Identification Of M6a Related Lncrnas
To distinguish mRNAs and lncRNAs, data on m6A-related gene expression were retrieved from transcriptome data. Next, The connection between m6A-related gene expression and lncRNAs was analyzed via co-expression analysis (table S1). A network plot was generated to visualize this correlation (Figure 1A). A forest plot was used to visualize the univariate Cox regression analysis (Figure 1B, table S2). heatmaps and box plots were constructed using the differential expression of prognosis-related m6A-related lncRNAs between tumor and normal tissues, as shown in Figure 2A and 2B. 30 m6A prognosis-related lncRNAs were identified that were differently expressed between tumor and surrounding normal tissues. LINC02604、AC116914.2、 Z84485.1、 ZNF32−AS2、 AC004076.2、 AL138921.1、 TMEM147−AS1、 SNHG20、 PTOV1−AS2 and AC004148.1 were dramatically higher in BLCA tissues than in normal adjacent tissues (p < 0. 001). The expression level of ATP1B3−AS1、AC025280.1、AC012568.1、BDNF−AS was markedly lower in BLCA tissues than in normal tissues (p < 0. 01).
Figure 2. Different expression of m6A prognostic related lncRNAs. A: Boxplot of the difference expression of m6A prognostic related lncRNAs among tumor and normal tissue. *:P < 0.05; **:P < 0.01; ***:P < 0.001. B: Heatmap of the difference expression of m6A prognostic related lncRNAs among tumor and normal tissue. *:P < 0.05; **:P < 0.01; ***:P < 0.001. Red represents high expression, while blue represents low expression. The abscissa represents the sample, while the ordinate represents prognostic related lncRNA.
The Role of m6A-related lncRNAs
Based on the similarity demonstrated by the expression levels of m6A-related lncRNAs and the proportion of ambiguous clustering measure, the k = 2 was identified as having optimal clustering stability from k = 2 to 9. A total of 433 patients with BLCA were clustered into two subtypes, namely, cluster1 (n = 132) and cluster2 (n = 271), based on the expression levels of the m6A-related lncRNAs (Fig. 3, table S3). To assess the potential role of m6A-related lncRNAs, a survival study based on lncRNA subtypes was conducted, and the overall survival (OS) of cluster1 was higher than that of cluster2 (p = 0. 007), as shown in Fig. 4A. The expression of most m6A-related lncRNAs was lower in the cluster2 than in the cluster1, especially the expression levels of LINC0260、AC104532.2、AL022328.2 and EHMT2 − AS1 (Fig. 4B). While the expression of individual m6A-related lncRNAs was higher in the cluster2 than in the cluster1, such as AC097359.2 and AC025280.1 (Fig. 4B). Additionally, The clinicopathological features between the two subtypes were then compared (Fig. 4B). The cluster2 was preferentially associated with Stage III − IV (p < 0. 001).
Association Of Pd-l1 With M6a-related Lncrnas
To explore the correlation of PD-L1 with m6A-related lncRNAs, the differential expression of PD-L1 in two subtypes were assessed. The expression level of PD-L1 in the cluster2 was distinctly higher than that in the cluster1 (p<0.01; Fig.5A). However, there was no distinct difference in the expression of prognosis-related lncRNAs in the different tissues (Fig.5B), probably due to small sample size. In BLCA, a gene correlation study was performed to see if there was a link between the target gene and the prognostic m6A-related lncRNAs (Fig.6), and we found that the expression of PD-L1 had a significantly negative association with m6A-lncRNA LINC0260、AC104532. 2、AL022328.2 and EHMT2−AS1.
Figure 5. Difference expression of target gene in related subtype. The expression level of PD-L1 was upregulated in the cluster2 compared with cluster1(p < 0. 001). There was not significant difference between BLCA tissues and para-carcinoma tissues (p > 0. 05).
The Role of Immune cells Infiltration and the Tumor Microenvironment
Differential analyses of immune cell infiltration and immunological scores in distinct clusters were carried out to evaluate the effect of m6A-related lncRNAs on the TIME of BLCA (table S4). The infiltration abundance of 22 types of immune cells in each cluster was shown in Figure 7A. Additionally, cluster2 exhibits high levels of CD4 memory-activated T cells, macrophages M2, neutrophils and NK cells resting (P < 0. 05; Figure 7B-7E), whereas cluster1 was more correlated with plasma cells (P < 0. 05; Figure7F) and T cells regulatory (Tregs) (P < 0. 001; Figure 7A and 7G). As shown in Figure 8A-8C, All of the scores were higher in cluster2, indicating a higher level of immune-related cells in the tumor microenvironment (P < 0. 05) (table S5). We applied gene set enrichment analysis (GSEA) to explore the probable regulatory mechanisms underlying the variations in TIME between the two groupings. The hallmark pathways and functions involved in cluster2 included Chemokine Signaling Pathway、 Cytokine-Cytokine Receptor Interaction、Cell Adhesion Molecules (CAMs)、Natural Killer Cell Mediated Cytotoxicity、Antigen Processing and Presentation and ECM-Receptor Interaction(Figure 9A-9F). According to the results, the most enriched signaling pathway was “Chemokine signaling pathway”. These functions and pathways were up-regulated in cluster2, Both the FDR q-value and the FWER p-values were < 0. 05. These functions and pathways might be implicated in the distinct TIME of cluster1/2.
