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Identification of subtypes in digestive system tumors based on multi-omics data and graph convolutional network

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Abstract

Accurately predicting the molecular subtype of cancer patients is of great significance for personalized diagnosis and treatment of cancer. The progress of a large amount of multi-omics data and data-driven methods is expected to promote the molecular subtyping of cancer. Existing methods are limited by their ability to deal with high-dimensional data and the influence of misleading and unrelated factors, resulting in ambiguous and overlapping subtypes. This article proposes a method called Multi-Omics Subtypes of Digestive System Tumors (MSDST), which is used for subtype identification of digestive system tumors. The method learns a new representation of the relationship between samples from multi-omics data, and uses a self-encoding model composed of omics-specific graph convolutional networks to learn the high-level representation of each omics data feature while considering the prognosis prediction results. Finally, k-means algorithm is used to cluster samples for analysis. Compared with other state-of-the-art methods, our proposed method performs better in identifying digestive system tumor subtypes. Subsequent clinical data analysis and functional enrichment analysis further confirm the specific biological characteristics and functional differences of the identified subtypes. This research provides new ideas and methods for precision medicine, and is expected to promote personalized treatment and improve the prognosis of digestive system tumors.

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Data Availability

This study analyzed publicly available datasets generated by the Cancer Genome Atlas (TCGA), managed by the National Cancer Institute (NCI). These datasets can be found at: http://cancergenome.nih.gov.

References

  1. Arnold M, Abnet CC, Neale RE, Vignat J, Giovannucci EL, McGlynn KA, Bray F (2020) Global burden of 5 major types of gastrointestinal cancer. Gastroenterology 159(1):335–34915

    Article  Google Scholar 

  2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J Clin 71(3):209–249

  3. Liu Y, Sethi NS, Hinoue T, Schneider BG, Cherniack AD, Sanchez-Vega F, Seoane JA, Farshidfar F, Bowlby R, Islam M (2018) Comparative molecular analysis of gastrointestinal adenocarcinomas. Cancer Cell 33(4):721–7358

    Article  Google Scholar 

  4. Xie Y, Shi L, He X, Luo Y (2021) Gastrointestinal cancers in China, the USA, and Europe. Gastroenterol Rep 9(2):91–104

    Article  Google Scholar 

  5. Zhao L, Lee VH, Ng MK, Yan H, Bijlsma MF (2019) Molecular subtyping of cancer: current status and moving toward clinical applications. Brief Bioinf 20(2):572–584

    Article  Google Scholar 

  6. Wong ANN, He Z, Leung KL, To CCK, Wong CY, Wong SCC, Yoo JS, Chan CKR, Chan AZ, Lacambra MD (2022) Current developments of artificial intelligence in digital pathology and its future clinical applications in gastrointestinal cancers. Cancers 14(15):3780

    Article  Google Scholar 

  7. Wahid M, Ahmed G, Hussain S, Ansari AA (2023) A survey on cancer molecular subtype classification using deep learning. In: 2023 4th International conference on computing, mathematics and engineering technologies (iCoMET), pp 1–5

  8. Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metab Clin Exp 69S:36–40

    Article  Google Scholar 

  9. Vahabi N, Michailidis G (2022) Unsupervised multi-omics data integration methods: a comprehensive review. Front Genet 13

  10. Rakshit S, Saha I, Chakraborty SS, Plewczyski D (2018) Deep learning for integrated analysis of breast cancer subtype specific multi-omics data. In: TENCON 2018 - 2018 IEEE Region 10 Conference, pp 1917–1922

  11. Sun P, Wu Y, Yin C, Jiang H, Xu Y, Sun H (2022) Molecular subtyping of cancer based on distinguishing co-expression modules and machine learning. Front Genet 13

  12. Tian J, Zhu M, Ren Z, Zhao Q, Wang P, He CK, Zhang M, Peng X, Wu B, Feng R, Fu M (2022) Deep learning algorithm reveals two prognostic subtypes in patients with gliomas. BMC Bioinf 23(1):417

