MicroRNAs as biomarkers for prostate cancer prognosis: a systematic review and a systematic reanalysis of public data

Background Reliable prognostic biomarkers to distinguish indolent from aggressive prostate cancer (PCa) are lacking. Many studies investigated microRNAs (miRs) as PCa prognostic biomarkers, often reporting inconsistent findings. We present a systematic review of these; also systematic reanalysis of public miR-profile datasets to identify tissue-derived miRs prognostic of biochemical recurrence (BCR) in patients undergoing radical prostatectomy. Methods Independent PubMed searches were performed for relevant articles from January 2007 to December 2019. For the review, 128 studies were included. Pooled-hazard-ratios (HRs) for miRs in multiple studies were calculated using a random-effects model (REM). For the reanalysis, five studies were included and Cox proportional-hazard models, testing miR association with BCR, performed for miRs profiled in all. Results Systematic review identified 120 miRs as prognostic. Five (let-7b-5p, miR-145-5p, miR152-3p, miR-195-5p, miR-224-5p) were consistently associated with progression in multiple cohorts/studies. In the reanalysis, ten (let-7a-5p, miR-148a-3p, miR-203a-3p, miR-26b-5p, miR30a-3p, miR-30c-5p, miR-30e-3p, miR-374a-5p, miR-425-3p, miR-582-5p) were significantly prognostic of BCR. Of these, miR-148a-3p (HR = 0.80/95% CI = 0.68-0.94) and miR-582-5p (HR = 0.73/95% CI = 0.61-0.87) were also reported in prior publication(s) in the review. Conclusions Fifteen miRs were consistently associated with disease progression in multiple publications or datasets. Further research into their biological roles is warranted to support investigations into their performance as prognostic PCa biomarkers.

Predictors of Mortality in Patients with Advanced Cancer—A Systematic Review and Meta-Analysis

To timely initiate advance care planning in patients with advanced cancer, physicians should identify patients with limited life expectancy. We aimed to identify predictors of mortality. To identify the relevant literature, we searched Embase, MEDLINE, Cochrane Central, Web of Science, and PubMed databases between January 2000–April 2020. Identified studies were assessed on risk-of-bias with a modified QUIPS tool. The main outcomes were predictors and prediction models of mortality within a period of 3–24 months. We included predictors that were studied in ≥2 cancer types in a meta-analysis using a fixed or random-effects model and summarized the discriminative ability of models. We included 68 studies (ranging from 42 to 66,112 patients), of which 24 were low risk-of-bias, and 39 were included in the meta-analysis. Using a fixed-effects model, the predictors of mortality were: the surprise question, performance status, cognitive impairment, (sub)cutaneous metastases, body mass index, comorbidity, serum albumin, and hemoglobin. Using a random-effects model, predictors were: disease stage IV (hazard ratio [HR] 7.58; 95% confidence interval [CI] 4.00–14.36), lung cancer (HR 2.51; 95% CI 1.24–5.06), ECOG performance status 1+ (HR 2.03; 95% CI 1.44–2.86) and 2+ (HR 4.06; 95% CI 2.36–6.98), age (HR 1.20; 95% CI 1.05–1.38), male sex (HR 1.24; 95% CI 1.14–1.36), and Charlson comorbidity score 3+ (HR 1.60; 95% CI 1.11–2.32). Thirteen studies reported on prediction models consisting of different sets of predictors with mostly moderate discriminative ability. To conclude, we identified reasonably accurate non-tumor specific predictors of mortality. Those predictors could guide in developing a more accurate prediction model and in selecting patients for advance care planning.

Complication Differences Between the Tumescent and Non-Tumescent Dissection Techniques for Mastectomy: A Meta-Analysis

PurposeWe conducted a systematic literature search and pooled data from studies to compare the incidence of complications between the tumescent and non-tumescent techniques for mastectomy.MethodsWe searched PubMed, Embase, BioMed Central, Ovid, and CENTRAL databases for studies comparing the two mastectomy techniques up to November 1st, 2020. We used a random-effects model to calculate odds ratios (OR) with 95% confidence intervals (CI).ResultsNine studies were included with one randomized controlled trial (RCT). Meta-analysis indicated no statistically significant difference in the incidence of total skin necrosis (OR 1.18 95% CI 0.71, 1.98 I2 = 82% p=0.52), major skin necrosis (OR 1.58 95% CI 0.69, 3.62 I2 = 71% p=0.28), minor skin necrosis (OR 1.11 95% CI 0.43, 2.85 I2 = 72% p=0.83), hematoma (OR 1.19 95% CI 0.80, 1.79 I2 = 4% p=0.39), and infections (OR 0.87 95% CI 0.54, 1.40 I2 = 54% p=0.56) between tumescent and non-tumescent groups. Analysis of studies using immediate alloplastic reconstruction revealed no statistically significant difference in the incidence of explantation between the two groups (OR 0.78 95% CI 0.46, 1.34 I2 = 62% p=0.37). Multivariable-adjusted ORs on total skin necrosis were available from three studies. Pooled analysis indicated no statistically significant difference between tumescent and non-tumescent groups (OR 1.72 95% CI 0.72, 4.13 I2 = 87% p=0.23).ConclusionLow-quality evidence derived mostly from non-randomized studies is indicative of no difference in the incidence of skin necrosis, hematoma, seroma, infection, and explantation between the tumescent and non-tumescent techniques of mastectomy. There is a need for high-quality RCTs to further strengthen the evidence.

