Competitive Endogenous RNA Landscape in Epstein-Barr Virus Associated Nasopharyngeal Carcinoma

Hijacking and rewiring of host CircRNA/miRNA/mRNA competitive endogenous RNA (ceRNA) regulatory networks by oncoviruses during development of viral cancers

A significant portion of human cancers are caused by oncoviruses (12%-25%). Oncoviruses employ various strategies to promote their replication and induce tumourigenesis in host cells, one of which involves modifying the gene expression patterns of the host cells, leading to the rewiring of genes and resulting in significant changes in cellular processes and signalling pathways. In recent studies, a specific mode of gene regulation known as circular RNA (circRNA)-mediated competing endogenous RNA (ceRNA) networks has emerged as a key player in this context. CircRNAs, a class of non-coding RNA molecules, can interact with other RNA molecules, such as mRNAs and microRNAs (miRNAs), through a process known as ceRNA crosstalk. This interaction occurs when circRNAs, acting as sponges, sequester miRNAs, thereby preventing them from binding to their target mRNAs and modulating their expression. By rewiring the host cell genome, oncoviruses have the ability to manipulate the expression and activity of circRNAs, thereby influencing the ceRNA networks that can profoundly impact cellular processes such as cell proliferation, differentiation, apoptosis, and immune responses. This review focuses on a comprehensive evaluation of the latest findings on the involvement of virus-induced reprogramming of host circRNA-mediated ceRNA networks in the development and pathophysiology of human viral cancers, including cervical cancer, gastric cancer, nasopharyngeal carcinoma, Kaposi's sarcoma, hepatocellular carcinoma, and diffuse large B cell lymphoma. Understanding these mechanisms can improve our knowledge of how oncoviruses contribute to human tumourigenesis and identify potential targets for developing optimised therapies and diagnostic tools for viral cancers.

Identification of a lncRNA/circRNA-miRNA-mRNA network in Nasopharyngeal Carcinoma by deep sequencing and bioinformatics analysis

Background: Accumulating evidence indicates that non-coding RNAs (ncRNA), including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), can function as competitive endogenous RNAs (ceRNAs) by binding to microRNAs (miRNAs) and regulating host gene expression at the transcriptional or post-transcriptional level. Dysregulation in ceRNA network regulation has been implicated in the occurrence and development of cancer. However, the lncRNA/circRNA-miRNA-mRNA regulatory network is still lacking in nasopharyngeal carcinoma (NPC). Methods: Differentially expressed genes (DEGs) were obtained from our previous sequencing data and Gene Expression Omnibus (GEO). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) were used to explore the biological functions of these common DEGs. Through a series of bioinformatic analyses, the lncRNA/circRNA-miRNA-mRNA network was established. In additional, the external data GSE102349 was used to test the prognostic value of the hub mRNAs through the Kaplan-Meier method. Results: We successfully constructed a lncRNA/circRNA-miRNA-mRNA network in NPC, consisting of 16 lncRNAs, 6 miRNAs, 3 circRNAs and 10 mRNAs and found that three genes (TOP2A, ZWINT, TTK) were significantly associated with overall survival time (OS) in patients. Conclusion: The regulatory network revealed in this study may help comprehensively elucidate the ceRNA mechanisms driving NPC, and provide novel candidate biomarkers for evaluating the prognosis of NPC.

Lymph node metastasis-related gene signature shows good performance in predicting prognosis and immune infiltration in cervical cancer

Aims

This study aimed to construct a lymph node metastasis-related gene signature to predict prognosis and immune infiltration in patients with cervical cancer.

Methods

Clinical and RNA sequencing data of 193 patients with cervical cancer, which were divided into lymph node metastasis (N1) and non-lymph node metastasis (N0) groups, were acquired from TCGA. Differentially expressed genes (DEGs) between the N1 and N0 groups were detected, and protein-protein interaction combined with LASSO analysis was conducted to further screen lymph node metastasis-related genes. Univariate and multivariate Cox regression analyses were performed to establish a predictive signature. The genetic features, potential biological behavior, and immune infiltration characteristics of the predictive signature were explored. Furthermore, the sensitivity of patients to chemotherapy drugs was estimated based on the predictive signature and the expression of TEKT2 and RPGR was investigated in the cervical cancer tissue samples.

Results

A total of 271 lymph node metastasis-related DEGs, including 100 upregulated and 171 downregulated genes, were identified. Two genes, TEKT2 and RPGR, were associated with lymph node metastasis and prognosis in cervical cancer, and were used to construct a lymph node metastasis-related predictive signature. Based on the predictive signature, patients with cervical cancer were divided into high- and low-risk groups. The high-risk group, characterized by a higher tumor mutation burden and somatic mutation rate, indicated a poor overall survival. The activation of immune infiltration and increased expression of checkpoint genes were observed in the high-risk group, indicating that they might benefit from immunotherapy. Cytarabine, FH535, and procaspase-activating compound-1 were estimated as reasonable chemotherapy options for patients in the high-risk group, whereas two taxanes and five tyrosine kinase inhibitors, including etoposide and vinorelbine, had therapeutic significance for patients in the low-risk group. The expression of TEKT2 and RPGR was significantly downregulated in cervical cancer tissues, especially in metastatic lymph node tissues.

