The mediating effect of health behaviors on the association between job strain and mental health outcome: a national survey of police officers.

Police officers often face emotionally challenging interpersonal situations and numerous studies have demonstrated that policing is a stressful occupation. A study revealed a significant positive correlation between emotional demands among police officers and emotional dissonance, as well as burnout. Health-promoting behaviors can contribute to better overall health outcomes and reduce the risk of developing health problems, but there is limited research evaluating the association of job strain and health behaviors with mental health outcomes in police officers. The objective of this study was to assess the job strain associated with mental health mediated by health behaviors in Taiwanese police officers. This was a cross-sectional quantitative study conducted in Oct 2016. A total of 41,871 police officers (response rate was 79.7%) participated questionnaire that consisted of demographic information, job characteristics, health behaviors, and mental component summary (MCS) scores of the Short-Form Health Survey. Independent t-tests and one-way analysis of variance (One-way ANOVA) were conducted to assess the differences in mean MCS scores across various demographics, health behavior, and job characteristics. Multivariate regression analyses were used to assess the relationship between job strain and health behaviors with mental health outcomes. MCS scores were associated with job characteristics and health behaviors among police officers except for gender. After adjusting for covariates, multivariate analysis indicated that police officers with high job demands and high job strain index exhibited poor MCS scores. Job strain was significantly associated with MCS mediated by health behaviors (consumption of fruits and vegetables, and physical activity) in Taiwanese police officers. Since regular physical activity and increased vegetable and fruit consumption might alleviate the effects of job strain on mental health status, it is recommended that institutional policies be established to promote health-enhancing behaviors among police officers.

Mental Illness Diagnoses May Not Cause All Mental Symptoms: A Simulation Study for Major Depressive Episodes, Dysthymic Disorder, and Manic Episodes.

Objectives This study aims to understand the statistical significance of the associations between diagnoses and symptoms based on simulations that have been used to understand the interpretability of mental illness diagnoses. Methods The symptoms for the diagnosis of major depressive episodes, dysthymic disorder, and manic episodes were extracted from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR, American Psychiatric Association, Philadelphia, Pennsylvania). Without real-world symptom data, we simulated populations using various combinations of symptom prevalence and correlations. Assuming symptoms occurred with similar prevalence and correlations, for each combination of symptom prevalence (0.05, 0.1, 0.3, 0.5, and 0.7) and correlation (0, 0.1, 0.4, 0.7, and 0.9), 100 cohorts with 10,000 individuals were randomly created. Diagnoses were made according to the DSM-IV-TR criteria. The associations between the diagnoses and their input symptoms were quantified with odds ratios and correlation coefficients. P-values from 100 cohorts for each combination of symptom prevalence and correlation were summarized. Results Three mental illness diagnoses were not significantly correlated with their own symptoms in all simulations, particularly when symptoms were not correlated, except for the symptom in the major criteria of major depressive episodes or dysthymic disorder. The symptoms for the diagnosis of major depressive episodes and dysthymic disorder were significantly correlated with these two diagnoses in some simulations, assuming 0.1, 0.4, 0.7, or 0.9 symptom correlations, except for one symptom. The overlap in the input symptoms for the diagnosis of major depressive episodes and dysthymic disorder also leads to significant correlations between these two diagnoses, assuming 0.1, 0.4, 0.7, and 0.9 correlations between input symptoms. Manic episodes are not significantly associated with the input symptoms of major depressive episodes and dysthymic disorder. Conclusion There are challenges to establish the causation between psychiatric symptoms and mental illness diagnoses. There is insufficient prevalence and incidence data to show all psychiatric symptoms exist or can be observed in patients. The diagnostic accuracy of symptoms to detect a disease cause is far from perfect. Assuming the symptoms of three mood disorders may present in patients, three diagnoses are not significantly associated with all psychiatric symptoms used to diagnose them. The diagnostic criteria of the three diagnoses have not been designed to guarantee significant associations between symptoms and diagnoses. Because statistical associations are important for making causal inferences, there may be a lack of causation between diagnoses and symptoms. Previous research has identified factors that lead to insignificant associations between diagnoses and symptoms, including biases due to data processing and a lack of epidemiological evidence to support the design of mental illness diagnostic criteria.

