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291 results on '"Zhang, Liangyu"'

1. Federated Control in Markov Decision Processes

Author

Jin, Hao, Peng, Yang, Zhang, Liangyu, and Zhang, Zhihua

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

We study problems of federated control in Markov Decision Processes. To solve an MDP with large state space, multiple learning agents are introduced to collaboratively learn its optimal policy without communication of locally collected experience. In our settings, these agents have limited capabilities, which means they are restricted within different regions of the overall state space during the training process. In face of the difference among restricted regions, we firstly introduce concepts of leakage probabilities to understand how such heterogeneity affects the learning process, and then propose a novel communication protocol that we call Federated-Q protocol (FedQ), which periodically aggregates agents' knowledge of their restricted regions and accordingly modifies their learning problems for further training. In terms of theoretical analysis, we justify the correctness of FedQ as a communication protocol, then give a general result on sample complexity of derived algorithms FedQ-X with the RL oracle , and finally conduct a thorough study on the sample complexity of FedQ-SynQ. Specifically, FedQ-X has been shown to enjoy linear speedup in terms of sample complexity when workload is uniformly distributed among agents. Moreover, we carry out experiments in various environments to justify the efficiency of our methods.

Published
2024

2. Federated Reinforcement Learning with Constraint Heterogeneity

Author

Jin, Hao, Zhang, Liangyu, and Zhang, Zhihua

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We study a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. In our setting, we aim to solve a reinforcement learning problem with multiple constraints while $N$ training agents are located in $N$ different environments with limited access to the constraint signals and they are expected to collaboratively learn a policy satisfying all constraint signals. Such learning problems are prevalent in scenarios of Large Language Model (LLM) fine-tuning and healthcare applications. To solve the problem, we propose federated primal-dual policy optimization methods based on traditional policy gradient methods. Specifically, we introduce $N$ local Lagrange functions for agents to perform local policy updates, and these agents are then scheduled to periodically communicate on their local policies. Taking natural policy gradient (NPG) and proximal policy optimization (PPO) as policy optimization methods, we mainly focus on two instances of our algorithms, ie, {FedNPG} and {FedPPO}. We show that FedNPG achieves global convergence with an $\tilde{O}(1/\sqrt{T})$ rate, and FedPPO efficiently solves complicated learning tasks with the use of deep neural networks.

Published
2024

3. Statistical Efficiency of Distributional Temporal Difference Learning and Freedman's Inequality in Hilbert Spaces

Author

Peng, Yang, Zhang, Liangyu, and Zhang, Zhihua

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Distributional reinforcement learning (DRL) has achieved empirical success in various domains. One core task in DRL is distributional policy evaluation, which involves estimating the return distribution $\eta^\pi$ for a given policy $\pi$. Distributional temporal difference learning has been accordingly proposed, which extends the classic temporal difference learning (TD) in RL. In this paper, we focus on the non-asymptotic statistical rates of distributional TD. To facilitate theoretical analysis, we propose non-parametric distributional TD (NTD). For a $\gamma$-discounted infinite-horizon tabular Markov decision process, we show that for NTD with a generative model, we need $\tilde{O}(\varepsilon^{-2}\mu_{\min}^{-1}(1-\gamma)^{-3})$ interactions with the environment to achieve an $\varepsilon$-optimal estimator with high probability, when the estimation error is measured by the $1$-Wasserstein. This sample complexity bound is minimax optimal up to logarithmic factors. In addition, we revisit categorical distributional TD (CTD), showing that the same non-asymptotic convergence bounds hold for CTD in the case of the $1$-Wasserstein distance. We also extend our analysis to the more general setting where the data generating process is Markovian. In the Markovian setting, we propose variance-reduced variants of NTD and CTD, and show that both can achieve a $\tilde{O}(\varepsilon^{-2} \mu_{\pi,\min}^{-1}(1-\gamma)^{-3}+t_{mix}\mu_{\pi,\min}^{-1}(1-\gamma)^{-1})$ sample complexity bounds in the case of the $1$-Wasserstein distance, which matches the state-of-the-art statistical results for classic policy evaluation. To achieve the sharp statistical rates, we establish a novel Freedman's inequality in Hilbert spaces. This new Freedman's inequality would be of independent interest for statistical analysis of various infinite-dimensional online learning problems.

