Browsing by Subject "Non-coding RNAs"

Now showing 1 - 4 of 4
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    Epigenetic Crosstalk between the Tumor Microenvironment and Ovarian Cancer Cells: A Therapeutic Road Less Traveled
    (MDPI, 2018-08-30) Klymenko, Yuliya; Nephew, Kenneth P.; Biochemistry and Molecular Biology, School of Medicine
    Metastatic dissemination of epithelial ovarian cancer (EOC) predominantly occurs through direct cell shedding from the primary tumor into the intra-abdominal cavity that is filled with malignant ascitic effusions. Facilitated by the fluid flow, cells distribute throughout the cavity, broadly seed and invade through peritoneal lining, and resume secondary tumor growth in abdominal and pelvic organs. At all steps of this unique metastatic process, cancer cells exist within a multidimensional tumor microenvironment consisting of intraperitoneally residing cancer-reprogramed fibroblasts, adipose, immune, mesenchymal stem, mesothelial, and vascular cells that exert miscellaneous bioactive molecules into malignant ascites and contribute to EOC progression and metastasis via distinct molecular mechanisms and epigenetic dysregulation. This review outlines basic epigenetic mechanisms, including DNA methylation, histone modifications, chromatin remodeling, and non-coding RNA regulators, and summarizes current knowledge on reciprocal interactions between each participant of the EOC cellular milieu and tumor cells in the context of aberrant epigenetic crosstalk. Promising research directions and potential therapeutic strategies that may encompass epigenetic tailoring as a component of complex EOC treatment are discussed.
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    Erratum: Skeletal muscle-specific overexpression of miR-486 limits mammary tumor-induced skeletal muscle functional limitations
    (Elsevier, 2022-08-20) Wang, Ruizhong; Kumar, Brijesh; Doud, Emma H.; Mosley, Amber L.; Alexander, Matthew S.; Kunkel, Louis M.; Nakshatri, Harikrishna; Surgery, School of Medicine
    [This corrects the article DOI: 10.1016/j.omtn.2022.03.009.].
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    MiR-150 blunts cardiac dysfunction in mice with cardiomyocyte loss of β1-adrenergic receptor/β-arrestin signaling and controls a unique transcriptome
    (Springer Nature, 2022-12-30) Moukette, Bruno; Kawaguchi, Satoshi; Sepulveda, Marisa N.; Hayasaka, Taiki; Aonuma, Tatsuya; Liangpunsakul, Suthat; Yang, Lei; Dharmakumar, Rohan; Conway, Simon J.; Kim, Il-man; Anatomy, Cell Biology and Physiology, School of Medicine
    The β1-adrenergic receptor (β1AR) is found primarily in hearts (mainly in cardiomyocytes [CMs]) and β-arrestin-mediated β1AR signaling elicits cardioprotection through CM survival. We showed that microRNA-150 (miR-150) is upregulated by β-arrestin-mediated β1AR signaling and that CM miR-150 inhibits maladaptive remodeling post-myocardial infarction. Here, we investigate whether miR-150 rescues cardiac dysfunction in mice bearing CM-specific abrogation of β-arrestin-mediated β1AR signaling. Using CM-specific transgenic (TG) mice expressing a mutant β1AR (G protein-coupled receptor kinase [GRK]–β1AR that exhibits impairment in β-arrestin-mediated β1AR signaling), we first generate a novel double TG mouse line overexpressing miR-150. We demonstrate that miR-150 is sufficient to improve cardiac dysfunction in CM-specific GRK–β1AR TG mice following chronic catecholamine stimulation. Our genome-wide circular RNA, long noncoding RNA (lncRNA), and mRNA profiling analyses unveil a subset of cardiac ncRNAs and genes as heretofore unrecognized mechanisms for beneficial actions of β1AR/β-arrestin signaling or miR-150. We further show that lncRNA Gm41664 and GDAP1L1 are direct novel upstream and downstream regulators of miR-150. Lastly, CM protective actions of miR-150 are attributed to repressing pro-apoptotic GDAP1L1 and are mitigated by pro-apoptotic Gm41664. Our findings support the idea that miR-150 contributes significantly to β1AR/β-arrestin-mediated cardioprotection by regulating unique ncRNA and gene signatures in CMs.
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    Pseudogene-gene functional networks are prognostic of patient survival in breast cancer
    (BMC, 2020) Smerekanych, Sasha; Johnson, Travis S.; Huang, Kun; Zhang, Yan; Medicine, School of Medicine
    Background: Given the vast range of molecular mechanisms giving rise to breast cancer, it is unlikely universal cures exist. However, by providing a more precise prognosis for breast cancer patients through integrative models, treatments can become more individualized, resulting in more successful outcomes. Specifically, we combine gene expression, pseudogene expression, miRNA expression, clinical factors, and pseudogene-gene functional networks to generate these models for breast cancer prognostics. Establishing a LASSO-generated molecular gene signature revealed that the increased expression of genes STXBP5, GALP and LOC387646 indicate a poor prognosis for a breast cancer patient. We also found that increased CTSLP8 and RPS10P20 and decreased HLA-K pseudogene expression indicate poor prognosis for a patient. Perhaps most importantly we identified a pseudogene-gene interaction, GPS2-GPS2P1 (improved prognosis) that is prognostic where neither the gene nor pseudogene alone is prognostic of survival. Besides, miR-3923 was predicted to target GPS2 using miRanda, PicTar, and TargetScan, which imply modules of gene-pseudogene-miRNAs that are potentially functionally related to patient survival. Results: In our LASSO-based model, we take into account features including pseudogenes, genes and candidate pseudogene-gene interactions. Key biomarkers were identified from the features. The identification of key biomarkers in combination with significant clinical factors (such as stage and radiation therapy status) should be considered as well, enabling a specific prognostic prediction and future treatment plan for an individual patient. Here we used our PseudoFuN web application to identify the candidate pseudogene-gene interactions as candidate features in our integrative models. We further identified potential miRNAs targeting those features in our models using PseudoFuN as well. From this study, we present an interpretable survival model based on LASSO and decision trees, we also provide a novel feature set which includes pseudogene-gene interaction terms that have been ignored by previous prognostic models. We find that some interaction terms for pseudogenes and genes are significantly prognostic of survival. These interactions are cross-over interactions, where the impact of the gene expression on survival changes with pseudogene expression and vice versa. These may imply more complicated regulation mechanisms than previously understood. Conclusions: We recommend these novel feature sets be considered when training other types of prognostic models as well, which may provide more comprehensive insights into personalized treatment decisions.