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

JunJie Yu; WeiPu Mao; Si Sun; Qiang Hu; Can Wang; ZhiPeng Xu; RuiJi Liu; SaiSai Chen; Bin Xu; Ming Chen

doi:10.3389/fonc.2021.663263

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

Front Oncol. 2021 May 26:11:663263. doi: 10.3389/fonc.2021.663263. eCollection 2021.

Authors

JunJie Yu¹, WeiPu Mao¹, Si Sun¹, Qiang Hu¹, Can Wang¹, ZhiPeng Xu¹, RuiJi Liu¹, SaiSai Chen¹, Bin Xu², Ming Chen^{2

3}

Affiliations

¹ Medical College, Southeast University, Nanjing, China.
² Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China.
³ Department of Urology, Affiliated Lishui People's Hospital of Southeast University, Nanjing, China.

Abstract

Purpose: This 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.

Methods: The 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.

Results: We 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.

Conclusions: We constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.

Keywords: M6A; The Cancer Genome Atlas; kidney renal clear cell carcinoma; long non-coding RNA; prognostic signature.