CeRNA in cancer: possible functions and clinical implications

Competitive endogenous network of lncRNA, miRNA, and mRNA in the chemoresistance of gastrointestinal tract adenocarcinomas

Background: Gastric cancer (GC) is a common and deadly cancer worldwide. Molecular changes underlying the development of GC are not thoroughly understood. Therefore, we constructed and analyzed a novel four-component competing endogenous RNA (ceRNA) network to introduce plausible diagnostic and prognostic biomarkers in GC. Materials and Methods: Transcriptomics and circular RNA (circRNA) data were retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. After batch effect correction, differential expression analysis, and interaction prediction, a ceRNA network including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) was established. Enrichment analyses were performed, and a protein–protein interaction (PPI) network was constructed. Furthermore, a subnetwork was extracted, and using the quantitative real-time polymerase chain reaction (qRT-PCR) method, the expression changes of two hub ceRNAs were examined. Finally, survival analysis was performed to identify potential prognostic RNAs. Results: A four-component ceRNA network containing 822 nodes and 1365 edges was constructed. Enrichment analyses unveiled important signaling pathways and gene ontologies such as neuroactive ligand–receptor interaction and axonogenesis. The PPI network showed the interactions among mRNAs of the ceRNA network. qRT-PCR indicated downregulation of EPHA5 and SNAP91 mRNAs in GC compared to control tissues. Survival analyses revealed eight mRNAs and one lncRNA as potential prognostic biomarkers in GC. Conclusion: The established four-component network of ceRNAs in GC reveals a comprehensive view of the molecular and cellular characteristics of GC progression, which can be considered as a basis to examine and validate potential diagnostic and prognostic biomarkers as well as therapeutic targets.

Competing endogenous RNAs (ceRNAs) containing microRNA response elements can competitively interact with microRNA via miRNA response elements, which can combine non-coding RNAs with protein-coding RNAs through complex ceRNA networks. CeRNAs include non-coding RNAs (long non-coding RNAs, circular RNAs, and transcribed pseudogenes) and protein-coding RNAs (mRNAs). Molecular interactions in ceRNA networks can coordinate many biological processes; however, they may also lead to ceRNA network imbalance and thus contribute to cancer occurrence when disturbed. Recent studies indicate that many dysregulated RNAs derived from lung cancer may function as ceRNAs to regulate multitudinous biological functions for lung cancer, including tumor cell proliferation, apoptosis, growth, invasion, migration, and metastasis. This study therefore reviewed the research progress in the field of non-coding and protein-coding RNAs as ceRNAs in lung cancer, and highlighted validated ceRNAs involved in biological lung cancer functions. Furthermore, the roles of ceRNAs as novel prognostic and diagnostic biomarkers were also discussed. Interpreting the involvement of ceRNAs networks in lung cancer will provide new insight into cancer pathogenesis and treatment strategies.

Simple SummaryCompetitive endogenous RNAs (ceRNAs) can regulate gene expression at the posttranscriptional level by competitively binding to microRNAs. In cancer, ceRNA activity is dysregulated. The imbalanced ceRNA-related regulatory network has been theorized to play an important role in cancer progression. In this review, we summarize the biological functions and clinical implications of long noncoding RNAs and circular RNAs as ceRNAs in nasopharyngeal carcinoma (NPC).Nasopharyngeal carcinoma (NPC) is a kind of head-and-neck malignant tumor, and distant metastasis treatment resistance is the leading cause of patient death. In-depth understanding of NPC progression and treatment failure remains to be explored. Long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) are noncoding RNAs that play key regulatory role in shaping tumor cell activities. Recent studies have revealed that lncRNA and circRNA function as competitive endogenous RNAs (ceRNAs) by regulating the posttranscriptional expression of genes as miRNA baits. The imbalanced ceRNA networks derived from lncRNA/circRNA-miRNA-mRNA interaction are widely found to contribute to NPC development. Herein, we summarize typical examples of lncRNA/circRNA-associated ceRNAs in recent years, which involved the potential molecular mechanisms in the regulation of proliferation, apoptosis, treatment resistance and metastasis of NPC, and discuss their potential clinical significance in the prognosis and treatment of NPC. Interpreting the involvement of ceRNAs networks will provide new insight into the pathogenesis and treatment strategies of NPC. However, ceRNA regulatory mechanism has some limitations currently. Screening the most effective ceRNA targets and the clinical application of ceRNA still has many challenges.