The Upper Limits of Risk Ratios and Recommendations for Reporting Risk Ratios, Odds Ratios, and Rate Ratios.

Background Relative measures, including risk ratios (RRs) and odds ratios (ORs), are reported in many epidemiological studies. RRs represent how many times a condition is likely to develop when exposed to a risk factor. The upper limit of RRs is the multiplicative inverse of the baseline incidence. Ignoring the upper limits of RRs can lead to reporting exaggerated relative effect sizes. Objectives This study aims to demonstrate the importance of such upper limits for effect size reporting via equations, examples, and simulations and provide recommendations for the reporting of relative measures. Methods Equations to calculate RRs and their 95% confidence intervals (CIs) were listed. We performed simulations with 10,000 simulated subjects and three population variables: proportions at risk (0.05, 0.1, 0.3, 0.5, and 0.8), baseline incidence (0.05, 0.1, 0.3, 0.5, and 0.8), and RRs (0.5, 1.0, 5.0, 10.0, and 25.0). Subjects were randomly assigned with a risk based on the set of proportions-at-risk values. A disease occurred based on the baseline incidence among those not at risk. The incidence of those at risk was the product of the baseline incidence and the RRs. The 95% CIs of RRs were calculated according to Altman. Results The calculation of RR 95% CIs is not connected to the RR upper limits in equations. The RRs in the simulated populations at risk could reach the upper limits of RRs: multiplicative inverse of the baseline incidence. The upper limits to the derived RRs were around 1.25, 2, 3.3, 10, and 20, when the assumed baseline incidence rates were 0.8, 0.5, 0.3, 0.2, and 0.05, respectively. We demonstrated five scenarios in which the RR 95% CIs might exceed the upper limits. Conclusions Statistical significance does not imply the RR 95% CIs not exceeding the upper limits of RRs. When reporting RRs or ORs, the RR upper limits should be assessed. The rate ratio is also subject to a similar upper limit. In the literature, ORs tend to overestimate effect sizes. It is recommended to correct ORs that aim to approximate RRs assuming outcomes are rare. A reporting guide for relative measures, RRs, ORs, and rate ratios, is provided. Researchers are recommended to report whether the 95% CIs of relative measures, RRs, ORs, and rate ratios, overlap with the range of upper limits and discuss whether the relative measure estimates may exceed the upper limits.

Critical Hierarchical Appraisal and Reporting Tool for Composite Measures (CHAOS).

Background Composite measures are often used to represent certain concepts that cannot be measured with single variables and can be used as diagnoses, prognostic factors, or outcomes in clinical or health research. For example, frailty is a diagnosis confirmed based on the number of age-related symptoms and has been used to predict major health outcomes. However, undeclared assumptions and problems are prevalent among composite measures. Thus, we aim to propose a reporting guide and an appraisal tool for identifying these assumptions and problems. Methods We developed this reporting and assessment tool based on evidence and the consensus of experts pioneering research on index mining and syndrome mining. We designed a development framework for composite measures and then tested and revised it based on several composite measures commonly used in medical research, such as frailty, body mass index (BMI), mental illness diagnoses, and innovative indices mined for mortality prediction. We extracted review questions and reporting items from various issues identified by the development framework. This panel reviewed the identified issues, considered other aspects that might have been neglected in previous studies, and reached a consensus on the questions to be used by the reporting and assessment tool. Results We selected 19 questions in seven domains for reporting or critical assessment. Each domain contains review questions for authors and readers to critically evaluate the interpretability and validity of composite measures, which include candidate variable selection, variable inclusion and assumption declaration, data processing, weighting scheme, methods to aggregate information, composite measure interpretation and justification, and recommendations on the use. Conclusions For all seven domains, interpretability is central with respect to composite measures. Variable inclusion and assumptions are important clues to show the connection between composite measures and their theories. This tool can help researchers and readers understand the appropriateness of composite measures by exploring various issues. We recommend using this Critical Hierarchical Appraisal and repOrting tool for composite measureS (CHAOS) along with other critical appraisal tools to evaluate study design or risk of bias.

