Assessing the perspective of well-being of older patients with multiple morbidities by using the LAVA tool-a person-centered approach

BACKGROUND: Older patients with multiple morbidities are a particularly vulnerable population that is likely to face complex medical decisions at some time in their lives. A patient-centered medical care fosters the inclusion of the patients’ perspectives, priorities, and complaints into clinical decision making. METHODS: This article presents a short and non-normative assessment tool to capture the priorities and problems of older patients. The so-called LAVA (“Life and Vitality Assessment”) tool was developed for practical use in seniors in the general population and for residents in nursing homes in order to gain more knowledge about the patients themselves as well as to facilitate access to the patients. The LAVA tool conceptualizes well-being from the perspectives of older individuals themselves rather than from the perspectives of outside individuals. RESULTS: The LAVA tool is graphically presented and the assessment is explained in detail. Exemplarily, the outcomes of the assessments with the LAVA of three multimorbid older patients are presented and discussed. In each case, the assessment pointed out resources as well as at least one problem area, rated as very important by the patients themselves. CONCLUSIONS: The LAVA tool is a short, non-normative, and useful approach that encapsulates the perspectives of well-being of multimorbid patients and gives insights into their resources and problem areas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-021-02342-3

On dynamic network entropy in cancer

The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network to induce a stochastic dynamics on the network, we here demonstrate that cancer cells are characterised by an increase in the dynamic network entropy, compared to cells of normal physiology. Using a fundamental relation between the macroscopic resilience of a dynamical system and the uncertainty (entropy) in the underlying microscopic processes, we argue that cancer cells will be more robust to random gene perturbations. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local dynamic entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local network dynamics. In particular, we also find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in the dynamic network entropy. In summary, our results support the view that the observed increased robustness of cancer cells to perturbation and therapy may be due to an increase in the dynamic network entropy that allows cells to adapt to the new cellular stresses. Conversely, genes that exhibit local flux entropy decreases in cancer may render cancer cells more susceptible to targeted intervention and may therefore represent promising drug targets.Comment: 10 pages, 3 figures, 4 tables. Submitte

Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment

Gene-expression signature-based disease classification and clinical outcome prediction has not been widely introduced in clinical medicine as initially expected, mainly due to the lack of extensive validation needed for its clinical deployment. Obstacles include variable measurement in microarray assay, inconsistent assay platform, analytical requirement for comparable pair of training and test datasets, etc. Furthermore, as medical device helping clinical decision making, the prediction needs to be made for each single patient with a measure of its reliability. To address these issues, there is a need for flexible prediction method less sensitive to difference in experimental and analytical conditions, applicable to each single patient, and providing measure of prediction confidence. The nearest template prediction (NTP) method provides a convenient way to make class prediction with assessment of prediction confidence computed in each single patient's gene-expression data using only a list of signature genes and a test dataset. We demonstrate that the method can be flexibly applied to cross-platform, cross-species, and multiclass predictions without any optimization of analysis parameters

Alteration of the embryo transcriptome of hexaploid winter wheat (Triticum aestivum cv. Mercia) during maturation and germination

Grain dormancy and germination are areas of biology that are of considerable interest to the cereal community. We have used a 9,155-feature wheat unigene cDNA microarray resource to investigate changes in the wheat embryo transcriptome during late grain development and maturation and during the first 48 h of postimbibition germination. In the embryo 392 mRNAs accumulated by twofold or greater over the time course from 21 days postanthesis (dpa) to 40 dpa and on through 1 and 2 days postgermination. These included mRNAs encoding proteins involved in amino acid biosynthesis and metabolism, cell division and subsequent cell development, signal transduction, lipid metabolism, energy production, protein turnover, respiration, initiation of transcription, initiation of translation and ribosomal composition. A number of mRNAs encoding proteins of unknown function also accumulated over the time course. Conversely 163 sequences showed decreases of twofold or greater over the time course. A small number of mRNAs also showed rapid accumulation specifically during the first 48 h of germination. We also examined alterations in the accumulation of transcripts encoding proteins involved in abscisic acid signalling. Thus, we describe changes in the level of transcripts encoding wheat Viviparous 1 (Vp1) and other interacting proteins. Interestingly, the transcript encoding wheat Viviparous-interacting protein 1 showed a pattern of accumulation that correlates inversely with germination. Our data suggests that the majority of the transcripts required for germination accumulate in the embryo prior to germination and we discuss the implications of these findings with regard to manipulation of germination in wheat