Competing endogenous RNAs (ceRNAs) refer to RNA transcripts, such as mRNAs, non-coding RNAs, pseudogene transcripts, and circular RNAs, that can regulate each other by competing for the same pool of miRNAs. ceRNAs involve in the pathogenesis of several common cancers such as prostate cancer, liver cancer, breast cancer, lung cancer, gastric cancer, endometrial cancer, and so on. ceRNA activity is determined by factors such as miRNA/ceRNA abundance, ceRNAs binding affinity to miRNAs, RNA editing, and RNA-binding proteins. The alteration of any of these factors may lead to ceRNA network imbalance and thus contribute to cancer initiation and progression. There are generally three steps in ceRNA research conductions: ceRNA prediction, ceRNA validation, and ceRNA functional investigation. Deciphering ceRNA interplay in cancer provides new insight into cancer pathogenesis and opportunities for therapy exploration. In this review, we try to give readers a concise and reliable illustration on the mechanism, functions, research approaches, and perspective of ceRNA in cancer.

Obesity is becoming an epidemic of widespread concern, but the underlying causes remain elusive. In this study, whole transcriptome RNA sequencing revealed differential profiles of noncoding (nc) RNAs and mRNAs in visceral adipose tissue from obese (BMI > 32.5kg/m2) and lean (BMI < 20kg/m2) individuals, with 1920 differentially expressed genes, 1466 long noncoding (lnc) RNAs, 122 micro (mi) RNAs, and 52 circular (circ) RNAs identified. Gene Set Enrichment Analysis, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis revealed that these ncRNAs were involved in inflammation-related pathways that included cytokine-cytokine receptor interaction, the tumor necrosis factor and nuclear factor kappa B signaling pathways. The results indicated a critical role of inflammation in the pathogenesis of obesity. The network interaction of lncRNA, circRNA, and miRNA revealed a competing endogenous (ce) RNA network that was associated with inflammation. The ceRNA network included circORC5/miR-197-5p/TNFRSF10D and circNTRK2/miR-760/LAT, which were dysregulated in obese patients. In conclusion, this whole transcriptome study provided a pool of data that will be useful for identifying biomarkers of obesity and identified an obesity-associated ceRNA network that is regulated by circORC5 and circNTRK2.

The expression of pepsinogen C (PGC) is considered an ideal negative biomarker of gastric cancer, but its pathological mechanisms remain unclear. This study aims to analyze competing endogenous RNA (ceRNA) networks related to PGC expression at a post-transcriptional level and build an experimental basis for studying the role of PGC in the progression of gastric cancer. RNA sequencing technology was used to detect the differential expression (DE) profiles of PGC-related long non-coding (lnc)RNAs, circular (circ)RNAs, and mRNAs. Ggcorrplot R package and online database were used to construct DElncRNAs/DEcircRNAs co-mediated PGC expression-related ceRNA networks. In vivo and in vitro validations were performed using quantitative reverse transcription-PCR (qRT-PCR). RNA sequencing found 637 DEmRNAs, 698 DElncRNAs, and 38 DEcircRNAs. The PPI network of PGC expression-related mRNAs consisted of 503 nodes and 1179 edges. CFH, PPARG, and MUC6 directly interacted with PGC. Enrichment analysis suggested that DEmRNAs were mainly enriched in cancer-related pathways. Eleven DElncRNAs, 13 circRNAs, and 35 miRNA-mRNA pairs were used to construct ceRNA networks co-mediated by DElncRNAs and DEcircRNAs that were PGC expression-related. The network directly related to PGC was as follows: SNHG16/hsa_circ_0008197-hsa-mir-98-5p/hsa-let-7f-5p/hsa-let-7c-5p-PGC. qRT-PCR validation results showed that PGC, PPARG, SNHG16, and hsa_circ_0008197 were differentially expressed in gastric cancer cells and tissues: PGC positively correlated with PPARG (r = 0.276, P = 0.009), SNHG16 (r = 0.35, P = 0.002), and hsa_circ_0008197 (r = 0.346, P = 0.005). PGC-related DElncRNAs and DEcircRNAs co-mediated complicated ceRNA networks to regulate PGC expression, thus affecting the occurrence and development of gastric cancer at a post-transcriptional level. Of these, the network directly associated with PGC expression was a SNHG16/hsa_circ_0008197-mir-98-5p/hsa-let-7f-5p/hsa-let-7c-5p - PGC axis. This study may form a foundation for the subsequent exploration of the possible regulatory mechanisms of PGC in gastric cancer.

