Ion channels are involved in the mechanism of anesthetic action and side effect. The transcription and expression of ion channel genes can be modulated by general anesthetics. The adverse effect of continuous infusion of etomidate has been concerned. However, the effects of etomidate on mRNA expressions of ion channel genes remain unclear. In this study, we exposed Daphnia pulex in 250 μmol/L of etomidate for 240 min and observed the change of heart rate, phototactic behavior and blood glucose during the period of exposure, as well as the mRNA expressions of 120 ion channel genes at the end of the experiment. Compared to the controls, heart rate, phototactic behavior and blood glucose were not influenced by 250 μmol/L of etomidate. According to the quantitative PCR results, 18 of 120 Daphnia pulex ion channel genes transcripts were affected by persistent 240 min exposure to 250 μmol/L of etomidate: 2 genes were upregulated and 16 genes were down-regulated, suggesting that etomidate showed effects on many different ion channels in transcription level. Systematical exploration of transcriptional changes of ion channels could contribute to understanding of the pharmacological mechanism of etomidate.
Objective The aim of this study is to review the association between long non-coding RNA (lncRNA) and papillary thyroid carcinoma (PTC). Method The relevant literatures about lncRNA associated with PTC were retrospectively analyzed and summarized. Results The expression levels of noncoding RNA associated with MAP kinase pathway and growth arrest (NAMA), PTC susceptibility candidate 3 (PTCSC3), BRAF activated non-coding RNA (BANCR), maternally expressed gene 3 (MEG3), NONHSAT037832, and GAS8-AS1 in PTC tissues were significantly lower than those in non-thyroid carcinoma tissues. The expression levels of ENST00000537266, ENST00000426615, XLOC051122, XLOC006074, HOX transcript antisense RNA (HOTAIR), antisense noncoding RNA in the INK4 locus (ANRIL), and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in PTC tissues were upregulated in PTC tissues, comparing with the non-thyroid carcinoma tissues. These lncRNAs were possibly involved in cell proliferation, migration, and apoptosis of PTC. Conclusion LncRNAs may provide new insights into the molecular mechanism and gene-targeted therapy of PTC and become new molecular marker for the diagnosis of PTC.
Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanism of action between drugs, it is expensive to screen new drug combinations through trials. It is well known that virtual screening of computational models can effectively reduce the test cost. Recently, foreign scholars successfully predicted the synergistic value of new drug combinations on cancer cell lines by using deep learning model DeepSynergy. However, DeepSynergy is a two-stage method and uses only one kind of feature as input. In this study, we proposed a new end-to-end deep learning model, MulinputSynergy which predicted the synergistic value of drug combinations by integrating gene expression, gene mutation, gene copy number characteristics of cancer cells and anticancer drug chemistry characteristics. In order to solve the problem of high dimension of features, we used convolutional neural network to reduce the dimension of gene features. Experimental results showed that the proposed model was superior to DeepSynergy deep learning model, with the mean square error decreasing from 197 to 176, the mean absolute error decreasing from 9.48 to 8.77, and the decision coefficient increasing from 0.53 to 0.58. This model could learn the potential relationship between anticancer drugs and cell lines from a variety of characteristics and locate the effective drug combinations quickly and accurately.
Cancer gene expression data have the characteristics of high dimensionalities and small samples so it is necessary to perform dimensionality reduction of the data. Traditional linear dimensionality reduction approaches can not find the nonlinear relationship between the data points. In addition, they have bad dimensionality reduction results. Therefore a multiple weights locally linear embedding (LLE) algorithm with improved distance is introduced to perform dimensionality reduction in this study. We adopted an improved distance to calculate the neighbor of each data point in this algorithm, and then we introduced multiple sets of linearly independent local weight vectors for each neighbor, and obtained the embedding results in the low-dimensional space of the high-dimensional data by minimizing the reconstruction error. Experimental result showed that the multiple weights LLE algorithm with improved distance had good dimensionality reduction functions of the cancer gene expression data.
RNA can be labeled by more than 170 chemical modifications after transcription, and these chemical modifications are collectively referred to as RNA modifications. It opened a new chapter of epigenetic research and became a major research hotspot in recent years. RNA modification regulates the expression of genes from the transcriptome level by regulating the fate of RNA, thus participating in many biological processes and disease occurrence and development. With the deepening of research, the diversity and complexity of RNA modification, as well as its physiological significance and potential as a therapeutic target, can not be ignored.