Published
2024

6. Estimation and Inference in Distributional Reinforcement Learning

Author

Zhang, Liangyu, Peng, Yang, Liang, Jiadong, Yang, Wenhao, and Zhang, Zhihua

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In this paper, we study distributional reinforcement learning from the perspective of statistical efficiency. We investigate distributional policy evaluation, aiming to estimate the complete return distribution (denoted $\eta^\pi$) attained by a given policy $\pi$. We use the certainty-equivalence method to construct our estimator $\hat\eta^\pi$, given a generative model is available. In this circumstance we need a dataset of size $\widetilde O\left(\frac{|\mathcal{S}||\mathcal{A}|}{\varepsilon^{2p}(1-\gamma)^{2p+2}}\right)$ to guarantee the $p$-Wasserstein metric between $\hat\eta^\pi$ and $\eta^\pi$ less than $\varepsilon$ with high probability. This implies the distributional policy evaluation problem can be solved with sample efficiency. Also, we show that under different mild assumptions a dataset of size $\widetilde O\left(\frac{|\mathcal{S}||\mathcal{A}|}{\varepsilon^{2}(1-\gamma)^{4}}\right)$ suffices to ensure the Kolmogorov metric and total variation metric between $\hat\eta^\pi$ and $\eta^\pi$ is below $\varepsilon$ with high probability. Furthermore, we investigate the asymptotic behavior of $\hat\eta^\pi$. We demonstrate that the ``empirical process'' $\sqrt{n}(\hat\eta^\pi-\eta^\pi)$ converges weakly to a Gaussian process in the space of bounded functionals on Lipschitz function class $\ell^\infty(\mathcal{F}_{\text{W}})$, also in the space of bounded functionals on indicator function class $\ell^\infty(\mathcal{F}_{\text{KS}})$ and bounded measurable function class $\ell^\infty(\mathcal{F}_{\text{TV}})$ when some mild conditions hold. Our findings give rise to a unified approach to statistical inference of a wide class of statistical functionals of $\eta^\pi$.

Published
2023

7. Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning

Author

Zhang, Liangyu, Peng, Yang, Yang, Wenhao, and Zhang, Zhihua

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We propose a novel generalization of constrained Markov decision processes (CMDPs) that we call the \emph{semi-infinitely constrained Markov decision process} (SICMDP). Particularly, we consider a continuum of constraints instead of a finite number of constraints as in the case of ordinary CMDPs. We also devise two reinforcement learning algorithms for SICMDPs that we call SI-CRL and SI-CPO. SI-CRL is a model-based reinforcement learning algorithm. Given an estimate of the transition model, we first transform the reinforcement learning problem into a linear semi-infinitely programming (LSIP) problem and then use the dual exchange method in the LSIP literature to solve it. SI-CPO is a policy optimization algorithm. Borrowing the ideas from the cooperative stochastic approximation approach, we make alternative updates to the policy parameters to maximize the reward or minimize the cost. To the best of our knowledge, we are the first to apply tools from semi-infinitely programming (SIP) to solve constrained reinforcement learning problems. We present theoretical analysis for SI-CRL and SI-CPO, identifying their iteration complexity and sample complexity. We also conduct extensive numerical examples to illustrate the SICMDP model and demonstrate that our proposed algorithms are able to solve complex sequential decision-making tasks leveraging modern deep reinforcement learning techniques., Comment: Shorter version accepted at NeurIPS 2022

Published
2023

8. A cooperative network at the nuclear envelope counteracts LINC-mediated forces during oogenesis in C. elegans.

Author

Liu, Chenshu, Rex, Rachel, Lung, Zoe, Wang, John, Wu, Fan, Kim, Hyung, Zhang, Liangyu, Sohn, Lydia, and Dernburg, Abby

Subjects
Animals, Nuclear Envelope, Caenorhabditis elegans, Oogenesis, Oocytes, Cell Nucleus, Mammals, Laminin, Caenorhabditis elegans Proteins
Abstract

Oogenesis involves transduction of mechanical forces from the cytoskeleton to the nuclear envelope (NE). In Caenorhabditis elegans, oocyte nuclei lacking the single lamin protein LMN-1 are vulnerable to collapse under forces mediated through LINC (linker of nucleoskeleton and cytoskeleton) complexes. Here, we use cytological analysis and in vivo imaging to investigate the balance of forces that drive this collapse and protect oocyte nuclei. We also use a mechano-node-pore sensing device to directly measure the effect of genetic mutations on oocyte nuclear stiffness. We find that nuclear collapse is not a consequence of apoptosis. It is promoted by dynein, which induces polarization of a LINC complex composed of Sad1 and UNC-84 homology 1 (SUN-1) and ZYGote defective 12 (ZYG-12). Lamins contribute to oocyte nuclear stiffness and cooperate with other inner nuclear membrane proteins to distribute LINC complexes and protect nuclei from collapse. We speculate that a similar network may protect oocyte integrity during extended oocyte arrest in mammals.