Surgery vs. Biopsy in the Treatment of Butterfly Glioblastoma: A Systematic Review and Meta-Analysis

Butterfly glioblastomas (bGBM) are grade IV gliomas that spread to bilateral hemispheres by infiltrating the corpus callosum. Data on the effect of surgery are limited to small case series. The aim of this meta-analysis was to compare resection vs. biopsy in terms of survival outcomes and postoperative complications. A systematic review of the literature was conducted using PubMed, EMBASE, and Cochrane databases through March 2021 in accordance with the PRISMA checklist. Pooled hazard ratios were calculated and meta-analyzed in a random-effects model including assessment of heterogeneity. Out of 3367 articles, seven studies were included with 293 patients. Surgical resection was significantly associated with longer overall survival (HR 0.39, 95%CI 0.2–0.55) than biopsy. Low heterogeneity was observed (I2: 0%). In further analysis, the effect persisted in extent of resection subgroups of both ≥80% and <80%. No statistically significant difference between surgery and biopsy was detected in terms of postoperative complications, although these were numerically larger for surgery. In patients with bGBM, surgical resection was associated with longer survival prospects compared with biopsy.

Improving the Interpretation of Random Effects Regression Results

Mummolo and Peterson improve the use and interpretation of fixed-effects models by pointing out that unit intercepts fundamentally reduce the amount of variation of variables in fixed-effects models. Along a similar vein, we make two claims in the context of random effects models. First, we show that potentially large reductions in variation, in this case caused by quasi-demeaning, also occur in models using random effects. Second, in many instances, what authors claim to be a random effects model is actually a pooled model after the quasi-demeaning process, affecting how we should interpret the model. A literature review of random effects models in top journals suggests that both points are currently not well understood. To better help users interested in improving their interpretation of random effects models, we provide Stata and R programs to easily obtain post-estimation quasi-demeaned variables.

On estimating a constrained bivariate random effects model for meta-analysis of test accuracy studies

Tailored meta-analysis uses setting-specific knowledge for the test positive rate and disease prevalence to constrain the possible values for a test's sensitivity and specificity. The constrained region is used to select those studies relevant to the setting for meta-analysis using an unconstrained bivariate random effects model (BRM). However, sometimes there may be no studies to aggregate, or the summary estimate may lie outside the plausible or “applicable” region. Potentially these shortcomings may be overcome by incorporating the constraints in the BRM to produce a constrained model. Using a penalised likelihood approach we developed an optimisation algorithm based on co-ordinate ascent and Newton-Raphson iteration to fit a constrained bivariate random effects model (CBRM) for meta-analysis. Using numerical examples based on simulation studies and real datasets we compared its performance with the BRM in terms of bias, mean squared error and coverage probability. We also determined the ‘closeness’ of the estimates to their true values using the Euclidian and Mahalanobis distances. The CBRM produced estimates which in the majority of cases had lower absolute mean bias and greater coverage probability than the BRM. The estimated sensitivities and specificity for the CBRM were, in general, closer to the true values than the BRM. For the two real datasets, the CBRM produced estimates which were in the applicable region in contrast to the BRM. When combining setting-specific data with test accuracy meta-analysis, a constrained model is more likely to yield a plausible estimate for the sensitivity and specificity in the practice setting than an unconstrained model.

A Systematic Review and Meta-Analysis of the Effectiveness of Neuroprotectants for Paclitaxel-Induced Peripheral Neuropathy

BackgroundPaclitaxel-induced peripheral neuropathy (PIPN) is a disabling side effect of paclitaxel with few effective preventive strategies. We aim to determine the efficacy of pharmacological and non-pharmacological neuroprotective interventions in preventing PIPN incidence.MethodsBiomedical literature databases were searched from years 2000 to 2021 for trials comparing neuroprotective interventions and control. Meta-analysis was performed using the random-effects model. The primary outcome was the incidence of PIPN.ResultsOf 24 relevant controlled trials, 14 were eligible for meta-analysis. Pooled results from seven non-pharmacological trials were associated with a statistically significant 48% relative reduction of PIPN risk with low heterogeneity. Conversely, pooled results from six pharmacological trials were associated with a significant 20% relative reduction of PIPN risk with moderate heterogeneity. Both pharmacological and non-pharmacological approaches appear effective in reducing PIPN incidence in the treatment arm compared to control (pooled RR &lt; 1).ConclusionCurrent evidence suggests that both interventions may reduce PIPN risk. Non-pharmacological interventions appear more effective than pharmacological interventions.

α-Linolenic Acid and Risk of Heart Failure: A Meta-Analysis

Background: The α-linolenic acid is a plant origin n-3 fatty acid that may reduce the risk of cardiovascular disease. However, the effect of α-linolenic acid (ALA) on the risk of heart failure (HF) remains unclear. In this meta-analysis, we aimed to determine the role of ALA in the risk of incident HF.Methods: Electronic databases were searched for studies up to August 10, 2021. Studies were included for meta-analysis if the adjusted risk of HF in different dietary intake or circulating levels of ALA was reported. We used the random-effects model to calculate the estimated hazard ratios (HRs) and 95% CI for higher ALA.Results: A total of 6 studies (7 cohorts) comprising 135,270 participants were included for meta-analysis. After a median follow-up duration of 10 years, 5,905 cases of HF were recorded. No significant heterogeneity was observed among all the included studies. Random-effects model analyses showed that there was no significant association between ALA and the risk of incident HF, either assessed as quintiles (highest quintile vs. lowest quintile: HR = 0.95, 95% CI = 0.86–1.06) or per 1 SD increment (HR = 0.99, 95% CI = 0.95–1.01). Furthermore, we did not observe any association between ALA and the risk of HF in subgroup analyses performed according to age, sex, follow-up duration, and measuring method of ALA.Conclusions: We found no association between ALA and the risk of incident HF, suggesting that ALA might not be effective in the prevention of HF.