Frailty does not cause all frail symptoms: United States Health and Retirement Study.

BACKGROUND: Frailty is associated with major health outcomes. However, the relationships between frailty and frailty symptoms haven't been well studied. This study aims to show the associations between frailty and frailty symptoms. METHODS: The Health and Retirement Study (HRS) is an ongoing longitudinal biannual survey in the United States. Three of the most used frailty diagnoses, defined by the Functional Domains Model, the Burden Model, and the Biologic Syndrome Model, were reproduced according to previous studies. The associations between frailty statuses and input symptoms were assessed using odds ratios and correlation coefficients. RESULTS: The sample sizes, mean ages, and frailty prevalence matched those reported in previous studies. Frailty statuses were weakly correlated with each other (coefficients = 0.19 to 0.38, p < 0.001 for all). There were 49 input symptoms identified by these three models. Frailty statuses defined by the three models were not significantly correlated with one or two symptoms defined by the same models (p > 0.05 for all). One to six symptoms defined by the other two models were not significantly correlated with each of the three frailty statuses (p > 0.05 for all). Frailty statuses were significantly correlated with their own bias variables (p < 0.05 for all). CONCLUSION: Frailty diagnoses lack significant correlations with some of their own frailty symptoms and some of the frailty symptoms defined by the other two models. This finding raises questions like whether the frailty symptoms lacking significant correlations with frailty statuses could be included to diagnose frailty and whether frailty exists and causes frailty symptoms.

Diagnostic accuracy of symptoms for an underlying disease: a simulation study.

Symptoms have been used to diagnose conditions such as frailty and mental illnesses. However, the diagnostic accuracy of the numbers of symptoms has not been well studied. This study aims to use equations and simulations to demonstrate how the factors that determine symptom incidence influence symptoms' diagnostic accuracy for disease diagnosis. Assuming a disease causing symptoms and correlated with the other disease in 10,000 simulated subjects, 40 symptoms occurred based on 3 epidemiological measures: proportions diseased, baseline symptom incidence (among those not diseased), and risk ratios. Symptoms occurred with similar correlation coefficients. The sensitivities and specificities of single symptoms for disease diagnosis were exhibited as equations using the three epidemiological measures and approximated using linear regression in simulated populations. The areas under curves (AUCs) of the receiver operating characteristic (ROC) curves was the measure to determine the diagnostic accuracy of multiple symptoms, derived by using 2 to 40 symptoms for disease diagnosis. With respect to each AUC, the best set of sensitivity and specificity, whose difference with 1 in the absolute value was maximal, was chosen. The results showed sensitivities and specificities of single symptoms for disease diagnosis were fully explained with the three epidemiological measures in simulated subjects. The AUCs increased or decreased with more symptoms used for disease diagnosis, when the risk ratios were greater or less than 1, respectively. Based on the AUCs, with risk ratios were similar to 1, symptoms did not provide diagnostic values. When risk ratios were greater or less than 1, maximal or minimal AUCs usually could be reached with less than 30 symptoms. The maximal AUCs and their best sets of sensitivities and specificities could be well approximated with the three epidemiological and interaction terms, adjusted R-squared ≥ 0.69. However, the observed overall symptom correlations, overall symptom incidence, and numbers of symptoms explained a small fraction of the AUC variances, adjusted R-squared ≤ 0.03. In conclusion, the sensitivities and specificities of single symptoms for disease diagnosis can be explained fully by the at-risk incidence and the 1 minus baseline incidence, respectively. The epidemiological measures and baseline symptom correlations can explain large fractions of the variances of the maximal AUCs and the best sets of sensitivities and specificities. These findings are important for researchers who want to assess the diagnostic accuracy of composite diagnostic criteria.

