The circular RNA hsa_circ_000780 as a potential molecular diagnostic target for gastric cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE184882 | GEO | 2021/09/28
REPOSITORIES: GEO
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Project description:MicroRNA (miRNA) sponges containing miRNA complementary binding sites constitute a potentially useful strategy for miRNA-inhibition therapeutics in cancer patients. Recently, naturally occurring circular RNAs (circRNAs) have been revealed to function as efficient microRNA sponges. We hypothesized that synthetic circRNA sponges targeting oncomiRs could be constructed and used to achieve potentially therapeutic microRNA loss of function. In this study, linear RNA molecules containing five miR-21 binding sites were transcribed in vitro. After dephosphorylation by calf intestinal phosphatase and phosphorylation by T4 polynucleotide kinase, circRNA sponges were circularized using 5’-3’ end ligation by T4 RNA ligase 1. Synthetic circular sponge stability was assayed in the presence of RNase R or fetal bovine serum. Luciferase reporter and cell proliferation assays were performed to assess competitive inhibition of miR-21 activity by circRNA sponges in NCI-N87 gastric cancer cells. Tandem Mass Tag (TMT) labeling proteomics analysis and Western blotting were performed to delineate effects of circRNA sponges on miR-21 downstream targeted proteins. Our experiments revealed that artificial circRNA sponges can be synthesized using enzymatic ligation. These synthetic circRNA sponges are more resistant than their linear RNA counterparts to nuclease degradation in vitro. They effectively suppress the activity of miR-21 on its downstream protein targets, including the important cancer protein DAXX. Finally, they also inhibit gastric cancer cell proliferation. Our results suggest that synthetic circRNA sponges represent a rapid, effective, convenient strategy to achieve loss of miRNA function in vitro, with potential future therapeutic application in vivo.
2018-10-22 | PXD008584 | Pride
Project description:Background: Circular RNAs (circRNAs) have attracted increasing attention in recent years for their potential application as disease biomarkers due to their high abundance and stability. In this study, we attempted to screen circRNAs that can be used to predict postoperative recurrence and survival in patients with gastric cancer (GC). Methods: High-throughput RNA sequencing was used to identify differentially expressed circRNAs in GC patients with different prognoses. The expression level of circRNAs in the training set (n=136) and validation set (n=167) was detected by quantitative real-time PCR (qRT-PCR). Kaplan–Meier estimator, receiver operating characteristic (ROC) curve and cox regression analysis were used to evaluate the prognostic value of circRNAs on recurrence-free survival (RFS) and overall survival (OS) in GC patients. CeRNA network prediction, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the circRNAs with prognostic significance. Results: A total of 259 differentially expressed circRNAs were identified in GC patients with different RFS. We found two circRNAs (hsa_circ_0005092 and hsa_circ_0002647) that highly expressed in GC patients with good prognoses, and subsequently established a predictive model for postoperative recurrence and prognosis evaluation, named circPanel. Patients with circPanellow might have shorter recurrence-free survival (RFS) and overall survival (OS). We also performed circRNA-miRNA-mRNA network prediction and functional analysis for hsa_circ_0005092 and hsa_circ_0002647. Conclusions: CircPanel has the potential to be a prognostic biomarker in GC patients with greater accuracy than a single circRNA and certain traditional tumor markers (e.g., CEA, CA19-9 and CA724).
2021-08-12 | GSE181769 | GEO
Project description:The circRNAs expression profiles in 5 gastric cancer (GC) and their matched non-gastric cancer (non-GC) tissues were detected by human circRNA expression profile chips, and the differentially expression circRNAs between GC and matched non-GC tissues were then identified. A total of 713 circRNAs were differentially expressed in GC tissues vs. non-GC tissues (fold change≥2.0, p<0.05) among all the candidate circRNAs (62998) detected in both GC and non-GC tissues according to the T-test. 191/713 circRNAs were significantly upregulated in GC tissues, whereas 522/713 circRNAs were significantly downregulated in GC tissues. GO and KEGG pathway analyses showed that many dysregulated circRNAs may be functionally GC-related, thereby contributing to the development of GC.
2017-06-19 | GSE100170 | GEO
Project description:The circular RNA hsa_circ_000780 as a potential molecular diagnostic target for gastric cancer
| PRJNA766718 | ENA
Project description:Accumulating evidence has shown that circRNAs played critical roles in cancer and oncology. However, the role and mechanisms of circRNAs in gastric cancer (GC) remains not fully understood. CircRNA microarray analysis is one of reliable and convenient methods to detect differentially expressed circular RNAs. The aims of this study are to investigate the differentially expressed circRNAs in GC via human circRNA microarray analysis, and illuminate the role and underlying mechanisms of hsa_circ_0025506, i.e., circGPRC5A(e2), in GC.
