Skip to main content

Advertisement

SpringerLink
Log in

Using machine learning and RNA to enhance the efficacy of anti-tumor immunotherapy

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

RNA (Ribonucleic Aci) plays a crucial role in a variety of physiological and pathological processes. Over the past few decades, researchers have designed many types of RNAs and their derivatives and applied them to different fields of biology. Studies have confirmed that RNA molecules play an important role in tumor growth, metastasis, metabolism and immune escape. Based on the research of tumor-associated RNA and the application of related technologies, RNA has been designed and applied in tumor immunotherapy. Machine learning (ML) algorithms can be used to solve tumor type classification predictions with the large-scale data, some with high prediction accuracy and others with limited accuracy. For the early detection of tumors, this paper proposes to use machine learning and RNA technology to establish a tumor type prediction model to enhance the effect of anti-tumor immunotherapy. The model first preprocesses the data. Then, this paper uses an improved principal component analysis algorithm to perform feature dimensionality reduction and data fusion on the RNA expression and DNA methylation data of patients with 32 tumor types. Finally, the data is fed into a tumor type classifier based on the CNN-LSTM model for training and prediction. The experimental results show that the method proposed in this paper has excellent performance in tumor classification, and has important guiding significance for the early diagnosis and treatment of tumor patients, which further indicates that the intelligent ML method can be used for data analysis in the field of tumor type prediction.

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

References

  1. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674

    Article  Google Scholar 

  2. Barrett RL, Puré E (2020) Cancer-associated fibroblasts and their influence on tumor immunity and immunotherapy. Elife 9:e57243

    Article  Google Scholar 

  3. Starnes CO (1992) Coley’s toxins in perspective. Nature 357(6373):11–12

    Article  Google Scholar 

  4. Sharma P, Allison JP (2015) Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 161(2):205–214

    Article  Google Scholar 

  5. Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, Chow LQ, Vokes EE, Felip E, Holgado E, Barlesi F, Brahmer JR (2015) Nivolumab versus docetaxel in advanced nonsquamous non–small-cell lung cancer. New Engl J Med 373(17):1627–1639

    Article  Google Scholar 

  6. Motzer RJ, Escudier B, McDermott DF, George S, Hammers HJ, Srinivas S, Tykodi SS, Sosman JA, Procopio G, Plimack ER, Castellano D, Sharma P (2015) Nivolumab versus everolimus in advanced renal-cell carcinoma. New Engl J Med 373(19):1803–1813

    Article  Google Scholar 

  7. Rosenberg JE, Hoffman-Censits J, Powles T, Van Der Heijden MS, Balar AV, Necchi A, Dawson N, O’Donnell PH, Balmanoukian A, Loriot Y, Srinivas S, Dreicer R (2016) Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387(10031):1909–1920

    Article  Google Scholar 

  8. Wang Z, Jensen MA, Zenklusen JC (2016) A practical guide to the cancer genome atlas (TCGA). In: Statistical Genomics. Humana Press, New York, NY, (pp 111–141)

  9. Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, Hasz R, Walters G, Garcia F, Young N, Foster B, Moore HF (2013) The genotype-tissue expression (GTEx) project. Nat Genet 45(6):580–585

    Article  Google Scholar 

  10. Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4–5):198–211

    Article  Google Scholar 

  11. Rendon-Gonzalez E, Ponomaryov V (2016) Automatic Lung nodule segmentation and classification in CT images based on SVM. In: 2016 9th international Kharkiv symposium on physics and engineering of microwaves, millimeter and submillimeter waves (MSMW), IEEE, (pp 1–4)

  12. Suthaharan S (2016) Support vector machine. In: Machine learning models and algorithms for big data classification. Springer, Boston, MA. (pp 207–235)

  13. Qiao Z, Kewen X, Panpan W, Wang H (2017) Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm. Pattern Recognit Image Anal 27(4):855–862

