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- Thomas, HS, et al.
(author)
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- 2019
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swepub:Mat__t
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- Campbell, PJ, et al.
(author)
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Pan-cancer analysis of whole genomes
- 2020
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In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 578:7793, s. 82-
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Journal article (peer-reviewed)abstract
- Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1–3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10–18.
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- Glasbey, JC, et al.
(author)
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- 2021
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swepub:Mat__t
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- Ruilope, LM, et al.
(author)
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Design and Baseline Characteristics of the Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease Trial
- 2019
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In: American journal of nephrology. - : S. Karger AG. - 1421-9670 .- 0250-8095. ; 50:5, s. 345-356
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Journal article (peer-reviewed)abstract
- <b><i>Background:</i></b> Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. <b><i>Patients and</i></b> <b><i>Methods:</i></b> The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate ≥25 mL/min/1.73 m<sup>2</sup> and albuminuria (urinary albumin-to-creatinine ratio ≥30 to ≤5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level α = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. <b><i>Conclusions:</i></b> FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049.
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- Du, QQ, et al.
(author)
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FASN promotes lymph node metastasis in cervical cancer via cholesterol reprogramming and lymphangiogenesis
- 2022
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In: Cell death & disease. - : Springer Science and Business Media LLC. - 2041-4889. ; 13:5, s. 488-
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Journal article (peer-reviewed)abstract
- Cervical cancer (CC) patients with lymph node metastasis (LNM) have a poor prognosis. Clarification of the detailed mechanisms underlying LNM may provide potential clinical therapeutic targets for CC patients with LNM. However, the molecular mechanism of LNM in CC is unclear. In the present study, we demonstrated that fatty acid synthase (FASN), one of the key enzymes in lipid metabolism, had upregulated expression in the CC samples and was correlated with LNM. Moreover, multivariate Cox proportional hazards analysis identified FASN as an independent prognostic factor of CC patients. Furthermore, gain-of-function and loss-of-function approaches showed that FASN promoted CC cell migration, invasion, and lymphangiogenesis. Mechanistically, on the one hand, FASN could regulate cholesterol reprogramming and then activate the lipid raft-related c-Src/AKT/FAK signaling pathway, leading to enhanced cell migration and invasion. On the other hand, FASN induced lymphangiogenesis by secreting PDGF-AA/IGFBP3. More importantly, knockdown of FASN with FASN shRNA or the inhibitors C75 and Cerulenin dramatically diminished LNM in vivo, suggesting that FASN plays an essential role in LNM of CC and the clinical application potential of FASN inhibitors. Taken together, our findings uncover a novel molecular mechanism in LNM of CC and identify FASN as a novel prognostic factor and potential therapeutic target for LNM in CC.
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