Analysis of Circular RNA-Related Competing Endogenous RNA Identifies the Immune-Related Risk Signature for Colorectal Cancer

Abstract

Background: Recent papers have described circular RNAs (circRNAs) playing important roles in the development and progression of colorectal cancer (CRC). However, the expression profiles of circRNAs and their functions in CRC have rarely been studied. The objective was to identify circRNAs involved in the carcinogenesis and progression of CRC and to explore potential molecular mechanisms as a competitive endogenous RNA (ceRNA). Moreover, we aimed to establish an immune-related gene signature for predicting the overall survival (OS) of CRC. Methods: The expression patterns of circRNA, miRNA, mRNA, and clinicopathological data were collected from the GEO and TCGA databases. A ceRNA network would be established, and the functional enrichment analyses were performed. The proteinprotein interaction network (PPI) was constructed, and hub genes were identified using a cytohub plugin. Subsequently, an immune-related signature was developed based on mRNAs in the ceRNA network. In addition, OS-nomogram was constructed by combining an immune-related signature and clinicopathological characterization to predict the OS. Results: We established a circRNA-miRNA-mRNA ceRNA network. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the mRNAs were mainly enriched in neuroactive ligand-receptor interaction, Wnt signaling pathway, cell adhesion molecules (CAMs), and renin secretion. PPI network and module analysis identified 10 hub genes, and the circRNA-miRNA hub gene regulatory modules was established. After univariate and multivariate analysis, seven immune-related genes in the ceRNA network were used to construct the immune-related signature. Patients were divided into low-risk and high-risk groups, and there were significant differences in the OS. The ROC of the nomogram indicated the satisfactory accuracy and predictive power. Furthermore, we established a prognostic nomogram based on immune-related risk score and clinical characterization. The ROC and calibration curves revealed the accuracy of the nomogram. In addition, the high-risk score was positively correlated with six immune infiltrating cells (P < 0.05).

A seven-lncRNA signature for predicting Ewing’s sarcoma

“…The lncRNA testis developmental related gene 1 (TDRG1) has been identified as a proto-oncogene for many tumor types, including gastric carcinoma, cervical cancer, epithelial ovarian cancer, endometrial cancer, and testicular germ cell tumors (Ma et al, 2020;Peng et al, 2019). LINC00623 also plays a role in hormone-related cancers (Wang et al, 2020).…”

“…AUC values ranged from 0.5 to 1.0, with 0.5 indicating a random probability and 1.0 indicating the perfect ability. It is generally considered that AUC >0.6 as a screening standard has a good predictive value (Li et al, 2020;Song et al, 2020).…”

Identification of Candidate Biomarkers Correlated Poorer Prognosis of Breast Cancer via Bioinformatics Method

“…After enrichment of the DEGs, we construct the PPI network based on String online database (https://string-db.org/) for all DEGs [9] and Combine score greater than 0.9 as the cutoff criterion, and then the network was loaded to the Cytoscape (version 3.7.0) software, and cytoHubba plugin was curried to predict hub genes [10].…”

An Immune-Gene-Based Classifier Predicts Prognosis in Patients With Cervical Squamous Cell Carcinoma

“…Immune-related prognostic gene signatures based on breast cancer, lung cancer, hepatocellular carcinoma have been reported ( Budhu et al, 2006 ; Roepman et al, 2009 ; Bedognetti et al, 2015 ). Immune-related prognostic nomogram incorporating immune gene signature and the other clinical information were also shown good performance in colorectal cancer ( Song et al, 2020 ) and lower-grade glioma ( Zhang et al, 2020 ). For the CC, Liu and colleagues ( Liu et al, 2020 ) established a two-immune-gene signature ( CD6 and CD1C ) for predicting prognosis.…”