Identification Of M6a-related Lncrnas Involved In Blca Development
Firstly, we identified 14086 lncRNAs in 407 BLCA samples from TCGA datasets. We then extracted the expression matrixes of 21 m6A-related genes from the TCGA datasets, respectively. A lncRNA which expression value was correlated with one or more of the 21 m6A-related genes (| Pearson R| > 0.4 and p < 0.001) was defined as a m6A-related lncRNAs. Pearson correlation analysis was performed to search m6A-related lncRNAs. we obtained 741 lncRNAs which were significantly correlated with m6A-related genes. The network diagram of co-expression between 741 lncRNAs and 21 m6A-related genes was shown in Fig. 1a. Combined with the prognostic information, univariate Cox regression was performed to screen the m6A-related lncRNAs related to the prognosis of from 741 lncRNAs related to m6A (p < 0.001). We found that 9 m6A-related lncRNAs were significantly correlated with the OS of BLCA patients (Fig. 1b). Then transcriptome profile of m6A-related lncRNA was thoroughly investigated. The information of lncRNA related to m6A was extracted, and the difference analysis was carried out between the normal tissues of bladder and BLCA tissues. The results showed that 9 m6A-related lncRNAs had differences(*P < 0.05,** P < 0.01,***P < 0.001; Fig. 1c, d).
Correlation of the m6A-related lncRNAs with clinicopathological features of BLCA patients
To develop a prognostic signature based upon 9 m6A-related lncRNAs, we sought to stratify 407 BLCA patient samples by consensus clustering analysis (Fig. 2a, b). Based on the cumulative distribution function (CDF) value, k = 2 was the optimal cluster number to divide the BLCA cohort, namely, cluster 1 and cluster 2 (Fig. 2c). Furthermore, we evaluated the associations between clusters and clinicopathological features. Compared with cluster 2, m6A-related lncRNAs were more highly expressed in cluster 1 (Fig. 2g). Moreover, we explored the prognostic implications of the lncRNA by integrating them with survival information. The analysis was performed by using the Kaplan-Meier plotter, and we found that patients in cluster 1 had a better prognosis, whereas patients in lncRNA clusters 2 had the unfavorable outcomes (log rank test, p = 0.016; Fig. 2f). Studies have found that increased Foxp3 expression may be an independent predictor of poor prognosis of BLCA . Targeted gene methylation modification provides new insights for clinical treatment design of some diseases. Understanding the characteristics and regulatory mechanism of Foxp3 protein is helpful to design a reasonable treatment plan for BLCA. Additionally, the two genomic clusters showed significant differences in Foxp3 expression levels. Compared with cluster 1, the expression level of Foxp3 in cluster 2 was relatively high (log rank test, p < 0.001; Fig. 2d). In addition, Foxp3 expression in BLCA was higher than that in normal tissues (log rank test, p < 0.05; Fig. 2e). At the same time, we analyzed the correlation between the m6A-related lncRNAs. Among them, we focused on the significant negative correlation between Foxp3 closely related to the PTOV1 − AS2, AC104564.3, EHMT2-AS1 and AC004148.1. (Fig. 2h). Taken together, the consistency between different lncRNAs clusters and prognostic profiles shows that our classification method is scientific and reasonable.
Consensus clustering for m6A-related lncRNAs associated with distinct immune cell infiltration
To unravel the underlying biological characteristics of distinct m6A-related lncRNAs subtypes. First, we performed the CIBERSORT algorithms to quantify the activity or enrichment levels of immune cells in BLCA tumor tissues. Among the two main m6A-Related lncRNAs subtypes, we performed differential analysis to determine the immune cell infiltration among these subtypes by limma packages of R software. The cluster 1 exhibited the highest Tregs infiltration. Cluster 2 showed an escalated memory activated CD4+T cells and immunosuppression associated M2 macrophages and neutrophils infiltration (Fig. 3a). To investigate the effect of m6A related-lncRNA on the tumor immune microenvironment (TIME) of BLCA, we performed the ESTIMATE algorithms to quantify the activity or enrichment levels of immunoscore, StromalScore and ESTIMATE Score in BLCA tumor tissues. Compared with cluster 1, StromalScore,ESTIMATEScore and ImmuneScore are more highly expressed in cluster 2 (Fig. 3b-d). In addition, to elucidate the potential regulatory mechanisms resulting in differences in the m6A related-lncRNA between the two subgroups, we performed gene set enrichment analysis (GSEA). The characteristics of the patient in cluster 1 were base excision repair, notch signaling pathway, mTOR signaling pathwayand and regulation of autophagy (Fig. 3e-h) and so on. Gene set enrichment analysis showed that there were several tumor and inflammatory markers enriched in cluster 2 subgroup, such as cytokine-cytokine-receptor-interaction, cell adhesion molecule cam, chemokine signal transduction pathway and T cell receptor signal transduction pathway (Fig. 3i-l). These results may give us some insights into the cellular biological effects associated with m6A-related lncRNA prognostic markers.
