Long-term disease burden and survivorship issues after surgery and radiotherapy of intracranial meningioma patients

BACKGROUND Many intracranial meningioma patients have an impaired health-related quality of life (HRQoL) and neurocognitive functioning up to 4 yr after intervention. OBJECTIVE To assess the long-term (≥5 yr) disease burden of meningioma patients. METHODS In this multicenter cross-sectional study, patients ≥5 yr after intervention (including active magnetic resonance imaging (MRI) surveillance) were included and assessed for HRQoL (Short-Form Health Survey 36), neurocognitive functioning (neuropsychological assessment), anxiety and depression (Hospital Anxiety and Depression Scale), and work productivity (Short Form-Health and Labour Questionnaire). Multivariable and propensity score regression analyses were used to compare patients and controls, and different treatment strategies corrected for possible confounders. Clinically relevant differences were reported. RESULTS At a median of 9 yr follow-up after intervention, meningioma patients (n = 190) reported more limitations due to physical (difference 12.5 points, P = .008) and emotional (13.3 points, P = .002) health problems compared with controls. Patients also had an increased risk to suffer from anxiety (odds ratio [OR]: 2.6, 95% CI: 1.2-5.7) and depression (OR: 3.7, 95% CI: 1.3-10.5). Neurocognitive deficits were found in 43% of patients. Although postoperative complications, radiotherapy, and reresection were associated with worse verbal memory, attention, and executive functioning when compared to patients resected once, the only clinically relevant association was between reresection and worse attention (–2.11, 95% CI: –3.52 to –0.07). Patients of working age less often had a paid job (48%) compared with the working-age Dutch population (72%) and reported more obstacles at work compared with controls. CONCLUSION In the long term, a large proportion of meningioma patients have impaired HRQoL, neurocognitive deficits, and high levels of anxiety or depression. Patients treated with 1 resection have the best neurocognitive functioning

High prevalence of chronic kidney disease in Iran: a large population-based study

<p>Abstract</p> <p>Background</p> <p>Chronic kidney disease (CKD) is a global public health threat, associated with an alarming increase in morbidity and mortality. The importance is the worldwide increase in its incidence and prevalence.</p> <p>Methods</p> <p>In this cross-sectional study, we estimate the prevalence and determine the associated factors of chronic kidney disease in a representative sample of 10063 participants aged over 20 years, in Tehran, Iran. Chronic kidney disease was defined as estimated glomerular filtration rate less than 60 mL/min/1.73 m2. Glomerular filtration rate was estimated from abbreviated prediction equation provided by the Modification of Diet in Renal Disease study (MDRD).</p> <p>Results</p> <p>Overall prevalence of CKD with the abbreviated MDRD equation was 18.9% (95% confidence interval (CI) 18.2, 20.6). Age adjusted prevalence of CKD was 14.9% (95%CI 14.2, 15.6). Factors associated to CKD include age(years)(odds ratio(OR) 1.1, 95% CI 1.0 to 1.2), female gender (OR 3.1, 95% CI 2.6, 3.7), BMI (BMI 25 to <30 OR 1.5, 95% CI 1.3, 1.8 and BMI ≥ 30 OR 1.6, 95% CI 1.3, 2.0), high waist circumference (OR 1.2, 95% CI 1.1, 1.4), hypertension (OR 1.2, 95% CI 1.1, 1.4), and dyslipidemia (OR 1.3, 95% CI 1.1, 1.5).</p> <p>Conclusion</p> <p>CKD with its high prevalence poses a definite health threat in Iran.</p

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

Forouzanfar MH, Afshin A, Alexander LT, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. LANCET. 2016;388(10053):1659-1724.Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors-the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57.8% (95% CI 56.6-58.8) of global deaths and 41.2% (39.8-42.8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211.8 million [192.7 million to 231.1 million] global DALYs), smoking (148.6 million [134.2 million to 163.1 million]), high fasting plasma glucose (143.1 million [125.1 million to 163.5 million]), high BMI (120.1 million [83.8 million to 158.4 million]), childhood undernutrition (113.3 million [103.9 million to 123.4 million]), ambient particulate matter (103.1 million [90.8 million to 115.1 million]), high total cholesterol (88.7 million [74.6 million to 105.7 million]), household air pollution (85.6 million [66.7 million to 106.1 million]), alcohol use (85.0 million [77.2 million to 93.0 million]), and diets high in sodium (83.0 million [49.3 million to 127.5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Copyright (C) The Author(s). Published by Elsevier Ltd

The circadian clock as a potential biomarker and therapeutic target in pancreatic cancer

