Exploration of the Differentially Expressed Long Noncoding RNAs and Genes of Morphine Tolerance via Bioinformatic Analysis

Qiu, Yong; Meng, Ling‐Bing; Di, Chen-Yi; Huo, Yu-Hu; Yao, Bo-Chen; Zhang, Tengjiao; Hua, Zhen

doi:10.1089/cmb.2019.0188

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…In addition to animal experiments, Qiu et al [ 111 ] screened and identified differentially expressed lncRNAs related to morphine tolerance by mining public databases through bioinformation technology. For example, the expression levels of XR_006440, XR_009493, AF196267, MRAK150340 and MRAK037188 were downregulated and those of MRAK046606, XR_005988, DQ266361, UC.167- and UC.468+ were upregulated, and these differentially expressed lncRNAs and pivotal genes could provide new research targets for the diagnosis and treatment of morphine tolerance.…”

Section: Lncrna and Opioid Tolerancementioning

confidence: 99%

Noncoding RNAs: Novel Targets for Opioid Tolerance

Deng

Zou

2023

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As a global health problem, chronic pain is one of the leading causes of disability, and it imposes a huge economic and public health burden on families and society. Opioids represent the cornerstone of analgesic drugs. However, opioid tolerance caused by long-term application of opioids is a major factor leading to drug withdrawal, serious side effects caused by dose increases, and even the death of patients, placing an increasing burden on individuals, medicine, and society. Despite efforts to develop methods to prevent and treat opioid tolerance, no effective treatment has yet been found. Therefore, understanding the mechanism underlying opioid tolerance is crucial for finding new prevention and treatment strategies. Noncoding RNAs (ncRNAs) are important parts of mammalian gene transcriptomes, and there are thousands of unique noncoding RNA sequences in cells. With the rapid development of high-throughput genome technology, research on ncRNAs has become a hot topic in biomedical research. In recent years, studies have shown that ncRNAs mediate physiological and pathological processes, including chromatin remodeling, transcription, posttranscriptional modification and signal transduction, which are key regulators of physiological processes in developmental and disease environments and have become biomarkers and potential therapeutic targets for various diseases. An increasing number of studies have found that ncRNAs are closely related to the development of opioid tolerance. In this review, we have summarized the evidence that ncRNAs play an important role in opioid tolerance and that ncRNAs may be novel targets for opioid tolerance.

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Section: Lncrna and Opioid Tolerancementioning

confidence: 99%

Noncoding RNAs: Novel Targets for Opioid Tolerance

Deng

Zou

2023

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“…As such, exploring the exact molecular mechanisms of action, as well as reliable therapeutic targets for TSCC has attracted wide attention ( 15 ). With the development of gene sequencing technology, a large number of DEGs have been identified in various tumor types ( 38 , 39 ). DEGs play several roles in the occurrence and development of various diseases, including transcriptional regulation, post-transcriptional processing and regulation of protein expression.…”

Section: Introductionmentioning

confidence: 99%

SPP1 and FN1 are significant gene biomarkers of tongue squamous cell carcinoma

Liu

Zhang

et al. 2021

Oncol Lett

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Tongue squamous cell carcinoma (TSCC) is one of the most common malignant tumor types in the oral and maxillofacial region. The etiology and pathogenesis behind TSCC is complicated. In the present study, three gene expression profiles, namely GSE31056, GSE13601 and GSE78060, were downloaded from the Gene Expression Omnibus (GEO). The GEO2R online tool was utilized to identify differentially expressed genes (DEGs) between TSCC and normal tissue samples. Furthermore, a protein-protein interaction (PPI) network was constructed and hub genes were validated and analyzed. A total of 83 common DEGs were obtained in three datasets, including 48 upregulated and 35 downregulated genes. Pathway enrichment analysis indicated that DEGs were primarily enriched in cell adhesion, extracellular matrix (ECM) organization, and proteolysis.

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“…Bioinformatics tools can screen differentially expressed genes (DEGs) between diseased and normal tissues [3,7,8]. These DEGs are related to the pathological stage, lesion grade, and prognosis of patients.…”

Section: Introductionmentioning

confidence: 99%

Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model

Liu

Xiao

et al. 2020

BioMed Research International

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Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, which represents the 9th most frequently diagnosed cancer. However, the molecular mechanism of occurrence and development of ccRCC is indistinct. Therefore, the research aims to identify the hub biomarkers of ccRCC using numerous bioinformatics tools and functional experiments. Methods. The public data was downloaded from the Gene Expression Omnibus (GEO) database, and the differently expressed genes (DEGs) between ccRCC and normal renal tissues were identified with GEO2R. Protein-protein interaction (PPI) network of the DEGs was constructed, and hub genes were screened with cytoHubba. Then, ten ccRCC tumor samples and ten normal kidney tissues were obtained to verify the expression of hub genes with the RT-qPCR. Finally, the neural network model was constructed to verify the relationship among the genes. Results. A total of 251 DEGs and ten hub genes were identified. AURKB, CCNA2, TPX2, and NCAPG were highly expressed in ccRCC compared with renal tissue. With the increasing expression of AURKB, CCNA2, TPX2, and NCAPG, the pathological stage of ccRCC increased gradually (P<0.05). Patients with high expression of AURKB, CCNA2, TPX2, and NCAPG have a poor overall survival. After the verification of RT-qPCR, the expression of hub genes was same as the public data. And there were strong correlations between the AURKB, CCNA2, TPX2, and NCAPG with the verification of the neural network model. Conclusion. After the identification and verification, AURKB, CCNA2, TPX2, and NCAPG might be related to the occurrence and malignant progression of ccRCC.

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Exploration of the Differentially Expressed Long Noncoding RNAs and Genes of Morphine Tolerance via Bioinformatic Analysis

Cited by 7 publications

References 28 publications

Noncoding RNAs: Novel Targets for Opioid Tolerance

Noncoding RNAs: Novel Targets for Opioid Tolerance

SPP1 and FN1 are significant gene biomarkers of tongue squamous cell carcinoma

Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model

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