Construction Of The M6a-lps In The Tcga- Train Dataset
Firstly, sort out the clinical data of BLCA patients, and normal samples and samples without survival data were deleted. A total of (n = 407) patients were included for lncRNAs model construction. Using computer-generated random numbers, the entire TCGA-BLCA data set (n = 407) was divided into a TCGA-Train(204 cases) and a TCGA-Test (203 cases). The patient’s survival time, survival status and m6A-related lncRNAs expression data were combined. Model construction in the TCGA-Train, and then the best model selection in the TCGA-Test. To build the the prognostic model of m6A-related lncRNAs for forecasting the OS of BLCA patients, we performed a LASSO Cox analysis on the basis of the 9 m6A-related prognostic lncRNAs in the TCGA-Train and it generated the m6A-LPS which contains five lncRNAs and coefficient of each (Fig. 4a, d). The m6A-LPS involved five lncRNAs, and for each patient in the TCGA-Train, a risk score was calculated based on the coefficient for each lncRNA. Patients in the TCGA-Train were divided into low and high-risk subgroups based on the median value of risk scores. Kaplan–Meier survival curves depicted that BLCA patients with higher risk scores had worse clinical outcomes (lower OS rates and a shorter OS time) (Fig. 4b). Risk score and survival status distributions are plotted in Fig. 5A. And the ROC curves demonstrated that m6A-LPS harbored a promising ability to predict OS in the TCGA-Train (AUC = 0.713; Fig. 4e).
Validation Of The M6a-lps In The Tcga-test Dataset
To validate the prognostic ability of m6A-LPS, we calculated risk scores for patients in the TCGA-Test cohort using the same formula. BLCA patients in the TCGA-Test dataset were assigned to low-risk and high-risk groups based on the median risk score. The results were consistent with the findings in the TCGA-train dataset: BLCA patients with higher risk scores had lower OS rates and a shorter OS time in the TCGA-test dataset (Fig. 4c). Risk score and survival status distributions showed that patients with higher risk scores had shorter OS time and dead status (Fig. 5c). The ROC analysis also indicated that m6A-LPS had a strong prognostic value for BLCA patients in the TCGA-test dataset (AUC = 0.682; Fig. 4f). These results showed that the prognostic model of m6A-LPS had a robust and stable OS-predictive ability.
The m6A-LPS was an independent prognostic factor for BLCA patients
We used univariate and multivariate Cox analyses to assess whether the m6A-LPS was an independent prognostic factor for patients with BLCA. Based on the data of BLCA patients in the TCGA-train dataset, univariate Cox analysis indicated that the m6A-LPS was remarkably associated with OS [hazard ratio (HR):1.329,95% CI: 1.176–1.501, p < 0.001] and multivariate Cox analysis further showed that the prognostic model of m6A-related lncRNAs was an independent predictor of OS (HR: 1.302, 95% CI: 1.147–1.478, p < 0.001; Fig. 5b). The conclusion was validated in the TCGA-test dataset, which confirmed that the prognostic model of m6A-related lncRNAs was an independent predictor of OS for BLCA patients in the TCGA-test validation dataset (univariate: HR: 3.027, 95% CI: 1.645–5.571, p < 0.001; multivariate: HR: 3.272, 95% CI: 1.601–6.685, p = 0.001; Fig. 5d). These results indicated that our the m6A-LPS, as an independent prognostic indicator, might be useful for clinical prognosis evaluation.
Relationship between the m6A-LPS and multiple linicopathological features of BLCA
The m6A-LPS vary differently among clinicopathological characteristics (Fig. 6a). When the data is divided into high-risk and low-risk groups, it can be observed that AL136295.2, AC104564.3, EHMT2 − AS1 and AC116914.2 are in a high expression state in the low-risk group, while TP1B3 − AS1 were highly expressed in the high-risk group. Afterward, we tried to determine whether clinicopathological characteristics are related to m6A-LPS. Above all, we conducted a stratified analysis to confirm whether the risk score is different in clinical subgroups. In order to better show the correlation between clinicopathological characteristics and risk score, a sample with a higher BLCA risk score was deleted. The results showed that BLCA patients with stage III-IV, cluster 2, Age > 65 years, and T3-4 stage had a higher risk score. Low-Grade and ImmuneScore had a lower risk score (Fig. 6b-g). In addition, we conducted a stratified analysis to confirm whether it retains its ability to predict OS in each subgroup for better evaluate the prognostic ability of the risk Score. In contrast with patients with lower risk, higher risk BLCA patients had worse OS in the aged ≤ 65 or > 65 years, Male, Grade, T0-2 or T3-4 stage, M0 stage, N0-1, and Stage I-II or Stage III-IV subgroups (Fig. 6i-r). We also analyzed the vital immune checkpoints, i.e. The Kruskal-Wallis test was used to detect the significant differences between the Foxp3 expression level in two distinct cluster subtypes (Fig. 6h). From the above findings we concluded that the novel prognostic signature integrated by m6A-LPS could independently predict the prognosis of BLCA patients.
Effect of risk score alterations of the m6A-LPS on immune cell infiltration
The relationship between the risk score and infiltration levels of twenty-two immune cell types was analyzed to estimate the effect of five the m6A-related lncRNA prognostic signatures on the BLCA immune microenvironment. A significantly negative correlation was observed between the risk score and infiltration levels of the plasma cells, naive B cells, CD8 T cells, Tregs, and T cells gamma delta (p < 0.05; Fig. 7a–e). The risk score was positively correlated with the infiltration levels of neutrophils, mast cells activated,NK cells resting, M0 macrophages and M1 macrophages (p < 0.05; Fig. 7f–j). This result confirmed that m6A regulator-based risk signatures were implicated in the BLCA immune microenvironment.