Why Mental Illness Diagnoses Are Wrong: A Pilot Study on the Perspectives of the Public.

Background: Mental illness diagnostic criteria are made based on assumptions. This pilot study aims to assess the public's perspectives on mental illness diagnoses and these assumptions. Methods: An anonymous survey with 30 questions was made available online in 2021. Participants were recruited via social media, and no personal information was collected. Ten questions focused on participants' perceptions regarding mental illness diagnoses, and 20 questions related to the assumptions of mental illness diagnoses. The participants' perspectives on these assumptions held by professionals were assessed. Results: Among 14 survey participants, 4 correctly answered the relationships of 6 symptom pairs (28.57%). Two participants could not correctly conduct the calculations involved in mood disorder diagnoses (14.29%). Eleven (78.57%) correctly indicated that 2 or more sets of criteria were available for single diagnoses of mental illnesses. Only 1 (7.14%) correctly answered that the associations between symptoms and diagnoses were supported by including symptoms in the diagnostic criteria of the diagnoses. Nine (64.29%) correctly answered that the diagnosis variances were not fully explained by their symptoms. The confidence of participants in the major depressive disorder diagnosis and the willingness to take medications for this diagnosis were the same (mean = 5.50, standard deviation [SD] = 2.31). However, the confidence of participants in the symptom-based diagnosis of non-solid brain tumor was significantly lower (mean = 1.62, SD = 2.33, p < 0.001). Conclusion: Our study found that mental illness diagnoses are wrong from the perspectives of the public because our participants did not agree with all the assumptions professionals make about mental illness diagnoses. Only a minority of our participants obtained correct answers to the calculations involved in mental illness diagnoses. In the literature, neither patients nor the public have been engaged in formulating the diagnostic criteria of mental illnesses.

Simulation study to demonstrate biases created by diagnostic criteria of mental illnesses: major depressive episodes, dysthymia, and manic episodes.

OBJECTIVES: Composite diagnostic criteria alone are likely to create and introduce biases into diagnoses that subsequently have poor relationships with input symptoms. This study aims to understand the relationships between the diagnoses and the input symptoms, as well as the magnitudes of biases created by diagnostic criteria and introduced into the diagnoses of mental illnesses with large disease burdens (major depressive episodes, dysthymic disorder, and manic episodes). SETTINGS: General psychiatric care. PARTICIPANTS: Without real-world data available to the public, 100 000 subjects were simulated and the input symptoms were assigned based on the assumed prevalence rates (0.05, 0.1, 0.3, 0.5 and 0.7) and correlations between symptoms (0, 0.1, 0.4, 0.7 and 0.9). The input symptoms were extracted from the diagnostic criteria. The diagnostic criteria were transformed into mathematical equations to demonstrate the sources of biases and convert the input symptoms into diagnoses. PRIMARY AND SECONDARY OUTCOMES: The relationships between the input symptoms and diagnoses were interpreted using forward stepwise linear regressions. Biases due to data censoring or categorisation introduced into the intermediate variables, and the three diagnoses were measured. RESULTS: The prevalence rates of the diagnoses were lower than those of the input symptoms and proportional to the assumed prevalence rates and the correlations between the input symptoms. Certain input or bias variables consistently explained the diagnoses better than the others. Except for 0 correlations and 0.7 prevalence rates of the input symptoms for the diagnosis of dysthymic disorder, the input symptoms could not fully explain the diagnoses. CONCLUSIONS: There are biases created due to composite diagnostic criteria and introduced into the diagnoses. The design of the diagnostic criteria determines the prevalence of the diagnoses and the relationships between the input symptoms, the diagnoses, and the biases. The importance of the input symptoms has been distorted largely by the diagnostic criteria.

Opportunities and Challenges From Leading Trends in a Biomonitoring Project: Canadian Health Measures Survey 2007-2017.

