factor 1 in Endometrial Cancer in Patients With Polycystic Ovary Syndrome: Protocol for a Methodological Approach Using Cell Culture Experiments.

BACKGROUND: Endometrial cancer (EC) is the most common gynecological cancer in women globally. It is linked to increasing obesity rates and longer life spans. The molecular mechanisms leading to EC are unclear; however, women with polycystic ovary syndrome (PCOS) have a 3- to 5-fold increased EC risk. According to a pilot study conducted in the United Kingdom, insulin-like growth factor-1 (IGF-1) gene and protein were raised in the endometrium and blood of women with EC and PCOS, compared with those without PCOS (controls). Therefore, raised serum IGF-1 levels may contribute to an increased EC risk in women with PCOS, but it is necessary to test this hypothesis since not all studies have demonstrated this association. OBJECTIVE: This study aims to investigate the role of IGF-1 in mediating EC risk in PCOS. This will be achieved by evaluating the proliferative effects of PCOS serum, IGF-1, and IGF-1 antagonist on human endometrial cancer 1-A and 1-B cell lines, with a comparison to controls (using serum from women without PCOS and cell culture media). The study will also identify differentially expressed genes and pathways activated by various treatments. METHODS: We intend to recruit 20 women with PCOS and 20 women without PCOS for this cross-sectional study. All experiments will be carried out 4 times to ensure consistency. We will perform transcriptomic and phosphoproteomic profiling to identify differentially expressed genes and phosphoproteins between different treatments using RNA sequencing and phosphoproteomics. We will also perform bioinformatics pathway analysis to identify whether any unique collection of genes or phosphoproteins explains increased EC risk in PCOS. The primary outcome measure will be the cell proliferation (growth) difference measured by cell index values. Our protocol stands out due to its unique approach; no previous study has used this approach to investigate the oncogenic effect of serum from women with PCOS. Additionally, no previous study has considered the differential mutations of genes related to the insulin signaling pathway across various types of human EC cell lines and the potential impact of these variations on their experimental findings. RESULTS: Participants are currently being recruited. It is expected that preliminary findings suitable for analysis and publication will be available by the summer of 2024. CONCLUSIONS: Although we currently do not have any results to report, sharing our protocol at this stage will aid in research collaboration, provide an opportunity for early feedback, and help reduce duplication of effort by other research groups. The findings of our study will have broader implications. A deeper understanding of the mechanisms underlying the regulation of the IGF system in PCOS and EC will improve our ability to develop effective treatment modalities for EC and will be a vital step toward reducing EC in women globally. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48127.

Transcriptomic analysis identifies four novel receptors potentially linking endometrial cancer with polycystic ovary syndrome and generates a transcriptomic atlas.

Polycystic Ovary Syndrome (PCOS) is associated with a 3 to 4-fold increased risk of endometrial cancer (EC), but molecular mechanisms are unclear. Upregulation of the IGF1 gene in PCOS endometrium may increase EC risk, but this is uncertain. We aimed to investigate links between EC and PCOS, by analysing publicly available transcriptomic data. The NCBI Gene Expression Omnibus was used to identify relevant studies. Differentially expressed genes (DEGs) were identified and analysed using Metascape to identify pathways of interest. PCOS DEGs that encode proteins secreted into blood were identified using the Human Protein Atlas blood protein database. EC DEGs that are cellular receptors were identified using EcoTyper. These were intersected to identify which EC receptors interact with PCOS secreted proteins. Seven receptors were identified in EC but only PTPRF, ITGA2, ITGA3 and ITGB4 genes were expressed on epithelial cells. Pathway enrichment of these genes showed that the major and common pathway involved was that of the PI3K-AKT signalling pathway which was consistent with a link between PCOS and EC. However, IGF1 was down regulated in PCOS and EC. These findings hold significant promise for improving our understanding of mechanistic pathways leading to EC in PCOS.

Identification of Specific Biomarkers and Pathways in the Treatment Response of Infliximab for Inflammatory Bowel Disease: In-Silico Analysis.

BACKGROUND: Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal tract. In biological therapy, infliximab became the first anti-tumor necrosis factor (TNF) agent approved for IBD. Despite this success, infliximab is expensive, often ineffective, and associated with adverse events. Prediction of infliximab resistance would improve overall potential outcomes. Therefore, there is a pressing need to widen the scope of investigating the role of genetics in IBD to their association with therapy response. METHODS: In the current study, an in-silico analysis of publicly available IBD patient transcriptomics datasets from Gene Expression Omnibus (GEO) are used to identify subsets of differentially expressed genes (DEGs) involved in the pathogenesis of IBD and may serve as potential biomarkers for Infliximab response. Five datasets were found that met the inclusion criteria. The DEGs for datasets were identified using limma R packages through the GEOR2 tool. The probes' annotated genes in each dataset intersected with DGEs from all other datasets. Enriched gene Ontology Clustering for the identified genes was performed using Metascape to explore the possible connections or interactions between the genes. RESULTS: 174 DEGs between IBD and healthy controls were found from analyzing two datasets (GSE14580 and GSE73661), indicating a possible role in the pathogenesis of IBD. Of the 174 DEGs, five genes (SELE, TREM1, AQP9, FPR2, and HCAR3) were shared between all five datasets. Moreover, these five genes were identified as downregulated in the infliximab responder group compared to the non-responder group. CONCLUSIONS: We hypothesize that alteration in the expression of these genes leads to an impaired response to infliximab in IBD patients. Thus, these genes can serve as potential biomarkers for the early detection of compromised infliximab response in IBD patients.

In Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkers.

BACKGROUND: Breast cancer is the most common type of cancer among women and is classified into multiple subtypes. Triple-negative breast cancer (TNBC) is the most aggressive subtype, with high mortality rates and limited treatment options such as chemotherapy and radiation. Due to the heterogeneity and complexity of TNBC, there is a lack of reliable biomarkers that can be used to aid in the early diagnosis and prognosis of TNBC in a non-invasive screening method. AIM: This study aims to use in silico methods to identify potential biomarkers for TNBC screening and diagnosis, as well as potential therapeutic markers. METHODS: Publicly available transcriptomic data of breast cancer patients published in the NCBI's GEO database were used in this analysis. Data were analyzed with the online tool GEO2R to identify differentially expressed genes (DEGs). Genes that were differentially expressed in more than 50% of the datasets were selected for further analysis. Metascape, Kaplan-Meier plotter, cBioPortal, and the online tool TIMER were used for functional pathway analysis to identify the biological role and functional pathways associated with these genes. Breast Cancer Gene-Expression Miner v4.7 was used to validify the obtained results in a larger cohort of datasets. RESULTS: A total of 34 genes were identified as differentially expressed in more than half of the datasets. The DEG GATA3 had the highest degree of regulation, and it plays a role in regulating other genes. The estrogen-dependent pathway was the most enriched pathway, involving four crucial genes, including GATA3. The gene FOXA1 was consistently down-regulated in TNBC in all datasets. CONCLUSIONS: The shortlisted 34 DEGs will aid clinicians in diagnosing TNBC more accurately as well as developing targeted therapies to improve patient prognosis. In vitro and in vivo studies are further recommended to validate the results of the current study.

The Role of Systems Biology in Deciphering Asthma Heterogeneity.

Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.

Healthcare Derived Smart Watches and Mobile Phones are Contaminated Niches to Multidrug Resistant and Highly Virulent Microbes.

Background: As high touch wearable devices, the potential for microbial contamination of smart watches is high. In this study, microbial contamination of smart watches of healthcare workers (HCWs) was assessed and compared to the individual's mobile phone and hands. Methods: This study was part of a larger point prevalence survey of microbial contamination of mobile phones of HCWs at the emergency unit of a tertiary care facility. Swabs from smart watches, mobile phones and hands were obtained from four HCWs with dual ownership of these digital devices. Bacterial culture was carried out for all samples and those from smart watches and mobile phones were further assessed using shotgun metagenomic sequencing. Results: Majority of the participants were females (n/N = 3/4; 75%). Although they all use their digital devices at work and believe that these devices could harbour microbes, cleaning in the preceding 24 hours was reported by one individual. Predominant organisms identified on bacterial culture were multidrug resistant Staphylococcus hominis and Staphylococcus epidermidis. At least one organism identified from the hands was also detected on all mobile phones and two smart watches. Shotgun metagenomics analysis demonstrated greater microbial number and diversity on mobile phones compared to smart watches. All devices had high signatures of Pseudomonas aeruginosa and associated bacteriophages and antibiotic resistance genes. Almost half of the antibiotic resistance genes (n/N = 35/75;46.6%) were present on all devices and majority were related to efflux pumps. Of the 201 virulence factor genes (VFG) identified, majority (n/N = 148/201;73%) were associated with P. aeruginosa with 96% (n/N = 142/148) present on smart watches and mobile phones. Conclusion: This first report on microbial contamination of smart watches using metagenomics next generation sequencing showed similar pattern of contamination with microbes, VFG and antibiotic resistance genes across digital devices. Further studies on microbial contamination of wearable digital devices are urgently needed.

Metagenomic Sequencing and Reverse Transcriptase PCR Reveal That Mobile Phones and Environmental Surfaces Are Reservoirs of Multidrug-Resistant Superbugs and SARS-CoV-2.

