Up-regulated METTL3 was significantly related to PCa as well as the high risk PCa
A total of 496 PCa tissues and 51 normal prostate tissues from TCGA dataset were used to analyze the relationship between METTL3 and PCa. The result showed that the expression level of METTL3 in the PCa tissues was significantly higher than that in the normal prostate tissues (Figure 2A). According to GS, the 496 PCa tissues were divided into three groups including the low-risk (GS<7) (45) group, the moderate-risk (GS=7) (246) group and the high-risk (GS>7) (205) group. We compared the expression levels of METTL3 in these three groups and found that there was a significant difference among them (P<0.001). As can been seen in Figure 2B, the expression level of METTL3 in the high-risk group was significantly higher than that in the low-risk group (P<0.01) as well as the moderate-risk group (P<0.001). Furthermore, through observing the expression level of METTL3 protein by using the PCa TMA dataset, a similar result was got. It was not only that the expression level of METTL3 in the PCa tissues was visibly higher than that in the normal tissues and the AD tissues, but also that the expression level of METTL3 in the high risk PCa tissues (GS>7) was obviously higher than that in the low-moderate risk PCa tissues (GS≤7) (Figure 2C). Taken together, these results suggest that the up-regulated METTL3 is showed close relationship to PCa as well as the high risk PCa.
Up-regulated METTL3 was closely related to the poor PFS of the PCa patients
490 PCa tissues from TCGA cohort were used to evaluate the prognostic value of METTL3 in PCa. PFS analysis via uni-variable Cox regression algorithm showed that the up-regulated METTL3 was significantly related to the poor PFS of the PCa patients (Hazard ratio (HR): 1.58; 95% confidence interval (CI): 1.022-2.449; P <0.05). According to the optimal cutoff value (4.98) of METTL3, PCa patients were divided into two groups, the group of the patients with high expression level of METTL3 and the group of the patients with low expression level of METTL3. Kaplan-Meier curve showed that the patients with high expression level of METTL3 had poorer both short-term and long-term PFS than those with low expression level of METTL3 (Log Rank (Mantel-Cox): X2=6.957, P<0.01; Breslow (Generalized Wilcoxon): X2=5.562, P<0.05; Figure 2D). 515 PCa patients from the cohort integrated by TCGA cohort and ICGC cohort were used for verification. Consistently, the similar results were got (Figure 2E). Above all, we suggest that METTL3 have the potential to be the prognostic predictor for PCa.
Mechanism of METTL3 that was closely related to the prognosis of PCa
In order to uncover the mechanism of METTL3 involved in the prognosis of PCa, firstly, twenty-five m6A methyltransferase METTL3 target genes were identified (S-Table 1) [see Additional file 1], which met the criteria mentioned in “Materials and Methods”. Then, these target genes were used to perform the PFS analysis via LASSO Cox regression with 490 PCa tissues from TCGA cohort to establish the M-RM, which was consist of seven m6A methyltransferase METTL3 target genes including small nuclear ribo-nucleoprotein U1 sub-unit 70 coding gene (SNRNP70), claudin 15 coding gene (CLDN15), non-SMC condensin I complex sub-unit H coding gene (NCAPH), DExD-box helicase 39A coding gene (DDX39A), RAD54 like coding gene (RAD54L), essential meiotic structure-specific endonuclease subunit 2 coding gene (EME2), and melanocortin 1 receptor coding gene (MC1R) (Figure 3A). The formula of the M-RM score for each patient was: M-RM score = (0.221 * expression level of SNRNP70) + (0.233 * expression level of CLDN15) + (0.008 * expression level of NCAPH) + (0.001 * expression level of DDX39A) + (0.516 * expression level of RAD54L) + (0.014 * expression level of EME2) + (0.324 * expression level of MC1R). Subsequently, the preliminary evaluation of these seven m6A methyltransferase METTL3 target genes and the M-RM were performed. These seven target genes were all moderately positively correlated to METTL3, and significantly related to the PFS of PCa (Figure 3B). The time-dependent ROC showed that the M-RM had a good diagnostic performance for 1-year, 3-year and 5-year PFS events with the area under the curve (AUC) of 0.78 (95% CI: 0.71-0.85),0.78 (95% CI: 0.72-0.84), and 0.74 (95% CI: 0.65-0.82) (Figure 3C). According to the optimal cutoff value of the M-RM score, the PCa patients were divided into the high M-RM score group and the low M-RM score group. As shown in Figure 3D, the gene expression levels of variables in the M-RM and the number of the PFS events in the same period of time in the high M-RM score group were both obviously higher than those in the low M-RM score group. In addition, Kaplan-Meier curve showed that the patients with high RM score had poorer both short-term and long-term PFS than those with low PFS score (Log Rank (Mantel-Cox): X2=46.131, P<0.0001; Breslow (Generalized Wilcoxon): X2=40.082, P<0.0001; Figure 3E). This result was confirmed by using the cohort integrated by TCGA cohort and ICGC cohort subsequently (Log Rank (Mantel-Cox): X2=22.123, P<0.0001; Breslow (Generalized Wilcoxon): X2=18.510, P<0.0001; Figure 3F). Thus, we believed that METTL3 might affect the prognosis of PCa mainly through regulating the expression of these seven m6A methyltransferase METTL3 target genes in the M-RM. Finally, in order to further understand how the M-RM affected the prognosis of PCa, ssGSEA was performed. And based on it, we found that the M-RM score was significantly positively correlated to the activity of the cell cycle related KEGG pathways, such as cell cycle, DNA replication, base excision repair, etc. (Figure 4). Taken together, we suggest that METTL3 may affect the prognosis of PCa mainly through regulating the expression levels of their target genes which were closely related to the process of cell cycle.
Nomogram construction and evaluation
METTL3 and the clinical risk factors including age, GS, pathological T (pT) staging and pathological N (pN) staging were used for the PFS analysis via multi-variable Cox regression algorithm. The risk model (RM) (X2=33.03, P<0.0001) integrated the METTL3 and pT staging for was established (S-Table 2) [see Additional file 1]. PFS analyses via Kaplan-Meier and uni-variate Cox regression algorithm showed that the RM was significantly related to the prognosis of PCa patients. The patients with high RM score had poorer both short-term and long-term PFS than those with low RM score (Log Rank (Mantel-Cox): X2=22.951, P<0.0001; Breslow (Generalized Wilcoxon): X2=15.925, P<0.0001; Figure 5A). The nomogram based on the RM was drawn to provide the quantitative approach to predict the probability of 1-year, 3-year and 5-year PFS for the PCa patients (Figure 5B). The C-index of the RM was 0.729 (95% CI: 0.651-0.808; P<0.0001). And the calibration curve showed that the RM-based nomogram-predicted 3-year and 5-year PFS of the PCa patients was highly fit with the observed ones (Figure 5C).