Skip to main content

Advertisement

SpringerLink
Log in

Bladder cancer gene expression prediction with explainable algorithms

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this study, we aimed to classify bladder cancer patients using tumoral and non-tumoral gene expression data. In this way, we aimed to determine which genes are effective on tumoral and normal tissues. In addition, for this purpose, we planned to perform this classification using interpretable methods (The aim of this study was to classify bladder cancer patients using gene expression data from tumoral and non-tumoral tissues. By doing so, we wanted to determine which genes were effective on both tumoral and normal tissues. Moreover, for this purpose, we planned to use interpretable methods for this classification.). Analyses using permutation feature importance (PFI), SHapley Additive exPlanations (SHAP), local interpretable model-agnostic explanations (LIME), and Anchor methods on data from Gene Expression Omnibus (GEO) and Curated Microarray Database we did (We performed analyses using permutation feature importance (PFI), SHapley Additive exPlanations (SHAP), local interpretable model-agnostic explanations (LIME), and Anchor methods on data from Gene Expression Omnibus (GEO) and Curated Microarray Database.). These are eXplainable methods used to determine the importance of genes in classification. According to the results of our study, the most important genes were determined as LINC00161, ACACB, and CBARP according to the PFI method, HSPA6, STON2, and RFC2 according to the SHAP method, PRUNE2 and ABCC13 according to the LIME method, and TMEM74, KLHL10, and GAMT according to the Anchor method. This study shows that genes involved in other cancer types are also effective in bladder cancer. In addition, it has been observed that using explainable methods in cancer data can support prognosis and treatment in the clinic.

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

Similar content being viewed by others

Data availability

Data openly available in a public repository (https://www.ncbi.nlm.nih.gov/geo/), (https://sbcb.inf.ufrgs.br/cumida).

References

  1. WHO. Bladder cancer. https://www.iarc.who.int/cancer-type/bladder-cancer/ (accessed 2023).

  2. Segundo-Val IS, Sanz-Lozano CS (2016) Introduction to the gene expression analysis. Methods Mol Biol 1434:29–43. https://doi.org/10.1007/978-1-4939-3652-6_3

    Article  Google Scholar 

  3. Vadapalli S, Abdelhalim H, Zeeshan S, Ahmed Z (2022) Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine. Brief Bioinf 23(5):191. https://doi.org/10.1093/bib/bbac191

    Article  Google Scholar 

  4. Abbas M, El-Manzalawy Y (2020) Machine learning based refined differential gene expression analysis of pediatric sepsis. BMC Med ical Genom 13(1):122. https://doi.org/10.1186/s12920-020-00771-4

    Article  Google Scholar 

  5. Guneri-Sozeri PY, Erkek-Ozhan S (2022) Identification of the gene expression changes and gene regulatory aspects in ELF3 mutant bladder cancer. Mol Biol Rep 49(4):3135–3147. https://doi.org/10.1007/s11033-022-07145-2

    Article  Google Scholar 

  6. Zaravinos A, Lambrou GI, Volanis D, Delakas D, Spandidos DA (2011) Spotlight on differentially expressed genes in urinary bladder cancer. PLoS ONE 6(4):e18255. https://doi.org/10.1371/journal.pone.0018255

    Article  Google Scholar 

  7. Khalsan M et al (2022) A survey of machine learning approaches applied to gene expression analysis for cancer prediction. IEEE Access 10:27522–27534. https://doi.org/10.1109/ACCESS.2022.3146312

    Article  Google Scholar 

  8. Rukhsar L, Bangyal WH, Ali Khan MS, Ag Ibrahim AA, Nisar K, Rawat DB (2022) Analyzing RNA-Seq gene expression data using deep learning approaches for cancer classification. Appl Sci 12(4):1850. https://doi.org/10.3390/app12041850

    Article  Google Scholar 

  9. Almarzouki HZ (2022) Deep-learning-based cancer profiles classification using gene expression data profile. J Healthcare Eng 2022:4715998. https://doi.org/10.1155/2022/4715998

    Article  Google Scholar 

  10. Chen K et al (2021) Identification and validation of hub genes associated with bladder cancer by integrated bioinformatics and experimental assays. Front Oncol Original Res 11:782981. https://doi.org/10.3389/fonc.2021.782981

    Article  Google Scholar 

  11. Wagner A (2022) AI predicts the effectiveness and evolution of gene promoter sequences. Nature 603:384. https://doi.org/10.1038/d41586-022-00384-0

