Skip to main content

Advertisement

SpringerLink
Log in

Development of a prognostic signature for immune-associated genes in bladder cancer and exploring potential drug findings

  • Urology - Original Paper
  • Published:
International Urology and Nephrology Aims and scope Submit manuscript

Abstract

Background

Bladder cancer, predominantly affecting men, is a prevalent malignancy of the urinary system. Although platinum-based chemotherapy has demonstrated certain enhancements in overall survival when compared to surgery alone, the efficacy of treatments is impeded by the unfavorable side effects of conventional chemotherapy medications. Nonetheless, immunotherapy exhibits potential in the treatment of bladder cancer.

Methods

To create an immune-associated prognostic signature for bladder cancer, bioinformatics analyses were performed utilizing The Cancer Genome Atlas (TCGA) database in this study. By identifying differential gene expressions between the high-risk and low-risk groups, a potential therapeutic drug was predicted using the Connectivity Map database. Subsequently, the impact of this drug on the growth of T24 cells was validated through MTT assay and 3D cell culture techniques.

Results

The signature included 1 immune-associated LncRNA (NR2F1-AS1) and 16 immune-associated mRNAs (DEFB133, RBP7, PDGFRA, CGB3, PDGFD, SCG2, ADCYAP1R1, OPRL1, PGR, PSMD1, TANK, PRDX1, ADIPOR2, S100A8, AHNAK, EGFR). Based on the assessment of risk scores, the patients were classified into cohorts of low-risk and high-risk individuals. The cohort with low risk demonstrated a considerably higher likelihood of survival in comparison to the group with high risk. Furthermore, variations in immune infiltration were noted among the two categories. Cephaeline, a possible medication, was discovered by analyzing variations in gene expression. It exhibited promise in suppressing the viability and growth of T24 bladder cancer cells.

Conclusion

The novel predictive pattern allows for efficient categorization of patients with bladder cancer, enabling focused and rigorous treatment for those expected to have a worse prognosis. The discovery of a possible curative medication establishes a basis for forthcoming immunotherapy trials in bladder cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

The data of RNA-HTSeq and clinical information data of BLCA patients were derived from the following resources available in the public domain: https://portal.gdc.cancer.gov/; The data that support the findings of this study in Figure 13 are openly available in figshare at https://doi.org/10.6084/m9.figshare.24168177. 

References

  1. Black PC, Sridhar S (2017) New research in bladder cancer, ASCO-GU 2017. Can Urol Assoc J 11:S160–S162. https://doi.org/10.5489/cuaj.4606

    Article  PubMed  PubMed Central  Google Scholar 

  2. Luo H, Xu C, Liu Z et al (2019) Neural differentiation of bone marrow mesenchymal stem cells with human brain-derived neurotrophic factor gene-modified in functionalized self-assembling peptide hydrogel in vitro. J Cell Biochem 120:2828–2835. https://doi.org/10.1002/jcb.26408

    Article  CAS  PubMed  Google Scholar 

  3. Arab S, Hadjati J (2019) Adenosine blockage in tumor microenvironment and improvement of cancer immunotherapy. Immune Netw 19:e23. https://doi.org/10.4110/in.2019.19.e23

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wolacewicz M, Hrynkiewicz R, Grywalska E et al (2020) Immunotherapy in bladder cancer: current methods and future perspectives. Cancers (Basel). https://doi.org/10.3390/cancers12051181

    Article  PubMed  PubMed Central  Google Scholar 

  5. Londono MC, Reig M, Group RM (2020) Multidisciplinary clinical approach to cancer patients with immune-related adverse events induced by checkpoint inhibitors. Cancers (Basel). https://doi.org/10.3390/cancers12113446

    Article  PubMed  Google Scholar 

  6. Yang F, Zeng Z, Li J et al (2018) PD-1/PD-L1 axis, rather than high-mobility group alarmins or CD8+ tumor-infiltrating lymphocytes, is associated with survival in head and neck squamous cell carcinoma patients who received surgical resection. Front Oncol 8:604. https://doi.org/10.3389/fonc.2018.00604

