CircRNAs as Potential Blood Biomarkers and Key Elements in Regulatory Networks in Gastric Cancer.
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ABSTRACT:
SUBMITTER: Reis-das-Merces L
PROVIDER: S-EPMC8776217 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background We aimed to identify the key differentially expressed genes (DEGs) associated with poor prognosis in gastric cancer (GC) and to elucidate the underlying molecular mechanisms in order to provide a therapeutic target for this disease. Methods The DEGs common in two datasets, GSE54129 and GSE79973, were screened. GO and KEGG enrichment analyses were then performed for these DEGs using DAVID’s tool. STRING and the Cytoscope software were also used to analyze the protein-protein interaction (PPI) networks of the DEGs common between the two datasets. Results A total of 164 common DEGs were identified from GSE79973 and GSE54129 datasets, 42 were up-regulated and 122 were down-regulated in GC. KEGG analysis demonstrated that up-regulated DEGs were mainly enriched for focal adhesion, ECM-receptor interaction, PI3K-Akt signaling pathway, protein digestion and absorption, and vascular smooth muscle contraction, while down-regulated DEGs were enriched for chemical carcinogenesis, metabolism of xenobiotics by cytochrome P450, drug metabolism-cytochrome P450, and retinol metabolism (P<0.05). Obtained PPI network for the 164 DEGs via Cytotype software, using MCODE app of Cytotype software we identified 13 hub genes. Twelve of these genes were found to be associated with poor prognosis in GC by survival analysis. Post validation by the GEPIA, Oncomine, and Human Protein Atlas databases, eight genes (COL4A1, COL6A3, COL1A2, COL1A1, THBS2, COL11A1, SPP1, and FN1) were found to be up-regulated in GC tissues and correlated with poor prognosis of GC. Conclusions COL4A1, COL6A3, COL1A2, COL1A1, THBS2, COL11A1, SPP1, and FN1 could serve as potential targets for GC diagnosis and prognosis.
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Project description:Regulatory elements determine the connectivity of molecular networks and mediate a variety of regulatory processes ranging from DNA looping to transcriptional, posttranscriptional, and posttranslational regulation. This review highlights our current understanding of the different types of regulatory elements found in molecular networks with a focus on DNA regulatory elements. We highlight technical advances and current challenges for the mapping of regulatory elements at the genome-wide scale, and describe new computational methods to uncover these elements via reconstructing regulatory networks from large genomic datasets. WIREs Syst Biol Med 2017, 9:e1374. doi: 10.1002/wsbm.1374 For further resources related to this article, please visit the WIREs website.
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Project description:CircRNAs are novel endogenous non-coding small RNAs involved in the regulation of multiple biological processes. However, little is known regarding circRNAs in ovarian development and maturation of fish. Our study, for the first time, provides the genome-wide overview of the types and relative abundances of circRNAs in tongue sole tissues during three ovarian developmental stages. We detected 6790 circRNAs in the brain, 5712 in the pituitary gland, 4937 in the ovary and 4160 in the liver. Some circRNAs exhibit tissue-specific expression, and qRT-PCR largely confirmed 6 differentially expressed (DE) circRNAs. Gene Ontology and KEGG pathway analyses of DE mRNAs were performed. Some DE circRNA parental genes were closely associated with biological processes in key signalling pathways and may play essential roles in ovarian development and maturation. We found that the selected circRNAs were involved in 10 pathways. RNase R digestion experiment and Sanger sequencing verified that the circRNA had a ring structure and was RNase R resistant. qRT-PCR results largely confirmed differential circRNA expression patterns from the RNA-seq data. These findings indicate that circRNAs are widespread in terms of present in production-related tissues of tongue sole with potentially important regulatory roles in ovarian development and maturation.
| S-EPMC8468179 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is one of the most common cancers all over the world, causing high mortality. Gastric cancer screening is one of the effective strategies used to reduce mortality. We expect that good biomarkers can be discovered to diagnose and treat gastric cancer as early as possible.MethodsWe download four gene expression profiling datasets of gastric cancer (GSE118916, GSE54129, GSE103236, GSE112369), which were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between gastric cancer and adjacent normal tissues were detected to explore biomarkers that may play an important role in gastric cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlap genes were conducted by the Metascape online database; the protein-protein interaction (PPI) network was constructed by the STRING online database, and we screened the hub genes of the PPI network using the Cytoscape software. The survival curve analysis was conducted by km-plotter and the stage plots of hub genes were created by the GEPIA online database. PCR, WB, and immunohistochemistry were used to verify the expression of hub genes. A neural network model was established to quantify the predictors of gastric cancer.ResultsThe relative expression level of cadherin-3 (CDH3), lymphoid enhancer-binding factor 1 (LEF1), and matrix metallopeptidase 7 (MMP7) were significantly higher in gastric samples, compared with the normal groups (p<0.05). Receiver operator characteristic (ROC) curves were constructed to determine the effect of the three genes' expression on gastric cancer, and the AUC was used to determine the degree of confidence: CDH3 (AUC = 0.800, P<0.05, 95% CI =0.857-0.895), LEF1 (AUC=0.620, P<0.05, 95%CI=0.632-0.714), and MMP7 (AUC=0.914, P<0.05, 95%CI=0.714-0.947). The high-risk warning indicator of gastric cancer contained 8<CDH3<15 and 10<expression of LEF1<16.ConclusionsCDH3, LEF1, and MMP7 can be used as candidate biomarkers to construct a neural network model from hub genes, which may be helpful for the early diagnosis of gastric cancer.
