Metformin use and hospital attendance-related resources utilization among diabetic patients with prostate cancer on androgen deprivation therapy: A population-based cohort study

Background Androgen deprivation therapy (ADT), used increasingly in the treatment of prostate cancer (PCa), negatively influences glycemic control in diabetes and is associated with an increased risk of diabetes complications where hospitalization commonly ensues. Metformin could decrease the metabolic consequences of ADT and enhance its effect. This study examined the association of metformin use with healthcare resources utilization among diabetic, PCa patients receiving ADT. Methods Diabetic adults with PCa on ADT in Hong Kong between December 1999 and March 2021 were identified. Patients with <6 months of concurrent metformin and ADT use were excluded. All included patients were followed up until September 2021. The outcomes were hospital attendances and related costs. Results In total, 1,284 metformin users and 687 non-users were studied. Over 8,045 person-years, 9,049 accident and emergency (A&E), and 21,262 inpatient attendances, with 11,2781 days of hospitalization were observed. Metformin users had significantly fewer A&E attendances (incidence rate ratio (IRR): 0.61 [95% confidence interval 0.54–0.69], p < 0.001), inpatient attendances (IRR: 0.57 [0.48–0.67], p < 0.001), and days of hospitalization (IRR: 0.55 [0.42–0.72], p < 0.001). Annual attendance costs were lower for metformin users than non-users (cost ratio: 0.28 [0.10–0.80], p = 0.017). Conclusions Metformin use was associated with decreased hospital attendances, days of hospitalization, and associated costs, which could help reduce healthcare resource utilization following ADT in the treatment of PCa

Highly sensitive proximity mediated immunoassay reveals HER2 status conversion in the circulating tumor cells of metastatic breast cancer patients

<p>Abstract</p> <p>Background</p> <p>The clinical benefits associated with targeted oncology agents are generally limited to subsets of patients. Even with favorable biomarker profiles, many patients do not respond or acquire resistance. Existing technologies are ineffective for treatment monitoring as they provide only static and limited information and require substantial amounts of tissue. Therefore, there is an urgent need to develop methods that can profile potential therapeutic targets with limited clinical specimens during the course of treatment.</p> <p>Methods</p> <p>We have developed a novel proteomics-based assay, Collaborative Enzyme Enhanced Reactive-immunoassay (CEER) that can be used for analyzing clinical samples. CEER utilizes the formation of unique immuno-complex between capture-antibodies and two additional detector-Abs on a microarray surface. One of the detector-Abs is conjugated to glucose oxidase (GO), and the other is conjugated to Horse Radish Peroxidase (HRP). Target detection requires the presence of both detector-Abs because the enzyme channeling event between GO and HRP will not occur unless both Abs are in close proximity.</p> <p>Results</p> <p>CEER was able to detect single-cell level expression and phosphorylation of human epidermal growth factor receptor 2 (HER2) and human epidermal growth factor receptor 1 (HER1) in breast cancer (BCa) systems. The shift in phosphorylation profiles of receptor tyrosine kinases (RTKs) and other signal transduction proteins upon differential ligand stimulation further demonstrated extreme assay specificity in a multiplexed array format. HER2 analysis by CEER in 227 BCa tissues showed superior accuracy when compared to the outcome from immunohistochemistry (IHC) (83% vs. 96%). A significant incidence of HER2 status alteration with recurrent disease was observed via circulating tumor cell (CTC) analysis, suggesting an evolving and dynamic disease progression. HER2-positive CTCs were found in 41% (7/17) while CTCs with significant HER2-activation without apparent over-expression were found in 18% (3/17) of relapsed BCa patients with HER2-negative primary tumors. The apparent 'HER2 status conversion' observed in recurrent BCa may have significant implications on understanding breast cancer metastasis and associated therapeutic development.</p> <p>Conclusion</p> <p>CEER can be multiplexed to analyze pathway proteins in a comprehensive manner with extreme specificity and sensitivity. This format is ideal for analyzing clinical samples with limited availability.</p