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hu-Lieskovan S, Bhaumik S, Dhodapkar K et al (2020) SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery. J Immunother Cancer. https://doi.org/10.1136/jitc-2020-000705

    Article  PubMed  PubMed Central  Google Scholar 

  8. Linden A, Yarnold PR (2017) Modeling time-to-event (survival) data using classification tree analysis. J Eval Clin Pract 23:1299–1308. https://doi.org/10.1111/jep.12779

    Article  PubMed  Google Scholar 

  9. Nagashima K, Sato Y (2017) Information criteria for Firth’s penalized partial likelihood approach in Cox regression models. Stat Med 36:3422–3436. https://doi.org/10.1002/sim.7368

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16:385–395. https://doi.org/10.1002/(sici)1097-0258(19970228)16:4%3c385::aid-sim380%3e3.0.co;2-3

    Article  CAS  PubMed  Google Scholar 

  11. Ritchie ME, Phipson B, Wu D et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl Acids Res 43:e47. https://doi.org/10.1093/nar/gkv007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. Gene Ontol Consort Nat Genet 25:25–29. https://doi.org/10.1038/75556

    Article  CAS  Google Scholar 

  13. Kanehisa M, Furumichi M, Tanabe M et al (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucl Acids Res 45:D353–D361. https://doi.org/10.1093/nar/gkw1092

    Article  CAS  PubMed  Google Scholar 

  14. Yu G, Wang LG, Han Y et al (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284–287. https://doi.org/10.1089/omi.2011.0118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chen B, Khodadoust MS, Liu CL et al (2018) Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol 1711:243–259. https://doi.org/10.1007/978-1-4939-7493-1_12

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Musa A, Ghoraie LS, Zhang SD et al (2017) A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 18:903. https://doi.org/10.1093/bib/bbx023

    Article  PubMed  PubMed Central  Google Scholar 

  17. Han J, Gu X, Li Y et al (2020) Mechanisms of BCG in the treatment of bladder cancer-current understanding and the prospect. Biomed Pharmacother 129:110393. https://doi.org/10.1016/j.biopha.2020.110393

    Article  CAS  PubMed  Google Scholar 

  18. Pettenati C, Ingersoll MA (2018) Mechanisms of BCG immunotherapy and its outlook for bladder cancer. Nat Rev Urol 15:615–625. https://doi.org/10.1038/s41585-018-0055-4

    Article  CAS  PubMed  Google Scholar 

  19. Gomes-Giacoia E, Miyake M, Goodison S et al (2014) Intravesical ALT-803 and BCG treatment reduces tumor burden in a carcinogen induced bladder cancer rat model; a role for cytokine production and NK cell expansion. PLoS ONE 9:e96705. https://doi.org/10.1371/journal.pone.0096705

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Huntington ND, Legrand N, Alves NL et al (2009) IL-15 trans-presentation promotes human NK cell development and differentiation in vivo. J Exp Med 206:25–34. https://doi.org/10.1084/jem.20082013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Rosser CJ, Tikhonenkov S, Nix JW et al (2021) Safety, tolerability, and long-term clinical outcomes of an IL-15 analogue (N-803) admixed with bacillus calmette-guerin (BCG) for the treatment of bladder cancer. Oncoimmunology 10:1912885. https://doi.org/10.1080/2162402X.2021.1912885

    Article  PubMed  PubMed Central  Google Scholar 

  22. Jacob JB, Jacob MK, Parajuli P (2021) Review of immune checkpoint inhibitors in immuno-oncology. Adv Pharmacol 91:111–139. https://doi.org/10.1016/bs.apha.2021.01.002

    Article  CAS  PubMed  Google Scholar 

  23. Alanee S, Sana S, El-Zawahry A et al (2021) Phase I trial of intravesical Bacillus Calmette-Guerin combined with intravenous pembrolizumab in recurrent or persistent high-grade non-muscle-invasive bladder cancer after previous Bacillus Calmette-Guerin treatment. World J Urol 39:3807–3813. https://doi.org/10.1007/s00345-021-03716-3

    Article  CAS  PubMed  Google Scholar 

  24. Ward Grados DF, Ahmadi H, Griffith TS et al (2022) Immunotherapy for bladder cancer: latest advances and ongoing clinical trials. Immunol Investig 51:2226–2251. https://doi.org/10.1080/08820139.2022.2118606