Background: Biomonitoring can be conducted by assessing the levels of chemicals in human bodies and their surroundings, for example, as was done in the Canadian Health Measures Survey (CHMS). This study aims to report the leading increasing or decreasing biomarker trends and determine their significance. Methods: We implemented a trend analysis for all variables from CHMS biomonitoring data cycles 1-5 conducted between 2007 and 2017. The associations between time and obesity were determined with linear regressions using the CHMS cycles and body mass index (BMI) as predictors. Results: There were 997 unique biomarkers identified and 86 biomarkers with significant trends across cycles. Nine of the 10 leading biomarkers with the largest decreases were environmental chemicals. The levels of 1,2,3-trimethyl benzene, dodecane, palmitoleic acid, and o-xylene decreased by more than 60%. All of the 10 chemicals with the largest increases were environmental chemicals, and the levels of 1,2,4-trimethylbenzene, nonanal, and 4-methyl-2-pentanone increased by more than 200%. None of the 20 biomarkers with the largest increases or decreases between cycles were associated with BMI. Conclusions: The CHMS provides the opportunity for researchers to determine associations between biomarkers and time or BMI. However, the unknown causes of trends with large magnitudes of increase or decrease and their unclear impact on Canadians' health present challenges. We recommend that the CHMS plan future cycles on leading trends and measure chemicals with both human and environmental samples.

Using syndrome mining with the Health and Retirement Study to identify the deadliest and least deadly frailty syndromes.

Syndromes are defined with signs or symptoms that occur together and represent conditions. We use a data-driven approach to identify the deadliest and most death-averse frailty syndromes based on frailty symptoms. A list of 72 frailty symptoms was retrieved based on three frailty indices. We used data from the Health and Retirement Study (HRS), a longitudinal study following Americans aged 50 years and over. Principal component (PC)-based syndromes were derived based on a principal component analysis of the symptoms. Equal-weight 4-item syndromes were the sum of any four symptoms. Discrete-time survival analysis was conducted to compare the predictive power of derived syndromes on mortality. Deadly syndromes were those that significantly predicted mortality with positive regression coefficients and death-averse ones with negative coefficients. There were 2,797 of 5,041 PC-based and 964,774 of 971,635 equal-weight 4-item syndromes significantly associated with mortality. The input symptoms with the largest regression coefficients could be summed with three other input variables with small regression coefficients to constitute the leading deadliest and the most death-averse 4-item equal-weight syndromes. In addition to chance alone, input symptoms' variances and the regression coefficients or p values regarding mortality prediction are associated with the identification of significant syndromes.

Composite diagnostic criteria are problematic for linking potentially distinct populations: the case of frailty.

Composite diagnostic criteria are common in frailty research. We worry distinct populations may be linked to each other due to complicated criteria. We aim to investigate whether distinct populations might be considered similar based on frailty diagnostic criteria. The Functional Domains Model for frailty diagnosis included four domains: physical, nutritive, cognitive and sensory functioning. Health and Retirement Study participants with two or more deficiencies in the domains were diagnosed frail. The survival distributions were analyzed using discrete-time survival analysis. The distributions of the demographic characteristics and survival across the groups diagnosed with frailty were significantly different (p < 0.05). A deficiency in cognitive functioning was associated with the worst survival pattern compared with a deficiency in the other domains (adjusted p < 0.05). The associations of the domains with mortality were cumulative without interactions. Cognitive functioning had the largest effect size for mortality prediction (Odds ratios, OR = 2.37), larger than that of frailty status (OR = 1.92). The frailty diagnostic criteria may take distinct populations as equal and potentially assign irrelevant interventions to individuals without corresponding conditions. We think it necessary to review the adequacy of composite diagnostic criteria in frailty diagnosis.

Blockade of STAT3 Signaling Contributes to Anticancer Effect of 5-Acetyloxy-6,7,8,4'-Tetra-Methoxyflavone, a Tangeretin Derivative, on Human Glioblastoma Multiforme Cells.

