The tumor suppressor gene hypermethylated in cancer 1 is transcriptionally regulated by E2F1

The Hypermethylated in Cancer 1 (HIC1) gene encodes a zinc finger transcriptional repressor that cooperates with p53 to suppress cancer development. We and others recently showed that HIC1 is a transcriptional target of p53. To identify additional transcriptional regulators of HIC1, we screened a set of transcription factors for regulation of a human HIC1 promoter reporter. We found that E2F1 strongly activates the full-length HIC1 promoter reporter. Promoter deletions and mutations identified two E2F responsive elements in the HIC1 core promoter region. Moreover, in vivo binding of E2F1 to the HIC1 promoter was shown by chromatin immunoprecipitation assays in human TIG3 fibroblasts expressing tamoxifen-activated E2F1. In agreement, activation of E2F1 in TIG3-E2F1 cells markedly increased HIC1 expression. Interestingly, expression of E2F1 in the p53(-/-) hepatocellular carcinoma cell line Hep3B led to an increase of endogenous HIC1 mRNA, although bisulfite genomic sequencing of the HIC1 promoter revealed that the region bearing the two E2F1 binding sites is hypermethylated. In addition, endogenous E2F1 induced by etoposide treatment bound to the HIC1 promoter. Moreover, inhibition of E2F1 strongly reduced the expression of etoposide-induced HIC1. In conclusion, we identified HIC1 as novel E2F1 transcriptional target in DNA damage responses.

Patient-reported outcomes in meta-analyses - part 2: methods for improving interpretability for decision-makers

Systematic reviews and meta-analyses of randomized trials that include patient-reported outcomes (PROs) often provide crucial information for patients, clinicians and policy-makers facing challenging health care decisions. Based on emerging methods, guidance on improving the interpretability of meta-analysis of patient-reported outcomes, typically continuous in nature, is likely to enhance decision-making. The objective of this paper is to summarize approaches to enhancing the interpretability of pooled estimates of PROs in meta-analyses. When differences in PROs between groups are statistically significant, decision-makers must be able to interpret the magnitude of effect. This is challenging when, as is often the case, clinical trial investigators use different measurement instruments for the same construct within and between individual randomized trials. For such cases, in addition to pooling results as a standardized mean difference, we recommend that systematic review authors use other methods to present results such as relative (relative risk, odds ratio) or absolute (risk difference) dichotomized treatment effects, complimented by presentation in either: natural units (e.g. overall depression reduced by 2.4 points when measured on a 50-point Hamilton Rating Scale for Depression); minimal important difference units (e.g. where 1.0 unit represents the smallest difference in depression that patients, on average, perceive as important the depression score was 0.38 (95%CI 0.30 to 0.47) units less than the control group); or a ratio of means (e.g. where the mean in the treatment group is divided by the mean in the control group, the ratio of means is 1.27, representing a 27%relative reduction in the mean depression score).