Oncogene-induced senescence is an important tumour-suppressing mechanism to prevent both premalignant transformation and cancer progression. Overcoming this process is a critical step in early cancer development. The druggable orphan nuclear receptor TLX (NR2E1) is characterized as an important regulator of neural stem cells and is also implicated in the development of some brain tumours. However, its exact functional roles in cancer growth regulation still remain unclear. Here we report that TLX can act as a promoter of tumourigenesis in prostate cancer by suppressing oncogene-induced senescence. We determined that TLX exhibited an increased expression in high-grade prostate cancer tissues and many prostate cancer cell lines. Functional studies revealed that TLX could perform an oncogenic function in prostate cancer cells, as its knockdown triggered cellular senescence and cell growth arrest in vitro and in vivo, whereas its over-expression promoted the malignant growth of prostate cancer cells. Furthermore, enhancement of TLX activity, by either ectopic expression or ligand stimulation, could potently prevent doxorubicin-induced senescence in prostate cancer cells and also allow prostatic epithelial cells to escape oncogene-induced senescence induced either by activated oncogene H-Ras(G12V) or knockdown of tumour suppressor PTEN, via a mechanism of direct but differential transcriptional regulation of two senescence-associated genes, repression of CDKN1A and transactivation of SIRT1. Together, our present study shows, for the first time, that TLX may play an important role in prostate carcinogenesis through its suppression of oncogene-induced senescence, and also suggests that targeting the senescence-regulatory TLX is of potential therapeutic significance in prostate cancer.
Targeting of steroidogenic enzymes (e.g., abiraterone acetate targeting CYP17A1) has been developed as a novel therapeutic strategy against metastatic castration-resistant prostate cancer (CRPC). However, resistance to steroidal inhibitors inevitably develops in patients, the mechanisms of which remain largely unknown. Liver receptor homolog-1 (LRH-1, ) is a nuclear receptor, originally characterized as an important regulator of some liver-specific metabolic genes. Here, we report that LRH-1, which exhibited an increased expression pattern in high-grade prostate cancer and CRPC xenograft models, functions to promote androgen biosynthesis via its direct transactivation of several key steroidogenic enzyme genes, elevating intratumoral androgen levels and reactivating AR signaling in CRPC xenografts as well as abiraterone-treated CRPC tumors. Pharmacologic inhibition of LRH-1 activity attenuated LRH-1-mediated androgen deprivation and anti-androgen resistance of prostate cancer cells. Our findings not only demonstrate the significant role of LRH-1 in the promotion of intratumoral androgen biosynthesis in CRPC via its direct transcriptional control of steroidogenesis, but also suggest targeting LRH-1 could be a potential therapeutic strategy for CRPC management. These findings not only demonstrate the significant role of the nuclear receptor LRH-1 in the promotion of intratumoral androgen biosynthesis in CRPC via its direct transcriptional control of steroidogenesis, but also suggest targeting LRH-1 could be a potential therapeutic strategy for CRPC management. .
We study the multi-channel sparse blind deconvolution (MCS-BD) problem, whose task is to simultaneously recover a kernel a and multiple sparse inputs txiu p i"1 from their circulant convolution yi " a f xi (i " 1,¨¨¨, p). We formulate the task as a nonconvex optimization problem over the sphere. Under mild statistical assumptions of the data, we prove that the vanilla Riemannian gradient descent (RGD) method, with random initializations, provably recovers both the kernel a and the signals txiu p i"1 up to a signed shift ambiguity. In comparison with state-of-the-art results, our work shows significant improvements in terms of sample complexity and computational efficiency. Our theoretical results are corroborated by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods on both synthetic and real datasets. 1 Here, (i) BGpθq and BRpθq denote Bernoulli-Gaussian and Bernoulli-Rademacher distribution, respectively; (ii) θ P r0, 1s is the Bernoulli parameter controlling the sparsity level of x i ; (iii) ε denotes the recovery precision of global solution a‹, i.e., min ℓ }a´s ℓ ra‹s} ď ε; (iv) r O and r Ω hides logpnq, θ and other factors. 2 Recently, similar loss has been considered for short and sparse deconvolution [ZKW18] and complete dictionary learning [ZYL`19]. 3 As the tail of BGpθq distribution is heavier than that of BRpθq, their sample complexity would be even worse if BGpθq model was considered. 4 We say x obeys a Bernoulli-Rademacher distribution when x " b d r where d denotes point-wise product, b follows i.i.d. Bernoulli distribution and r follows i.i.d. Rademacher distribution.Contributions of this paper. In this work, we introduce an efficient optimization method for solving MCS-BD. We consider a natural nonconvex formulation based on a smooth relaxation of ℓ 1 -loss. Under mild assumptions of the data, we prove the following result.With random initializations, a vanilla RGD efficiently finds an approximate solution, which can be refined by a subgradient method that converges exactly to the target solution in a linear rate.We summarize our main result in Table 1. By comparison 5 with [LB18], our approach demonstrates substantial improvements for solving MCS-BD in terms of both sample and time complexity. Moreover, our experimental results imply that our analysis is still far from tight -the phase transitions suggest that p ě Ωppoly logpnqq samples might be sufficient for exact recovery, which is favorable for applications (as real data in form of images can have millions of pixels, resulting in huge dimension n). Our analysis is inspired by recent results on orthogonal dictionary learning [GBW18, BJS18], but much of our theoretical analysis is tailored for MCS-BD with a few extra new ingredients. Our work is the first result provably showing that vanilla gradient descent type methods with random initialization solve MCS-BD efficiently. Moreover, our ideas could potentially lead to new algorithmic guarantees for other nonconvex problems such a...
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