    Article  Google Scholar 

  14. Kouzani AZ, Lee SLA, Hu EJ (2008) Lung nodules detection by ensemble classification. In: 2008 IEEE international conference on systems, man and cybernetics, IEEE, (pp 324–329)

  15. Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW (2012) GenBank. Nucleic Acids Res 41(D1):D36–D42

    Article  Google Scholar 

  16. McEntyre J, Sarkans U, Brazma A (2015) The BioStudies database. Mol Syst Biol 11(12):847

    Article  Google Scholar 

  17. da Silva DSM, da Silva WM, RuiZhe G, Bernardi AP, Mariano AM, Holanda M (2019) Big data trends in bioinformatics. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM), IEEE, (pp 1862–1867)

  18. Schuster SC (2008) Next-generation sequencing transforms today’s biology. Nat Methods 5(1):16–18

    Article  Google Scholar 

  19. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  20. Sahiner B, Chan HP, Petrick N, Wei D, Helvie MA, Adler DD, Goodsitt MM (1996) Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging 15(5):598–610

    Article  Google Scholar 

  21. Lo SCB, Lin JS, Freedman MT, Mun SK (1993) Computer-assisted diagnosis of lung nodule detection using artificial convoultion neural network. In: Medical Imaging 1993: Image Processing. SPIE. (Vol. 1898, pp 859–869)

  22. Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recogn Lett 125:1–6

    Article  Google Scholar 

  23. Abbass HA (2002) An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif Intell Med 25(3):265–281

    Article  Google Scholar 

  24. Fogel DB, Wasson EC III, Boughton EM (1995) Evolving neural networks for detecting breast cancer. Cancer Lett 96(1):49–53

    Article  Google Scholar 

  25. Abdikenov B, Iklassov Z, Sharipov A, Hussain S, Jamwal PK (2019) Analytics of heterogeneous breast cancer data using neuroevolution. IEEE Access 7:18050–18060

    Article  Google Scholar 

  26. Liu J, Xu B, Zheng C, Gong Y, Garibaldi J et al (2018) An end-to-end deep learning histochemical scoring system for breast cancer TMA. IEEE Trans Med Imaging 38(2):617–628

    Article  Google Scholar 

  27. Lu H, Wang H, Yoon SW (2019) A dynamic gradient boosting machine using genetic optimizer for practical breast cancer prognosis. Expert Syst Appl 116:340–350

    Article  Google Scholar 

  28. Oza NC, Russell S (2001) Experimental comparisons of online and batch versions of bagging and boosting. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp 359–364)

  29. He XG, Li CJ, Rui JW (2018) Research on dimensional reduction of sparse matrix data based on information entropy. J Univ Electron Sci Technol China 47(02):235–241

    Google Scholar 

  30. Jing WAN, Fan WU, Yunbin HE, Song LI (2020) Clustering algorithm for high-dimensional data under new dimensionality reduction criteria. J Front Comput Sci Technol 14(1):96

    Google Scholar 

  31. Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 international conference on engineering and technology (ICET), IEEE, (pp 1–6)

  32. Mostavi M, Chiu YC, Huang Y, Chen Y (2020) Convolutional neural network models for cancer type prediction based on gene expression. BMC Med Genomics 13(5):1–13

    Google Scholar 

Download references

Acknowledgements

Scientific Research Foundation of Yunnan Provincial Education Department Teacher Project (Granted No. 2019J0567) and Yunnan Provincial Science and Technology Department Local University Joint Project (Granted No. 202001BA070001-191).

Author information

Authors and Affiliations

  1. Kunming College, Kunming, 650214, China

    Yunfang Wei & Yingzhen Su

Authors
  1. Yunfang Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingzhen Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yingzhen Su.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest in this paper.

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 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

Wei, Y., Su, Y. Using machine learning and RNA to enhance the efficacy of anti-tumor immunotherapy. Evol. Intel. 16, 1555–1563 (2023). https://doi.org/10.1007/s12065-022-00781-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-022-00781-4

Keywords

Search

Navigation