A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer

Results

The study’s flowchart is depicted in Figure 1, with a total of 394 BLCA patients enrolled from the TCGA-BLCA cohort, and their clinical characteristics outlined in detail in Table 1.

Figure 1. Flowchart of data collection and analysis. Abbreviations: BLCA: Bladder urothelial carcinoma; TCGA: The Cancer Genome Atlas; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology; lncRNAs: long noncoding RNAs; LASSO: least absolute shrinkage and selection operator; ROC: receiver operating characteristic; GSEA: gene enrichment analysis; ssGSEA: single-sample gene set enrichment analysis.

Table 1. The clinical characteristics of patients in different cohorts.

Variables	TCGA cohort (n = 394)	Training cohort (n = 198)	Testing cohort (n = 196)
Age (%)
≤65	158 (40.1%)	85 (42.9%)	73 (37.2%)
>65	236 (59.9%)	113 (57.1%)	123 (62.8%)
Gender (%)
Female	103 (26.1%)	49 (33.6%)	54 (27.6%)
Male	291 (73.9%)	149 (66.4%)	142 (72.4%)
Grade (%)
High Grade	373 (94.7%)	184 (92.9%)	189 (96.4%)
Low Grade	18 (4.6%)	12 (6.1%)	6 (3.1%)
Unknow	3 (0.7%)	2 (1%)	1 (0.5%)
Stage (%)
I + II	125 (31.7%)	66 (33.3%)	59 (30.1%)
III + IV	267 (67.8%)	130 (65.7%)	137 (69.9%)
Unknow	2 (0.5%)	2 (1%)	0 (0%)
T (%)
T0	1 (0.3%)	0 (0%)	1 (0.5%)
T1 + T2	115 (29.2%)	61 (30.8%)	54 (27.6%)
T3 + T4	246 (62.4%)	123 (62.1%)	123 (62.8%)
TX + Unknow	32 (8.1%)	14 (7.1%)	18 (9.1%)
N (%)
N0	228 (57.9%)	120 (60.6%)	108 (55.1%)
N1–3	125 (31.7%)	62 (31.3%)	63 (32.1%)
NX + Unknow	41 (10.4%)	16 (8.1%)	25 (12.8%)
M (%)
M0	188 (47.7%)	94 (47.5%)	94 (48.0%)
M1	10 (2.6%)	5 (2.5%)	5 (2.5%)
MX + Unknow	196 (49.7%)	99 (50%)	97 (49.5%)
Abbreviations: T: tumor; M: metastasis; N: lymph node.

Construction of the cuproptosis-related lncRNA predictive signature

A total of 548 cuproptosis-related lncRNAs were identified (Supplementary Table 1). Univariate Cox regression analysis revealed that 135 lncRNAs were associated with the prognosis of BLCA patients (Supplementary Figure 1). LASSO Cox regression model screened 20 lncRNAs (Figure 2A, 2B, Supplementary Table 2). 20 lncRNAs were subjected to multivariate Cox regression analysis to create a predictive signature, which identified a final set of 8 lncRNAs (AC005261.1, AC008074.2, AC021321.1, AL024508.2, AL354919.2, ARHGAP5-AS1, LINC01106, LINC02446). The expression heatmap of 8 cuproptosis-related lncRNAs in BLCA patients was shown in Figure 2C. We further visualized lncRNAs using Cytoscape software and the R package “ggalluvial”. The co-expression network contained 9 pairs of lncRNA-mRNAs (Figure 2D). AC005261.1 had co-expressive relationship with the DLAT; AC008074.2 had co-expressive relationship with LIPT1; AC021321.1 had co-expressive relationship with DBT; AL024508.2 had co-expressive relationship with LIAS and LIPT1; AL354919.2 had co-expressive relationship with SLC31A1; ARHGAP5-AS1 had co-expressive relationship with FDX1; LINC01106 had co-expressive relationship with LIPT1; LINC02446 had co-expressive relationship with NLRP3. AC005261.1, AC008074.2, AC021321.1, AL024508.2, AL354919.2, LINC01106 and LINC02446 were protective factors, while ARHGAP5-AS was risk factor (Figure 2E). The risk score was calculated as follows: Risk score = 0.372 × expression level of ARHGAP5-AS1 − 0.500 × expression level of AC005261.1 − 0.620 × expression level of AC008074.2 − 1.204 × expression level of AC021321.1 − 0.415 × expression level of AL024508.2 − 0.592 × expression level of AL354919.2 − 0.613 × expression level of LINC01106 − 0.738 × expression level of LINC02446. After evaluating the risk score of every BLCA patient using the aforementioned formula, the patients were stratified into high-risk group and low-risk group according to the median of the risk score. Compared with the low-risk group, Figure 3A showed that the high-risk group experienced considerably reduced OS time (p < 0.001). The risk scores of the high-risk and low-risk groups are shown in Figure 3B. As the risk score increased, univariate and multivariate Cox regression analyses were conducted on the available variables to ascertain their association with a higher number of patient fatalities (Figure 3C). Univariate Cox regression analysis indicated that age, stage, T stage, N stage and risk score showed a significant association with the OS (Figure 3D). Additionally, multivariate Cox regression analysis demonstrated that, according to Figure 3E, risk score was the independent predictor of OS. The risk score’s AUC was 0.782, which was the best among the available variables in predicting the prognosis of BLCA patients (Figure 3F). According to time-ROC analysis, the predictive signature has a great predictive performance, with AUCs of 0.757, 0.775 and 0.777 at 1-, 3-, and 5 years, respectively (Figure 3G). Furthermore, after analyzing the differences in clinicopathological variables between the high-risk and low-risk groups, we found that there were differences in stage (p < 0.001), T stage (p < 0.05), N stage (p < 0.05), grade (P < 0.01), and fustat (p < 0.001) between the high-risk and low-risk groups (Figure 4).