RNA regulation in immunity.

We aimed at exploring the role of gene expression changes regulated by non-coding RNAs in ossification of ligamentum flavum (OLF). Three microarray datasets, including long non-coding RNA (lncRNA)/mRNA expression profile (GSE106253), circular RNA (circRNA) expression profile (GSE106255), and microRNA (miRNA) expression profile (GSE106256), were downloaded from the public Gene Expression Omnibus repository. The differentially expressed (DE) mRNAs, lncRNAs, miRNAs, and circRNAs in OLF tissues were analyzed, compared with normal tissues. Two competing endogenous RNA (ceRNA) networks with lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA interactions were constructed, separately. Random walk with the restart model was applied to calculate the correlations of mRNAs with the published OLF-related genes. The top 50 mRNAs were subjected to function enrichment analysis and active small-molecule prediction. Total 2323 DE mRNAs, 1168 lncRNAs, 336 circRNAs, and 29 miRNAs were identified based on the microarray datasets. The LncRNA-related ceRNA network was constructed with 614 lncRNA-miRNA, 494 miRNA-mRNA, and 2099 lncRNA-mRNA interaction pairs; the circRNA-related ceRNA network was constructed with 153 circRNA-miRNA, 190 miRNA-mRNA, and 210 circRNA-mRNA interaction pairs. There were 17 OLF-related genes retrieved from previous literature, such as NPPS, COL6A1, and COL11A2, among which COL6A1 was the overlapped gene with mRNAs in the ceRNA network. Subsequently, top 50 mRNAs that closely correlated with COL6A1 in the ceRNA network were captured and these genes were closely related with the collagen catabolic process, regulation of cell growth, and neuronal action potential. DRD1 and COL6A1 were predicted to be the targets by small active molecule drugs. The collagen catabolic process may be implicated in OLF development. COL6A1 and DRD1 may be the candidate targets for OLF. However, further validations were needed.

Integrated Analyses of Competing Endogenous RNA Network Reveal Potential Therapeutic Targets in Chronic Lymphocytic Leukemia

BackgroundAlthough chronic inflammation, oxidative stress, airway remodeling, and protease-antiprotease imbalance have been implicated in chronic obstructive pulmonary disease (COPD), the exact pathogenesis is still obscure. Gene transcription and post-transcriptional regulation have been taken into account as key regulators of COPD occurrence and development. Identifying the hub genes and constructing biological regulatory networks at the post-transcriptional level will help extend current knowledge on COPD pathogenesis and develop potential drugs.MethodsAll lung tissues from non-smokers (n = 6), smokers without COPD (smokers, n = 7), and smokers with COPD (COPD, n = 7) were collected to detect messenger RNA (mRNA), microRNA (miRNA), circular RNA (circRNA), and long non-coding RNA (lncRNA) expression and identify the hub genes. Biological regulatory networks were constructed at the post-transcriptional level, including the RNA-binding protein (RBP)-hub gene interaction network and the competitive endogenous RNA (ceRNA) network. In addition, we assessed the composition and abundance of immune cells in COPD lung tissue and predicted potential therapeutic drugs for COPD. Finally, the hub genes were confirmed at both the RNA and protein levels.ResultsAmong the 20 participants, a total of 121169 mRNA transcripts, 1871 miRNA transcripts, 4244 circRNA transcripts, and 122130 lncRNA transcripts were detected. There were differences in the expression of 1561 mRNAs, 48 miRNAs, 33 circRNAs, and 545 lncRNAs between smokers and non-smokers, as well as 1289 mRNAs, 69 miRNAs, 32 circRNAs, and 433 lncRNAs between smokers and COPD patients. 18 hub genes were identified in COPD. TGF-β signaling and Wnt/β-catenin signaling may be involved in the development of COPD. Furthermore, the circRNA/lncRNA-miRNA-mRNA ceRNA networks and the RBP-hub gene interaction network were also constructed. Analysis of the immune cell infiltration level revealed that M2 macrophages and activated NK cells were increased in COPD lung tissues. Finally, we identified that the ITK inhibitor and oxybutynin chloride may be effective in treating COPD.ConclusionsWe identified several novel hub genes involved in COPD pathogenesis. TGF-β signaling and Wnt/β-catenin signaling were the most dysregulated pathways in COPD patients. Our study constructed post-transcriptional biological regulatory networks and predicted small-molecule drugs for the treatment of COPD, which enhanced the existing understanding of COPD pathogenesis and suggested an innovative direction for the therapeutic intervention of the disease.