Overexpression of IL-7 enhances cisplatin resistance in glioma

Cisplatin is one of the most commonly used chemotherapeutic agents for glioma patients. In this study, array comparative genomic hybridization (aCGH) was used to identify genes associated with cisplatin resistance in a human glioma cell line. The cisplatin-resistant U251/CP2 cell line was derived by stepwise selection using cisplatin. The genetic aberrations of the U251 parental cell line and the U251/CP2 cells were analyzed using aCGH. RT-PCR was used to detect the expression of the altered genes revealed by aCGH. The sensitivity of glioma cells to cisplatin was determined by using the MTT assay. Apoptosis was detected using flow cytometry and western blot analysis. The IC50 value of cisplatin in U251/CP2 cells was five times higher than its IC50 in U251 cells. The U251 cells lost at least one copy each of the CFHR1 and CFHR3 genes, and both CFHR1 and CFHR3 were homozygously deleted in U251/CP2 cells. The U251/CP2 cells gained two to three copies of C8orf70 and IL-7 genes. IL-7 mRNA expression was studied in 12 glioma cell lines, and expression was positively correlated with the IC50 of cisplatin. Furthermore, IL-7 mRNA expression was also positively correlated with the IC50 of cisplatin in 91 clinical glioma specimens. Additionally, treatment with recombinant human IL-7 (rhIL-7) enhanced cisplatin resistance and increased the relative growth rate of the glioma cells. Moreover, the apoptosis induced by cisplatin could be inhibited by IL-7. In conclusion, our results suggest that IL-7 may play an important role in cisplatin resistance in glioma

Kernel Spectral Clustering and applications

In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane in a high dimensional space induced by a kernel. In addition, the multi-way clustering can be obtained by combining a set of binary decision functions via an Error Correcting Output Codes (ECOC) encoding scheme. Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing. In the validation stage model selection is performed to obtain tuning parameters, like the number of clusters present in the data. This is a major advantage compared to classical spectral clustering where the determination of the clustering parameters is unclear and relies on heuristics. Once a KSC model is trained on a small subset of the entire data, it is able to generalize well to unseen test points. Beyond the basic formulation, sparse KSC algorithms based on the Incomplete Cholesky Decomposition (ICD) and $L_0$, $L_1, L_0 + L_1$, Group Lasso regularization are reviewed. In that respect, we show how it is possible to handle large scale data. Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented. Finally, real-world applications such as image segmentation, power load time-series clustering, document clustering and big data learning are considered.Comment: chapter contribution to the book "Unsupervised Learning Algorithms

Threshold selection in gene co-expression networks using spectral graph theory techniques

Abstract Background Gene co-expression networks are often constructed by computing some measure of similarity between expression levels of gene transcripts and subsequently applying a high-pass filter to remove all but the most likely biologically-significant relationships. The selection of this expression threshold necessarily has a significant effect on any conclusions derived from the resulting network. Many approaches have been taken to choose an appropriate threshold, among them computing levels of statistical significance, accepting only the top one percent of relationships, and selecting an arbitrary expression cutoff. Results We apply spectral graph theory methods to develop a systematic method for threshold selection. Eigenvalues and eigenvectors are computed for a transformation of the adjacency matrix of the network constructed at various threshold values. From these, we use a basic spectral clustering method to examine the set of gene-gene relationships and select a threshold dependent upon the community structure of the data. This approach is applied to two well-studied microarray data sets from Homo sapiens and Saccharomyces cerevisiae. Conclusion This method presents a systematic, data-based alternative to using more artificial cutoff values and results in a more conservative approach to threshold selection than some other popular techniques such as retaining only statistically-significant relationships or setting a cutoff to include a percentage of the highest correlations

Novel Genetic Risk and Metabolic Signatures of Insulin Signaling and Androgenesis in the Anovulation of Polycystic Ovary Syndrome

