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Journal ArticleDOI

PPAR-gamma induced AKT3 expression increases levels of mitochondrial biogenesis driving prostate cancer.

02 Mar 2021-Oncogene (Nature Publishing Group)-Vol. 40, Iss: 13, pp 2355-2366
TL;DR: In this article, a functional effect of PPARG on AKT serine/threonine kinase 3 (AKT3) was identified, which resulted in a more aggressive disease phenotype.
Abstract: Peroxisome Proliferator-Activated Receptor Gamma (PPARG) is one of the three members of the PPAR family of transcription factors. Besides its roles in adipocyte differentiation and lipid metabolism, we recently demonstrated an association between PPARG and metastasis in prostate cancer. In this study a functional effect of PPARG on AKT serine/threonine kinase 3 (AKT3), which ultimately results in a more aggressive disease phenotype was identified. AKT3 has previously been shown to regulate PPARG co-activator 1 alpha (PGC1α) localisation and function through its action on chromosome maintenance region 1 (CRM1). AKT3 promotes PGC1α localisation to the nucleus through its inhibitory effects on CRM1, a known nuclear export protein. Collectively our results demonstrate how PPARG over-expression drives an increase in AKT3 levels, which in turn has the downstream effect of increasing PGC1α localisation within the nucleus, driving mitochondrial biogenesis. Furthermore, this increase in mitochondrial mass provides higher energetic output in the form of elevated ATP levels which may fuel the progression of the tumour cell through epithelial to mesenchymal transition (EMT) and ultimately metastasis.

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Citations
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Journal ArticleDOI
TL;DR: Current knowledge of the import and export of the components of three large gene expression machines - the core replisome, RNA polymerase II and the ribosome - is described, pointing out the questions that persist about how such large macromolecular complexes are trafficked to serve their function in a designated subcellular location.

56 citations

Journal ArticleDOI
TL;DR: In this article, the authors highlight the recent progress of Akt signaling pathway, review the up-to-date data from clinical studies, and discuss the potential biomarkers that may help personalized treatment of cancer with Akt inhibitors, and also discuss how Akt may confer the vulnerability of cancer cells to some kinds of anticancer agents.
Abstract: Biomarkers-guided precision therapeutics has revolutionized the clinical development and administration of molecular-targeted anticancer agents. Tailored precision cancer therapy exhibits better response rate compared to unselective treatment. Protein kinases have critical roles in cell signaling, metabolism, proliferation, survival and migration. Aberrant activation of protein kinases is critical for tumor growth and progression. Hence, protein kinases are key targets for molecular targeted cancer therapy. The serine/threonine kinase Akt is frequently activated in various types of cancer. Activation of Akt promotes tumor progression and drug resistance. Since the first Akt inhibitor was reported in 2000, many Akt inhibitors have been developed and evaluated in either early or late stage of clinical trials, which take advantage of liquid biopsy and genomic or molecular profiling to realize personalized cancer therapy. Two inhibitors, capivasertib and ipatasertib, are being tested in phase III clinical trials for cancer therapy. Here, we highlight recent progress of Akt signaling pathway, review the up-to-date data from clinical studies of Akt inhibitors and discuss the potential biomarkers that may help personalized treatment of cancer with Akt inhibitors. In addition, we also discuss how Akt may confer the vulnerability of cancer cells to some kinds of anticancer agents.

49 citations

Journal ArticleDOI
TL;DR: In this article , the authors proposed metabolic interventions aiming to reduce lipid availability to cancer cells or to exacerbate their metabolic vulnerabilities provide promising therapeutic opportunities to prevent cancer progression and treat metastasis.

32 citations

Journal ArticleDOI
09 Jul 2021-Cancers
TL;DR: In this article, the authors analyzed current literatures on distinct functions of Akt isoforms in breast cancer and proposed an approach to target the Akt signaling pathway for cancer therapy, which is critical to effectively target this pathway.
Abstract: Akt, also known as protein kinase B (PKB), belongs to the AGC family of protein kinases. It acts downstream of the phosphatidylinositol 3-kinase (PI3K) and regulates diverse cellular processes, including cell proliferation, cell survival, metabolism, tumor growth and metastasis. The PI3K/Akt signaling pathway is frequently deregulated in breast cancer and plays an important role in the development and progression of breast cancer. There are three closely related members in the Akt family, namely Akt1(PKBα), Akt2(PKBβ) and Akt3(PKBγ). Although Akt isoforms share similar structures, they exhibit redundant, distinct as well as opposite functions. While the Akt signaling pathway is an important target for cancer therapy, an understanding of the isoform-specific function of Akt is critical to effectively target this pathway. However, our perception regarding how Akt isoforms contribute to the genesis and progression of breast cancer changes as we gain new knowledge. The purpose of this review article is to analyze current literatures on distinct functions of Akt isoforms in breast cancer.

23 citations

Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis, and the prognostic value and molecular mechanism of these hub genes using Kaplan-Meier (KM) curve and Gene Set Enrichment Analysis (GSEA).
Abstract: Breast cancer (BC) is a malignancy with high incidence among women in the world. This study aims to screen key genes and potential prognostic biomarkers for BC using bioinformatics analysis. Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and then the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed to investigate the biological function of DEGs. Additionally, hub genes were screened by constructing a protein-protein interaction (PPI) network. Then, we explored the prognostic value and molecular mechanism of these hub genes using Kaplan-Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). As a result, 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. The DEGs were mainly related to cell cycles and cell proliferation by GO and KEGG pathway analysis. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree were identified initially, among which, 11 hub genes were significantly correlated with the prognosis of BC patients based on the Kaplan-Meier-plotter. GSEA reviewed that these hub genes correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. In conclusion, this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

20 citations

References
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Journal ArticleDOI
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .

47,038 citations

Journal ArticleDOI
TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
Abstract: As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

37,898 citations

Journal ArticleDOI
TL;DR: TopHat2 is described, which incorporates many significant enhancements to TopHat, and combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes.
Abstract: TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.

11,380 citations

Journal ArticleDOI
TL;DR: The overall cancer death rate decreased from 215.1 (per 100,000 population) in 1991 to 168.7 in 2011, a total relative decline of 22%.
Abstract: Each year the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the National Cancer Institute (Surveillance, Epidemiology, and End Results [SEER] Program), the Centers for Disease Control and Prevention (National Program of Cancer Registries), and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. A total of 1,658,370 new cancer cases and 589,430 cancer deaths are projected to occur in the United States in 2015. During the most recent 5 years for which there are data (2007-2011), delay-adjusted cancer incidence rates (13 oldest SEER registries) declined by 1.8% per year in men and were stable in women, while cancer death rates nationwide decreased by 1.8% per year in men and by 1.4% per year in women. The overall cancer death rate decreased from 215.1 (per 100,000 population) in 1991 to 168.7 in 2011, a total relative decline of 22%. However, the magnitude of the decline varied by state, and was generally lowest in the South (∼15%) and highest in the Northeast (≥20%). For example, there were declines of 25% to 30% in Maryland, New Jersey, Massachusetts, New York, and Delaware, which collectively averted 29,000 cancer deaths in 2011 as a result of this progress. Further gains can be accelerated by applying existing cancer control knowledge across all segments of the population.

10,989 citations

Journal ArticleDOI
29 Jun 2007-Cell
TL;DR: Those Akt substrates that are most likely to contribute to the diverse cellular roles of Akt, which include cell survival, growth, proliferation, angiogenesis, metabolism, and migration are discussed.

5,505 citations