RNA sequencing steps toward the first line

• DNA is the sequence that codes for proteins.
• Messenger RNA is transcribed from the DNA sequence of genes and translated into protein.
• It can be difficult to predict how a change in the DNA sequence will affect messenger RNA and protein quantity and quality.
• DNA translocation changes can cause the joining of sequences from two different genes or different parts of the same gene.
• DNA sequencing is often used clinically to predict how DNA changes might affect proteins.
• Alternatively, RNA sequencing can be used as a more direct measure of the effect of DNA changes on the protein products.
• This sequencing is important for identifying changes in cancer that may indicate response to targeted therapy, prognosis, or diagnosis.

     

Comprehensive interrogation of gene lists from genome-scale cancer screens with oncoEnrichR

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based service hosted by the Galaxy platform ( https://oncotools.elixir.no ), and can also be accessed as a stand-alone R package ( https://github.com/sigven/oncoEnrichR ).     

Prostate cancer incidence in men with prostate-specific antigen below 3 ng/mL: The Finnish Randomized Study of Screening for Prostate Cancer

Prostate-specific antigen (PSA)-based screening for prostate cancer (PCa) can reduce PCa mortality, but also involves overdetection of low-risk disease with potential adverse effects. We evaluated PCa incidence among men with PSA below 3 ng/mL and no PCa diagnosis at the first screening round of the Finnish Randomized Study of Screening for PCa. Follow-up started at the first screening attendance and ended at PCa diagnosis, emigration, death or the common closing date (December 2016), whichever came first. Cox regression analysis was used to estimate hazard ratios and their confidence intervals (CI). Among men with PSA <3 ng/mL, cumulative PCa incidence was 9.1% after 17.6 years median follow-up. Cumulative incidence was 3.6% among men with baseline PSA 0 to 0.99 ng/mL, 11.5% in those with PSA 1.0 to 1.99 ng/mL and 25.7% among men with PSA 2 to 2.99 ng/mL (hazard ratio 9.0, 95% CI: 7.9-10.2 for the latter). The differences by PSA level were most striking for low-risk disease based on Gleason score and EAU risk group. PSA values <1 ng/mL indicate a very low 20-year risk, while at PSA 2 to 2.99 ng/mL risks are materially higher, with 4- to 5-fold risk for aggressive disease. Using risk-stratification and appropriate rescreening intervals will reduce screening intensity and overdetection. Using cumulative incidence of clinically significant PCa (csPCa) as the criterion, rescreening intervals could range from approximately 3 years for men with initial PSA 2 to 2.99 ng/mL, 6 years for men with PSA 1 to 1.99 ng/mL to 10 years for men with PSA <1 ng/mL.     

Trends in cancer incidence by socioeconomic deprivation in Germany in 2007 to 2018: An ecological registry-based study

Age-standardized cancer incidence has decreased over the last years for many cancer sites in developed countries. Whether these trends led to narrowing or widening socioeconomic inequalities in cancer incidence is unknown. Using cancer registry data covering 48 million inhabitants in Germany, the ecological association between age-standardized total and site specific (colorectal, lung, prostate and breast) cancer incidence in 2007 to 2018 and a deprivation index on district level (aggregated to quintiles) was investigated. Incidence in the most and least deprived districts were compared using Poisson models. Average annual percentage changes (AAPCs) and differences in AAPCs between deprivation quintiles were assessed using Joinpoint regression analyses. Age-standardized incidence decreased strongly between 2007 and 2018 for total cancer and all cancer sites (except female lung cancer), irrespective of the level of deprivation. However, differences in the magnitude of trends across deprivation quintiles resulted in increasing inequalities over time for total cancer, colorectal and lung cancer. For total cancer, the incidence rate ratio between the most and least deprived quintile increased from 1.07 (95% confidence interval: 1.01-1.12) to 1.23 (1.12-1.32) in men and from 1.07 (1.01-1.13) to 1.20 (1.14-1.26) in women. Largest inequalities were observed for lung cancer with 82% (men) and 88% (women) higher incidence in the most vs the least deprived regions in 2018. The observed increase in inequalities in cancer incidence is in alignment with trends in inequalities in risk factor prevalence and partly utilization of screening. Intervention programs targeted at socioeconomically deprived and urban regions are highly needed.     

Redox active or thiol reactive? Optimization of rapid screens to identify less evident nuisance compounds

Compounds that exhibit assay interference or undesirable mechanisms of bioactivity are routinely encountered in assays at various stages of drug discovery. We observed that assays for the investigation of thiol-reactive and redox-active compounds have not been collected in a comprehensive review. Here, we review these assays and subject them to experimental optimization to improve their reliability. We demonstrate the usefulness of our assay cascade by assaying a library of bioactive compounds, chemical probes, and a set of approved drugs. These high-throughput assays should complement the array of wet-lab and in silico assays during the initial stages of hit discovery campaigns to pursue only hit compounds with tractable mechanisms of action.     

童年期不良经历对个体健康影响的研究进展

童年期不良经历(ACEs)作为一项全球性的严峻公共卫生挑战,其对全生命周期的健康影响不容小觑。因此本文从心理健康、生理健康、性传播疾病及危险性行为、健康危险行为4个方面对ACEs的健康影响进行综述,为ACEs及其可能健康结局提供参考。     

Effects of the Cerebral Aneurysm Treatment Method on Coil Packing Density and Its Relationship with the Ostium Area

PurposeTo test the following hypotheses: (a) balloon or stent assistance increases coil packing density (CPD) in the endovascular treatment of intracranial aneurysms, and (b) CPD correlates to ostium area (OA) and aneurysm volume (AV).Materials and MethodsThis retrospective study included 60 aneurysms (54 ruptured and 6 unruptured) treated with simple coiling (SC) (n = 18), balloon-assisted coiling (BAC) (n = 7), or stent-assisted coiling (SAC) (n = 35) at the authors’ institution between August 2017 and December 2019. AV and OA measurements were obtained from 3-dimensional digital subtraction angiography images using commercial software. Coil sizes were retrieved from patient files, and coil volume (CV) measurements were obtained from https://www.angiocalc.com/. Analysis of covariance, multivariate covariance analysis, and Pearson correlation analyses were performed.ResultsThe median value for AV, CV, CPD, and OA was 63.4 mm³ (range, 5.5–1,771.4 mm³), 23.13 mm³ (range, 2.03–296.95 mm³), 33.29% (range, 13.41%–81.02%), and 10.7 mm² (range, 2.7–49.9 mm²), respectively. Multivariate analysis showed that the CPD values were not significantly different among the treatment groups, although OA significantly differed between the SC and SAC groups (P < .05). Pearson correlations showed that similar to AV, OA was negatively correlated with CPD (r = ?0.321, P < .05).ConclusionsThe CPD value in cerebral aneurysms treated with BAC or SAC did not differ from that in aneurysms treated with SC.