Modulations of cell cycle checkpoints during HCV associated disease

Background Impaired proliferation of hepatocytes has been reported in chronic Hepatitis C virus infection. Considering the fundamental role played by cell cycle proteins in controlling cell proliferation, altered regulation of these proteins could significantly contribute to HCV disease progression and subsequent hepatocellular carcinoma (HCC). This study aimed to identify the alterations in cell cycle genes expression with respect to early and advanced disease of chronic HCV infection. Methods Using freshly frozen liver biopsies, mRNA levels of 84 cell cycle genes in pooled RNA samples from patients with early or advanced fibrosis of chronic HCV infection were studied. To associate mRNA levels with respective protein levels, four genes (p27, p15, KNTC1 and MAD2L1) with significant changes in mRNA levels (\u3e 2-fold, p-value \u3c 0.05) were selected, and their protein expressions were examined in the liver biopsies of 38 chronic hepatitis C patients. Results In the early fibrosis group, increased mRNA levels of cell proliferation genes as well as cell cycle inhibitor genes were observed. In the advanced fibrosis group, DNA damage response genes were up-regulated while those associated with chromosomal stability were down-regulated. Increased expression of CDK inhibitor protein p27 was consistent with its mRNA level detected in early group while the same was found to be negatively associated with liver fibrosis. CDK inhibitor protein p15 was highly expressed in both early and advanced group, but showed no correlation with fibrosis. Among the mitotic checkpoint regulators, expression of KNTC1 was significantly reduced in advanced group while MAD2L1 showed a non-significant decrease. Conclusion Collectively these results are suggestive of a disrupted cell cycle regulation in HCV-infected liver. The information presented here highlights the potential of identified proteins as predictive factors to identify patients with high risk of cell transformation and HCC development

A Systems Biology-Based Classifier for Hepatocellular Carcinoma Diagnosis

AIM: The diagnosis of hepatocellular carcinoma (HCC) in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis. METHODS AND RESULTS: In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71%) and area under ROC curve (approximating 1.0), and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers. CONCLUSION: Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier

Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships

Global analysis of DNA methylation in early-stage liver fibrosis

<p>Abstract</p> <p>Background</p> <p>Liver fibrosis is caused by chemicals or viral infection. The progression of liver fibrosis results in hepatocellular carcinogenesis in later stages. Recent studies have revealed the importance of DNA hypermethylation in the progression of liver fibrosis to hepatocellular carcinoma (HCC). However, the importance of DNA methylation in the early-stage liver fibrosis remains unclear.</p> <p>Methods</p> <p>To address this issue, we used a pathological mouse model of early-stage liver fibrosis that was induced by treatment with carbon tetrachloride (CCl₄) for 2 weeks and performed a genome-wide analysis of DNA methylation status. This global analysis of DNA methylation was performed using a combination of methyl-binding protein (MBP)-based high throughput sequencing (MBP-seq) and bioinformatic tools, IPA and Oncomine. To confirm functional aspect of MBP-seq data, we complementary used biochemical methods, such as bisulfite modification and <it>in-vitro</it>-methylation assays.</p> <p>Results</p> <p>The genome-wide analysis revealed that DNA methylation status was reduced throughout the genome because of CCl₄treatment in the early-stage liver fibrosis. Bioinformatic and biochemical analyses revealed that a gene associated with fibrosis, <it>secreted phosphoprotein 1 </it>(<it>Spp1</it>), which induces inflammation, was hypomethylated and its expression was up-regulated. These results suggest that DNA hypomethylation of the genes responsible for fibrosis may precede the onset of liver fibrosis. Moreover, <it>Spp1 </it>is also known to enhance tumor development. Using the web-based database, we revealed that <it>Spp1 </it>expression is increased in HCC.</p> <p>Conclusions</p> <p>Our study suggests that hypomethylation is crucial for the onset of and in the progression of liver fibrosis to HCC. The elucidation of this change in methylation status from the onset of fibrosis and subsequent progression to HCC may lead to a new clinical diagnosis.</p

REGγ is associated with multiple oncogenic pathways in human cancers

<p>Abstract</p> <p>Background</p> <p>Recent studies suggest a role of the proteasome activator, REGγ, in cancer progression. Since there are limited numbers of known REGγ targets, it is not known which cancers and pathways are associated with REGγ.</p> <p>Methods</p> <p>REGγ protein expressions in four different cancers were investigated by immunohistochemistry (IHC) analysis. Following NCBI Gene Expression Omnibus (GEO) database search, microarray platform validation, differential expressions of REGγ in corresponding cancers were statistically analyzed. Genes highly correlated with REGγ were defined based on Pearson's correlation coefficient. Functional links were estimated by Ingenuity Core analysis. Finally, validation was performed by RT-PCR analysis in established cancer cell lines and IHC in human colon cancer tissues</p> <p>Results</p> <p>Here, we demonstrate overexpression of REGγ in four different cancer types by micro-tissue array analysis. Using meta-analysis of publicly available microarray databases and biological studies, we verified elevated REGγ gene expression in the four types of cancers and identified genes significantly correlated with REGγ expression, including genes in p53, Myc pathways, and multiple other cancer-related pathways. The predicted correlations were largely consistent with quantitative RT-PCR analysis.</p> <p>Conclusions</p> <p>This study provides us novel insights in REGγ gene expression profiles and its link to multiple cancer-related pathways in cancers. Our results indicate potentially important pathogenic roles of REGγ in multiple cancer types and implicate REGγ as a putative cancer marker.</p