Understanding the (epi)genetic dysregulation in Parkinson's disease through an integrative brain competitive endogenous RNA network

SummaryCircular RNAs (circRNAs) have been identified as naturally occurring RNAs that are highly represented in the eukaryotic transcriptome. Although a large number of circRNAs have been reported, the underlying regulatory mechanism of circRNAs biogenesis remains largely unknown. Here, we integrated in-depth multi-omics data including epigenome, transcriptome, and non-coding RNA and identified candidate circRNAs in six cellular contexts. Next, circRNAs were divided into two classes (high versus low) with different expression levels. Machine learning models were constructed that predicted circRNA expression levels based on 11 different histone modifications and host gene expression. We found that the models achieve great accuracy in predicting high versus low expressed circRNAs. Furthermore, the expression levels of host genes of circRNAs, H3k36me3, H3k79me2, and H4k20me1 contributed greatly to the classification models in six cellular contexts. In summary, all these results suggest that epigenetic modifications, particularly histone modifications, can effectively predict expression levels of circRNAs.

Colorectal cancer (CRC) is a common type of cancer and the second leading cause of cancer-related deaths worldwide. Competing endogenous RNAs (ceRNAs) that contain microRNA response elements (MREs) are involved in CRC progression. They can compete with microRNAs (miRNAs) via their MREs, which can combine non-coding and coding RNAs via complex ceRNA networks. This molecular interaction has the potential to affect a wide variety of biological processes, and many cancers can occur as a result of an imbalanced ceRNA network. Recent research indicates that numerous dysregulated RNAs in CRC may function as ceRNAs, regulating multiple biological functions of the tumour, including proliferation, apoptosis, metastasis, invasion and migration. In this review, we discuss the role of protein-coding and non-coding RNAs, such as long non-coding RNAs, circular RNAs and pseudogenes, in the occurrence of ceRNA networks in CRC, and their function in cancer-related pathways, such as Wnt/β-catenin, mitogen-activated protein kinase and transforming growth factor-β signalling pathways. Additionally, we discuss validated ceRNAs associated with CRC biological functions and their potential role as novel prognostic and diagnostic biomarkers. Examining the role of ceRNAs in CRC sheds new light on cancer treatment and pathogenesis.

Cervical cancer is the third most common cancer worldwide and the fourth leading cause of cancer-associated mortality in women. Accumulating evidence indicates that long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) may play key roles in the carcinogenesis of different cancers; however, little is known about the mechanisms of lncRNAs and circRNAs in the progression and metastasis of cervical cancer. In this study, we explored the expression profiles of lncRNAs, circRNAs, miRNAs, and mRNAs in HPV16 (human papillomavirus genotype 16) mediated cervical squamous cell carcinoma and matched adjacent non-tumor (ATN) tissues from three patients with high-throughput RNA sequencing (RNA-seq). In total, we identified 19 lncRNAs, 99 circRNAs, 28 miRNAs, and 304 mRNAs that were commonly differentially expressed (DE) in different patients. Among the non-coding RNAs, 3 lncRNAs and 44 circRNAs are novel to our knowledge. Functional enrichment analysis showed that DE lncRNAs, miRNAs, and mRNAs were enriched in pathways crucial to cancer as well as other gene ontology (GO) terms. Furthermore, the co-expression network and function prediction suggested that all 19 DE lncRNAs could play different roles in the carcinogenesis and development of cervical cancer. The competing endogenous RNA (ceRNA) network based on DE coding and non-coding RNAs showed that each miRNA targeted a number of lncRNAs and circRNAs. The link between part of the miRNAs in the network and cervical cancer has been validated in previous studies, and these miRNAs targeted the majority of the novel non-coding RNAs, thus suggesting that these novel non-coding RNAs may be involved in cervical cancer. Taken together, our study shows that DE non-coding RNAs could be further developed as diagnostic and therapeutic biomarkers of cervical cancer. The complex ceRNA network also lays the foundation for future research of the roles of coding and non-coding RNAs in cervical cancer.