Published
2023

10. Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach

Author

Lu, Miao, Yang, Wenhao, Zhang, Liangyu, and Zhang, Zhihua

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In an Markov decision process (MDP), unobservable confounders may exist and have impacts on the data generating process, so that the classic off-policy evaluation (OPE) estimators may fail to identify the true value function of the target policy. In this paper, we study the statistical properties of OPE in confounded MDPs with observable instrumental variables. Specifically, we propose a two-stage estimator based on the instrumental variables and establish its statistical properties in the confounded MDPs with a linear structure. For non-asymptotic analysis, we prove a $\mathcal{O}(n^{-1/2})$-error bound where $n$ is the number of samples. For asymptotic analysis, we prove that the two-stage estimator is asymptotically normal with a typical rate of $n^{1/2}$. To the best of our knowledge, we are the first to show such statistical results of the two-stage estimator for confounded linear MDPs via instrumental variables.

Published
2022

11. The Construction of the Improvement System of Ideological, Political and Educational Effects of College Physical Education Courses Based on Data Discovery

Author

Zou, Ning, Zhang, Liangyu, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Zhang, Yinjun, editor, and Shah, Nazir, editor

Published
2024
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16. Voltage sag sensitive load type identification based on power quality monitoring data

Author

Zhang Yi, Zhang Liangyu, Liu Bijie, Chen Jintao, and Yao Wenxu

Subjects
Sensitive load, Power quality monitoring data, Voltage sag, Load identification, Machine learning, Production of electric energy or power. Powerplants. Central stations, TK1001-1841
Abstract

This paper focuses on identifying voltage sag-sensitive loads within unknown load type or when the load is already in operation. To achieve this, a new method of sensitive load identification based on power quality monitoring data is proposed. Firstly, the active power RMS monitoring data is used as the base data. The Hodrick-Prescott filtering and sliding mean segmentation are used to divide the period of the voltage sag event. Next, based on the division result, the differences of steady-state power quality monitoring data before and after each event are calculated as the dataset. The dynamic K-means is used to divide various load action areas. Finally, the voltage tolerance curves of each action area are fitted and compared with the preset curves, then according to the constituted rules in this paper, the type of sensitive load contained by user is recognized. The feasibility and accuracy of the proposed method are verified by analyzing the simulation examples and actual power quality monitoring data.© 2017 Elsevier Inc. All rights reserved.

Published
2024
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20. Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptotics

Author

Yang, Wenhao, Zhang, Liangyu, and Zhang, Zhihua

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In this paper, we study the non-asymptotic and asymptotic performances of the optimal robust policy and value function of robust Markov Decision Processes(MDPs), where the optimal robust policy and value function are solved only from a generative model. While prior work focusing on non-asymptotic performances of robust MDPs is restricted in the setting of the KL uncertainty set and $(s,a)$-rectangular assumption, we improve their results and also consider other uncertainty sets, including $L_1$ and $\chi^2$ balls. Our results show that when we assume $(s,a)$-rectangular on uncertainty sets, the sample complexity is about $\widetilde{O}\left(\frac{|\mathcal{S}|^2|\mathcal{A}|}{\varepsilon^2\rho^2(1-\gamma)^4}\right)$. In addition, we extend our results from $(s,a)$-rectangular assumption to $s$-rectangular assumption. In this scenario, the sample complexity varies with the choice of uncertainty sets and is generally larger than the case under $(s,a)$-rectangular assumption. Moreover, we also show that the optimal robust value function is asymptotic normal with a typical rate $\sqrt{n}$ under $(s,a)$ and $s$-rectangular assumptions from both theoretical and empirical perspectives.