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor with poor prognosis, largely due to resistance to current radiotherapy and Temozolomide chemotherapy. The constitutive activation of Signal Transducer and Activator of Transcription 3 (STAT3) is evidenced as a pivotal driver of GBM pathogenesis and therapy resistance, and hence, is a promising GBM drug target. 5-acetyloxy-6,7,8,4'-tetramethoxyflavone (5-AcTMF) is an acetylated derivative of Tangeretin which is known to exert anticancer effects on breast, colon, lung, and multiple myeloma; however, its effect on GBM remains elusive. Herein, we reported that 5-AcTMF suppressed the viability and clonogenicity along with inducing apoptosis in multiple human GBM cell lines. Mechanistic analyses further revealed that 5-AcTMF lowered the levels of Tyrosine 705-phosphorylated STAT3 (p-STAT3), a canonical marker of STAT3 activation, but also dampened p-STAT3 upregulation elicited by Interleukin-6. Notably, ectopic expression of dominant-active STAT3 impeded 5-AcTMF-induced suppression of viability and clonogenicity plus apoptosis induction in GBM cells, confirming the prerequisite of STAT3 blockage for the inhibitory action of 5-AcTMF on GBM cell survival and growth. Additionally, 5-AcTMF impaired the activation of STAT3 upstream kinase JAK2 but also downregulated antiapoptotic BCL-2 and BCL-xL in a STAT3-dependent manner. Moreover, the overexpression of either BCL-2 or BCL-xL abrogated 5-AcTMF-mediated viability reduction and apoptosis induction in GBM cells. Collectively, we, for the first time, revealed the anticancer effect of 5-AcTMF on GBM cells, which was executed via thwarting the JAK2-STAT3-BCL-2/BCL-xL signaling axis. Our findings further implicate the therapeutic potential of 5-AcTMF for GBM treatment.

Dodecyl gallate induces apoptosis by upregulating the caspase-dependent apoptotic pathway and inhibiting the expression of anti-apoptotic Bcl-2 family proteins in human osteosarcoma cells.

Dodecyl gallate (DG) is a gallic acid ester that has been shown to inhibit tumor growth. The aim of this study was to investigate the mechanism by which DG induces antiproliferative and apoptotic effects in MG-63 human osteosarcoma cells. Dose- and time-dependent cytotoxic effects of DG were determined using an MTT assay. The results showed that the half-maximal inhibitory concentration (IC50) of DG in MG-63 cells was 31.15 µM at 24 h, 10.66 µM at 48 h, and 9.06 µM at 72 h. Flow cytometric analysis demonstrated that exposure to 20 and 40 µM DG resulted in an increase in the sub-G1 phase population and in S-phase cell cycle arrest. Furthermore, western blot analysis of apoptosis-related protein expression revealed an increase in the activation of caspases 8 and 3, cleavage of poly (ADPribose) polymerase (PARP), and disruption of mitochondrial membrane permeability was measured by flow cytometry. An increase in the Bax/Bcl-2 ratio and a decrease in the expression of inhibitor of apoptosis protein (IAP) family members, namely X-linked inhibitor of apoptosis protein and survivin, were also observed following DG treatment. These data provide insight into the molecular mechanisms governing the ability of DG to induce apoptosis in human osteosarcoma cells in vitro.

Left common basal pyramid torsion following left upper lobectomy/segmentectomy.

Lobar or segmental lung torsion is a severe complication of lung resection. To the best of our knowledge, common basal pyramid torsion has never been reported. We describe a case of left basal pyramid torsion after left upper lobectomy and superior segmentectomy, which was successfully treated by thoracoscopic surgery.

Effects of teriparatide on lung function and pain relief in women with multiple osteoporotic vertebral compression fractures.

