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1.
J Cell Biochem ; 123(12): 2079-2092, 2022 12.
Article in English | MEDLINE | ID: mdl-36191155

ABSTRACT

Prostate cancer (PCa) represents the second most common cancer in men and affects millions worldwide. Chemotherapy is a common treatment for PCa but the development of resistance is often a problem during therapy. NRF2 (nuclear factor erythroid 2-related factor 2) is one of the major transcription factors regulating antioxidant enzymes and is also involved with drug efflux and detoxification. Cancer cells submitted to chemotherapy often promote NRF2 activation to benefit themselves with the cytoprotective response. Here, we found that DU145 and PC3 PCa cell lines have different responses regarding NRF2 activation, when subjected to arsenite-induced stress, even in the presence of MG132, a proteasome inhibitor. We also observed that only in PC3 cells treated with arsenite, NRF2 was able to translocate to the nucleus. To better understand the role of NRF2 in promoting chemoresistance, we performed CRISPR knockout of NRF2 (NKO) in DU145 and PC3 cells. The effectiveness of the knockout was confirmed through the downregulation of NRF2 targets (p < 0.0001). PC3 NKO cells exhibited higher levels of reactive oxygen species (ROS) compared to wild-type cells (p < 0.0001), while this alteration was not observed in DU145 NKO cells. Despite no modulation in ROS content, a lower IC50 value (p < 0.05) for cisplatin was observed in DU145 NKO cells, suggesting that the knockout sensitized the cells to the treatment. Besides, the treatment of DU145 NKO with cisplatin led cells to apoptosis as observed by the increased levels of PARP1 cleavage (p < 0.05), possibly triggered by increased DNA damage. Reduced levels of KU70 and phospho-CHK2 (p < 0.05) were also detected. The data presented here support that NRF2 is a mediator of oncogenesis and could be a potential target to sensitize PCa cells to chemotherapy, reinforcing the importance of knowing the specific genetic and biochemical characteristics of the cancer cells for a more effective approach against cancer.


Subject(s)
Arsenites , Prostatic Neoplasms , Male , Humans , Cisplatin/pharmacology , Cisplatin/therapeutic use , Reactive Oxygen Species/metabolism , NF-E2-Related Factor 2/genetics , NF-E2-Related Factor 2/metabolism , Arsenites/pharmacology , Arsenites/therapeutic use , Prostatic Neoplasms/drug therapy , Prostatic Neoplasms/genetics , Prostatic Neoplasms/metabolism , Apoptosis , Cell Line, Tumor
2.
Int J Mol Sci ; 22(5)2021 Feb 28.
Article in English | MEDLINE | ID: mdl-33670895

ABSTRACT

Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.


Subject(s)
Breast Neoplasms/genetics , Gene Regulatory Networks , RNA, Long Noncoding/metabolism , Breast Neoplasms/diagnosis , Breast Neoplasms/metabolism , Cell Line, Tumor , Computational Biology , Female , Gene Expression Regulation, Neoplastic , Humans , MCF-7 Cells , Prognosis
3.
Mol Ther Nucleic Acids ; 14: 301-317, 2019 Mar 01.
Article in English | MEDLINE | ID: mdl-30654191

ABSTRACT

KRAS is one of the most frequently mutated proto-oncogenes in pancreatic ductal adenocarcinoma (PDAC) and aberrantly activated in triple-negative breast cancer (TNBC). A profound role of microRNAs (miRNAs) in the pathogenesis of human cancer is being uncovered, including in cancer therapy. Using in silico prediction algorithms, we identified miR-873 as a potential regulator of KRAS, and we investigated its role in PDAC and TNBC. We found that reduced miR-873 expression is associated with shorter patient survival in both cancers. miR-873 expression is significantly repressed in PDAC and TNBC cell lines and inversely correlated with KRAS levels. We demonstrate that miR-873 directly bound to the 3' UTR of KRAS mRNA and suppressed its expression. Notably, restoring miR-873 expression induced apoptosis; recapitulated the effects of KRAS inhibition on cell proliferation, colony formation, and invasion; and suppressed the activity of ERK and PI3K/AKT, while overexpression of KRAS rescued the effects mediated by miR-873. Moreover, in vivo delivery of miR-873 nanoparticles inhibited KRAS expression and tumor growth in PDAC and TNBC tumor models. In conclusion, we provide the first evidence that miR-873 acts as a tumor suppressor by targeting KRAS and that miR-873-based gene therapy may be a therapeutic strategy in PDAC and TNBC.

4.
Clin Genet ; 95(5): 558-568, 2019 05.
Article in English | MEDLINE | ID: mdl-30614523

ABSTRACT

Breast Cancer (BC) is the most commonly diagnosed cancer and is the leading cause of cancer deaths in women. BC is a heterogeneous disease with different clinical and genetic features. According to immunohistochemical markers, BC is subdivided into four main subtypes: luminal A, luminal B, ERBB2 positive and triple negative. Long non-coding RNAs (lncRNAs) are transcripts with more than 200 nucleotides and deregulated lncRNAs are associated with human diseases, including BC. In order to improve BC molecular classification, non-coding RNAs (ncRNAs), including lncRNAs, have been used. In this review, we focus on lncRNAs with differential expression in BC subtypes and how these RNAs may act to contribute to BC heterogeneity. We also emphasize the potential of these lncRNAs as biomarkers.


Subject(s)
Breast Neoplasms/classification , Breast Neoplasms/genetics , Gene Expression Profiling , Gene Expression Regulation, Neoplastic , RNA, Long Noncoding/genetics , Estrogens/metabolism , Female , Humans , RNA, Long Noncoding/metabolism , Receptor, ErbB-2/metabolism
5.
Noncoding RNA ; 4(2)2018 May 11.
Article in English | MEDLINE | ID: mdl-29751665

ABSTRACT

Multifactorial diseases such as cancer, cardiovascular conditions and neurological, immunological and metabolic disorders are a group of diseases caused by the combination of genetic and environmental factors. High-throughput RNA sequencing (RNA-seq) technologies have revealed that less than 2% of the genome corresponds to protein-coding genes, although most of the human genome is transcribed. The other transcripts include a large variety of non-coding RNAs (ncRNAs), and the continuous generation of RNA-seq data shows that ncRNAs are strongly deregulated and may be important players in pathological processes. A specific class of ncRNAs, the long non-coding RNAs (lncRNAs), has been intensively studied in human diseases. For clinical purposes, lncRNAs may have advantages mainly because of their specificity and differential expression patterns, as well as their ideal qualities for diagnosis and therapeutics. Multifactorial diseases are the major cause of death worldwide and many aspects of their development are not fully understood. Recent data about lncRNAs has improved our knowledge and helped risk assessment and prognosis of these pathologies. This review summarizes the involvement of some lncRNAs in the most common multifactorial diseases, with a focus on those with published functional data.

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