Published
2021

23. Intervention Generative Adversarial Networks

Author

Liang, Jiadong, Zhang, Liangyu, Zhang, Cheng, and Zhang, Zhihua

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem. The main idea is to introduce a regularization term that we call intervention loss into the objective. We refer to the resulting generative model as Intervention Generative Adversarial Networks (IVGAN). By perturbing the latent representations of real images obtained from an auxiliary encoder network with Gaussian invariant interventions and penalizing the dissimilarity of the distributions of the resulting generated images, the intervention loss provides more informative gradient for the generator, significantly improving GAN's training stability. We demonstrate the effectiveness and efficiency of our methods via solid theoretical analysis and thorough evaluation on standard real-world datasets as well as the stacked MNIST dataset.

Published
2020

31. Integrated analysis of single-cell and bulk-RNA sequencing for the cellular senescence in prognosis of lung adenocarcinoma.

Author

Yu, Fengqiang, Zhang, Liangyu, Zhang, Xun, Zeng, Jianshen, and Lai, Fancai

Abstract

Non-small cell lung cancer (NSCLC), half of which are lung adenocarcinoma (LUAD), is one of the most widely spread cancers in the world. Telomerase, which maintains telomere length and chromosomal integrity, enables cancer cells to avoid replicative senescence. When telomerase is inhibited, cancer cells' senescence began, preventing them from growing indefinitely. Cellular senescence and telomeres are intrinsically linked. As of yet, still laking a systematic study of the involvement of telomere-senescence related genes in lung adenocarcinoma development. In this study, myeloid cells were identified as the cell type which are most correlated with cellular senescence based on its highest telomere-related gene activity. GO, KEGG, GSEA and GSVA analyses were used to explore the biological function of telomere-senescence related genes in LUAD. The combined analysis of single-cell RNA-sequencing and bulk-RNA sequencing identified a gene signature composed of 14 genes which can accurately predict the prognosis of patients with LUAD. In one training and four validation sets, patients with higher Telomere Related Gene Signature (TRGS) had a worse prognosis than those with lower TRGS. Different TRGS patient groups showed varying degrees of immune cell infiltration, frequency of gene missense mutation, sensitivity to different drugs, and tumor mutation burden (TMB). Collectively, we developed a brand new signature composed of telomere-senescence related genes that can accurately predicts patients' prognosis in LUAD, which provides new insights for future research into the role of cellular senescence in LUAD. [ABSTRACT FROM AUTHOR]

Published
2025
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33. A compartmentalized signaling network mediates crossover control in meiosis.

Author

Zhang, Liangyu, Köhler, Simone, Rillo-Bohn, Regina, and Dernburg, Abby F

Subjects
Synaptonemal Complex, Animals, Caenorhabditis elegans, Signal Transduction, Cell Compartmentation, Chromosome Segregation, Meiosis, Crossing Over, Genetic, C. elegans, RING finger ligase, Turing pattern, cell biology, chromosomes, crossover control, gene expression, homologous recombination, meiosis, synaptonemal Complex, Crossing Over, Genetic, Genetics, 1.1 Normal biological development and functioning, Generic Health Relevance, Biochemistry and Cell Biology
Abstract

During meiosis, each pair of homologous chromosomes typically undergoes at least one crossover (crossover assurance), but these exchanges are strictly limited in number and widely spaced along chromosomes (crossover interference). The molecular basis for this chromosome-wide regulation remains mysterious. A family of meiotic RING finger proteins has been implicated in crossover regulation across eukaryotes. Caenorhabditis elegans expresses four such proteins, of which one (ZHP-3) is known to be required for crossovers. Here we investigate the functions of ZHP-1, ZHP-2, and ZHP-4. We find that all four ZHP proteins, like their homologs in other species, localize to the synaptonemal complex, an unusual, liquid crystalline compartment that assembles between paired homologs. Together they promote accumulation of pro-crossover factors, including ZHP-3 and ZHP-4, at a single recombination intermediate, thereby patterning exchanges along paired chromosomes. These proteins also act at the top of a hierarchical, symmetry-breaking process that enables crossovers to direct accurate chromosome segregation.