    Article  Google Scholar 

  13. Cascianelli S, Molineris I, Isella C, Masseroli M, Medico E (2020) Machine learning for RNA sequencing-based intrinsic subtyping of breast cancer. Sci Rep 10(1):14071

    Article  Google Scholar 

  14. Li S, Yang Y, Wang X, Li J, Yu J, Li X, Wong K-C (2022) Colorectal cancer subtype identification from differential gene expression levels using minimalist deep learning. BioData Mining 15(1):12

    Article  Google Scholar 

  15. Chen R, Yang L, Goodison S, Sun Y (2019) Deep learning approach to identifying breast cancer subtypes using high-dimensional genomic data. arXiv:629865

  16. Yang B, Xin T-T, Pang S-M, Wang M, Wang Y-J (2021) Deep subspace mutual learning for cancer subtypes prediction. Bioinformatics 37(21):3715–3722

    Article  Google Scholar 

  17. Xu J, Wu P, Chen Y, Meng Q, Dawood H, Dawood H (2019) A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data. BMC Bioinf 20(1):527

    Article  Google Scholar 

  18. Hashimoto N, Fukushima D, Koga R, Takagi Y, Ko K, Kohno K, Nakaguro M, Nakamura S, Hontani H, Takeuchi I (2019) Multi-scale domain-adversarial multiple-instance cnn for cancer subtype classification with unannotated histopathological images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3852–3861

  19. ElKarami B, Alkhateeb A, Qattous H, Alshomali L, Shahrrava B (2022) Multi-omics data integration model based on umap embedding and convolutional neural network. Cancer Inf 21:11769351221124204

    Google Scholar 

  20. Zhan Q, Wen C, Zhao Y, Fang L, Jin Y, Zhang Z, Zou S, Li F, Yang Y, Wu L (2021) Identification of copy number variation-driven molecular subtypes informative for prognosis and treatment in pancreatic adenocarcinoma of a Chinese cohort. Ebiomed 74

  21. Qattous H, Azzeh M, Ibrahim R, Al-Ghafer IA, Al Sorkhy M, Alkhateeb A (2024) Pacmap-embedded convolutional neural network for multi-omics data integration. Heliyon 10(1)

  22. Madhumita, Paul S (2022) Capturing the latent space of an autoencoder for multi-omics integration and cancer subtyping. Comput Biol Med 148:105832

  23. Li X, Ma J, Leng L, Han M, Li M, He F, Zhu Y (2022) Mogcn: a multi-omics integration method based on graph convolutional network for cancer subtype analysis. Front Genet 13:806842

    Article  Google Scholar 

  24. Gao F, Wang W, Tan M, Zhu L, Zhang Y, Fessler E, Vermeulen L, Wang X (2019) Deepcc: a novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis 8(9):44

    Article  Google Scholar 

  25. Franco EF, Rana P, Cruz A, Calderon VV, Azevedo V, Ramos RTJ, Ghosh P (2021) Performance comparison of deep learning autoencoders for cancer subtype detection using multi-omics data. Cancers (Basel) 13(9)

  26. Dai W, Yue W, Peng W, Fu X, Liu L, Liu L (2022) Identifying cancer subtypes using a residual graph convolution model on a sample similarity network. Genes 13(1):65

    Article  Google Scholar 

  27. Yin C, Cao Y, Sun P, Zhang H, Li Z, Xu Y, Sun H (2022) Molecular subtyping of cancer based on robust graph neural network and multi-omics data integration. Front Genet 13:884028

    Article  Google Scholar 

  28. Ge S, Liu J, Cheng Y, Meng X, Wang X (2022) Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping. Brief Bioinf 24(1)

  29. Zhang G, Peng Z, Yan C, Wang J, Luo J, Luo H (2022) Multigatae: a novel cancer subtype identification method based on multi-omics and attention mechanism. Front Genet 13:855629

    Article  Google Scholar 

  30. Shi X, Liang C, Wang H (2023) Multiview robust graph-based clustering for cancer subtype identification. IEEE/ACM Trans Comput Biol Bioinf 20(1):544–556

    Article  Google Scholar 

  31. Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM (2013) The cancer genome atlas pan-cancer analysis project. Nat Genet 45(10):1113–1120

    Article  Google Scholar 

  32. Wang T, Shao W, Huang Z, Tang H, Zhang J, Ding Z, Huang K (2021) Mogonet integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nat Commun 12(1):3445

    Article  Google Scholar 

  33. Wang L, Ding Z, Tao Z, Liu Y, Fu Y (2019) Generative multi-view human action recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 6212–6221

  34. Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24

    Article  MathSciNet  Google Scholar 

  35. Li B, Wang T, Nabavi S (2021) Cancer molecular subtype classification by graph convolutional networks on multi-omics data. In: Proceedings of the 12th ACM conference on bioinformatics, computational biology, and health informatics, pp 1–9

  36. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu X, Liu S, Bo X, Yu G (2021) clusterprofiler 4.0: a universal enrichment tool for interpreting omics data. The Innovation 2(3):100141

  37. Akiba T, Sano S, Yanase T, Ohta T, Koyama M (2019) Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 2623–2631

  38. Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, Haibe-Kains B, Goldenberg A (2014) Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 11(3):333–337

    Article  Google Scholar 

  39. Song W, Wang W, Dai D-Q (2021) Subtype-weslr: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data. Briefings in Bioinformatics 23(1)

  40. Meng C, Helm D, Frejno M, Kuster B (2016) mocluster: identifying joint patterns across multiple omics data sets. J Proteome Res 15(3):755–765

    Article  Google Scholar 

  41. Rappoport N, Shamir R (2019) Nemo: cancer subtyping by integration of partial multi-omic data. Bioinformatics 35(18):3348–3356

    Article  Google Scholar 

  42. Wu D, Wang D, Zhang MQ, Gu J (2015) Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. BMC Genom 16(1):1022

    Article  Google Scholar 

  43. Mo Q, Shen R, Guo C, Vannucci M, Chan KS, Hilsenbeck SG (2017) A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics 19(1):71–86

    Article  MathSciNet  Google Scholar 

  44. Shi Q, Zhang C, Peng M, Yu X, Zeng T, Liu J, Chen L (2017) Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data. Bioinformatics 33(17):2706–2714

    Article  Google Scholar 

  45. Zhang C, Chen Y, Zeng T, Zhang C, Chen L (2022) Deep latent space fusion for adaptive representation of heterogeneous multi-omics data. Brief Bioinf 23(2):600

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. U20A20225 and U2013601), in part by Anhui Province Natural Science Funds for Distinguished Young Scholar (Grant No. 2308085J02), in part by the Science and Technology Innovation 2030 - "New Generation Artificial Intelligence" Major Project (Grant No. 2022ZD0116305), in part by Innovation Leading Talent of Anhui Province TeZhi plan, in part by the Natural Science Foundation of Hefei, China (Grant No. 202321), and in part by the CAAI-Huawei Mind Spore Open Fund (Grant No. CAAIXSJLJJ-2022-011A). Thanks to the funding support from Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot (Grant No. IFCIR2024001).

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Author notes

  1. Lin Zhou and Ning Wang have contributed equally to this work.

Authors and Affiliations

  1. School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, Anhui, China

    Lin Zhou, Hongbo Gao & Yi Zhou

  2. Institute of Advanced Technology, University of Science and Technology of China, Hefei, 230088, Anhui, China

    Hongbo Gao

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore, Singapore

    Hongbo Gao

  4. Chongqing College of Mobile Communication, Chongqing, 400044, China

    Ning Wang

  5. Department of Breast Center, West District of The Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, Anhui, China

    Zhengzhi Zhu

  6. School of Physics and Electronic Information, Anhui Normal University, Wuhu, 241002, Anhui, China

    Mingxing Fang

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  1. Lin Zhou
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Correspondence to Zhengzhi Zhu or Hongbo Gao.

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Zhou, L., Wang, N., Zhu, Z. et al. Identification of subtypes in digestive system tumors based on multi-omics data and graph convolutional network. Int. J. Mach. Learn. & Cyber. 15, 3567–3577 (2024). https://doi.org/10.1007/s13042-024-02109-3

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