Figure 2. Construction of the cuproptosis-related lncRNAs predictive signature. (A) LASSO coefficient profiles of the expression of 135 lncRNAs. (B) Selection of the penalty parameter (λ) in the LASSO model via 20-fold cross-validation. (C) The expression levels of eight cuproptosis-related lncRNAs in tumor and normal tissues. (D) The co-expression network of prognostic cuproptosis-related lncRNAs. (E) Sankey diagram of prognostic cuproptosis-based lncRNAs. Abbreviations: lncRNAs: long noncoding RNAs; N: normal; T: tumor.

Figure 3. The correlation between the predictive signature and the prognosis of BLCA patients. (A) Kaplan-Meier analysis of the OS rate of BLCA patients in the high-risk and low-risk groups. (B) The distribution of the risk score among BLCA patients. (C) The number of dead and alive patients with different risk scores. Blue represents the number of survivors, and yellow represents the number of deaths. (D) Forest plot for univariate Cox regression analysis. (E) Forest plot for multivariate Cox regression analysis. (F) The ROC curve of the risk score and clinicopathological variables. (G) ROC curve and AUCs at 1-year, 3-years and 5-years survival for the predictive signature. Abbreviations: BLCA: Bladder urothelial carcinoma; OS: overall survival; ROC: receiver operating characteristic; AUC: area under the curve; T: tumor; N: lymph node; M: metastasis.

Figure 4. Distribution heat map of five prognostic cuproptosis-related lncRNAs and clinicopathological variables in the high-risk and low-risk groups. Abbreviations: lncRNAs: long noncoding RNAs; T: tumor; N: lymph node; M: metastasis.

Design of a nomogram

We created a nomogram including clinicopathological factors and risk score to simplify the clinical use of the predictive signature. The nomogram showed that it could help us predict the 1-, 3-, and 5-year survival rates of BLCA (Figure 5A). The calibration curves displayed a strong correlation between the predicted and actual 1-, 3-, and 5-year overall survival rates, as illustrated in Figure 5B–5D.

Figure 5. Nomogram creation and validation. (A) A nomogram combining clinicopathological variables and risk score predicts 1-year, 3-year, and 5-year OS rate of BLCA patients. (B–D) The calibration curves for the OS nomogram model in BLCA at 1-year, 3-year, and 5-year. Abbreviations: OS: overall survival; BLCA: Bladder urothelial carcinoma; N: lymph node; M: metastasis.

Relationship between the predictive signature and the prognosis of BLCA patients among the clinicopathological variables

We performed a subgroup analysis of the different age, gender, grade, stage and Tumor Node Metastasis (TNM) stage to study the relationship between the predictive signature and the prognosis of BLCA patients. We found that for the age, gender, high grade, stage III–IV, T3–4 stage, N stage and M0 stage, the OS of BLCA patients in the high-risk group was shorter than that in the low-risk group (Figure 6).

Figure 6. Kaplan-Meier survival curves of high-risk and low-risk groups among patients sorted according to different clinicopathological variables. (A, B) Age; (C, D) Gender; (E) High Grade; (F) Stage III–IV; (G) T3–4 stage; (H, I) N Stage; (J) M0 stage. Abbreviations: T: tumor; N: lymph node; M: metastasis.

Internal validation of the cuproptosis-related lncRNA predictive signature

To verify the accuracy of the predictive signature for OS based on the entire TCGA cohort, we randomly divided the 394 BLCA patients into two cohorts, training cohort and testing cohort (Table 1). In the training cohort, the OS rate of patients in the high-risk group was lower than that of the low-risk group (Figure 7A, p = 1.276e-12).