Background: Mobile phones of healthcare workers (HCWs) can act as fomites in the dissemination of microbes. This study was carried out to investigate microbial contamination of mobile phones of HCWs and environmental samples from the hospital unit using a combination of phenotypic and molecular methods. Methods: This point prevalence survey was carried out at the Emergency unit of a tertiary care facility. The emergency unit has two zones, a general zone for non-COVID-19 patients and a dedicated COVID-19 zone for confirmed or suspected COVID-19 patients. Swabs were obtained from the mobile phones of HCWs in both zones for bacterial culture and shotgun metagenomic analysis. Metagenomic sequencing of pooled environmental swabs was conducted. RT-PCR for SARS-CoV-2 detection was carried out. Results: Bacteria contamination on culture was detected from 33 (94.2%) mobile phones with a preponderance of Staphylococcus epidermidis (n/N = 18/35), Staphylococcus hominis (n/N = 13/35), and Staphylococcus haemolyticus (n/N = 7/35). Two methicillin-sensitive and three methicillin-resistant Staphylococcus aureus, and one pan-drug-resistant carbapenemase producer Acinetobacter baumannii were detected. Shotgun metagenomic analysis showed high signature of Pseudomonas aeruginosa in mobile phone and environmental samples with preponderance of P. aeruginosa bacteriophages. Malassezia and Aspergillus spp. were the predominant fungi detected. Fourteen mobile phones and one environmental sample harbored protists. P. aeruginosa antimicrobial resistance genes mostly encoding for efflux pump systems were detected. The P. aeruginosa virulent factor genes detected were related to motility, adherence, aggregation, and biofilms. One mobile phone from the COVID-19 zone (n/N = 1/5; 20%) had positive SARS-CoV-2 detection while all other phone and environmental samples were negative. Conclusion: The findings demonstrate that mobile phones of HCWs are fomites for potentially pathogenic and highly drug-resistant microbes. The presence of these microbes on the mobile phones and hospital environmental surfaces is a concern as it poses a risk of pathogen transfer to patients and dissemination into the community.

Antimicrobial Activity of Phytic Acid: An Emerging Agent in Endodontics.

Background: Phytic acid (IP6) is a promising and emerging agent, and because of its unique structure and distinctive properties, it lends itself to several applications in dentistry. Recently, IP6 was proposed as a potential chelating agent in endodontics. However, there is limited knowledge regarding its antimicrobial and antibiofilm effectiveness. The aims of this study, were therefore to evaluate the antimicrobial and antibiofilm activities of IP6 against a range of microbial species and compare these with ethylenediaminetetraacetic acid (EDTA) and sodium hypochlorite (NaOCl). The contact time required for IP6 to exert its bactericidal effect on Enterococcus faecalis was also determined. Methods: The inhibitory and biocidal activities of IP6, EDTA and NaOCl were assessed using a broth microdilution assay against 11 clinical and reference strains of bacteria and a reference strain of Candida albicans. The contact time required for various IP6 concentrations to eliminate planktonic cultures of E. faecalis was determined using a membrane filtration method according to BS-EN-1040:2005. IP6 bactericidal activity was also evaluated using fluorescent microscopy, and the antibiofilm activity of the test agents was also determined. Results: IP6 was biocidal against all tested microorganisms. At concentrations of 0.5%, 1% and 2%, IP6 required 5 min to exert a bactericidal effect on E. faecalis, while 5% IP6 was bactericidal after 30 s. IP6 also eradicated biofilms of the tested microorganisms. In conclusion, IP6 had notable antimicrobial effects on planktonic and biofilm cultures and exhibited rapid bactericidal effects on E. faecalis. This research highlighted, for the first time the antimicrobial and antibiofilm properties of IP6, which could be exploited, not only in dental applications, but also other fields where novel strategies to counter antimicrobial resistance are required.

COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets.

Background: To develop anti-viral drugs and vaccines, it is crucial to understand the molecular basis and pathology of COVID-19. An increase in research output is required to generate data and results at a faster rate, therefore bioinformatics plays a crucial role in COVID-19 research. There is an abundance of transcriptomic data from studies carried out on COVID-19, however, their use is limited by the confounding factors pertaining to each study. The reanalysis of all these datasets in a unified approach should help in understanding the molecular basis of COVID-19. This should allow for the identification of COVID-19 biomarkers expressed in patients and the presence of markers specific to disease severity and condition. Aim: In this study, we aim to use the multiple publicly available transcriptomic datasets retrieved from the Gene Expression Omnibus (GEO) database to identify consistently differential expressed genes in different tissues and clinical settings. Materials and Methods: A list of datasets was generated from NCBI's GEO using the GEOmetadb package through R software. Search keywords included SARS-COV-2 and COVID-19. Datasets in human tissues containing more than ten samples were selected for this study. Differentially expressed genes (DEGs) in each dataset were identified. Then the common DEGs between different datasets, conditions, tissues and clinical settings were shortlisted. Results: Using a unified approach, we were able to identify common DEGs based on the disease conditions, samples source and clinical settings. For each indication, a different set of genes have been identified, revealing that a multitude of factors play a role in the level of gene expression. Conclusion: Unified reanalysis of publically available transcriptomic data showed promising potential in identifying core targets that can explain the molecular pathology and be used as biomarkers for COVID-19.