Pancreatic cancer (PC) has a very high mortality rate globally. Despite ongoing efforts, its prognosis has not improved significantly over the last two decades. Thus, further approaches for optimizing treatment are required. Various biological processes oscillate in a circadian rhythm and are regulated by an endogenous clock. The machinery controlling the circadian cycle is tightly coupled with the cell cycle and can interact with tumor suppressor genes/oncogenes; and can therefore potentially influence cancer progression. Understanding the detailed interactions may lead to the discovery of prognostic and diagnostic biomarkers and new potential targets for treatment. Here, we explain how the circadian system relates to the cell cycle, cancer, and tumor suppressor genes/oncogenes. Furthermore, we propose that circadian clock genes may be potential biomarkers for some cancers and review the current advances in the treatment of PC by targeting the circadian clock. Despite efforts to diagnose pancreatic cancer early, it still remains a cancer with poor prognosis and high mortality rates. While studies have shown the role of molecular clock disruption in tumor initiation, development, and therapy resistance, the role of circadian genes in pancreatic cancer pathogenesis is not yet fully understood and further studies are required to better understand the potential of circadian genes as biomarkers and therapeutic targets.</p

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies

Role of gut bacterial and non-bacterial microbiota in alcohol-associated liver disease: Molecular mechanisms, biomarkers and therapeutic prospective

Alcohol-associated liver disease (ALD) comprises a spectrum of liver diseases that include: steatosis to alcohol-associated hepatitis, cirrhosis, and ultimately hepatocellular carcinoma. The pathophysiology and potential underlying mechanisms for alcohol-associated liver disease are unclear. Moreover, the treatment of ALD remains a challenge. Intestinal microbiota include bacteria, fungi, and viruses, that are now known to be important in the development of ALD. Alcohol consumption can change the gut microbiota and function leading to liver disease. Given the importance of interactions between intestinal microbiota, alcohol, and liver injury, the gut microbiota has emerged as a potential biomarker and therapeutic target. This review focuses on the potential mechanisms by which the gut microbiota may be involved in the pathogenesis of ALD and explains how this can be translated into clinical management. We discuss the potential of utilizing the gut microbiota signature as a biomarker in ALD patients. Additionally, we present an overview of the prospect of modulating the intestinal microbiota for the management of ALD

The therapeutic potential value of Cancer-testis antigens in immunotherapy of gastric cancer

Gastric cancer (GC) is the fourth most common cause of mortality and the fifth for incidence, globally. Diagnosis, early prognosis, and therapy remains challenging for this condition, and new tumor-associated antigens are required for its detection and immunotherapy. Cancer-testis antigens (CTAs) are a subfamily of tumor-associated antigens (TAAs) that have been identified as potential biomarkers and targets for cancer immunotherapy. The CTAs-restricted expression pattern in tumor cells and their potential immunogenicity identify them as attractive target candidates in CTA-based diagnosis or prognosis or immunotherapy. To date, numerous studies have reported the dysregulation of CTAs in GC. Several clinical trials have been done to assess CTA-based immunotherapeutic potential in the treatment of GC patients. NY-ESO-1, MAGE, and KK-LC-1 have been used in GC clinical trials. We review recent studies that have investigated the potential of the CTAs in GC regarding the expression, function, aggressive phenotype, prognosis, and immunological responses as well as their possible clinical significance as immunotherapeutic targets with a focus on challenges and future interventions

Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis

Abstract: Colorectal cancer (CRC) is the third most common cause of cancer-related deaths. The five-year relative survival rate for CRC is estimated to be approximately 90% for patients diagnosed with early stages and 14% for those diagnosed at an advanced stages of disease, respectively. Hence, the development of accurate prognostic markers is required. Bioinformatics enables the identification of dysregulated pathways and novel biomarkers. RNA expression profiling was performed in CRC patients from the TCGA database using a Machine Learning approach to identify differential expression genes (DEGs). Survival curves were assessed using Kaplan–Meier analysis to identify prognostic biomarkers. Furthermore, the molecular pathways, protein–protein interaction, the co-expression of DEGs, and the correlation between DEGs and clinical data have been evaluated. The diagnostic markers were then determined based on machine learning analysis. The results indicated that key upregulated genes are associated with the RNA processing and heterocycle metabolic process, including C10orf2, NOP2, DKC1, BYSL, RRP12, PUS7, MTHFD1L, and PPAT. Furthermore, the survival analysis identified NOP58, OSBPL3, DNAJC2, and ZMYND19 as prognostic markers. The combineROC curve analysis indicated that the combination of C10orf2 -PPAT- ZMYND19 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.98, 1.00, and 0.99, respectively. Eventually, ZMYND19 gene was validated in CRC patients. In conclusion, novel biomarkers of CRC have been identified that may be a promising strategy for early diagnosis, potential treatment, and better prognosis.</p

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

Abstract Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan–Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein–protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC