Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer

Yuanyuan He; Haodong Wei; Siqing Liao; Ruiming Ou; Yuqiang Xiong; Yongchun Zuo; Lei Yang

doi:10.2174/0115748936288453240124082031

ISSN: 1574-8936
E-ISSN: 2212-392X

Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer
Authors: Yuanyuan He¹, Haodong Wei¹, Siqing Liao¹, Ruiming Ou², Yuqiang Xiong¹, Yongchun Zuo^3,4,5 and Lei Yang¹
View Affiliations Hide Affiliations

Affiliations: ¹ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China ; ² College of Basic Medicine, Harbin Medical University, Harbin, 150081, China ; ³ The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China ; ⁴ Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd. Hohhot, 010010, China ; ⁵ Inner Mongolia International Mongolian Hospital, Hohhot, 010065, China
Source: Current Bioinformatics, Volume 19, Issue 10, Dec 2024, p. 963 - 976
DOI: https://doi.org/10.2174/0115748936288453240124082031
- Received: 20 Oct 2023
- Accepted: 17 Jan 2024
- Available online: 06 Feb 2024

Abstract

Background

Bladder cancer is a prevalent malignancy globally, characterized by rising incidence and mortality rates. Stratifying bladder cancer patients into different subtypes is crucial for the effective treatment of this form of cancer. Therefore, there is a need to develop a stratification model specific to bladder cancer.

Purpose

This study aims to establish a prognostic prediction model for bladder cancer, with the primary goal of accurately predicting prognosis and treatment outcomes.

Methods

We collected datasets from 10 bladder cancer datasets sourced from the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA) databases, and IMvigor210 dataset. The machine learning based on feature selection algorithms were used to generate 96 models for establishing the risk score for each patient. Based on the risk score, all the patients were classified into two different risk score groups.

Results

The two groups of bladder cancer patients exhibited significant differences in prognosis, biological functions, and drug sensitivity. Nomogram model demonstrated that the risk score had a robust predictive effect with good clinical utility.

Conclusion

The risk score constructed in this study can be utilized to predict the prognosis, response to drug treatment, and immunotherapy of bladder cancer patients, providing assistance for personalized clinical treatment of bladder cancer.

Article metrics loading...

/content/journals/cbio/10.2174/0115748936288453240124082031

2024-02-06

2024-11-22

From This Site

/content/journals/cbio/10.2174/0115748936288453240124082031

dcterms_title,dcterms_subject,pub_keyword

-contentType:Contributor -contentType:Concept -contentType:Institution

10

5

Full text loading...

References

SungH. FerlayJ. SiegelR.L. Global cancer statistics 2020: GLOBOCAN estimates of Incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.202171320924910.3322/caac.21660 33538338
[Google Scholar]
RobertsonA.G. KimJ. Al-AhmadieH. Comprehensive molecular characterization of muscle-invasive bladder cancer.Cell20171713540556.e2510.1016/j.cell.2017.09.007 28988769
[Google Scholar]
van KesselK.E.M. ZuiverloonT.C.M. AlbertsA.R. BoormansJ.L. ZwarthoffE.C. Targeted therapies in bladder cancer: An overview of in vivo research.Nat. Rev. Urol.2015121268169410.1038/nrurol.2015.231 26390971
[Google Scholar]
BrayF. FerlayJ. SoerjomataramI. SiegelR.L. TorreL.A. JemalA. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.201868639442410.3322/caac.21492 30207593
[Google Scholar]
KamounA. de ReynièsA. AlloryY. A consensus molecular classification of muscle-invasive bladder cancer.Eur. Urol.202077442043310.1016/j.eururo.2019.09.006 31563503
[Google Scholar]
JubberI. OngS. BukavinaL. Epidemiology of bladder cancer in 2023: A systematic review of risk factors.Eur. Urol.202384217619010.1016/j.eururo.2023.03.029 37198015
[Google Scholar]
TutsoyO. TanrikuluM.Y. Priority and age specific vaccination algorithm for the pandemic diseases: A comprehensive parametric prediction model.BMC Med. Inform. Decis. Mak.2022221410.1186/s12911‑021‑01720‑6 34991566
[Google Scholar]
TutsoyO. Graph theory based large-scale machine learning with multi-dimensional constrained optimization approaches for exact epidemiological modeling of pandemic diseases.IEEE Trans. Pattern Anal. Mach. Intell.20234589836984510.1109/TPAMI.2023.3256421 37028303
[Google Scholar]
HänzelmannS. CasteloR. GuinneyJ. GSVA: Gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics2013141710.1186/1471‑2105‑14‑7 23323831
[Google Scholar]
SuD. WangS. XiQ. Prognostic and predictive value of a metabolic risk score model in breast cancer: An immunogenomic landscape analysis.Brief. Funct. Genomics202221212814110.1093/bfgp/elab040 34755827
[Google Scholar]
SuD. LuQ. PanY. Immune-related gene-based prognostic signature for the risk stratification analysis of breast cancer.Curr. Bioinform.202217219620510.2174/1574893616666211005110732
[Google Scholar]
WangS. ZhangQ. YuC. CaoY. ZuoY. YangL. Immune cell infiltration-based signature for prognosis and immunogenomic analysis in breast cancer.Brief. Bioinform.20212222020203110.1093/bib/bbaa026 32141494
[Google Scholar]
WangS. XiongY. ZhangQ. Clinical significance and immunogenomic landscape analyses of the immune cell signature based prognostic model for patients with breast cancer.Brief. Bioinform.2020 33302293
[Google Scholar]
MiaoY.R. ZhangQ. LeiQ. ImmuCellAI: A unique method for comprehensive T-cell subsets abundance prediction and its application in cancer immunotherapy.Adv. Sci.202077190288010.1002/advs.201902880 32274301
[Google Scholar]
YangL. LvY. WangS. Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou’s 5-steps rule.Genomics202011221500151510.1016/j.ygeno.2019.08.021 31472243
[Google Scholar]
XiaoY. MaD. ZhaoS. Multi-omics profiling reveals distinct microenvironment characterization and suggests immune escape mechanisms of triple-negative breast cancer.Clin. Cancer Res.201925165002501410.1158/1078‑0432.CCR‑18‑3524 30837276
[Google Scholar]
LeoneR.D. PowellJ.D. Metabolism of immune cells in cancer.Nat. Rev. Cancer202020951653110.1038/s41568‑020‑0273‑y 32632251
[Google Scholar]
XiaoY. MaD. YangY.S. Comprehensive metabolomics expands precision medicine for triple-negative breast cancer.Cell Res.202232547749010.1038/s41422‑022‑00614‑0 35105939
[Google Scholar]
ChuG. JiX. WangY. NiuH. Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for muscle-invasive urothelial cancer.Mol. Ther. Nucleic Acids20233311012610.1016/j.omtn.2023.06.001 37449047
[Google Scholar]
LiuZ. LiuL. WengS. Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.Nat. Commun.202213181610.1038/s41467‑022‑28421‑6 35145098
[Google Scholar]
NingJ. SunK. FanX. Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer.Sci. Rep.2023131701910.1038/s41598‑023‑34291‑9 37120631
[Google Scholar]
LiuJ. ShiY. ZhangY. Multi-omics identification of an immunogenic cell death-related signature for clear cell renal cell carcinoma in the context of 3P medicine and based on a 101-combination machine learning computational framework.EPMA J.202314227530510.1007/s13167‑023‑00327‑3 37275552
[Google Scholar]
QinH. AbulaitiA. MaimaitiA. Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma.J. Transl. Med.202321158810.1186/s12967‑023‑04468‑x 37660060
[Google Scholar]
WangL. LiuZ. LiangR. Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer.eLife202211e8015010.7554/eLife.80150 36282174
[Google Scholar]
ColapricoA. SilvaT.C. OlsenC. TCGAbiolinks: An R/Bioconductor package for integrative analysis of TCGA data.Nucleic Acids Res.2016448e71e110.1093/nar/gkv1507 26704973
[Google Scholar]
MariathasanS. TurleyS.J. NicklesD. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells.Nature2018554769354454810.1038/nature25501 29443960
[Google Scholar]
WuY. YangS. MaJ. Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level.Cancer Discov.202212113415310.1158/2159‑8290.CD‑21‑0316 34417225
[Google Scholar]
JassalB. MatthewsL. ViteriG. The reactome pathway knowledgebase.Nucleic Acids Res.202048D1D498D503 31691815
[Google Scholar]
LeekJ.T. JohnsonW.E. ParkerH.S. JaffeA.E. StoreyJ.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments.Bioinformatics201228688288310.1093/bioinformatics/bts034 22257669
[Google Scholar]
LiuZ. GuoC. DangQ. Integrative analysis from multi-center studies identities a consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer.EBioMedicine20227510375010.1016/j.ebiom.2021.103750 34922323
[Google Scholar]
XuH. LiuZ. WengS. Artificial intelligence‐driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi‐center integration analysis.Mol. Oncol.202216224023404210.1002/1878‑0261.13313 36083778
[Google Scholar]
PickettK.L. SureshK. CampbellK.R. DavisS. Juarez-ColungaE. Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.BMC Med. Res. Methodol.202121121610.1186/s12874‑021‑01375‑x 34657597
[Google Scholar]
GoemanJ.J. L1 penalized estimation in the Cox proportional hazards model.Biom. J.2010521708410.1002/bimj.200900028 19937997
[Google Scholar]
RobinsonM.D. McCarthyD.J. SmythG.K. edgeR: A bioconductor package for differential expression analysis of digital gene expression data.Bioinformatics201026113914010.1093/bioinformatics/btp616 19910308
[Google Scholar]
WuT. HuE. XuS. ClusterProfiler 4.0: A universal enrichment tool for interpreting omics data.Innovation20212310014110.1016/j.xinn.2021.100141 34557778
[Google Scholar]
SubramanianA. TamayoP. MoothaV.K. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles.Proc. Natl. Acad. Sci. USA200510243155451555010.1073/pnas.0506580102 16199517
[Google Scholar]
LiberzonA. BirgerC. ThorvaldsdóttirH. GhandiM. MesirovJ.P. TamayoP. The molecular signatures database (MSigDB) hallmark gene set collection.Cell Syst.20151641742510.1016/j.cels.2015.12.004 26771021
[Google Scholar]
BhattacharyaS. DunnP. ThomasC.G. ImmPort, toward repurposing of open access immunological assay data for translational and clinical research.Sci. Data20185118001510.1038/sdata.2018.15 29485622
[Google Scholar]
CharoentongP. FinotelloF. AngelovaM. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade.Cell Rep.201718124826210.1016/j.celrep.2016.12.019 28052254
[Google Scholar]
MaeserD. GruenerR.F. HuangR.S. oncoPredict: An R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data.Brief. Bioinform.2021226bbab26010.1093/bib/bbab260 34260682
[Google Scholar]
YangW. SoaresJ. GreningerP. Genomics of drug sensitivity in cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells.Nucleic Acids Res.201341Database issueD955D961 23180760
[Google Scholar]

/content/journals/cbio/10.2174/0115748936288453240124082031

Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer

Curr Bioinform 19, 963 (2024); https://doi.org/10.2174/0115748936288453240124082031

/content/journals/cbio/10.2174/0115748936288453240124082031

Data & Media loading...

Supplements

Article Type: Research Article

Keyword(s): algorithms; Bladder cancer; drug sensitivity; immunotherapy; machine learning; prognosis

Most Cited Most Cited RSS feed

- A Review of Ensemble Methods in Bioinformatics
  
  Authors: Pengyi Yang, Yee Hwa Yang, Bing B. Zhou and Albert Y. Zomaya
- Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis
  
  Authors: Masahiro Sugimoto, Masato Kawakami, Martin Robert, Tomoyoshi Soga and Masaru Tomita
- Distance-based Support Vector Machine to Predict DNA N6- methyladenine Modification
  
  Authors: Haoyu Zhang, Quan Zou, Ying Ju, Chenggang Song and Dong Chen
- A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods
  
  Authors: Jun Zhang and Bin Liu
- Molecular Genetic Markers: Discovery, Applications, Data Storage and Visualisation
  
  Authors: Chris Duran, Nikki Appleby, David Edwards and Jacqueline Batley
- A Brief Survey of Machine Learning Methods in Protein Sub-Golgi Localization
  
  Authors: Wuritu Yang, Xiao-Juan Zhu, Jian Huang, Hui Ding and Hao Lin
- Cancer Diagnosis Through IsomiR Expression with Machine Learning Method
  
  Authors: Zhijun Liao, Dapeng Li, Xinrui Wang, Lisheng Li and Quan Zou
- The Advances and Challenges of Deep Learning Application in Biological Big Data Processing
  
  Authors: Li Peng, Manman Peng, Bo Liao, Guohua Huang, Weibiao Li and Dingfeng Xie
- Gene Expression Profile Classification: A Review
  
  Authors: Musa H. Asyali, Dilek Colak, Omer Demirkaya and Mehmet S. Inan
- Relevance of Molecular Docking Studies in Drug Designing
  
  Authors: Ritu Jakhar, Mehak Dangi, Alka Khichi and Anil K. Chhillar
More Less

Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer

Abstract

Most Read This Month

Most Cited Most Cited RSS feed