Identification of an m6A-Related lncRNA Signature for Predicting the Prognosis in Patients With Kidney Renal Clear Cell Carcinoma

Abstract

PurposeThis study aimed to construct an m6A-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data obtained from The Cancer Genome Atlas (TCGA) database.MethodsThe KIRC patient data were downloaded from TCGA database and m6A-related genes were obtained from published articles. Pearson correlation analysis was implemented to identify m6A-related lncRNAs. Univariate, Lasso, and multivariate Cox regression analyses were used to identifying prognostic risk-associated lncRNAs. Five lncRNAs were identified and used to construct a prognostic signature in training set. Kaplan–Meier curves and receiver operating characteristic (ROC) curves were applied to evaluate reliability and sensitivity of the signature in testing set and overall set, respectively. A prognostic nomogram was established to predict the probable 1-, 3-, and 5-year overall survival of KIRC patients quantitatively. GSEA was performed to explore the potential biological processes and cellular pathways. Besides, the lncRNA/miRNA/mRNA ceRNA network and PPI network were constructed based on weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analysis was used to identify the biological functions of m6A-related lncRNAs.ResultsWe constructed and verified an m6A-related lncRNAs prognostic signature of KIRC patients in TCGA database. We confirmed that the survival rates of KIRC patients with high-risk subgroup were significantly poorer than those with low-risk subgroup in the training set and testing set. ROC curves indicated that the prognostic signature had a reliable predictive capability in the training set (AUC = 0.802) and testing set (AUC = 0.725), respectively. Also, we established a prognostic nomogram with a high C-index and accomplished good prediction accuracy. The lncRNA/miRNA/mRNA ceRNA network and PPI network, as well as functional enrichment analysis provided us with new ways to search for potential biological functions.ConclusionsWe constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.

Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

“…Weighted gene co-expression network analysis (WGCNA) was developed by Horvath and Zhang in 2005 ( 20 ). At present, WGCNA is becoming a powerful approach to detecting gene modules, exploring the correlation of the modules and phenotypes, and discovering hub genes that regulate critical biological processes ( 21 , 22 ).…”

Identification of a N6-Methyladenosine (m6A)-Related lncRNA Signature for Predicting the Prognosis and Immune Landscape of Lung Squamous Cell Carcinoma

“…m6A-related lncRNAs demonstrated promising roles in tumor diagnosis, prognosis, and treatment. Yu et al constructed an m6A-related lncRNA signature which could accurately predict the survival of kidney renal clear cell carcinoma patients (24). Moreover, m6A regulators also demonstrated potential therapeutical roles in disease treatment (25), such as FTO inhibitors MO-I-500 (26) and FB23-2 (27).…”

Data_Sheet_1.xlsx

“…The areas under the ROC curves of the survival model for predicting 3-, 5-, 7-, and 10-year survival rates were 0.734, 0.752, 0.763, and 0.787, respectively, indicating that the risk model had a high predictive value (Figures 7E-H). Survival probability predicted by our angiogenesis prognostic signature was superior to the m6A-related lncRNA signature constructed by Yu et al (25) and the seven-MDEG signature constructed by Hu et al (26). For the patients with KIRC whose survival time was 1-4-years, our prognostic signature also showed a higher predictive accuracy compared with the autophagy-related long non-coding RNA signature constructed by Yu et al (24).…”

“…Currently, other predictors of risk or survival based on different mechanisms or aspects do exist for KIRC. For example, Yu et al constructed two prognostic signatures on the basis of autophagy-associated long non-coding RNAs (lncRNA) and m6A-related lncRNAs, respectively, both of which could effectively predict the outcome of patients with KIRC (24,25). In another study, a 7-methylated differentially expressed gene signature was found to be a powerful prognostic factor for these patients (26).…”

“…A receiver operating characteristic (ROC) curve was generated using the "survival-ROC" package in R to obtain the area under the ROC curve. To highlight the superiority and accuracy of our model, we carefully compared our prognostic model with another three prognostic signatures (an autophagyrelated long non-coding RNA signature constructed by Yu et al, an m6A-related lncRNA signature constructed by Yu et al, and a seven-MDEG signature constructed by Hu et al) (24)(25)(26). All the genes used for the construction of prognostic signatures of Yu and HU were obtained.…”