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

Noncoding RNAs: Novel Targets for Opioid Tolerance

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.

LncRNA MRAK159688 facilitates morphine tolerance by promoting REST-mediated inhibition of mu opioid receptor in rats

Morphine tolerance (MT) caused by the long-term use of morphine is a major medical problem. The molecular mechanism of morphine tolerance remains elusive. Here, we established a morphine tolerance model in rats and verified whether the long noncoding RNA (lncRNA) MRAK159688 is involved in morphine tolerance and its specific molecular mechanism. We show the significant upregulation of MRAK159688 expression in the spinal cord of morphine-tolerant rats. Overexpression of MRAK159688 by a lentivirus reduces the analgesic efficacy of morphine and induces pain behavior. Downregulation of MRAK159688 using a small interfering RNA (siRNA) attenuates the formation of morphine tolerance, partially reverses the development of morphine tolerance and alleviates morphine-induced hyperalgesia. MRAK159688 is located in the nucleus and cytoplasm of neurons, and it colocalizes with repressor element-1 silencing transcription factor (REST) in the nucleus. MRAK159688 potentiates the expression and function of REST, thereby inhibiting the expression of mu opioid receptor (MOR) and subsequently inducing morphine tolerance. Moreover, REST overexpression blocks the effects of MRAK159688 siRNA on relieving morphine tolerance. In general, chronic morphine administration-mediated upregulation of MRAK159688 in the spinal cord contributes to morphine tolerance and hyperalgesia by promoting REST-mediated inhibition of MOR. MRAK159688 downregulation may represent a novel RNA-based therapy for morphine tolerance.

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

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. A total of 63 nodes and 218 edges were included in the PPI network. The top 11 candidate hub genes were acquired, namely plasminogen activator urokinase (PLAU), signal transducer and activator of transcription 1, C-X-C motif chemokine ligand 12, matrix metallopeptidase (MMP) 13, secreted phosphoprotein 1 (SPP1), periostin, MMP1, MMP3, fibronectin 1 (FN1), serpin family E member 1 and snail family transcriptional repressor 2. Overall, 83 DEGs and 11 hub genes were screened from TSCC and normal individuals using bioinformatics and microarray technology. These genes may be used as diagnostic and therapeutic biomarkers for TSCC. In addition, SPP1 and FNl were identified as potential biomarkers for the progression of TSCC.

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

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.