    Article  Google Scholar 

  12. Abbod MFMF et al. (2006) Artificial intelligence technique for gene expression profiling of urinary bladder cancer. In: 2006 3rd International IEEE conference intelligent systems, 4–6 Sept 2006, pp 646–651. https://doi.org/10.1109/IS.2006.348495

  13. Altmann A, Toloşi L, Sander O, Lengauer T (2010) Permutation importance: a corrected feature importance measure. Bioinformatics 26(10):1340–1347

    Article  Google Scholar 

  14. Li J et al (2023) Identification of genes related to immune enhancement caused by heterologous ChAdOx1-BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods. Front Immunol 14:1131051. https://doi.org/10.3389/fimmu.2023.1131051

    Article  Google Scholar 

  15. Shew M et al (2021) MicroRNA profiling as a methodology to diagnose Ménière’s disease: potential application of machine learning. Otolaryngol Head Neck Surg 164(2):399–406. https://doi.org/10.1177/0194599820940649

    Article  Google Scholar 

  16. Bazaga A, Leggate D, Weisser H (2020) Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology. Sci Rep 10(1):10787. https://doi.org/10.1038/s41598-020-67846-1

    Article  Google Scholar 

  17. Shapley L (1953) A value for n-person games. Princeton University Press, Princeton, pp 307–317. https://doi.org/10.1515/9781400881970-018

  18. Derks J, Peters H (1993) A shapley value for games with restricted coalitions. Int J Game Theory 21(4):351–60. Available: https://EconPapers.repec.org/RePEc:spr:jogath:v:21:y:1993:i:4:p:351-60.

  19. Sanchez K, Kamal K, Manjaly P, Ly S, Mostaghimi A (2023) Clinical application of artificial intelligence for non-melanoma skin cancer. Current Treatment Options Oncol 24(4):373–379. https://doi.org/10.1007/s11864-023-01065-4

    Article  Google Scholar 

  20. Kumar S, Das A (2023) Peripheral blood mononuclear cell derived biomarker detection using eXplainable Artificial Intelligence (XAI) provides better diagnosis of breast cancer. Comput Biol Chem 104:107867. https://doi.org/10.1016/j.compbiolchem.2023.107867

    Article  Google Scholar 

  21. Zhu K et al (2022) A novel 10-gene ferroptosis-related prognostic signature in acute myeloid leukemia. Front Oncol 12:1023040. https://doi.org/10.3389/fonc.2022.1023040

    Article  Google Scholar 

  22. Palatnik de Sousa I, Maria Bernardes Rebuzzi Vellasco M, Costa da Silva E (2019) Local interpretable model-agnostic explanations for classification of lymph node metastases. Sensors 19(13), 2969. Available: https://www.mdpi.com/1424-8220/19/13/2969

  23. Lai Y et al (2022) Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning. Front Immunol 13:1046410. https://doi.org/10.3389/fimmu.2022.1046410

    Article  Google Scholar 

  24. Oni O, Qiao S (2019) Model-agnostic interpretation of cancer classification with multi-platform genomic data, pp 34–41

  25. Modhukur V et al. (2021) Machine learning approaches to classify primary and metastatic cancers using tissue of origin-based DNA methylation profiles. Cancers (Basel) 13(15):3768. Available: https://www.mdpi.com/2072-6694/13/15/3768

  26. Marco Tulio Ribeiro SS, Guestrin C (2018) Anchors: high-precision model-agnostic explanations. . Available: https://homes.cs.washington.edu/~marcotcr/aaai18.pdf

  27. Edgar R, Domrachev M, Lash AE (2002) "Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30(1):207–210. https://doi.org/10.1093/nar/30.1.207

    Article  Google Scholar 

  28. Feltes BC, Chandelier E, Grisci B, Dorn M (2019) CuMiDa: an extensively curated microarray database for benchmarking and testing of machine learning approaches in cancer research. J Comput Biol 26. https://doi.org/10.1089/cmb.2018.0238

  29. Sherman BT et al (2022) DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res 50(W1):W216-w221. https://doi.org/10.1093/nar/gkac194

    Article  Google Scholar 

  30. Botchkarev A (2018) Performance metrics (error measures) in machine learning regression. Forecast Prognost Prop Typol

  31. Vujovic ZD (2021) Classification model evaluation metrics. Int J Adv Comput Sci Appl 12(6):599–606

  32. De Diego IM, Redondo AR, Fernandez RR, Navarro J, Moguerza JM (2022) General performance score for classification problems. Appl Intell 52(10):12049–12063. https://doi.org/10.1007/s10489-021-03041-7

