Identification of circRNA–miRNA–mRNA regulatory network in gastric cancer by analysis of microarray data

Guan, Yongjun; Ma, Jianying; Song, Wei

doi:10.1186/s12935-019-0905-z

Cited by 88 publications

(71 citation statements)

References 34 publications

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“…Besides, Sun et al and Guan et al also performed bioinformatical analysis for circRNAs in hepatocellular carcinoma and gastric cancer, which used different methods and studied the ceRNA in different angles. 31,32 Thus, it would be reasonable to believe that bioinformatics provide guidance for our study. Certainly, further rigorous experiments are necessary to validate these findings.…”

Section: Dovepressmentioning

confidence: 99%

<p>Identification of circRNA-miRNA-mRNA Networks for Exploring the Fundamental Mechanism in Lung Adenocarcinoma</p>

Liang¹,

Zhang²,

Zhang³

et al. 2020

OTT

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Background: Circular RNAs (circRNAs) were elucidated to act as competing endogenous RNAs (ceRNAs) and to play significant roles in cancer initiation and progression. We aimed to identify the important circRNAs in lung adenocarcinoma and intended to predict the functions of them via the bioinformatics analysis. Methods: We extracted data from three Gene Expression Omnibus (GEO) datasets, GSE101586, GSE101684, and GSE104854, and identified the common differentially expressed circRNAs. Agarose gel electrophoresis and Sanger sequencing were used to verify these significant circRNAs. Then, qRT-PCR was performed to validate the expression through matched tissues and cell lines. Afterwards, a ceRNA network was constructed and functional analysis was performed to predict the potential mechanisms of circRNAs. Results: Five circRNAs (hsa_circ_0072088, hsa_circ_0082564, hsa_circ_0008274, hsa_-circ_0000519 and hsa_circ_0003528) were identified differentially expressed in the three datasets. Following the Agarose gel electrophoresis and qRT-PCR validation, hsa_circ_0072088 and hsa_-circ_0008274 were chosen for further analysis. A ceRNA network of hsa_circ_0072088 and hsa_circ_0008274 was built based on the CSCD/TargetScan/MiRTarbase/StarBase 3.0. Then, the functional analysis showed that several meaningful terms were identified, such as "epithelial to mesenchymal transition (EMT)", "Cellular response to TGF-β stimulus", "MAPK signaling pathway", and "PI3K-AKT signaling pathway". Finally, several significant circRNA/miRNA/mRNA regulatory axes, which were predicted relating to cancer progress, were noted from the network. Conclusion: We identified significant circRNAs and meaningful circRNA-miRNA-mRNA networks to provide novel insight into pathogenesis and therapy of lung adenocarcinoma.

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Section: Dovepressmentioning

confidence: 99%

<p>Identification of circRNA-miRNA-mRNA Networks for Exploring the Fundamental Mechanism in Lung Adenocarcinoma</p>

Liang¹,

Zhang²,

Zhang³

et al. 2020

OTT

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“…The function of hsa_circ_0005699 in cancer was controversial. hsa_circ_0005699 was previously reported to downregulate MCM8 and NCAPD2 expression by acting as a sponge of hsa-miR-504, functioning as tumor suppressor in gastric cancer [29]. Our study suggested that hsa_circ_0005699 may promote PTC as a sponge of hsa-miR-139-3p and upregulate SPC24 and CDKN2A expression.…”

Section: Discussionmentioning

confidence: 50%

Progression Risk Assessment of Post-Surgical Papillary Thyroid Carcinoma Based on Circular RNA-Associated Competing Endogenous RNA Mechanisms

et al. 2020

Preprint

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BackgroundAccurate risk assessment of post-surgical progression in papillary thyroid carcinoma (PTC) patients is critical. Exploring key differentially expressed mRNAs (DE-mRNAs) regulated by differentially expressed circRNAs (DE-circRNAs) via the ceRNA mechanism could help establish a novel assessment tool. MethodsceRNA network was established based on differentially expressed RNAs and correlation analysis. DE-mRNAs within the ceRNA network associated with progression-free interval (PFI) of PTC were identified to construct a prognostic ceRNA regulatory subnetwork. LASSO-Cox regression was applied to identify hub DE-mRNAs and establish a novel DE-mRNA signature in predicting PFI of PTC.ResultsSix hub DE-mRNAs, namely CLCNKB, FXBO27, FXYD6, RIMS2, SPC24, and CDKN2A, were identified to be most significantly related to the PFI of PTC and a prognostic DE-mRNA signature was proposed. A nomogram incorporating the DE-mRNA signature and clinical parameters was established to improve the progression risk assessment in post-surgical PTC, which was superior to the ATA risk stratification system and MACIS score AJCC staging system.ConclusionsBased on the circRNA-associated ceRNA RNA mechanism, a DE-mRNA signature and prognostic nomogram was established, which may improve the progression risk assessment in post-surgical PTC.

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“…The DEGs from all gene microarray datasets and RNA-seq data were obtained using the RRA method. This method uses a probabilistic model for aggregation, which is resilient to noise, and can calculate the probability of signi cance of all elements and perform the nal ranking [33][34][35][36][37]. The cutoff P value for the RRA was set as 0.05.…”

Section: The Potential Mechanism Of Bsg Expression In Thyroid Cancer mentioning

confidence: 99%

Immunohistochemical Basigin Expression Level in Thyroid Cancer Tissues

Guo

Tang

Pang

et al. 2020

Preprint

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Background: Thyroid cancer (TC) is the most common endocrine malignancy, Basigin (also known as BSG) plays a crucial role in tumor cell invasion, metastasis, and angiogenesis. This study was designed to identify the change of BSG expression in TC and its possible potential mechanism. Methods: The BSG expression levels in TC were demonstrated using data collected from in-house immunohistochemical (IHC), RNA-sequencing (RNA-seq), microarrays and literatures. Integrated analysis was performed to determined BSG expression levels in TC comprehensively. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed with the integration of BSG co-expressed genes and differentially expressed genes (DEGs) in TC tissues to explore the potential mechanisms of BSG in TC. Results: The protein expression level of BSG was significantly higher in TC cases based on the IHC experiments. In addition, the combined SMD for BSG expression was 0.39 (p<0.0001), the diagnostic odds ratio was 3.69, and the AUC of the sROC curve was 0.6986 using 1182 TC cases and 437 non-cancerous cases from 17 independent datasets. Furthermore, BSG co-expressed genes tended to be enriched in gene terms of the extracellular matrix (ECM), cell adhesion, and cell-cell interactions. The expression levels of nine hub BSG co-expressed genes were markedly up-regulated in TC cases. Conclusion: BSG expression levels were closely correlated with the progression of TC and may affect the signals of the ECM, cell adhesion, and cell-cell interactions.

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Identification of circRNA–miRNA–mRNA regulatory network in gastric cancer by analysis of microarray data

Cited by 88 publications

References 34 publications

<p>Identification of circRNA-miRNA-mRNA Networks for Exploring the Fundamental Mechanism in Lung Adenocarcinoma</p>

<p>Identification of circRNA-miRNA-mRNA Networks for Exploring the Fundamental Mechanism in Lung Adenocarcinoma</p>

Progression Risk Assessment of Post-Surgical Papillary Thyroid Carcinoma Based on Circular RNA-Associated Competing Endogenous RNA Mechanisms

Immunohistochemical Basigin Expression Level in Thyroid Cancer Tissues

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