Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Sep 13:14:1215296.
doi: 10.3389/fphar.2023.1215296. eCollection 2023.

Identification of novel lactate metabolism-related lncRNAs with prognostic value for bladder cancer

Xiushen Wang #  1 , Jing Pan #  2   3 , Qiutong Guan  2 , Ninghui Ren  2 , Ping Wang  1 , Minjie Wei  2 , Zhenhua Li  2
Affiliations

Identification of novel lactate metabolism-related lncRNAs with prognostic value for bladder cancer

Xiushen Wang et al. Front Pharmacol. .

Abstract

Background: Bladder cancer (BCA) has high recurrence and metastasis rates, and current treatment options show limited efficacy and significant adverse effects. It is crucial to find diagnostic markers and therapeutic targets with clinical value. This study aimed to identify lactate metabolism-related lncRNAs (LM_lncRNAs) to establish a model for evaluating bladder cancer prognosis. Method: A risk model consisting of lactate metabolism-related lncRNAs was developed to forecast bladder cancer patient prognosis using The Cancer Genome Atlas (TCGA) database. Kaplan‒Meier survival analysis, receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) were used to evaluate the reliability of risk grouping for predictive analysis of bladder cancer patients. The results were also validated in the validation set. Chemotherapeutic agents sensitive to lactate metabolism were assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Results: As an independent prognostic factor for patients, lactate metabolism-related lncRNAs can be used as a nomogram chart that predicts overall survival time (OS). There were significant differences in survival rates between the high-risk and low-risk groups based on the Kaplan‒Meier survival curve. decision curve analysis and receiver operating characteristic curve analysis confirmed its good predictive capacity. As a result, 22 chemotherapeutic agents were predicted to positively affect the high-risk group. Conclusion: An lactate metabolism-related lncRNA prediction model was proposed to predict the prognosis for patients with bladder cancer and chemotherapeutic drug sensitivity in high-risk groups, which provided a new idea for the prognostic evaluation of the clinical treatment of bladder cancer.

Keywords: bladder cancer; lactate metabolism; lncRNAs; molecular subtype; prognostic model.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Study flowchart. Three hundred thirty lactate-related mRNAs and 780 related lncRNAs (LRLs) were obtained from the TCGA and MSigDB databases. Then, 426 lactate-related differentially expressed lncRNAs (LDELs) were identified according to their differential expression in the tumor and adjacent tumor. Next, univariate Cox, Lasso, and multivariate Cox analyses were applied to screen for prognostic LDELs. Based on this analysis, a 5-LDEL signature was constructed. Subsequently, GSEA analyses, immune-related analyses, m6A-related analyses, and drug sensitivity assays were applied to identify the potential function of this signature. Finally, 2 internal validations were conducted to explore the expression and function of these LDELs.
FIGURE 2
FIGURE 2
Lactate signature construction. (A) Volcano map for differentially expressed lncRNAs. (B) Heatmap for differentially expressed lncRNAs. (C) Risk score distribution and survival status of the two risk groups. (D) Kaplan‒Meier curve analysis (K-M curve analysis) for the cohort. (E) The Sankey diagram presents the detailed connection between lactate-related lncRNAs and lactate-related genes.
FIGURE 3
FIGURE 3
Stability verification of the lactate-related lncRNA signature model in the training cohort. (A-B) The 1-year AUC of this signature lncRNA was 0.681, and the 5-year AUC was 0.691. (C) The predicted 3-, 5-, and 10-year survival receiver operating characteristic (ROC) curves of the new lncRNA features were 0.67, 0.68, and 0.69, respectively. (D) The model’s decision curve analysis (DCA) also shows that the model has good profitability.
FIGURE 4
FIGURE 4
ROC validation and Kaplan‒Meier curve analysis for the lactate-related lncRNA signature. (A-B) The ROC areas were 0.670 and 0.702 in validation sets 1 (dataset 2: n = 197) and 2 (dataset 3: 196), respectively. (C–D) Prolonged OS in low-risk versus high-risk patients in both validation cohorts (log-rank test, p < 0.001).
FIGURE 5
FIGURE 5
Independent prognostic value of the LDEL risk model. (A, B) Univariate (A) and multivariate Cox (B) analyses in the training cohort. (C) A nomogram for the lactate lncRNA signature. (D) A nomogram for both prognostic lactate lncRNAs and pathological factors. (E) C-index analysis of the nomogram.
FIGURE 6
FIGURE 6
(A) Enrichment of genes in the representative pathways by GSEA function analysis. (B) Immune scores for the high-risk and low-risk groups. (C) The stromal score for the high-risk and low-risk groups.
FIGURE 7
FIGURE 7
Correlation between LDELs and immunometabolic modification. (A) Expression of m6A genes between high- and low-risk subgroups (− p ≥ 0.1, ·p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). (B) Distribution of immune checkpoints between the high- and low-risk subgroups. (C) Protein‒protein interaction (PPI) network of 5 LDELs and lactate metabolism genes.
FIGURE 8
FIGURE 8
Comparative analysis of chemotherapy drugs with good efficacy in the high-risk group.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Antoni S., Ferlay J., Soerjomataram I., Znaor A., Jemal A., Bray F. (2017). Bladder cancer incidence and mortality: A global overview and recent trends. Eur. Urol. 71 (1), 96–108. 10.1016/j.eururo.2016.06.010 - DOI - PubMed
    1. Bader J. E., Voss K., Rathmell J. C. (2020). Targeting metabolism to improve the tumor microenvironment for cancer immunotherapy. Mol. Cell. 78 (6), 1019–1033. 10.1016/j.molcel.2020.05.034 - DOI - PMC - PubMed
    1. Bollard J., Miguela V., Ruiz de Galarreta M., Venkatesh A., Bian C. B., Roberto M. P., et al. (2017). Palbociclib (PD-0332991), a selective CDK4/6 inhibitor, restricts tumour growth in preclinical models of hepatocellular carcinoma. Gut 66 (7), 1286–1296. 10.1136/gutjnl-2016-312268 - DOI - PMC - PubMed
    1. Brown T. P., Bhattacharjee P., Ramachandran S., Sivaprakasam S., Ristic B., Sikder M. O. F., et al. (2020). The lactate receptor GPR81 promotes breast cancer growth via a paracrine mechanism involving antigen-presenting cells in the tumor microenvironment. Oncogene 39 (16), 3292–3304. 10.1038/s41388-020-1216-5 - DOI - PubMed
    1. Burns J. E., Hurst C. D., Knowles M. A., Phillips R. M., Allison S. J. (2021). The Warburg effect as a therapeutic target for bladder cancers and intratumoral heterogeneity in associated molecular targets. Cancer Sci. 112 (9), 3822–3834. 10.1111/cas.15047 - DOI - PMC - PubMed