Figure 7. Validation of the predictive signature for OS based on the entire TCGA dataset. (A) Kaplan-Meier survival curve in the training cohort. (B) Kaplan-Meier survival curve in the testing cohort. (C) ROC curve and AUCs at 1-year, 3-years and 5-years survival in the training cohort. (D) ROC curve and AUCs at 1-year, 3-years and 5-years survival in the testing cohort. Abbreviations: OS: overall survival; TCGA: The Cancer Genome Atlas. ROC: receiver operating characteristic; AUC: area under the curve.

According to Figure 7C, the AUC for 1-year survival was 0.775, 3-year survival was 0.842, and 5-year survival was 0.885. The prognosis of patients in the high-risk group in the testing cohort was also poorer than that of the low-risk group (Figure 7B, p = 0.0001). According to Figure 7D, the AUC for 1-year survival was 0.735, for 3-year survival it was 0.71, and for 5-year survival it was 0.688. These outcomes were in line with those of the entire TCGA cohort, proving that the predictive signature can serve as a reliable predictor of a patient’s prognosis for BLCA.

Functional enrichment analysis between different risk patients

To demonstrate the biological functions and pathways linked with risk score among patients with different risk levels, we conducted a functional enrichment analysis. By performing GSEA on the high-risk and low-risk groups, we were able to detect possible differences between them. As anticipated, we observed significant enrichment of immune-related biological processes and tumor-related signaling pathways in the high-risk group compared to the low-risk group. These pathways included cell cycle, ECM receptor interaction, tight junction, WNT signaling pathway, MAPK signaling pathway, P53 signaling pathway, TGF-beta signaling pathway, cell-cell junction, cellular response to copper ion, immunological memory process, T cell receptor complex, regulation of cellular response to hypoxia, etc. (Figure 8).

Figure 8. KEGG and GO enrichment analysis in the predictive signature between high-risk and low-risk groups. (A) KEGG enrichment analysis; (B) GO enrichment analysis. Abbreviations: KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology.

Immune infiltration level analysis

To analyze the level of immune infiltration, we utilized Principal component analysis (PCA) maps that enabled us to visualize patients’ distribution based on their whole genome, cuproptosis-related gene sets, cuproptosis-related lncRNAs, and predictive signature. The results showed that the predictive signature was the best for patients (Figure 9A–9D). Moreover, to investigate the association between risk scores and immune cells and pathways, we calculated the enrichment scores of ssGSEA concerning diverse immune cell subsets and their related pathways. The analysis confirmed the differences of immature dendritic cells (iDCs), macrophages, mast cells, T helper type 2 (Th2) cells, T regulatory cells (Tregs), chemokine receptor (CCR) and parainflammation between two risk groups (Figure 9E, 9F). Given the importance of checkpoint immunotherapy, furthermore observed to differ between two risk groups. When compared to the low-risk group, the high-risk group’s PD−L1 expression was much higher. Results indicated that anti-PD-1/L1 immunotherapy may be effective in high-risk individuals (Figure 10A). Along with immunotherapy, we also analyzed the effect of common drugs on the efficacy of BLCA. The results showed a lower IC50 value of cisplatin, docetaxel, imatinib, lapatinib, paclitaxel, parthenolide, pazopanib and thapsigargin in the high-risk group (Figure 10B–10I), but a higher IC50 value of methotrexate, MK.2206, MS.275, PD.0332991, temsirolimus, vinorelbine and vorinostat in the high-risk group (Figure 10J–10P), which aids in investigating specialized therapy options for BLCA patients in the high-risk and low-risk categories.

Figure 9. Patients with high-risk and low risk scores have different immune statuses. PCA maps show the distribution of patients based on the (A) whole genome; (B) cuproptosis-related gene sets; (C) cuproptosis-related lncRNAs; and (D) the predictive signature. Results for ssGSEA scores immune cells scores (E) and immune functions scores (F) between high and low risk groups in boxplots. Abbreviations: PCA: Principal component analysis; lncRNAs: long noncoding RNAs; ssGSEA: single-sample gene set enrichment analysis. aDCs: activated dendritic cells; iDCs: immature dendritic cells; NK: natural killer; pDCs: plasmacytoid dendritic cells; Tfh: T follicular helper; Th1: T helper type 1; Th2: T helper type 2; TIL: tumor-infiltrating lymphocyte; Treg: T regulatory cell; APC: antigen-presenting cell; CCR: chemokine receptor; HLA: human leukocyte antigen; MHC: major histocompatibility complex; IFN: interferon.