Discussion

The lymph node metastasis-related predictive signature based on TEKT2 and RPGR showed good performance in predicting the survival outcomes of patients with cervical cancer. The risk score of the predictive signature was related to genetic variation and immune infiltration, which could guide immunotherapy and chemotherapy strategies.

Screening of core genes and prediction of ceRNA regulation mechanism of circRNAs in nasopharyngeal carcinoma by bioinformatics analysis

Background: Nasopharyngeal carcinoma (NPC) represents a highly aggressive malignant tumor. Competing endogenous RNAs (ceRNA) regulation is a common regulatory mechanism in tumors. The ceRNA network links the functions between mRNAs and ncRNAs, thus playing an important regulatory role in diseases. This study screened the potential key genes in NPC and predicted regulatory mechanisms using bioinformatics analysis.

Methods: The merged microarray data of three NPC-related mRNA expression microarrays from the Gene Expression Omnibus (GEO) database and the expression data of tumor samples or normal samples from the nasopharynx and tonsil in The Cancer Genome Atlas (TCGA) database were both subjected to differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The results from two different databases were intersected with WGCNA results to obtain potential regulatory genes in NPC, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. The hub-gene in candidate genes was discerned through Protein-Protein Interaction (PPI) analysis and its upstream regulatory mechanism was predicted by miRwalk and circbank databases.

Results: Totally 68 upregulated genes and 96 downregulated genes in NPC were screened through GEO and TCGA. According to WGCNA, the NPC-related modules were screened from GEO and TCGA analysis results, and the genes in the modules were obtained. After the results of differential analysis and WGCNA were intersected, 74 differentially expressed candidate genes associated with NPC were discerned. Finally, fibronectin 1 (FN1) was identified as a hub-gene in NPC. Prediction of upstream regulatory mechanisms of FN1 suggested that FN1 may be regulated by ceRNA mechanisms involving multiple circRNAs, thereby influencing NPC progression through ceRNA regulation.

Conclusion: FN1 is identified as a key regulator in NPC development and is likely to be regulated by numerous circRNA-mediated ceRNA mechanisms.

CircMAN1A2 contributes to nasopharyngeal carcinoma progression via enhancing the ubiquitination of ATMIN through miR-135a-3p/UBR5 axis

Recently, the dysregulation of circRNAs has been increasingly implicated in the pathogenesis of nasopharyngeal carcinoma (NPC). Among these circRNAs, circMAN1A2 has been highlighted for the up-regulated expression in NPC, whereas the underlying mechanisms have not been clearly established. Thus, the aim of this study was to delineate the tumor-supporting role of circMAN1A2 in the oncogenesis and metastases of NPC. We validated through qRT-PCR that circMAN1A2 was highly expressed in NPC tissues and NPC cells. Survival analysis through Kaplan-Meier method showed that the overall survival, disease-free survival, and distant metastasis-free survival of patients was negatively correlated with the expression of circMAN1A2. Then, gain- and loss-of function assays demonstrated that circMAN1A2 knockdown could impede the proliferation, migration, invasion, and EMT in NPC cells. Further, we conducted dual luciferase reporter gene, RIP, and RNA pull down assays, unveiling that circMAN1A2 functioned as a sponge of miR-135a-3p, and miR-135a-3p targeted UBR5. Additionally, UBR5 interacted with ATMIN to foster the ubiquitination of ATMIN, thereby expediting the malignant behaviors of NPC cells as well as the lung and inguinal lymph node metastases of NPC tumors in vivo. Together, our study uncovered the tumor-initiating and pro-metastatic role of circMAN1A2-miR-135a-3p-UBR5-ATMIN axis in NPC regulation that may be a potential therapeutic target for human NPC.

Comprehensive analysis of aberrantly expressed circRNAs, mRNAs and lncRNAs in patients with nasopharyngeal carcinoma

BACKGROUND

The location of nasopharyngeal cancer is hidden, so it is difficult to diagnose at an early stage. In this study, we aimed to investigate the expression profiles of circRNAs, mRNAs and IncRNAs and to provide some basis for further studies.

METHODS

Expression profiles of circRNAs, mRNAs, and lncRNAs were analyzed using microarray techniques. The differentially expressed ncRNA was calculated by bioinformatics.

RESULTS

A total of 3048 circRNAs, 2179 lncRNAs, and 2015 mRNAs were detected to be significantly differentially expressed in NPC. The most upregulated circRNAs, lncRNAs, and mRNAs were hsa-circ-0067562, NONHSAT232922.1, and HOXB13, respectively. And, the most downregulated circRNAs, lncRNAs, and mRNAs were hsa_circ_0078837, lnc-TTC8-4:3, and LTF, respectively. The number of upregulated DE lncRNAs was more than twice than those downregulated. Our data showed that 80.44% of pairs of lncRNAs and cis-mRNAs demonstrated positive correlations. For lncRNAs and trans-mRNAs pairs, 53.7% of pairs showed positive correlation. LncRNA-mediated cis regulation is a prevalent regulatory mode in the development of nasopharyngeal carcinoma. CR1, LRMP and SORBS2 are predicted to be mediated not only by cis-acting lncRNA modes of action, but also by trans-acting lncRNA mechanisms. Additionally, we constructed a diagnostic prediction model with a high sensitivity and specificity.

CONCLUSION

Our study characterized the landscape of circRNAs, mRNAs and lncRNAs in NPC tissue and provided novel insights into the molecular mechanisms of NPC.

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.