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. 2021 Jan 11;21(1):46.
doi: 10.1186/s12935-020-01738-2.

Decreased expression of METTL14 predicts poor prognosis and construction of a prognostic signature for clear cell renal cell carcinoma

Yi Wang  1 , Rong Cong  2 , Shouyong Liu  2 , Bingye Zhu  1 , Xing Wang  3 , Qianwei Xing  4
Affiliations

Decreased expression of METTL14 predicts poor prognosis and construction of a prognostic signature for clear cell renal cell carcinoma

Yi Wang et al. Cancer Cell Int. .

Abstract

Background: METTL14, as one of N6-methyladenosine (m6A) related genes, has been found to be associated with promoting tumorigenesis in different types of cancers. This study was aimed to investigate the prognostic value of METTL14 in clear cell renal cell carcinoma (ccRCC).

Methods: We collected ccRCC patients' clinicopathological parameters information and 13 m6A related genes expression from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were conducted to investigate whether METTL14 could serve as an independent factor correlated with overall survival (OS). Gene Set Enrichment Analysis (GSEA) was carried out to identify METTL14-related signaling pathways. Moreover, a risk score (RS) was calculated to predict the prognosis of ccRCC. Quantitative real-time PCR (qRT-PCR) was also utilized to verify the expression of METTL14 in clinical specimens.

Results: Differently expressed m6A related genes were identified between ccRCC tissues and normal tissues. Therein, METTL14 was lowly expressed in ccRCC tissues and verified by qRT-PCR (all p < 0.01). Survival analysis indicated that high expression of METTL14 was associated with better OS (p = 1e-05). GSEA results revealed that high METTL14 expression was enriched in ERBB pathway, MAPK pathway, mTOR pathway, TGF-β pathway and Wnt pathway. Moreover, METTL14 was proved to be an independent prognostic factor by means of univariate and multivariate Cox regression analyses. Nomogram integrating both the METTL14 expression and clinicopathologic variables was also established to provide clinicians with a quantitative approach for predicting survival probabilities of ccRCC. Furthermore, a METTL14-based riskscore (RS) was developed with significant OS (p = 6.661e-16) and increased AUC of 0.856. Besides, significant correlated genes with METTL14 were also provided.

Conclusions: Our results indicated that METTL14 could serve as a favorable prognostic factor for ccRCC. Moreover, this study also provided a prognostic signature to predict prognosis of ccRCC and identified METTL14-related signaling pathways.

Keywords: Clear cell renal cell carcinoma; METTL14; N6-Methyladenosine; Overall survival; Risk score.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Relative expression of m6A-related genes in ccRCC; a Heatmap of ccRCC and normal tissues expressing the 13 m6A related genes in TCGA database; b boxplots of ccRCC and normal tissues expressing the 13 m6A related genes in TCGA database; c correlations of these 13 m6A related genes in the TCGA database
Fig. 2
Fig. 2
The expression of METTL14 in ccRCC tissues; a pairwise boxplot of the METTL14 expression between the ccRCC (n = 72) and matched normal tissues (n = 72) in TCGA dataset; b relative expression levels of the METTL14 expression between the ccRCC (n = 539) and normal tissues (n = 72) in TCGA dataset; c ROC curve performed to assess the OS predictive performance of METTL14 expression in TCGA dataset; d Kaplan-Meier curves for OS in the high-risk and low-risk groups when stratified by METTL14 expression; e qRT-PCR verification of METTL14 expression between ccRCC tissues (n = 6) and adjacent normal tissues (n = 6). The bar graphs represent means ± standard deviation. ***p < 0.001
Fig. 3
Fig. 3
Associations between METTL14 expression and clinicopathologic characteristics; a Grade; b Stage; c T stages; d M stages. ***p < 0.001
Fig. 4
Fig. 4
METTL14 could serve as an independent prognostic factor and established nomogram; a, b Univariate and multivariate cox regression analyses; c MultiROC analyses of OS for the METTL14 expression and classical clinicopathological parameters in TCGA dataset; d nomogram to predict the OS of ccRCC patients based on clinical parameters and METTL14 expression
Fig. 5
Fig. 5
Enrichment plots from gene set enrichment analysis (GSEA); a ERBB pathway; b MAPK pathway; c MTOR pathway; d pathway in cancer; e renal cell carcinoma; f TGF-β pathway; g Wnt pathway; h The seven most significantly enriched signaling pathways
Fig. 6
Fig. 6
Evaluation of the prognostic index (riskscore, RS) based on METTL14 expression and clinical characteristics in TCGA dataset; a Kaplan–Meier plot represents that patients in the high-risk group had significantly shorter overall survival time than those in the low-risk group; b the risk score distribution of patients in the TCGA dataset; c the higher the risk scores, the higher the numbers of dead persons; d ROC curve analysis for survival prediction by the RS based on the TCGA database
Fig. 7
Fig. 7
Correlations between METTL14 and related genes; The expression of METTL14 in ccRCC samples had negative correlations with a ADRM1; b C19orf53; c NUTF2; d PGLS; e POLR2J; The expression of METTL14 in ccRCC samples had positive correlations with f AQR; g IRE82; h UBE3A; i UBR1; j ZNF24; k Correlations of METTL14 and 10 METTL14-related genes in TCGA dataset
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