Figure 10. Comparison of treatment drugs sensitivity between high-risk and low-risk groups. (A) PD-L1 expression in high and low risk groups. (B) IC50 of cisplatin in high and low risk groups. (C) IC50 of docetaxel in high and low risk groups. (D) IC50 of imatinib in high and low risk groups. (E) IC50 of lapatinib in high and low risk groups. (F) IC50 of paclitaxel in high and low risk groups. (G) IC50 of parthenolide in high and low risk groups. (H) IC50 of pazopanib in high and low risk groups. (I) IC50 of thapsigargin in high and low risk groups. (J) IC50 of methotrexate in high and low risk groups. (K) IC50 of MK.2206 in high and low risk groups. (L) IC50 of MS.275 in high and low risk groups. (M) IC50 of PD.0332991 in high and low risk groups. (N) IC50 of temsirolimus in high and low risk groups. (O) IC50 of vinorelbine in high and low risk groups. (P) IC50 of vorinostat in high and low risk groups. Abbreviation: IC50: half-maximal inhibitory concentration.

The correlations between risk scores/8 cuproptosis-related lncRNAs and clinicopathological variables

We analyzed the correlation between clinicopathological variables and risk scores/8 cuproptosis-related lncRNAs using gene expression and corresponding clinical data from the TCGA database. Results showed that AC005261.1 was correlated with grade (Figure 11A); AC008074.2 was associated with fustat (Figure 11B); AC021321.1 was correlated with fustat, grade, stage, T stage and N stage (Figure 11C–11E, Supplementary Figure 2A, 2B); AL024508.2 was associated with fustat, grade, stage, T stage and N stage (Figure 11F–11H, Supplementary Figure 2C, 2D); AL354919.2 was correlated with age, fustat, grade, stage, and TNM stage (Figure 11I–11K, Supplementary Figure 2E–2H); LINC02446 was associated with grade and M stage (Figure 11L, Supplementary Figure 2I); risk scores were correlated with fustat, grade, stage, T stage and N stage (Figure 11M–11P, Supplementary Figure 2J).

Figure 11. The correlations between risk scores/8 cuproptosis-related lncRNAs and clinicopathological variables. (A) Correlation between AC005261.1 expression level and grade. (B) Correlation between AC008074.2 expression fustat. (C–E) Correlation between AC021321.1 expression level and fustat, N stage and stage. (F–H) Correlation between AL024508.2 expression level and fustat, N stage and stage. (I–K) Correlation between AL354919.2 and age, grade and T stage. (L) Correlation between LINC02446 expression level and M stage. (M–P) Correlation between risk scores expression level and fustat, grade, stage and N stage. Abbreviations: lncRNAs: long noncoding RNAs; T: tumor; N: lymph node; M: metastasis.

Validation of the expression of the cuproptosis-related lncRNAs in indicated cell lines

We chose three BLCA cell lines to examine the levels of their mRNA expression in order to further evaluate the expression of the cuproptosis-related lncRNAs. The control group was normal bladder cell line SV-HUC1. The results were shown in Figure 12. LncRNA AC005261.1, AC021321.1, AL024508.2, LINC02446 and LINC01106 were lowly expressed in tumor cells, while ARHGAP5-AS1 showed the opposite trend.

Figure 12. Validation of results through quantitative PCR. Relative mRNA expression of the cuproptosis-related lncRNAs in four cell lines (SV-HUC-1, 5637, BIU-87 and T24).

Abbreviations

BLCA: Bladder Urothelial Carcinoma; NMIBC: non-muscle invasive bladder cancer; MIBC: muscle invasive bladder cancer; TCA: tricarboxylic acid; LncRNAs: Long non-coding RNAs; TCGA: The Cancer Genome Atlas; LASSO: the least absolute shrinkage and selection operator; PPI: the protein-protein interaction; OS: overall survival; GSEA: Gene Set Enrichment Analysis; ssGSEA: Single sample gene set enrichment analysis; ROC: receiver operating characteristic; AUC: area under the curve; TNM: Tumor Node Metastasis; PCA: Principal component analysis; CRGs: cuproptosis-related genes.

Author Contributions

LW and XW designed the study and performed the experiments; LW and WC collected data and wrote the manuscript; YC, BC and YH performed the data analysis and drew the figures as well as tables; XW, LW, WC, BC, YH and LW reviewed and revised the manuscript. The final manuscript draft was read and approved by all authors.

Acknowledgments

The results shown here are in part based on data generated by the TCGA website (http://cancergenome.nih.gov/), the GENCODE project (Homo sapiens GRCh38) (https://gencodegenes.org).

Conflicts of Interest

The authors declare no conflicts of interest related to this study.

Funding

Jiaxing Science and Technology Program (2020AD30059); Jiaxing Medical Key Subject Funding of Zhejiang Province (2023-ZC-013); Jiaxing Key Laboratory of Precise Diagnosis and Treatment of Urological Tumor (2020-mnzdsys).

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