The discovery of microRNAs (miRNAs) has fundamentally transformed our understanding of gene regulation. The competing endogenous RNA (ceRNA) hypothesis postulates that messenger RNAs and other RNA transcripts, such as long non-coding RNAs and pseudogenes, can act as natural miRNA sponges. These RNAs influence each other’s expression levels by competing for the same pool of miRNAs through miRNA response elements on their target transcripts, thereby modulating gene expression and protein activity. In recent years, these ceRNA regulatory networks have gained considerable attention in cancer research. Several studies have identified cancer-specific ceRNA networks. Nevertheless, prior bioinformatic analyses have focused on long-non-coding RNA-associated ceRNA networks. Here, we identify an extended ceRNA network (including both long non-coding RNAs and pseudogenes) shared across a group of five hormone-dependent (HD) cancers, i.e., prostate, breast, colon, rectal, and endometrial cancers, using data from The Cancer Genome Atlas (TCGA). We performed a functional enrichment analysis for differentially expressed genes in the shared ceRNA network of HD cancers, followed by a survival analysis to determine their prognostic ability. We identified two long non-coding RNAs, nine genes, and seventy-four miRNAs in the shared ceRNA network across five HD cancers. Among them, two genes and forty-one miRNAs were associated with at least one HD cancer survival. This study is the first to investigate pseudogene-associated ceRNAs across a group of related cancers and highlights the value of this approach to understanding the shared molecular pathogenesis in a group of related diseases.

Objective: There is increasing use of open-source artificial pancreas systems (APS) in the management of Type 1 diabetes. Our aim was to assess the safety and efficacy of the automated insulin delivery system AndroidAPS (AAPS), compared with stand-alone pump therapy in people with type 1 diabetes. The primary outcome was the difference in the percentage of time in range (TIR, 70-180 mg/dL). Secondary aims included mean sensor glucose value and percent continuous glucose monitor (CGM) time below range (TBR, <70 mg/dL). Research Design and Methods: This open-label single-center randomized crossover study (ANZCTR, Australian New Zealand clinical trial registry, ANZCTR-ACTRN12620001191987) comprised 20 participants with type 1 diabetes on established pump therapy, assigned to either stand-alone insulin pump therapy or the open-source AAPS hybrid closed-loop system for four weeks, with crossover to the alternate arm for the following four weeks. The CGM outcome parameters were measured by seven-day CGM at baseline and the final week of each four-week study arm. Results: Twenty participants were recruited (60% women), aged 45.8 ± 15.9 years, with mean diabetes duration of 23.9 ± 13.2 years, baseline glycated hemoglobin (HbA1c) 7.5% ± 0.5% (58 ± 6 mmol/mol) and mean TIR 62.3% ± 12.9%. The change in TIR from baseline for AAPS compared with stand-alone pump therapy was 18.6% (11.4-25.9), ( P < .001), TIR 76.6% ± 11.7%, 58.0% ± 15.6%, for AAPS and stand-alone pump, respectively. Time glucose <54 mg/dL was not increased (mean = −2.0%, P = .191). No serious adverse events or episodes of severe hypoglycemia were recorded. Conclusions: This clinical trial of the open-source AAPS hybrid closed-loop system performed in an at-home setting demonstrated comparable safety to stand-alone pump therapy. The glycemic outcomes of AAPS were superior with improved TIR, and there was no significant difference in TBR compared with stand-alone pump therapy.

BackgroundHormone-dependent cancers (HDC) are among the leading causes of death worldwide among both men and women. Some of the established risk factors of HDC include unhealthy lifestyles, environmental factors, and genetic influences. Numerous studies have been conducted to understand gene–cancer associations. Transcriptome-wide association studies (TWAS) integrate data from genome-wide association studies (GWAS) and gene expression (expression quantitative trait loci – eQTL) to yield meaningful information on biological pathways associated with complex traits/diseases. Recently, TWAS have enabled the identification of novel associations between HDC risk and protein-coding genes.MethodsIn the present study, we performed a TWAS analysis using the summary data-based Mendelian randomization (SMR)–heterogeneity in dependent instruments (HEIDI) method to identify microRNAs (miRNAs), a group of non-coding RNAs (ncRNAs) associated with HDC risk. We obtained eQTL and GWAS summary statistics from the ncRNA-eQTL database and the National Human Genome Research Institute–European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog.ResultsWe identified 13 TWAS-significant miRNAs at cis regions (±1 Mb) associated with HDC risk (two, five, one, two, and three miRNAs for prostate, breast, ovarian, colorectal, and endometrial cancers, respectively). Among them, eight novel miRNAs were recognized in HDC risk. Eight protein-coding genes targeted by TWAS-identified miRNAs (SIRT1, SOX4, RUNX2, FOXA1, ABL2, SUB1, HNRNPH1, and WAC) are associated with HDC functions and signaling pathways.ConclusionOverall, identifying risk-associated miRNAs across a group of related cancers may help to understand cancer biology and provide novel insights into cancer genetic mechanisms. This customized approach can be applied to identify significant miRNAs in any trait/disease of interest.