    Article  CAS  Google Scholar 

  25. Inman BA, Longo TA, Ramalingam S et al (2017) Atezolizumab: a PD-L1-blocking antibody for bladder cancer. Clin Cancer Res 23:1886–1890. https://doi.org/10.1158/1078-0432.CCR-16-1417

    Article  CAS  PubMed  Google Scholar 

  26. Massard C, Gordon MS, Sharma S et al (2016) Safety and efficacy of durvalumab (MEDI4736), an anti-programmed cell death ligand-1 immune checkpoint inhibitor, in patients with advanced urothelial bladder cancer. J Clin Oncol 34:3119–3125. https://doi.org/10.1200/JCO.2016.67.9761

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Powles T, Park SH, Voog E et al (2020) Avelumab maintenance therapy for advanced or metastatic urothelial carcinoma. N Engl J Med 383:1218–1230. https://doi.org/10.1056/NEJMoa2002788

    Article  CAS  PubMed  Google Scholar 

  28. Feng L, Yang J, Zhang W et al (2022) Prognostic significance and identification of basement membrane-associated lncRNA in bladder cancer. Front Oncol 12:994703. https://doi.org/10.3389/fonc.2022.994703

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Li DX, Feng DC, Shi X et al (2023) Identification of endothelial-related molecular subtypes for bladder cancer patients. Front Oncol 13:1101055. https://doi.org/10.3389/fonc.2023.1101055

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen H, Yang W, Xue X et al (2022) Integrated analysis revealed an inflammatory cancer-associated fibroblast-based subtypes with promising implications in predicting the prognosis and immunotherapeutic response of bladder cancer patients. Int J Mol Sci. https://doi.org/10.3390/ijms232415970

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kashiwagi E, Abe T, Kinoshita F et al (2020) The role of adipocytokines and their receptors in bladder cancer: expression of adiponectin or leptin is an independent prognosticator. Am J Transl Res 12:3033–3045

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Herik Rodrigo AG, Tomonobu N, Yoneda H et al (2022) Toll-like receptor 4 promotes bladder cancer progression upon S100A8/A9 binding, which requires TIRAP-mediated TPL2 activation. Biochem Biophys Res Commun 634:83–91. https://doi.org/10.1016/j.bbrc.2022.09.116

    Article  CAS  PubMed  Google Scholar 

  33. Yasar O, Akcay T, Obek C et al (2017) Significance of S100A8, S100A9 and calprotectin levels in bladder cancer. Scand J Clin Lab Investig 77:437–441. https://doi.org/10.1080/00365513.2017.1336567

    Article  CAS  Google Scholar 

  34. Lyu ZJ, Wang Y, Huang JL et al (2021) Recurrent ZNF83-E293V mutation promotes bladder cancer progression through the NF-kappaB pathway via transcriptional dysregulation of S100A8. Mol Ther 29:275–290. https://doi.org/10.1016/j.ymthe.2020.09.004

    Article  CAS  PubMed  Google Scholar 

  35. Minami S, Sato Y, Matsumoto T et al (2010) Proteomic study of sera from patients with bladder cancer: usefulness of S100A8 and S100A9 proteins. Cancer Genom Proteom 7:181–189

    CAS  Google Scholar 

  36. Gao Z, Chen C, Gu P et al (2022) The tumor microenvironment and prognostic role of autophagy- and immune-related genes in bladder cancer. Cancer Biomark 35:293–303. https://doi.org/10.3233/CBM-220058

    Article  CAS  PubMed  Google Scholar 

  37. Luo Y, Chen L, Zhou Q et al (2020) Identification of a prognostic gene signature based on an immunogenomic landscape analysis of bladder cancer. J Cell Mol Med 24:13370–13382. https://doi.org/10.1111/jcmm.15960

