Dataset Information

CircRNAs as Potential Blood Biomarkers and Key Elements in Regulatory Networks in Gastric Cancer.

ABSTRACT: Gastric cancer (GC) is the fifth most common type of cancer and the third leading cause of cancer death in the world. It is a disease that encompasses a variety of molecular alterations, including in non-coding RNAs such as circular RNAs (circRNAs). In the present study, we investigated hsa_circ_0000211, hsa_circ_0000284, hsa_circ_0000524, hsa_circ_0001136 and hsa_circ_0004771 expression profiles using RT-qPCR in 71 gastric tissue samples from GC patients (tumor and tumor-adjacent samples) and volunteers without cancer. In order to investigate the suitability of circRNAs as minimally invasive biomarkers, we also evaluated their expression profile through RT-qPCR in peripheral blood samples from patients with and without GC (n = 41). We also investigated the predicted interactions between circRNA-miRNA-mRNA and circRNA-RBP using the KEGG and Reactome databases. Overall, our results showed that hsa_circ_0000211, hsa_circ_0000284 and hsa_circ_0004771 presented equivalent expression profiles when analyzed by different methods (RNA-Seq and RT-qPCR) and different types of samples (tissue and blood). Further, functional enrichment results identified important signaling pathways related to GC. Thus, our data support the consideration of circRNAs as new, minimally invasive biomarkers capable of aiding in the diagnosis of GC and with great potential to be applied in clinical practice.

SUBMITTER: Reis-das-Merces L

PROVIDER: S-EPMC8776217 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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CircRNAs as Potential Blood Biomarkers and Key Elements in Regulatory Networks in Gastric Cancer.

Reis-das-Mercês Laís L Vinasco-Sandoval Tatiana T Pompeu Rafael R Ramos Aline Cruz AC Anaissi Ana K M AKM Demachki Samia S de Assumpção Paulo Pimentel PP Vidal Amanda F AF Ribeiro-Dos-Santos Ândrea Â Magalhães Leandro L

International journal of molecular sciences 20220107 2

Gastric cancer (GC) is the fifth most common type of cancer and the third leading cause of cancer death in the world. It is a disease that encompasses a variety of molecular alterations, including in non-coding RNAs such as circular RNAs (circRNAs). In the present study, we investigated hsa_circ_0000211, hsa_circ_0000284, hsa_circ_0000524, hsa_circ_0001136 and hsa_circ_0004771 expression profiles using RT-qPCR in 71 gastric tissue samples from GC patients (tumor and tumor-adjacent samples) and v ...[more]

PMID: 35054834

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Project description:Lung cancer is the second most commonly diagnosed carcinoma and is the leading cause of cancer death. Although significant progress has been made towards its understanding and treatment, unraveling the complexities of lung cancer is still hampered by a lack of comprehensive knowledge on the mechanisms underlying the disease. High-throughput and multidimensional genomic data have shed new light on cancer biology. In this study, we developed a network-based approach integrating somatic mutations, the transcriptome, DNA methylation, and protein-DNA interactions to reveal the key regulators in lung adenocarcinoma (LUAD). By combining Bayesian network analysis with tissue-specific transcription factor (TF) and targeted gene interactions, we inferred 15 disease-related core regulatory networks in co-expression gene modules associated with LUAD. Through target gene set enrichment analysis, we identified a set of key TFs, including known cancer genes that potentially regulate the disease networks. These TFs were significantly enriched in multiple cancer-related pathways. Specifically, our results suggest that hepatitis viruses may contribute to lung carcinogenesis, highlighting the need for further investigations into the roles that viruses play in treating lung cancer. Additionally, 13 putative regulatory long non-coding RNAs (lncRNAs), including three that are known to be associated with lung cancer, and nine novel lncRNAs were revealed by our study. These lncRNAs and their target genes exhibited high interaction potentials and demonstrated significant expression correlations between normal lung and LUAD tissues. We further extended our study to include 16 solid-tissue tumor types and determined that the majority of these lncRNAs have putative regulatory roles in multiple cancers, with a few showing lung-cancer specific regulations. Our study provides a comprehensive investigation of transcription factor and lncRNA regulation in the context of LUAD regulatory networks and yields new insights into the regulatory mechanisms underlying LUAD. The novel key regulatory elements discovered by our research offer new targets for rational drug design and accompanying therapeutic strategies.

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.

Dataset Information

CircRNAs as Potential Blood Biomarkers and Key Elements in Regulatory Networks in Gastric Cancer.

Publications

CircRNAs as Potential Blood Biomarkers and Key Elements in Regulatory Networks in Gastric Cancer.

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