ABSTRACT
Androgen-regulated DNA damage response (DDR) is one of the essential mechanisms in prostate cancer (PCa), a hormone-sensitive disease. The heterogeneous nuclear ribonucleoprotein K (hnRNPK)-homology splicing regulatory protein known as far upstream element-binding protein 2 (KHSRP) is an RNA-binding protein that can attach to AU-rich elements in the 3' untranslated region (3'-UTR) of messenger RNAs (mRNAs) to mediate mRNA decay and emerges as a critical regulator in the DDR to preserve genome integrity. Nevertheless, how KHSRP responds to androgen-regulated DDR in PCa development remains unclear. This study found that androgen can significantly induce acetylation of KHSRP, which intrinsically drives tumor growth in xenografted mice. Moreover, enhanced KHSRP acetylation upon androgen stimuli impedes KHSRP-regulated DDR gene expression, as seen by analyzing RNA sequencing (RNA-seq) and Gene Set Enrichment Analysis (GSEA) datasets. Additionally, NAD-dependent protein deacetylase sirtuin-7 (SIRT7) is a promising deacetylase of KHSRP, and androgen stimuli impairs its interaction with KHSRP to sustain the increased KHSRP acetylation level in PCa. We first report the acetylation of KHSRP induced by androgen, which interrupts the KHSRP-regulated mRNA decay of the DDR-related genes to promote the tumorigenesis of PCa. This study provides insight into KHSRP biology and potential therapeutic strategies for PCa treatment, particularly that of castration-resistant PCa.
ABSTRACT
As a relatively rare population of cancer cells existing in the tumor microenvironment, cancer stem cells (CSCs) possess properties of immune privilege to evade the attack of immune system, regulated by the microenvironment of CSCs, the so-called CSCs niche. The bidirectional interaction of CSCs with tumor microenvironment (TME) components favors an immunosuppressive shelter for CSCs' survival and maintenance. Gastrointestinal cancer stem cells (GCSCs) are broadly regarded to be intimately involved in tumor initiation, progression, metastasis and recurrence, with elevated tumor resistance to conventional therapies, which pose a major hindrance to the clinical efficacy for treated patients with gastrointestinal malignancies. Thus, a multitude of efforts have been made to combat and eradicate GCSCs within the tumor mass. Among diverse methods of targeting CSCs in gastrointestinal malignancies, immunotherapy represents a promising strategy. And the better understanding of GCSCs immunomodulation and immunoresistance mechanisms is beneficial to guide and design novel GCSCs-specific immunotherapies with enhanced immune response and clinical efficacy. In this review, we have gathered available and updated information to present an overview of the immunoevasion features harbored by cancer stem cells, and we focus on the description of immune escape strategies utilized by CSCs and microenvironmental regulations underlying CSCs immuno-suppression in the context of gastrointestinal malignancies. Importantly, this review offers deep insights into recent advances of CSC-targeting immunotherapeutic approaches in gastrointestinal cancers.
ABSTRACT
As a popular machine learning method, neural networks can be used to solve many complex tasks. Their strong generalization ability comes from the representation ability of the basic neuron models. The most popular neuron model is the McCulloch-Pitts (MP) neuron, which uses a simple transformation to process the input signal. A common trend is to use the MP neuron to design various neural networks. However, the optimization of the neuron structure is rarely considered. Inspired by the elastic collision model in physics, we propose a new neuron model that can represent more complex distributions. We term it the Inter-layer Collision (IC) neuron which divides the input space into multiple subspaces to represent different linear transformations. Through this operation, the IC neuron enhances the non-linear representation ability and emphasizes useful input features for a given task. We build the IC networks by integrating the IC neurons into the fully-connected, the convolutional, and the recurrent structures. The IC networks outperform the traditional neural networks in a wide range of tasks. Besides, we combine the IC neuron with deep learning models and show the superiority of the IC neuron. Our research proves that the IC neuron can be an effective basic unit to build network structures and make the network performance better.