2023-01-25 | GSE194384 | GEO
Project description:Stomach cancer (GC) remains one of the most prevalent tumors globally, ranking third in terms of cancer-related mortality worldwide. Currently, the prognosis for gastric cancer following radical resection is still suboptimal, primarily due to metastasis. Therefore, it is crucial to explore target therapy as a means to address this issue. Circular RNAs (circRNAs) have emerged as pivotal regulators in the development and progression of cancer and hold promise as biomarkers for diagnosing and prognosticating various cancers. However, the precise mechanism by which circular RNA contributes to metastasis in gastric cancer remains unclear. Our objective is to validate the expression profile of circRNA in gastric cancer and investigate its functional role in metastasis within this context.
2024-07-31 | GSE248612 | GEO
Project description:The prognosis after curative resection of gastric cancer (GC) remains unsatisfactory, and thus, the development of treatments involving alternative molecular and genetic targets is critical. Circular RNAs (circRNAs), new stars of the non-coding RNA network, have been identified as critical regulators in various cancers. Here, we aimed to determine the circRNA expression profile and investigate the functional and prognostic significance of circRNA in GC. Using next-generation sequencing profiling, we first characterized an abundant circRNA, hsa_circ_0008549, derived from the OSBPL10 gene, and named it circOSBPL10. The expression of circOSBPL10 was found via quantitative real-time RT-PCR (qRT-PCR) to be upregulated in GC tissues, and silencing of circOSBPL10 significantly inhibited gastric cancer cell growth, migration and invasion in multiple experiments. We further confirmed that miR-136-5p is a downstream target of circOSBPL10 using RNA pull-down and luciferase reporter assays. Rescue experiments confirmed that circOSBPL10 regulates biological functions in GC cells via a circOSBPL10-miR-136-5p-WNT2 axis. In vivo experiments showed that circOSBPL10 promotes tumor growth and metastasis in mice. Furthermore, the level of circOSBPL10 was observed to be a prognostic marker of the overall survival and disease-free survival of patients with GC. Taken together, our findings reveal that circOSBPL10 may serve as a new proliferation factor and prognostic marker in gastric cancer.
2018-11-22 | GSE122796 | GEO
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
2024-07-23 | MODEL2407230001 | BioModels
Project description:Exosomes from the culture media (CM) of four GC cells (GCCs) and human gastric epithelial cells were isolated. Exosomal RNA was extracted, and circRNA microarray assay was performed to identify GC-specific exosomal circRNAs. A total of 58 exosomal circRNAs were expressed at considerable higher levels in GCCs than those in normal controls.The expression level of several top upregulated circRNAs were detected using qRT-PCR in both GC cells and clinical specimens of GC patients. More interesting, besides the high expression in GC cell derived exosomes, hsa_circ_0015286 was the uniquely upregulated circRNA in the plasma exosomes and tissues of GC patients..The result suggested that exosomal hsa_circ_0015286 may be a potential noninvasive biomarker for the diagnosis and prognosis monitoring of GC.
2022-05-12 | GSE202538 | GEO
Project description:Background: The lack of obvious symptoms of early gastric cancer (GC) as well as the absence of sensitive and specific biomarkers results in poor clinical outcomes. Tubulin is currently emerging as important regulators of the microtubule cytoskeleton and thus have a strong potential to be implicated in a number of disorders, however, its mechanism of action in gastric cancer is still unclear. Tubulin alpha-1C(TUBA1C) is a subtype of α-tubulin, high TUBA1C expression has been shown to be closely related to a poor prognosis in in various cancers,this study, for the first time, revealed the mechanism of TUBA1C promotes malignant progression of gastric cancer in vitro and in vivo. Methods: The expression of lncRNA EGFR-AS1 was detected in human GC cell lines by qRT–PCR. Mass spectrometry experiments following RNA pulldown assays found that EGFR-AS1 directly binds to TUBA1C, the CCK8, EdU, transwell, wound-healing, cell cycle assays and animal experiments were conducted to investigate the function of TUBA1C in GC. Combined with bioinformatics analyses, reveal interaction between Ki-67, E2F1, PCNA and TUBA1C by western blot. Rescue experiments furtherly demonstrated the relationship of EGFR-AS1and TUBA1C. Results: TUBA1C was proved to be a direct target of EGFR-AS1, TUBA1C promotes gastric cancer proliferation, migration and invasion by accelerating the progression of the cell cycle from the G1 phase to the S phase and activating the expression of oncogenes: Ki-67,E2F1 and PCNA. Conclusions: TUBA1C is a new potential target of LncRNA EGFR-AS1 promotes gastric cancer progression and could be a novel biomarker and therapeutic target for GC.
2023-05-10 | PXD040199 | Pride