Published
2018

38. Near Minimax-Optimal Distributional Temporal Difference Algorithms and The Freedman Inequality in Hilbert Spaces

Author

Peng, Yang, Zhang, Liangyu, Zhang, Zhihua, Peng, Yang, Zhang, Liangyu, and Zhang, Zhihua

Abstract

Distributional reinforcement learning (DRL) has achieved empirical success in various domains. One of the core tasks in the field of DRL is distributional policy evaluation, which involves estimating the return distribution $\eta^\pi$ for a given policy $\pi$. The distributional temporal difference (TD) algorithm has been accordingly proposed, which is an extension of the temporal difference algorithm in the classic RL literature. In the tabular case, \citet{rowland2018analysis} and \citet{rowland2023analysis} proved the asymptotic convergence of two instances of distributional TD, namely categorical temporal difference algorithm (CTD) and quantile temporal difference algorithm (QTD), respectively. In this paper, we go a step further and analyze the finite-sample performance of distributional TD. To facilitate theoretical analysis, we propose a non-parametric distributional TD algorithm (NTD). For a $\gamma$-discounted infinite-horizon tabular Markov decision process, we show that for NTD we need $\tilde{O}\left(\frac{1}{\varepsilon^{2p}(1-\gamma)^{2p+1}}\right)$ iterations to achieve an $\varepsilon$-optimal estimator with high probability, when the estimation error is measured by the $p$-Wasserstein distance. This sample complexity bound is minimax optimal (up to logarithmic factors) in the case of the $1$-Wasserstein distance. To achieve this, we establish a novel Freedman's inequality in Hilbert spaces, which would be of independent interest. In addition, we revisit CTD, showing that the same non-asymptotic convergence bounds hold for CTD in the case of the $p$-Wasserstein distance.

Published
2024

40. The auxin-inducible degradation (AID) system enables versatile conditional protein depletion in C. elegans.

Author

Zhang, Liangyu, Ward, Jordan D, Cheng, Ze, and Dernburg, Abby F

Subjects
Germ Cells, Cytoplasm, Animals, Caenorhabditis elegans, Embryo Loss, Indoleacetic Acids, Caenorhabditis elegans Proteins, Nuclear Proteins, Receptors, Cytoplasmic and Nuclear, Meiosis, Organ Specificity, Gene Deletion, Larva, Fertility, Proteolysis, Auxin, Auxin-inducible degradation, C. elegans, Degron, Genetic tool, Tissue-specific depletion, Genetics, Biotechnology, 1.1 Normal biological development and functioning, Generic Health Relevance, Receptors, Cytoplasmic and Nuclear, Biological Sciences, Medical and Health Sciences
Abstract

Experimental manipulation of protein abundance in living cells or organisms is an essential strategy for investigation of biological regulatory mechanisms. Whereas powerful techniques for protein expression have been developed in Caenorhabditis elegans, existing tools for conditional disruption of protein function are far more limited. To address this, we have adapted the auxin-inducible degradation (AID) system discovered in plants to enable conditional protein depletion in C. elegans. We report that expression of a modified Arabidopsis TIR1 F-box protein mediates robust auxin-dependent depletion of degron-tagged targets. We document the effectiveness of this system for depletion of nuclear and cytoplasmic proteins in diverse somatic and germline tissues throughout development. Target proteins were depleted in as little as 20-30 min, and their expression could be re-established upon auxin removal. We have engineered strains expressing TIR1 under the control of various promoter and 3' UTR sequences to drive tissue-specific or temporally regulated expression. The degron tag can be efficiently introduced by CRISPR/Cas9-based genome editing. We have harnessed this system to explore the roles of dynamically expressed nuclear hormone receptors in molting, and to analyze meiosis-specific roles for proteins required for germ line proliferation. Together, our results demonstrate that the AID system provides a powerful new tool for spatiotemporal regulation and analysis of protein function in a metazoan model organism.

Published
2015

45. A Sustainable Approach for Degradation of Alternariol by Peroxidase Extracted from Soybean Hulls: Performance, Pathway, and Toxicity Evaluation.

Author

Zhang, Xingke, Zheng, Hao, Lv, Hao, Yin, Jiyuan, Li, Yi, Zhang, Kexin, Zhang, Liangyu, Zhang, Wei, Wang, Zhixiang, Zhao, Lihong, and Guo, Yongpeng

Subjects
ANIMAL health, TOXICITY testing, APPLE juice, CORN flour, GRAPE juice
Abstract

