Background: Numerous types of research revealed that long noncoding RNAs (lncRNAs) played a significant role in immune response and the tumor microenvironment of bladder cancer (BLCA). Dysregulated lipid metabolism is considered to be one of the major risk factors for BLCA, the study aimed to detect the lipid metabolism-related lncRNAs (LMRLs) along with their potential prognostic values and immune correlations in BLCA.

Methods: We collected lipid metabolism-related genes, expression profiles, and clinical information on BLCA from the Molecular Signature Database (MSigDB) and the TCGA database, respectively. Differentially expressed lipid metabolism genes (DE-LMRGs) and differentially expressed long non-coding RNAs (DE-lncRNAs) were selected using the limma package. Spearman correlation analysis was employed to explore the correlations between DE-lncRNAs and DE-LMRGs and to further develop protein-protein interaction (PPI) networks and perform mutational analysis. The least absolute shrinkage and selection operator (LASSO) and univariate Cox analysis were then employed to construct a prognostic risk model. The performance of the model was evaluated using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and consistency indices. In addition, we downloaded the GSE31684 dataset for external validation of the prognostic signature. Moreover, we explored the association of the risk model with immune cell infiltration and chemotherapy response analysis to reveal the tumor immune microenvironment of BLCA. Finally, RT-qPCR was utilized to validate the expression of prognostic genes.

Results: A total of 48 DE-LncRNAs and 33 DE-LMRGs were found to be robustly correlated, and were used to construct a lncRNA-mRNA co-expression network, in which ACACB, ACOX2, and BCHE showed high mutation rates. Then, a risk model based on three LMRLs (RP11-465B22.8, MIR100HG, and LINC00865) was constructed. The risk model effectively distinguished between the clinical outcomes of BLCA patients, with high-risk scores indicating a worse prognosis and with substantial prognostic prediction accuracy. The model's results were consistent in the GSE31684 dataset. In addition, a nomogram was constructed based on the risk score, age, pathological T-stage, and pathological N-stage, which showed robust predictive power. Immune landscape analysis indicated that the risk model was significantly associated with T-cell CD4 memory activation, M1 macrophage, M2 macrophage, dendritic cell activation, and T-cell regulatory. We predicted that 49 drugs would perform satisfactorily in the high-risk group. Additionally, we found five m6A regulators associated with the high- and low-risk groups, suggesting that upstream regulation of LncRNA could be a novel target for BLCA treatment. Finally, RT-qPCR showed that RP11-465B22.8 was highly expressed in BLCA, while MIR100HG and LINC00865 were downregulated in BLCA.

Conclusion: Our findings suggest that the three LMRLs may serve as potential prognostic and immunotherapeutic biomarkers in BLCA. In addition, our study provides new ideas for understanding the pathogenic mechanisms and developing therapeutic strategies for BLCA patients.