ObjectiveTo understand progress of gene research for chronic venous ulcer (CVU) so as to seek for the best treatment strategy for it.MethodThe literatures about studies on gene polymorphism and variability that leaded to the occurrence and development of CVU in recent years were reviewed and analyzed.ResultsThe CVU was mainly caused by the chronic venous insufficiency (CVI). Many changes in the gene expression had been found in the curable CVU and incurable CVU. The expressions of regulated inflammatory genes, encoding extracellular peptide genes, and encoding different cellular pathways genes in the incurable CVU patients had remarkable differences as compared with the healthy individuals. Although there were more studies on incurable CVU than curable CVU, it was still unable to accurately predict the healing time of CVU. At the same time, genome-wide associations study had not been performed to find single nucleotide polymorphism related to the risk of CVU.ConclusionsAlthough CVU is mainly caused by CVI, not all patients with CVI have ulcer. At present, parts of risk factors of CVU have been known, such as age, iliofemoral vein embolism, deep vein insufficiency, hypertension, obesity, and so on. However, there are fewer studies on heredity, so it is necessary to strengthen its research. Gene expression and gene polymorphism have increasingly become focus of research on causes of chronic inflammation. Genome-wide association study is a gold standard of complex disease genetics, so it is neccessary to further search so as to better understand genetic basis and genetic background of CVU and find the best treatment strategy for improving ulcer healing.
Lung adenocarcinoma is a prevalent histological subtype of non-small cell lung cancer with different morphologic and molecular features that are critical for prognosis and treatment planning. In recent years, with the development of artificial intelligence technology, its application in the study of pathological subtypes and gene expression of lung adenocarcinoma has gained widespread attention. This paper reviews the research progress of machine learning and deep learning in pathological subtypes classification and gene expression analysis of lung adenocarcinoma, and some problems and challenges at the present stage are summarized and the future directions of artificial intelligence in lung adenocarcinoma research are foreseen.
It is generally considered that various regulatory activities between genes are contained in the gene expression datasets. Therefore, the underlying gene regulatory relationship and the biologically useful information can be found by modeling the gene regulatory network from the gene expression data. In our study, two unsupervised matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF), were proposed to identify significant genes and model the regulatory network using the microarray gene expression data of Alzheimer's disease (AD). By bio-molecular analyzing of the pathways, the differences between ICA and NMF have been explored and the fact, which the inflammatory reaction is one of the main pathological mechanisms of AD, is also emphasized. It was demonstrated that our study gave a novel and valuable method for the research of early detection and pathological mechanism, biomarkers' findings of AD.
Reconstruction of gene regulatory networks (GRNs) from large-scale expression data can mine the potential causality relationship among the genes and help understand the complex regulatory mechanisms. It is of utmost interest and has become a challenging computational problem for understanding the complex regulatory mechanisms in cellular systems. For the past decades, numerous theoretical and computational approaches have been introduced for inferring the GRNs. However, all existing methods of inferring GRNs from gene expression profiles have their strengths and weaknesses. In particular, many properties of GRNs, such as topology sparseness and non-linear dependence, are generally in regulation mechanism but are seldom taken into account simultaneously in one computational method. Some information theory algorithms do not recover the true positive edges that may have been deleted in an earlier computing process. These interaction relationships may reflect the actual relationship of genes. To overcome these disadvantages and to further enhance the precision and robustness of inferred GRNs, we presented an ensemble method, to infer GRNs from gene expression data by adopting two strategies of resampling and arithmetic mean fusion in this work. In this algorithm, the jackknife resampling procedure was first employed to form a series of sub-datasets of gene expression data, then the conditional mutual information was used to generate the corresponding sub-networks from the sub-datasets, and the final GRN was inferred by integrating these sub-networks with an arithmetic mean fusion strategy. Compared with those of the state-of-the-art algorithm on the benchmark synthetic GRNs datasets from the DREAM3 challenge and a real SOS DNA repair network, the results show that our method outperforms significantly LP, LASSO and ARANCE methods, and has a high and robust performance.
The microarray technology used in biological and medical research provides a new idea for the diagnosis and treatment of cancer. To find different types of cancer and to classify the cancer samples accurately, we propose a new cluster ensemble framework Dual Neural Gas Cluster Ensemble (DNGCE), which is based on neural gas algorithm, to discover the underlying structure of noisy cancer gene expression profiles. This framework DNGCE applies the neural gas algorithm to perform clustering not only on the sample dimension, but also on the attribute dimension. It also adopts the normalized cut algorithm to partition off the consensus matrix constructed from multiple clustering solutions. We obtained the final accurate results. Experiments on cancer gene expression profiles illustrated that the proposed approach could achieve good performance, as it outperforms the single clustering algorithms and most of the existing approaches in the process of clustering gene expression profiles.