Alternariol (AOH), an emerging mycotoxin, inevitably exists widely in various food and feed commodities with cereals and fruits being particularly susceptible, raising global concerns over its harm to human and livestock health. The development of eco-friendly and efficient strategies to decontaminate AOH has been an urgent task. This study provided insight into the utilization of crude soybean hull peroxidase as a powerful biocatalyst for degrading AOH. The results confirmed that crude soybean hull peroxidase (SHP) could catalyze the oxidation of AOH by use of H2O2 as a co-substrate. The optimum reaction conditions for SHP-catalyzed AOH degradation were recorded at pH 4.0–8.0, at 42–57 °C, and at H2O2 concentration of 100–500 μM. Mass analysis elucidated the degradation of AOH through hydroxylation and methylation by crude SHP. Moreover, toxicological analysis indicated that crude SHP-catalyzed AOH degradation detoxified the hepatotoxicity of this mycotoxin. The performance of crude SHP to degrade AOH in food matrices was further evaluated, and it was found that the enzyme agent could achieve AOH degradation by 77% in wheat flour, 84% in corn flour, 34% in grape juice, and 26% in apple juice. Collectively, these findings establish crude SHP as a promising candidate for effective AOH degradation, with potential applications in the food and feed industry. [ABSTRACT FROM AUTHOR]

Published
2024
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48. PLK1 phosphorylation of ZW10 guides accurate chromosome segregation in mitosis.

Author

Bellah, Sm Faysal, Xiong, Fangyuan, Dou, Zhen, Yang, Fengrui, Liu, Xing, Yao, Xuebiao, Gao, Xinjiao, and Zhang, Liangyu

Abstract

Stable transmission of genetic information during cell division requires faithful chromosome segregation. Mounting evidence has demonstrated that polo-like kinase 1 (PLK1) dynamics at kinetochores control correct kinetochore–microtubule attachments and subsequent silencing of the spindle assembly checkpoint. However, the mechanisms underlying PLK1-mediated silencing of the spindle checkpoint remain elusive. Here, we identified a regulatory mechanism by which PLK1-elicited zeste white 10 (ZW10) phosphorylation regulates spindle checkpoint silencing in mitosis. ZW10 is a cognate substrate of PLK1, and the phosphorylation of ZW10 at Ser12 enables dynamic ZW10–Zwint1 interactions. Inhibition of ZW10 phosphorylation resulted in misaligned chromosomes, while persistent expression of phospho-mimicking ZW10 mutant caused premature anaphase, in which sister chromatids entangled as cells entered anaphase. These findings reveal the previously uncharacterized PLK1–ZW10 interaction through which dynamic phosphorylation of ZW10 fine-tunes accurate chromosome segregation in mitosis. [ABSTRACT FROM AUTHOR]

Published
2024
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49. HOTAIR knockdown impairs metastasis of cervical cancer cells by down-regulating metastasis-related genes.

Author

Shi, Lei, Zhang, Dehui, Han, Huijuan, Zhang, Liangyu, Li, Sirui, Yang, Fang, and He, Caijun

Subjects
CERVICAL cancer, CANCER cells, CELL migration inhibition, CELL migration, METASTASIS, LINCRNA
Abstract

This study investigated the role of LncRNA HOTAIR knockdown in the biological impacts on cervical cancer cells. The HOTAIR gene in two human cervical cancer cell lines was silenced with small interfering (si) RNA siHOTAIR. Proliferation, apoptosis, migration and invasion of cells were assessed following the knockdown. The expressions of Notch1, EpCAM, E-cadherin, vimentin and STAT3 were assessed using qRT-PCR and Western blotting analysis. Compared with controls, HOTAIR levels were reduced significantly, the OD values of cells were significantly decreased in proliferation assays, cell apoptosis was significantly increased, cell migration and invasion were significantly reduced after HOTAIR knockdown. Molecular analysis showed that Notch1, EpCAM, vimentin and STAT3 expressions were decreased significantly, while the expression of E-cadherin was significantly increased after HOTAIR knockdown. Rescue experiments further confirmed that Notch1 and STAT3 were involved in siHOTAIR-mediated reduction of migration and invasion of cervical cancer cells. What is already known on this subject? Long non-coding RNAs including HOTAIR, is implicated in occurrence and development of cancer and have been explored to develop new therapeutic options for cancer. What do the results of this study add? HOTAIR silencing significantly reduces the viability and migration ability of cells and induces cell apoptosis, adding experimental data supporting the potential use of HOTAIR specific-siRNA as a therapeutic avenue for the cancer. What are the implications of these findings for clinical practice and/or further research? The finding from this study would help develop clinically applicable therapeutic avenues for the cancer and identify new treatment targets in the relevant pathways leading to new drugs or treatments. [ABSTRACT FROM AUTHOR]

Published
2023
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