BACKGROUND: Osteoporosis is predominantly a condition of the elderly. In this study, we evaluated the effects of teriparatide on lung function and pain relief in elderly women with multiple osteoporotic vertebral compression fractures. METHODS: A total of 37 patients who received teriparatide treatment during the period January 2010 to December 2011 were enrolled. Dual-energy X-ray absorptiometry scans were used to measure bone mineral density (BMD) and lung function was measured using a MasterScreen Body Jaeger spirometer. Forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) values were recorded. The Oswestry Disability Index (ODI) and the Visual Analog Scale (VAS) for pain were used to evaluate physical health and pain intensity, respectively, at baseline and after 6 months of teriparatide treatment. RESULTS: Mean BMD at the lumbar spine increased from 0.716 g/cm(2) at baseline to 0.829 g/cm(2) after 6 months of treatment. In addition, both mean FVC and FEV1 values after 6 months of treatment were significantly higher than baseline values (99.01% and 100.06% vs. 87.62% and 90.62%, respectively). Teriparatide treatment also resulted in a significant reduction in self-reported pain intensity and a significant improvement in physical health as measured by VAS and ODI scores, respectively. CONCLUSIONS: In addition to increasing BMD, teriparatide treatment improves the lung function and results in diminished pain intensity in women with multiple osteoporotic vertebral compression fractures.

Epithelial cell transformation sequence 2 is a potential biomarker of unfavorable survival in human gliomas.

BACKGROUND: High-grade primary gliomas are invasive and have poor outcome. The identification of biomarkers predictive of outcome in patients with gliomas is crucial for clinical follow-up. Epithelial cell transformation sequence 2 (ECT2) modulates cancer invasion, progression, metastasis and cell cycle regulation. However, its role in determining the clinical outcome of human gliomas warrants further elucidation. MATERIALS AND METHODS: This study hypothesized that ECT2 is over-expressed in human gliomas. We analysis de-linked data (GDS1815/219787_s_at/ECT2) in primary high-grade glioma, and exclude 23 sheets of data without detailed information. An additional database (GDS1962/234992_x_at/ECT2) was also included to evaluation ECT2 gene expression in each pathologic grading. RESULTS: Analysis of the Gene Expression Omnibus (GEO) profile showed that ECT2 mRNA expression level was higher in WHO grade IV (n = 81) than in grade II (n = 7, P = 0.0126) gliomas and non-tumor controls (n = 23; P = 1.65 Χ 10⁻8). Kaplan-Meier analysis showed unfavorable survival in patients with high ECT2 mRNA levels (n = 10) than in those with low ECT2 expression (n = 67) (median survival, 106 vs. 46 weeks, P < 0.0001, by log-rank test, Hazard ratio: 0.07850, 95% CI: 0.02402-0.2565). CONCLUSIONS: ECT2 expression is positively correlated with WHO pathologic grading and unfavorable survival, suggesting that ECT2 may be a potential therapeutic candidate in human gliomas.

Midkine expression in high grade gliomas: Correlation of this novel marker with proliferation and survival in human gliomas.

BACKGROUND: High-grade primary glioma have poor prognosis and predictive biomarkers is very important. Midkine (MDK), a heparin-binding growth factor, is important in regulating carcinogenesis, cell proliferation, mitogenesis, and angiogenesis. This study aimed to identify over-expression of MDK in gliomas and correlate this with clinical outcomes. The authors put forward their hypothesis correlating proliferation and poor survival with over-expression of this novel protein. METHODS: Two datasets from Gene Expression Omnibus (GEO) included human data of 100 and 180 patients, respectively. The MDK expression, World Health Organization (WHO) pathological grade, sex, age, and survival time were identified for statistical analysis. RESULTS: A search of the GEO profile revealed that MDK expression level was statistically greater in the WHO grade IV compared with grade II (P = 0.002), in grades III and IV compared with nontumor control (P = 0.044 and P < 0.001, respectively) after adjustments using the Bonferroni method. By the Kaplan-Meier survival curve, the high MDK expression group had poorer survival outcome (2.38-fold hazard, 95% confidence interval: 1.22-4.63) than the low MDK expression group after adjustments for WHO grade and age. CONCLUSIONS: Taken together, there is a positive correlation between MDK expression and WHO grading of human gliomas. Moreover, MDK over-expression is significant correlated to poor survival outcome in high-grade, suggesting that MDK may be an important therapeutic target.