Comprehensive Analysis of eRNAs and Genes Involved in Gastric Cancer and Constructing a Prognostic Model Predicting the Overall Survival of Patients

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Bahrami Basireh; Nikpour Parvaneh

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Isfahan Medical School Year:2023 | Volume:41 | Issue:724 Page(s): 488-495

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Comprehensive Analysis of eRNAs and Genes Involved in Gastric Cancer and Constructing a Prognostic Model Predicting the Overall Survival of Patients

Author(s)

Bahrami Basireh | Nikpour Parvaneh | Issue Writer Certificate

Keywords

EnhancerQ4

RNAQ4

Stomach NeoplasmsQ4

Survival analysisQ4

Systems biologyQ4

RNA-SeqQ4

Abstract

Background: Gastric cancer (GC) is one of the most prevalent cancers and the first cause of cancer-related deaths in Iran. These patients are diagnosed at advanced stages, which leads to poor prognosis because there is inadequate knowledge of molecular factors to provide diagnostic/prognostic biomarkers. The aim of the current study was the comprehensive analysis of Enhancer RNAs (eRNAs) to determine their molecular interactions with other genes and propose a survival-related model for patients. Methods: RNA-sequencing raw data of GC patients were downloaded from The Cancer Genome Atlas (TCGA), and differentially-expressed eRNAs and genes in tumors compared to non-tumor samples were extracted. The target genes of each eRNA were then identified based on physical distance, correlation, and a regulatory network was constructed with these elements. A prognostic model for predicting the overall survival of patients was eventually established by performing univariate and multivariate Cox regression analyses, and the performance of the model was surveyed. Findings: By performing differential expression analysis, 69 and 2606 differentially-expressed eRNAs and genes were extracted, respectively, and by identifying the relationships between these elements, a regulatory network consisting of 84 nodes and 119 edges was constructed. A three-components survival-related model subsequently was established which could predict patients' outcomes. Conclusion: Based on the results, the 3-component constructed model, including an eRNA and two other genes, can be considered as a possible prognostic tool; however, further research is needed to clinically implement it.

Cites

No record.

References