  33. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/a:1010933404324

    Article  Google Scholar 

  34. Octaviani TL, Rustam Z (2019) Random forest for breast cancer prediction. In: 4th International symposium on current progress in mathematics and sciences (ISCPMS). Univ Indonesia, Fac Math and Nat Sci, Depok, INDONESIA, vol 2168. In: AIP Conference Proceedings,30–31 Oct 2018. https://doi.org/10.1063/1.5132477. Available: <Go to ISI>://WOS:000519032600050

  35. Huljanah M, Rustam Z, Utama S, Siswantining T, Iop (2019) Feature selection using random forest classifier for predicting prostate cancer. In: presented at the 9TH annual basic science international conference 2019 (BASIC 2019)

  36. Huang M et al (2017) Head and neck cancer survival outcome prediction based on NRG oncology RTOG 0522 with random forests and random survival forests. Med Phys 44(6)

  37. Liu DF et al (2021) Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images. Arch Gynecol Obstetrics 303(3):811–820. https://doi.org/10.1007/s00404-020-05908-5.

  38. Santhanam R, Uzir N, Raman S, Banerjee S (2017) Experimenting XGBoost algorithm for prediction and classification of different datasets

  39. Deng XS, Li M, Deng SB, Wang L (2022) Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification. Med Biol Eng Comput 60(3):663–681. https://doi.org/10.1007/s11517-021-02476-x

    Article  Google Scholar 

  40. Ma BS et al (2022) Diagnostic classification of cancers using DNA methylation of paracancerous tissues. Sci Rep 12(1):10646. https://doi.org/10.1038/s41598-022-14786-7

    Article  MathSciNet  Google Scholar 

  41. Song YY, Lu Y (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 27(2):130–135. https://doi.org/10.11919/j.issn.1002-0829.215044

    Article  Google Scholar 

  42. Zhang Z (2016) Introduction to machine learning: k-nearest neighbors. Ann Transl Med 4(11):218. https://doi.org/10.21037/atm.2016.03.37

    Article  Google Scholar 

  43. Lee WM (2019) Supervised learning—classification using K‐nearest neighbors (KNN), pp 205–220

  44. Momodu A (2017) K-nearest neighbor implementation in python 3.6.1 from scratch

  45. Gao S, Li HM (2012) IEEE breast cancer diagnosis based on support vector machine. In: Presented at the 2012 2nd international conference on uncertainty reasoning and knowledge engineering (URKE)

  46. Chen LY, Li JT, Chang MM (2020) Cancer diagnosis and disease gene identification via statistical machine learning. Curr Bioinform 15(9):956–962. https://doi.org/10.2174/1574893615666200207094947

    Article  Google Scholar 

  47. Teeyapan K, Theera-Umpon N, Auephanwiriyakul S, IEEE (2015) Application of support vector based methods for cervical cancer cell classification. In: Presented at the proceedings 5th IEEE international conference on control system, computing and engineering (ICCSCE 2015)

  48. Liu TB, Zhang XM, Chen R, Deng XX, Fu B (2023) Development, comparison, and validation of four intelligent, practical machine learning models for patients with prostate-specific antigen in the gray zone. Front Oncol 13. Art no. 1157384. https://doi.org/10.3389/fonc.2023.1157384

  49. Akcay M, Etiz D, Celik O, Ozen A (2022) Evaluation of acute hematological toxicity by machine learning in gynecologic cancers using postoperative radiotherapy. Indian J Cancer 59(2):178–186. https://doi.org/10.4103/ijc.IJC_666_19

  50. Lei L, IEEE (2018) Research on logistic regression algorithm of breast cancer diagnose data by machine learning. In: presented at the 2018 international conference on robots and intelligent system (ICRIS 2018)

  51. Ramirez SG, Hales RC, Williams GP, Jones NL (2022) Extending SC-PDSI-PM with neural network regression using GLDAS data and Permutation Feature Importance. Environ Model Softw 157:105475

    Article  Google Scholar 

  52. Gramegna A, Giudici P (2021) SHAP and LIME: an evaluation of discriminative power in credit risk. Front Artif Intell 4. Art no. 752558. https://doi.org/10.3389/frai.2021.752558

  53. Holzinger A, Saranti A, Molnar C, Biecek P, Samek W (2022) Explainable AI methods—a brief overview. Springer International Publishing, pp 13–38

  54. Hagras H (2018) Toward human-understandable, explainable AI. Computer 51(9):28–36

    Article  Google Scholar 

  55. Shi Y, Zhou Y (2010) The role of surgery in the treatment of gastric cancer. J Surg Oncol 101(8):687–692. https://doi.org/10.1002/jso.21455

