Bladder cancer sample sources and grouping
Gene expression data (RNA-Seq), lncRNA sequencing data and corresponding clinical data of bladder cancer were downloaded from the TCGA database (https://portal.gdc.cancer.gov). According to the published research [13], 29 immune cell data sets were applied to evaluate the infiltration level of immune cells through the ssGSEA method. After that, patients were classified as the high and low immune cell infiltration groups using hclust package. The stromal score, immune score, and tumor purity score were calculated by the ESTIMATE algorithm to verify the effectiveness of ssGSEA groupings [14]. In addition, we assessed the difference between the two groups by analyzing the expression of the human leukocyte antigen (HLA) gene. CIBERSORT algorithm was employed to determine the infiltration of various immune cells in the tumor sample and verify the potency of the immune groupings again [15].
Screening Of Immune-related Lncrna In Bladder Cancer
We set | log2FC | > 0.5 and p < 0.05 as the standard to recognize the differentially expressed lncRNAs between the high and low immune cell infiltration groups by edgeR package. Differentially expressed lncRNAs between bladder cancer and paracancerous tissue were also identified by the same method. Venn diagram analysis was used to screen out immune-related lncRNAs in bladder cancer from the above two sets.
Construction Of Risk Score Model Based On Immune-related Lncrnas
In the training data set, univariate Cox regression was performed on immune-related lncRNAs to identify 38 prognosis-associated lncRNAs (Fig. 4a). LASSO regression analysis further screened 9 crucial lncRNAs (Fig. 4b, c). Survival analyses of immune-related lncRNAs revealed that 9 lncRNAs were significantly related to OS, including AC126773.2 (p = 0.039), RRN3P2 (p = 0.04), AL022322.1 (p = 0.003), Z84484.1 (p = 0.00001), AL645940.1 (p = 0.008), AL357054.4 (p = 0.043), AL662844.4 (p = 0.04), AL136084.3 (p = 0.013), and LINC01679 (p = 0.035) (Fig. 5). Multivariate Cox regression model calculated their βi (Table 1) to establish the risk score model: Risk score = . We set the median risk score as the cutoff and divided 411 patients into high-risk and low-risk groups. The Kaplan-Meier curve disclosed that the OS in the low-risk group was significantly better than that in the high-risk group (p = 7.542e-05) (Fig. 6a). The risk curve and scatter plot indicated that the risk coefficient and mortality of patients in the high-risk group were higher than those in the low-risk group (Fig. 6b, c). The heat map exhibited the expression profiles of the 9-lncRNAs signature in the high-risk and low-risk groups (Fig. 6d). The correlation status of B cells, CD4 + T cells, CD8 + T cells, dendrictic cells, macrophages, and neutrophils with risk score were ploted in Fig. 7 (Fig. 7a-f for the training data set and Fig. 7g-i for the testing data set). Similar results were obtained using the same method on the testing data set (Fig. 6e-h).
Table 1
The prognostic significance of the 9-lncRNAs signature
Immune-related gene | Coef | HR |
AC126773.2 | -0.245674785 | 0.782176559 |
RRN3P2 | -0.439240487 | 0.644525761 |
AL022322.1 | -0.210005673 | 0.810579648 |
Z84484.1 | -0.262440116 | 0.769172424 |
AL645940.1 | -0.136995376 | 0.871974258 |
AL357054.4 | -0.717602304 | 0.48792074 |
AL662844.4 | -0.731805115 | 0.481039873 |
AL136084.3 | 0.182874542 | 1.200663765 |
LINC01679 | -0.174892219 | 0.839547503 |
Establishment And Evaluation Of Nomogram
We evaluated the prognostic significance of risk score and clinical variables such as age, gender, and TNM stage by univariate and multivariate Cox regression analyses. The nomogram was established according to the results of multivariate Cox regression to predict each patient’s 3- and 5-year OS. We conducted the ROC curve analysis, concordance index (C-index) method, calibration curve method, and decision curve analysis (DCA) to assess the model’s accuracy. Finally, the testing set data was used to evaluate the above results.
Gene Set Enrichment Analysis
We performed GO enrichment analysis and KEGG pathway analysis on the differentially expressed genes between the high-risk and low-risk groups. GO enrichment analysis indicated that the genes were enriched in ephrin receptor signaling pathway, epidermal growth factor receptor (EGFR) signaling pathway, ERBB signaling pathway, mRNA splice site selection, DNA ADP ribosyltransferase activity, and T cell selection (Fig. 10a). KEGG pathway analysis showed that these genes were involved in amino sugar and nucleotide sugar metabolism, antigen processing and presentation, extracellular matrix (ECM) receptor interaction, focal adhesion, primary immunodeficiency, and T cell receptor signaling pathway (Fig. 10b). These findings may help researchers further explore the mechanism of immune-related lncRNA affecting the pathogenesis of bladder cancer.