Funding Information: The authors are grateful to all staff in the PCOSAct group for their effort in the collection of blood samples and clinical dataset which used in current study. Special thanks to Prof. Attila Toth from Institute of Physiological Chemistry, Dresden, Germany for the REC114 antibody. This study was supported by the National key Research and Development Program of China (2019YFC1709500); the National Collaboration Project of Critical Illness by Integrating Chinese Medicine and Western Medicine; the Project of Heilongjiang Province Innovation Team “TouYan;” the Yi-Xun Liu and Xiao-Ke Wu Academician Workstation; the Innovation Team of Reproductive Technique with Integrative Chinese Medicine and Western Medicine in Xuzhou City, China; Heilongjiang University of Chinese Medicine from the National Clinical Trial Base; Heilongjiang Provincial Clinical Research Center for Ovary Diseases; the Research Grant Council (T13-602/21-N, C5045-20EF, and 14122021); and Food and Health Bureau in Hong Kong, China (06171026). Ben Willem J. Mol is supported by a National Health and Medical Research Council (NHMRC) Investigator grant (GNT1176437). Ben Willem J. Mol reports consultancy for ObsEva and Merck and travel support from Merck. Xiaoke Wu, Yongyong Shi, and Chi Chiu Wang developed the research question and designed the study. Xiaoke Wu, Yongyong Shi, Yijuan Cao, and Chi Chiu Wang designed the analysis. Yongyong Shi and Zhiqiang Li contributed to the design of the experiment of whole-exome plus targeted SNP sequencing and the analysis, and interpreted the results. Jingshu Gao, Hui Chang, Duojia Zhang, Jing Cong, Yu Wang, Qi Wu, Xiaoxiao Han, Pui Wah Jacqueline Chung, Yiran Li, and Lin Zeng contributed to the experiment of metabolic profile and immunofluorescent staining and the analysis, and interpreted the results. Astrid Borchert and Hartmut Kuhn provided antibody support and advice. Xu Zheng and Lingxi Chen contributed to create the predictive model with deep machine learning. Jian Li, Qi Wu, Hongli Ma, Xu Zheng, and Lingxi Chen contributed to the analysis of the clinical characteristics and interpreted the results. Jian Li, Hongli Ma, Hui Chang, Jing Cong, and Chi Chiu Wang drafted the manuscript. All authors reviewed and revised the manuscript. Xiaoke Wu is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Xiaoke Wu, Chi Chiu Wang, Yijuan Cao, Jian Li, Zhiqiang Li, Hongli Ma, Jingshu Gao, Hui Chang, Duojia Zhang, Jing Cong, Yu Wang, Qi Wu, Xiaoxiao Han, Pui Wah Jacqueline Chung, Yiran Li, Xu Zheng, Lingxi Chen, Lin Zeng, Astrid Borchert, Hartmut Kuhn, Zijiang Chen, Ernest Hung Yu Ng, Elisabet Stener-Victorin, Heping Zhang, Richard S. Legro, Ben Willem J. Mol, and Yongyong Shi declare that they have no conflict of interest or financial conflicts to disclose. Funding Information: This study was supported by the National key Research and Development Program of China ( 2019YFC1709500 ); the National Collaboration Project of Critical Illness by Integrating Chinese Medicine and Western Medicine ; the Project of Heilongjiang Province Innovation Team “TouYan;” the Yi-Xun Liu and Xiao-Ke Wu Academician Workstation; the Innovation Team of Reproductive Technique with Integrative Chinese Medicine and Western Medicine in Xuzhou City , China; Heilongjiang University of Chinese Medicine from the National Clinical Trial Base ; Heilongjiang Provincial Clinical Research Center for Ovary Diseases ; the Research Grant Council ( T13-602/21-N , C5045-20EF , and 14122021 ); and Food and Health Bureau in Hong Kong, China ( 06171026 ). Publisher Copyright: © 2023Peer reviewedPublisher PD

Allo-network drugs: harnessing allostery in cellular networks

Allosteric drugs are increasingly used because they produce fewer side effects. Allosteric signal propagation does not stop at the 'end' of a protein, but may be dynamically transmitted across the cell. Here, we propose that the concept of allosteric drugs can be broadened to allo-network drugs, whose effects can propagate either within a protein, or across several proteins, to enhance or inhibit specific interactions along a pathway. We posit that current allosteric drugs are a special case of allo-network drugs, and suggest that allo-network drugs can achieve specific, limited changes at the systems level, and in this way can achieve fewer side effects and lower toxicity. Finally, we propose steps and methods to identify allo-network drug targets and sites outlining a new paradigm in systems-based drug design.Comment: 14 pages, 4 figures, 69 references, a cover story of the 2011 December issue of Trends in Pharmacological Science

Prediction of epigenetically regulated genes in breast cancer cell lines

Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fxed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically signifcant negative correlation between methylation profles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators

Energy and system size dependence of \phi meson production in Cu+Cu and Au+Au collisions

We study the beam-energy and system-size dependence of \phi meson production (using the hadronic decay mode \phi -- K+K-) by comparing the new results from Cu+Cu collisions and previously reported Au+Au collisions at \sqrt{s_NN} = 62.4 and 200 GeV measured in the STAR experiment at RHIC. Data presented are from mid-rapidity (|y|<0.5) for 0.4 < pT < 5 GeV/c. At a given beam energy, the transverse momentum distributions for \phi mesons are observed to be similar in yield and shape for Cu+Cu and Au+Au colliding systems with similar average numbers of participating nucleons. The \phi meson yields in nucleus-nucleus collisions, normalised by the average number of participating nucleons, are found to be enhanced relative to those from p+p collisions with a different trend compared to strange baryons. The enhancement for \phi mesons is observed to be higher at \sqrt{s_NN} = 200 GeV compared to 62.4 GeV. These observations for the produced \phi(s\bar{s}) mesons clearly suggest that, at these collision energies, the source of enhancement of strange hadrons is related to the formation of a dense partonic medium in high energy nucleus-nucleus collisions and cannot be alone due to canonical suppression of their production in smaller systems.Comment: 20 pages and 5 figure