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kriegmair MC, Wirtz RM, Worst TS et al (2018) Prognostic value of molecular breast cancer subtypes based on Her2, ESR1, PGR and Ki67 mRNA-expression in muscle invasive bladder cancer. Transl Oncol 11:467–476. https://doi.org/10.1016/j.tranon.2018.02.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Salah Fararjeh A, Al-Khader A, Al-Saleem M et al (2021) The prognostic significance of proteasome 26S subunit, non-ATPase (PSMD) genes for bladder urothelial carcinoma patients. Cancer Inform 20:11769351211067692. https://doi.org/10.1177/11769351211067692

    Article  PubMed  PubMed Central  Google Scholar 

  40. Kumari N, Vasudeva P, Tanwar P et al (2022) Peroxiredoxins—urinary surveillance biomarkers in urothelial cancer. J Cancer 13:2751–2756. https://doi.org/10.7150/jca.69811

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Jin K, Qiu S, Jin D et al (2021) Development of prognostic signature based on immune-related genes in muscle-invasive bladder cancer: bioinformatics analysis of TCGA database. Aging Albany NY 13:1859–1871. https://doi.org/10.18632/aging.103787

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Guo L, Wu Q, Ma Z et al (2021) Identification of immune-related genes that predict prognosis and risk of bladder cancer: bioinformatics analysis of TCGA database. Aging (Albany NY) 13:19352–19374. https://doi.org/10.18632/aging.203333

    Article  CAS  PubMed  Google Scholar 

  43. Silva LC, Borgato GB, Wagner VP et al (2022) Cephaeline is an inductor of histone H3 acetylation and inhibitor of mucoepidermoid carcinoma cancer stem cells. J Oral Pathol Med 51:553–562. https://doi.org/10.1111/jop.13252

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The present study was supported by the National Natural Science Foundation of China (82000230 to Gao Jinlai, 82104197 to Jin Jing), Science and Technology Bureau of Jiaxing (2020AY10021), Basic Public Welfare Research Project of Zhejiang Province (LGC21B050011) and the Qin Shen Scholar Program of Jiaxing University. We thank Jiaxing Medical Key Subject Funding of Zhejiang Province (2023-ZC-013), the Jiaxing Key Laboratory of Precise Diagnosis and Treatment of Urological Tumor (2020-mnzdsys) and Jiaxing Innovation Leading Program for Elite Talents (84122009) for their help.

Author information

Author notes

  1. Zhang Xiaoqin and Lu Zhouqi contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmacology, College of Medical, Jiaxing University, Jiaxing, 314000, China

    Zhang Xiaoqin, Lu Zhouqi, Feng Xinyi, Shen Bin, Wu Jiming, Liu Shihui, Zhou Bangwei & Gao Jinlai

  2. Departments of Central Laboratory, The Affiliated Hospital of Jiaxing University, Jiaxing, 314000, China

    Pan Huan

  3. Department of Urology, The Affiliated Hospital of Jiaxing University, Jiaxing, 314000, China

    Jin Jing & He Yi

Authors
  1. Zhang Xiaoqin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lu Zhouqi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pan Huan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feng Xinyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shen Bin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wu Jiming
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Liu Shihui
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhou Bangwei
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jin Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. He Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Gao Jinlai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The study was conceptualized by ZX and LZ. The experiments and data analysis were conducted by PH, SB, FX, WJ, ZB, and LS. The original draft of the paper was written by JJ, HY, and GJ. Constructive discussions involving JJ and GJ contributed to the enhancement of the paper. The final manuscript was read and approved by all authors.

Corresponding authors

Correspondence to Jin Jing, He Yi or Gao Jinlai.

Ethics declarations

Conflict of interest

No competing interests are declared by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

11255_2023_3796_MOESM1_ESM.pdf

Figure S1. Clinical information varied between the high and low-risk groups in the risk test database. Figure S2. Clinical information varied between the high and low-risk groups in the risk train database. Figure S3. Heatmap of immune cell expression between high-risk and low-risk groups. Figure S4. Barplot of immune cell expression between high-risk and low-risk groups (PDF 280 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiaoqin, Z., Zhouqi, L., Huan, P. et al. Development of a prognostic signature for immune-associated genes in bladder cancer and exploring potential drug findings. Int Urol Nephrol 56, 483–497 (2024). https://doi.org/10.1007/s11255-023-03796-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11255-023-03796-7

Keywords

Search

Navigation