Statin Use Significantly Improves Overall Survival in High-Grade Endometrial Cancer.

OBJECTIVE: Preclinical data and recent epidemiological studies suggest that statins have antiproliferative and antimetastatic effects in various cancer cells, and reduce cancer mortality and recurrence. We study the effect of statin use on survival outcomes and recurrence rates in patients with endometrial cancer with high-risk histology. MATERIALS AND METHODS: All patients receiving definitive therapy for high-risk endometrial cancer from 1995 to 2014 were retrospectively reviewed. Health characteristics at baseline were collected, and statin use was determined from medical records. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method and compared using the log-rank test. Cox proportional hazards regression models were used for univariate and multivariate analysis to determine independent factors associated with OS and PFS. RESULTS: A total of 199 patients were included in the study, of which 76 were hyperlipidemic and 50 used statins. The median follow-up time was 31 months from time of diagnosis. Hyperlipidemic patients who used statins had improved OS compared with hyperlipidemic patients not using statins (hazard ratio, 0.42; 95% confidence interval, 0.20-0.87; P = 0.02). Statin use was also associated with improved PFS (hazard ratio, 0.47; 95% confidence interval, 0.23-0.95; P = 0.04) on multivariate analysis. Hyperlipidemic patients who used statins had borderline improved freedom from local failure compared with hyperlipidemic cases not using statins (P = 0.08, log-rank test). Statin use was not found to be associated with improved cancer-specific mortality. CONCLUSIONS: Statin use is independently associated with significant improvements in PFS for the overall group and PFS and OS in the hyperlipidemic group.

Non-laser capture microscopy approach for the microdissection of discrete mouse brain regions for total RNA isolation and downstream next-generation sequencing and gene expression profiling.

As technological platforms, approaches such as next-generation sequencing, microarray, and qRT-PCR have great promise for expanding our understanding of the breadth of molecular regulation. Newer approaches such as high-resolution RNA sequencing (RNA-Seq)(1) provides new and expansive information about tissue- or state-specific expression such as relative transcript levels, alternative splicing, and micro RNAs(2-4). Prospects for employing the RNA-Seq method in comparative whole transcriptome profiling(5) within discrete tissues or between phenotypically distinct groups of individuals affords new avenues for elucidating molecular mechanisms involved in both normal and abnormal physiological states. Recently, whole transcriptome profiling has been performed on human brain tissue, identifying gene expression differences associated with disease progression(6). However, the use of next-generation sequencing has yet to be more widely integrated into mammalian studies. Gene expression studies in mouse models have reported distinct profiles within various brain nuclei using laser capture microscopy (LCM) for sample excision(7,8). While LCM affords sample collection with single-cell and discrete brain region precision, the relatively low total RNA yields from the LCM approach can be prohibitive to RNA-Seq and other profiling approaches in mouse brain tissues and may require sub-optimal sample amplification steps. Here, a protocol is presented for microdissection and total RNA extraction from discrete mouse brain regions. Set-diameter tissue corers are used to isolate 13 tissues from 750-µm serial coronal sections of an individual mouse brain. Tissue micropunch samples are immediately frozen and archived. Total RNA is obtained from the samples using magnetic bead-enabled total RNA isolation technology. Resulting RNA samples have adequate yield and quality for use in downstream expression profiling. This microdissection strategy provides a viable option to existing sample collection strategies for obtaining total RNA from discrete brain regions, opening possibilities for new gene expression discoveries.