Figure 9. Part of the GSEA results are listed above. high risk of m6A-related lncRNAs were enriched in multiple cancer-related functions and pathways, which were upregulated in class C2. Both FDR q-value and FWER p-Value < 0.01.
Construction Of Prognostic Signatures Based On M6a-related Lncrnas
A train group (70%) and a test group (30%) were utilized for the Kaplan-Meier survival analysis and associated plots to accurately predict the clinical outcome of m6A-related lncRNAs in BLCA patients, as shown in Fig. 10A -10B and Table S6 and S7. The results showed that the OS of patients in the high-risk group was significantly lower than that in the low-risk group, as shown in Fig. 10C-10D. The corresponding ROC curve was acquired using the time-ROC package to test the accuracy of our model for predicting the survival of patients with the disease (Fig. 10E-F). ROC curve analysis showed that the AUC value of test group was 0. 666, while the AUC value of train group was 0. 680, indicating the considerable accuracy of our model for predicting the survival of patients with BLCA. Univariate and multivariate Cox analyses were performed to evaluate whether risk score was an independent prognostic factor for BLCA patients. The distribution of the risk scores, OS, OS status, and expression profiles of the eleven m6A-related lncRNAs in TCGA training and validation cohorts was displayed in Fig. 11A-F. which revealed that the higher the immune scores, the higher the risk. Both in the test group and train group, after univariate analysis obtained factors related to OS in BLCA patients, multivariate Cox regression analysis showed that risk score, stage and age were identified as independent prognostic factors (P < 0. 01, HR > 1), in Fig. 12A-D. The link between risk score and clinical features, as well as cluster subgroups, was studied further. In the high-risk and low-risk groups, the heatmap showed the expression levels of eleven m6A-related lncRNAs(Fig. 13). AL136295.2 、BDNF − AS 、AC073575. 4 、AC074117. 1 、AC073534. 2 、AL022322. 1 、AC004076. 2 、AC104564. 3 、TMEM147 − AS1 、AC104532. 2 、AC116914. 2 were highly expressed in cluster1 and low risk group, while ATP1B3 − AS1 and AC097359. 2 were highly expressed in cluster2 and high-risk group. Furthermore, the different risk scores among subtype(P < 0.001), immune scores(P < 0.001), stage(P < 0.001) and N stage(P < 0.05) were examined in this heatmap. The results show that the risk score of cluster2、higher immune scores 、stage III − IV and N1–N3 is significantly higher. These findings indicate that the risk score of BLCA patients may profoundly influence clinical outcomes. To evaluate and verify if our model could be applied to diverse clinical parameters, we utilized model validation for clinical groups, as shown in Fig. 14. The result indicated that our model could be applied to the following different clinical parameters: age, gender, lymph node metastasis, stage and T stage (P < 0.05). In our BLCA model, genetic differential analysis was used to examine the expression differences of target genes in the different risk groups, as shown in Fig. 15. The expression level of PD-L1 was higher in high-risk group(P < 0. 001). The influence of eleven m6A-related lncRNAs on the BLCA immune microenvironment was estimated using the relationship between the risk score and immune infiltration levels(Fig. 16). The risk score was positively correlated with the infiltration levels of Macrophages M0, Macrophages M1, Macrophages M2, Mast cells activated, Neutrophils and NK resting cells with R > 0 and P < 0. 05. A significantly negative correlation was observed between the risk score and infiltration levels of Plasma cells, T cells gamma delta and T cells regulatory (Tregs) with R < 0 and P < 0.05. Risk signatures based on m6A-related lncRNAs are likely to have a role in BLCA's immune microenvironment regulation.
Figure 11. Distribution of risk score, OS, and OS status of the 11 prognostic m6A-related lncRNAs in the TCGA test cohort (A+C+E) and TCGA train cohort (B+D+F)
Figure 16. Relationships between the Risk Score and Infiltration Abundances of ten Immune Cell Types. (A) Macrophages M0, (B) Macrophages M1, (C) Macrophages M2, (D) Mast cells activated (E) Neutrophils and (F) NK resting cells are positive related with risk score, R > 0 and P < 0.05. (G) Plasma cells, (H) T cells gamma delta and (I) T regulatory cells, R < 0 and P < 0.05.