CA : A Cancer Journal for Clinicians

MicroRNAs (miRs, miRNAs) are small molecules of 18–22 nucleotides that serve as important regulators of gene expression at the post-transcriptional level. One of the mechanisms through which miRNAs regulate gene expression involves the interaction of their “seed” sequences primarily with 3′-end and more rarely with 5′-end, of mRNA transcribed from target genes. Numerous studies over the past decade have been devoted to quantitative and qualitative assessment of miRNAs expression and have shown remarkable changes in miRNA expression profiles in various diseases. Thus, profiling of miRNA expression can be an important tool for diagnostics and treatment of disease. However, less attention has been paid towards understanding the underlying reasons for changes in miRNA expression, especially in cancer cells. The purpose of this review is to analyze and systematize current data that explains reasons for changes in the expression of miRNAs. The review will cover both transcriptional (changes in gene expression and promoter hypermethylation) and post-transcriptional (changes in miRNA processing) mechanisms of regulation of miRNA expression, as well as effects of endogenous (hormones, cytokines) and exogenous (xenobiotics) compounds on the miRNA expression. The review will summarize the complex multilevel regulation of miRNA expression, in relation to cell type, physiological state of the body and various external factors.

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Regulatory mechanisms of microRNA expression

Members of the microRNA (miR)-30 family have been reported to promote adipogenesis and inhibit osteogenesis, yet their role in the regulation of thermogenesis remains unknown. In this study, we show that miR-30b/c concentrations are greatly increased during adipocyte differentiation and are stimulated by cold exposure or the β-adrenergic receptor activator. Overexpression and knockdown of miR-30b and -30c induced and suppressed, respectively, the expression of thermogenic genes such as UCP1 and Cidea in brown adipocytes. Forced expression of miR-30b/c also significantly increased thermogenic gene expression and mitochondrial respiration in primary adipocytes derived from subcutaneous white adipose tissue, demonstrating a promoting effect of miRNAs on the development of beige fat. In addition, knockdown of miR-30b/c repressed UCP1 expression in brown adipose tissue in vivo. miR-30b/c targets the 3′-untranslated region of the receptor-interacting protein 140 (RIP140), and overexpression of miR-30b/c significantly reduced RIP140 expression. Consistent with RIP140 as a target of miR-30b/c in regulating thermogenic gene expression, overexpression of RIP140 greatly suppressed the promoting effect of miR-30b/c on the expression of UCP1 and Cidea in brown adipocytes. Taken together, the data from our study identify miR-30b/c as a key regulator of thermogenesis and uncover a new mechanism underlying the regulation of brown adipose tissue function and the development of beige fat.

https://diabetes.diabetesjournals.org/content/diabetes/64/6/2056.full.pdf

miR-30 Promotes Thermogenesis and the Development of Beige Fat by Targeting RIP140

Chatbots are computer programs with which one can have a conversation. In this article, the authors describe how the GPT-4 chatbot, which has been given a general education, could affect the practice of medicine. 

Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine

Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine.

Noisy chaotic neural network (NCNN), which can exhibit stochastic chaotic simulated annealing (SCSA), has been proven to be a powerful tool in solving combinatorial optimization problems. In order to retain the excellent optimization property of SCSA and improve the optimization performance of the NCNN using hysteretic dynamics without increasing network parameters, we first construct an equivalent model of the NCNN and then control noises in the equivalent model to propose a novel hysteretic noisy chaotic neural network (HNCNN). Compared with the NCNN, the proposed HNCNN can exhibit both SCSA and hysteretic dynamics without introducing extra system parameters, and can increase the effective convergence toward optimal or near-optimal solutions at higher noise levels. Broadcast scheduling problem (BSP) in packet radio networks (PRNs) is to design an optimal time-division multiple-access (TDMA) frame structure with minimal frame length, maximal channel utilization, and minimal average time delay. In this paper, the proposed HNCNN is applied to solve BSP in PRNs to demonstrate its performance. Simulation results show that the proposed HNCNN with higher noise amplitudes is more likely to find an optimal or near-optimal TDMA frame structure with a minimal average time delay than previous algorithms.

Novel Hysteretic Noisy Chaotic Neural Network for Broadcast Scheduling Problems in Packet Radio Networks

To provide an ability to characterize local features for the chaotic neural network (CNN), Gauss wavelet is used for the self-feedback of the CNN with the dilation parameter acting as the bifurcation parameter. The exponentially decaying dilation parameter and the chaotically varying translation parameter not only govern the wavelet self-feedback transform but also enable the CNN to generate complex dynamics behavior preventing the network from being trapped in the local minima. Analysis of the energy function of the CNN indicates that the local characterization ability of the proposed CNN is effectively provided by the wavelet self-feedback in the manner of inverse wavelet transform and that the proposed CNN can achieve asymptotical stability. The experimental results on traveling salesman problem (TSP) suggest that the proposed CNN has a higher average success rate for obtaining globally optimal or near-optimal solutions.

A Novel Chaotic Neural Network With the Ability to Characterize Local Features and Its Application

Lung cancer, especially non-small cell lung cancer, is a leading cause of malignant tumor death worldwide. Understanding the mechanisms employed by the main regulators, such as microRNAs (miRNAs) and transcription factors (TFs), still remains elusive. The patterns of their cooperation and biological functions in the synergistic regulatory network have rarely been studied. Here, we describe the first miRNA-TF synergistic regulation network in human lung cancer. We identified important regulators (MYC, NFKB1, miR-590, and miR-570) and significant miRNA-TF synergistic regulatory motifs by random simulations. The two most significant motifs were the co-regulation of miRNAs and TFs, and TF-mediated cascade regulation. We also developed an algorithm to uncover the biological functions of the human lung cancer miRNA-TF synergistic regulatory network (regulation of apoptosis, cellular protein metabolic process, and cell cycle), and the specific functions of each miRNA-TF synergistic subnetwork. We found that the miR-17 family exerted important effects in the regulation of non-small cell lung cancer, such as in proliferation and cell cycle regulation by targeting the retinoblastoma protein (RB1) and forming a feed forward loop with the E2F1 TF. We proposed a model for the miR-17 family, E2F1, and RB1 to demonstrate their potential roles in the occurrence and development of non-small cell lung cancer. This work will provide a framework for constructing miRNA-TF synergistic regulatory networks, function analysis in diseases, and identification of the main regulators and regulatory motifs, which will be useful for understanding the putative regulatory motifs involving miRNAs and TFs, and for predicting new targets for cancer studies.

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Functional analysis of microRNA and transcription factor synergistic regulatory network based on identifying regulatory motifs in non-small cell lung cancer

In this paper, we first review Chen’s chaotic neural network model and then propose a novel wavelet chaotic neural network. Second,we apply them to search global minima of a continuous function, respectively. Meanwhile, the time evolution figures of the corresponding most positive Lyapunov exponent are given. Third, 10-city traveling salesman problem (TSP) is given to make a comparison between them. Finally we conclude that the novel wavelet chaotic neural network is more valid.

Wavelet chaotic neural networks and their application to optimization problems

Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

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Yao-qun Xu

Papers

Novel Hysteretic Noisy Chaotic Neural Network for Broadcast Scheduling Problems in Packet Radio Networks

A Novel Chaotic Neural Network With the Ability to Characterize Local Features and Its Application

Functional analysis of microRNA and transcription factor synergistic regulatory network based on identifying regulatory motifs in non-small cell lung cancer

Wavelet chaotic neural networks and their application to optimization problems

A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning