Notes: Questions with a hash tag (#) are displayed only when certain response is provided. Questions with an asterisk (*) allow participants to select more than one options. Screen Questions Did you take any clinical lab test (e.g., blood test, urine test, MRI, CT-scan) over the past six months? • Yes • No
Background/Aims: Cancer stem-like cells are the main cause of tumor occurrence, progression, and therapeutic resistance. However, the precise signals required for the maintenance of the stem-like traits of these cells in ovarian cancer remain elusive. We have thus worked to elucidate the functional role of Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ), a gene encoding the 14-3-3ζ protein, in the regulation of multidrug resistance and stem cell-like traits in ovarian cancer. Methods: We detected the YWHAZ levels in human ovarian cancer specimens and cell lines using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blots. MTS assays, soft agar colony formation assays, migration assays, cell cycle analysis, sphere formation assays, and flow cytometry were applied to investigate the functional role of YWHAZ in ovarian cancer. Results: Our data reveals substantially increased YWHAZ expression in both cisplatin- and paclitaxel-resistant ovarian cancer cells. Silencing YWHAZ restored the sensitivity of resistant ovarian cancer cells to cisplatin and paclitaxel. Furthermore, in vitro studies showed that down-regulation of YWHAZ inhibited cell cycle progression, migration, and the expression of stem cell markers. Moreover, tumorigenicity was suppressed in tumor-bearing BALB/c nude mice following YWHAZ knockdown. Additionally, we demonstrated that the expression of YWHAZ was directly down-regulated by miR-30e in resistant ovarian cancer cells. Conclusion: Our results have led to new insights into the essential role of YWHAZ in the regulation of tumourigenesis, stem-like traits, and drug resistance in ovarian cancer, thereby helping to identify a potential target for ovarian cancer therapy.
BACKGROUND: Network meta-analysis (NMA) is a popular tool to compare multiple treatments in medical research. It is frequently implemented via Bayesian methods. The prior choice of between-study heterogeneity is critical in Bayesian NMAs. This study evaluates the impact of different priors for heterogeneity on NMA results. METHODS:We identified all NMAs with binary outcomes published in The BMJ, JAMA, and The Lancet during 2010-2018, and extracted information about their prior choices for heterogeneity. Our primary analyses focused on those with publicly available full data. We re-analyzed the NMAs using 3 commonly-used non-informative priors and empirical informative log-normal priors. We obtained the posterior median odds ratios and 95% credible intervals of all comparisons, assessed the correlation among different priors, and used Bland-Altman plots to evaluate their agreement. The kappa statistic was also used to evaluate the agreement among these priors regarding statistical significance. RESULTS: Among the selected Bayesian NMAs, 52.3% did not specify the prior choice for heterogeneity, and 84.1% did not provide rationales. We re-analyzed 19 NMAs with full data available, involving 894 studies, 173 treatments, and 395,429 patients. The correlation among posterior median (log) odds ratios using different priors were generally very strong for NMAs with over 20 studies. The informative priors produced substantially narrower credible intervals than non-informative priors, especially for NMAs with few studies. Bland-Altman plots and kappa statistics indicated strong overall agreement, but this was not always the case for a specific NMA. CONCLUSIONS: Priors should be routinely reported in Bayesian NMAs. Sensitivity analyses are recommended to examine the impact of priors, especially for NMAs with relatively small sample sizes. Informative priors may produce substantially narrower credible intervals for such NMAs.
Objective: Network meta-analysis (NMA) is increasingly being used to synthesize direct and indirect evidence and help decision makers simultaneously compare multiple treatments. We empirically evaluate the incremental gain in precision achieved by incorporating indirect evidence in NMAs. Study Design and Setting:We performed both network and pairwise meta-analyses on 40 published datasets of multiple-treatment comparisons. Their results were compared using the recently proposed borrowing of strength (BoS) statistic, which quantifies the percentage reduction in the uncertainty of the effect estimate when adding indirect evidence to an NMA. Results:We analyzed 915 possible treatment comparisons, from which 484 (53%) had no direct evidence (BoS = 100%). In 181 comparisons with only one study contributing direct evidence, NMAs resulted in reduced precision (BoS < 0) and no appreciable improvements in precision (0 < BoS < 30%) for 104 (57.5%) and 23 (12.7%) comparisons, respectively. In 250 comparisons with at least two studies contributing direct evidence, NMAs provided increased precision with BoS ≥ 30% for 166 (66.4%) comparisons. Conclusion:Although NMAs have the potential to provide more precise results than those only based on direct evidence, the incremental gain may reliably occur only when at least two head-tohead studies are available and treatments are well connected. Researchers should routinely report and compare the results from both network and pairwise meta-analyses.
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