Human cytomegalovirus may promote tumour progression by upregulating arginase-2

BACKGROUND

Both arginase (ARG2) and human cytomegalovirus (HCMV) have been implicated in tumorigenesis. However, the role of ARG2 in the pathogenesis of glioblastoma (GBM) and the HCMV effects on ARG2 are unknown. We hypothesize that HCMV may contribute to tumorigenesis by increasing ARG2 expression.

RESULTS

ARG2 promotes tumorigenesis by increasing cellular proliferation, migration, invasion and vasculogenic mimicry in GBM cells, at least in part due to overexpression of MMP2/9. The nor-NOHA significantly reduced migration and tube formation of ARG2-overexpressing cells. HCMV immediate-early proteins (IE1/2) or its downstream pathways upregulated the expression of ARG2 in U-251 MG cells. Immunostaining of GBM tissue sections confirmed the overexpression of ARG2, consistent with data from subsets of Gene Expression Omnibus. Moreover, higher levels of ARG2 expression tended to be associated with poorer survival in GBM patient by analyzing data from TCGA.

METHODS

The role of ARG2 in tumorigenesis was examined by proliferation-, migration-, invasion-, wound healing- and tube formation assays using an ARG2-overexpressing cell line and ARG inhibitor, N (omega)-hydroxy-nor-L-arginine (nor-NOHA) and siRNA against ARG2 coupled with functional assays measuring MMP2/9 activity, VEGF levels and nitric oxide synthase activity. Association between HCMV and ARG2 were examined in vitro with 3 different GBM cell lines, and ex vivo with immunostaining on GBM tissue sections. The viral mechanism mediating ARG2 induction was examined by siRNA approach. Correlation between ARG2 expression and patient survival was extrapolated from bioinformatics analysis on data from The Cancer Genome Atlas (TCGA).

CONCLUSIONS

ARG2 promotes tumorigenesis, and HCMV may contribute to GBM pathogenesis by upregulating ARG2.

Overexpression of endothelin B receptor in glioblastoma: a prognostic marker and therapeutic target?

Background

Glioblastoma (GBM) is the most common malignant brain tumor with median survival of 12-15 months. Owing to uncertainty in clinical outcome, additional prognostic marker(s) apart from existing markers are needed. Since overexpression of endothelin B receptor (ETBR) has been demonstrated in gliomas, we aimed to test whether ETBR is a useful prognostic marker in GBM and examine if the clinically available endothelin receptor antagonists (ERA) could be useful in the disease treatment.

Methods

Data from The Cancer Genome Atlas and the Gene Expression Omnibus database were analyzed to assess ETBR expression. For survival analysis, glioblastoma samples from 25 Swedish patients were immunostained for ETBR, and the findings were correlated with clinical history. The druggability of ETBR was assessed by protein-protein interaction network analysis. ERAs were analyzed for toxicity in in vitro assays with GBM and breast cancer cells.

Results

By bioinformatics analysis, ETBR was found to be upregulated in glioblastoma patients, and its expression levels were correlated with reduced survival. ETBR interacts with key proteins involved in cancer pathogenesis, suggesting it as a druggable target. In vitro viability assays showed that ERAs may hold promise to treat glioblastoma and breast cancer.

Conclusions

ETBR is overexpressed in glioblastoma and other cancers and may be a prognostic marker in glioblastoma. ERAs may be useful for treating cancer patients.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4012-7) contains supplementary material, which is available to authorized users.

Conditional Disease Development extracted from Longitudinal Health Care Cohort Data using Layered Network Construction

Health care data holds great promise to be used in clinical decision support systems. However, frequent near-synonymous diagnoses recorded separately, as well as the sheer magnitude and complexity of the disease data makes it challenging to extract non-trivial conclusions beyond confirmatory associations from such a web of interactions. Here we present a systematic methodology to derive statistically valid conditional development of diseases. To this end we utilize a cohort of 5,512,469 individuals followed over 13 years at inpatient care, including data on disability pension and cause of death. By introducing a causal information fraction measure and taking advantage of the composite structure in the ICD codes, we extract an effective directed lower dimensional network representation (100 nodes and 130 edges) of our cohort. Unpacking composite nodes into bipartite graphs retrieves, for example, that individuals with behavioral disorders are more likely to be followed by prescription drug poisoning episodes, whereas women with leiomyoma were more likely to subsequently experience endometriosis. The conditional disease development represent putative causal relations, indicating possible novel clinical relationships and pathophysiological associations that have not been explored yet.

Accelerating translational research by clinically driven development of an informatics platform--a case study

Translational medicine is becoming increasingly dependent upon data generated from health care, clinical research, and molecular investigations. This increasing rate of production and diversity in data has brought about several challenges, including the need to integrate fragmented databases, enable secondary use of patient clinical data from health care in clinical research, and to create information systems that clinicians and biomedical researchers can readily use. Our case study effectively integrates requirements from the clinical and biomedical researcher perspectives in a translational medicine setting. Our three principal achievements are (a) a design of a user-friendly web-based system for management and integration of clinical and molecular databases, while adhering to proper de-identification and security measures; (b) providing a real-world test of the system functionalities using clinical cohorts; and (c) system integration with a clinical decision support system to demonstrate system interoperability. We engaged two active clinical cohorts, 747 psoriasis patients and 2001 rheumatoid arthritis patients, to demonstrate efficient query possibilities across the data sources, enable cohort stratification, extract variation in antibody patterns, study biomarker predictors of treatment response in RA patients, and to explore metabolic profiles of psoriasis patients. Finally, we demonstrated system interoperability by enabling integration with an established clinical decision support system in health care. To assure the usefulness and usability of the system, we followed two approaches. First, we created a graphical user interface supporting all user interactions. Secondly we carried out a system performance evaluation study where we measured the average response time in seconds for active users, http errors, and kilobits per second received and sent. The maximum response time was found to be 0.12 seconds; no server or client errors of any kind were detected. In conclusion, the system can readily be used by clinicians and biomedical researchers in a translational medicine setting.