Prognostic value and immune landscapes of disulfidptosis‑related lncRNAs in bladder cancer

Abstract

Disulfidptosis, which was recently identified, has shown promise as a potential cancer treatment. Nonetheless, the precise role of long non-coding RNAs (lncRNAs) in this phenomenon is currently unclear. To elucidate their significance in bladder cancer (BLCA), a signature of disulfidptosis-related lncRNAs (DRlncRNAs) was developed and their potential prognostic significance was explored. BLCA sample data were sourced from The Cancer Genome Atlas. A predictive signature comprising DRlncRNAs was formulated and subsequently validated. The combination of this signature with clinical characteristics facilitated the development of a nomogram with practical clinical utility. Additionally, enrichment analysis was conducted, the tumor microenvironment (TME) was assessed, the tumor mutational burden (TMB) was analyzed, and drug sensitivity was explored. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to quantify lncRNA expression. The results revealed an eight-gene signature based on DRlncRNAs was established, and the predictive accuracy of the nomogram that incorporated the risk score [area under the curve (AUC)=0.733] outperformed the nomogram without it (AUC=0.703). High-risk groups were associated with pathways such as WNT signaling, focal adhesion and cell cycle pathways. The TME study revealed that high-risk patients had increased immune infiltration, whereas the TMB and tumor immune dysfunction and exclusion scores in low-risk patients indicated a potentially robust immune response. Drug sensitivity analysis identified appropriate antitumor drugs for each group. RT-qPCR experiments validated significant differences in DRlncRNAs expression between normal and BLCA cell lines. In conclusion, the prognostic risk signature, which includes the eight identified DRlncRNAs, demonstrates promise for predicting prognosis of patients with BLCA and guiding the selection of suitable immunotherapy and chemotherapy strategies.

Keywords: BLCA; disulfidptosis; lncRNAs; prognostic signature.

Figure 1

Schematic diagram of the present study. BLCA, bladder cancer; lncRNA, long non-coding RNA; TCGA, The Cancer Genome Atlas; PCA, principal component analysis; TIDE, tumor immune dysfunction and exclusion; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; TMB, tumor mutation burden; TME, tumor microenvironment.

Figure 2

Identification of DRlncRNAs in patients with bladder cancer. (A) The protein-protein interaction network indicating the interactions among 6 disulfidptosis-related genes. (B) Volcano plot showing 287 differentially expressed DRlncRNAs. (C and D) Least absolute shrinkage and selection operator regression analysis. DRlncRNAs, disulfidptosis-related long non-coding RNAs.

Figure 3

Evaluation of the prognostic effectiveness of prognostic signature in the training group, test group and entire group. (A-C) The comparison of the Kaplan-Meier overall survival curves between low- and high-risk patients. (D-F) Receiver operating characteristic curves over one, three and five years. AUC, area under the curve.

Figure 4

Independent prognostic analysis and PCA. (A-D) PCA analysis based on all genes, all lncRNAs, disulfidptosis-related lncRNAs and risk signature. (E and F) Univariate and multivariate analyses. PCA, principal component analysis; lncRNA, long non-coding RNA; HR, hazard ratio; CI, confidence interval.

Figure 5

Further validation of signature impacts. (A-F) Kaplan-Meier analysis of overall survival in various clinical characteristics groups.

Figure 6

Development of a nomogram and the predictive performance of the signature. (A) Forecast analysis of nomogram for 1-, 3- and 5-years. (B) Receiver operating characteristic curves that include diverse clinical data. (C) Decision curve analysis. (D and E) Calibration curves for 1-, 3- and 5-year nomogram (D) with risk score and (E) without risk score. AUC, area under the curve.

Figure 7

TMB analysis. (A) Percentage bar graph. (B and C) Waterfall diagram of (B) high-risk groups and (C) low-risk groups. TMB, tumor mutation burden; H-, high; L-, low.

Figure 8

Immune infiltration landscape analysis. (A-D) Violin plots of differences in the microenvironment between the two groups. (E) The correlation between immune cells and risk scores under seven algorithms. (F) Single-sample gene set enrichment analysis evaluation of 28 immune cell infiltrations and (G) 13 immune function scores. ^*P<0.05, ^**P<0.01, ^***P<0.001 and ^****P<0.0001. ns, not significant (P>0.05).