    Article  Google Scholar 

  56. Wilusz JE, Sunwoo H, Spector DL (2009) Long non-coding RNAs: functional surprises from the RNA world. Genes Dev 23(13):1494–1504. https://doi.org/10.1101/gad.1800909

    Article  Google Scholar 

  57. Shen Y et al (2015) Prognostic and predictive values of long non-coding RNA LINC00472 in breast cancer. Oncotarget 6(11):8579–8592. https://doi.org/10.18632/oncotarget.3287

    Article  Google Scholar 

  58. Sun J et al (2015) A potential prognostic long non-coding RNA signature to predict metastasis-free survival of breast cancer patients. Sci Rep 5(1):16553. https://doi.org/10.1038/srep16553

  59. Li J et al (2014) LncRNA profile study reveals a three-lncRNA signature associated with the survival of patients with oesophageal squamous cell carcinoma. Gut 63(11):1700–1710. https://doi.org/10.1136/gutjnl-2013-305806

    Article  Google Scholar 

  60. Hu Y et al (2014) A long non-coding RNA signature to improve prognosis prediction of colorectal cancer. Oncotarget 5(8):2230–2242. https://doi.org/10.18632/oncotarget.1895

    Article  Google Scholar 

  61. Zhou M et al (2015) A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer. J Trans Med 13(1):231. https://doi.org/10.1186/s12967-015-0556-3

  62. Zhou M et al (2016) Comprehensive analysis of lncRNA expression profiles reveals a novel lncRNA signature to discriminate nonequivalent outcomes in patients with ovarian cancer. Oncotarget 7(22):32433–32448. https://doi.org/10.18632/oncotarget.8653

    Article  Google Scholar 

  63. Xu LC et al (2017) Up-regulation of LINC00161 correlates with tumor migration and invasion and poor prognosis of patients with hepatocellular carcinoma. Oncotarget 8(34):56168–56173. https://doi.org/10.18632/oncotarget.17040

    Article  Google Scholar 

  64. Li Z, Dou P, Liu T, He S (2017) Application of long non-coding RNAs in osteosarcoma: biomarkers and therapeutic targets. Cell Physiol Biochem 42(4):1407–1419. https://doi.org/10.1159/000479205

    Article  Google Scholar 

  65. Wang Y et al (2016) Long non-coding RNA LINC00161 sensitises osteosarcoma cells to cisplatin-induced apoptosis by regulating the miR-645-IFIT2 axis. Cancer Lett 382(2):137–146. https://doi.org/10.1016/j.canlet.2016.08.024

    Article  MathSciNet  Google Scholar 

  66. Shin SS et al (2017) HSPA6 augments garlic extract-induced inhibition of proliferation, migration, and invasion of bladder cancer EJ cells; Implication for cell cycle dysregulation, signaling pathway alteration, and transcription factor-associated MMP-9 regulation. PLoS ONE 12(2):e0171860. https://doi.org/10.1371/journal.pone.0171860

    Article  Google Scholar 

  67. Salameh A et al (2015) PRUNE2 is a human prostate cancer suppressor regulated by the intronic long non-coding RNA PCA3. Proc Natl Acad Sci USA 112(27):8403–8408. https://doi.org/10.1073/pnas.1507882112

    Article  Google Scholar 

  68. Zhou C, Li AH, Liu S, Sun H () Identification of an 11-autophagy-related-gene signature as promising prognostic biomarker for bladder cancer patients. Biology (Basel) 10(5). https://doi.org/10.3390/biology10050375

  69. Sun Y et al (2017) TMEM74 promotes tumor cell survival by inducing autophagy via interactions with ATG16L1 and ATG9A. Cell Death Dis 8(8):e3031. https://doi.org/10.1038/cddis.2017.370

Download references

Author information

Authors and Affiliations

  1. Bilecik Seyh Edebali University, Faculty of Engineering, Bioengineering Department, Bilecik, 11100, Türkiye

    Kevser Kübra Kırboğa

  2. Informatics Institute, Istanbul Technical University, Maslak, Istanbul, 34469, Türkiye

    Kevser Kübra Kırboğa

Authors
  1. Kevser Kübra Kırboğa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kevser Kübra Kırboğa.

Additional information

Publisher's Note

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

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

Kırboğa, K.K. Bladder cancer gene expression prediction with explainable algorithms. Neural Comput & Applic 36, 1585–1597 (2024). https://doi.org/10.1007/s00521-023-09142-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09142-3

Keywords

Search

Navigation