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Project description:Gastric cancer is one of the most commonly occurring cancers worldwide. Investigation of long noncoding RNAs is of increasing interest, particularly in relation to their contribution to progression and prognosis of gastric cancers; however, insufficient studies been performed investigating the part of long noncoding RNAs play in gastric cancer carcinogenesis. Patterns of dysregulated long noncoding RNA and messenger RNA between mucosa gastric cancer and adjacent normal tissues were identified using long noncoding RNAs microarray analysis. Quantitative real-time polymerase chain reaction was conducted as a means to verify the obtained data. Both Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were subsequently used to investigate the function of dysregulated long noncoding RNAs and messenger RNAs. Cis and trans action was used to predict the possible targets of long noncoding RNAs, and a coexpression network was created to simulate the complex intergenic interactions. Ninety-five dysregulated long noncoding RNAs and 123 messenger RNAs were identified, and quantitative real-time polymerase chain reaction was used to validate 6 filtered long noncoding RNAs. Gene Ontology and KEGG pathway analyses identified several remarkably biological processes and signaling pathways, including spliceosome, RNA transport, and ubiquitin-mediated proteolysis. The transcriptional factors MYC, GABPA, and E2F1 were found to play a central function in the long noncoding RNAs process, as indicated by the coexpression network. This study revealed the dysregulated long noncoding RNA profiles of mucosal gastric cancer. The results shed light on the biological function of long noncoding RNAs in gastric cancer pathogenesis. This provides useful information for exploring potential early screening biomarkers in gastric cancer.
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Project description:The expression of circular RNA (circRNA) may affect tumor progression. However, there have been no systemic meta-analysis for cancer diagnosis by using circRNAs in clinical till now. Herein, we aimed to collect and examined all the evidence on the potential role of circRNA as novel biomarker in human cancers.A comprehensive search strategy was used to search relevant literatures in the databases of PubMed, Embase, and the Web of Science from 2015 to August 2017. The correlation between circRNA expression and the diagnostic accuracy of tumor markers was analyzed. The methodological quality of each study was assessed by quality assessment for the diagnostic accuracies of the eligible studies (QUADAS-2). Statistical analysis was performed by applying the STATA (version 12.0) software.The present meta-analysis included 1752 patients with circRNA expression data of tumor and paired adjacent non-tumorous tissues from 17 publications (19 studies). The pooled sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnostic OR (DOR) with their 95% confidential intervals (95%CIs), and AUC values were 0.72 (0.67-0.76), 0.74 (0.69-0.78), 2.80 (2.40-3.10), 0.38 (0.33-0.44), 7.00 (6.00-9.00), and 0.79, respectively. Subgroup analyses showed that the expression of circRNA in tissues of hepatocellular carcinoma (HCC) group was more prone to be detected than other tumor types, with a high values of the specificity, DOR, and AUC.circRNAs might be suitable as diagnostic biomarkers for tumors, especially in HCC diagnosis. Further prospective studies on the diagnostic value of circRNAs from the different tumors are needed in the future.
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Project description:Gastric cancer (GC) is still an extremely severe health issue with high mortality due to the lacking of effective biomarkers. In this study, we aimed to investigate the alterations of salivary protein glycosylation related to GC and assess the possibility of salivary glycopatterns as potential biomarkers for the diagnosis of GC. Firstly, 94 patients with GC (n = 64) and atrophic gastritis (AG) (n = 30), as well as 30 age- and sex-matched healthy volunteers (HV) were enrolled in the test group to probe the difference of salivary glycopatterns using lectin microarrays, the results were validated by saliva microarrays and lectin blotting analysis. Then, the diagnostic model of GC (Model GC) and AG (Model AG) were constructed based on 15 candidate lectins which exhibited significant alterations of salivary glycopattern by logistic stepwise regression. Finally, two diagnostic models were assessed in the validation group including HV (n = 30) and patients with GC (n = 23) and AG (n = 24) and achieved high diagnostic power (Model GC (AUC: 0.89, sensitivity: 0.96 and specificity: 0.80), Model AG (AUC: 0.83, sensitivity: 0.92 and specificity: 0.72)). This study provides pivotal information to distinguish HV, AG and GC based on precise alterations in salivary glycopatterns, which have great potential to be biomarkers for diagnosis of GC.
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