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A New Paradigm in Tumor Therapy: Molecularly Targeted-Adoptive Cell Immunotherapy

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DOI: 10.23977/tranc.2024.050106 | Downloads: 6 | Views: 112

Author(s)

Yunhao Lei 1

Affiliation(s)

1 College of Medical Imaging, Dalian Medical University, Dalian, Liaoning, 116000, China

Corresponding Author

Yunhao Lei

ABSTRACT

In recent years, cancer has become a major disease that poses a major threat to human health. Tumor therapy is an effective means to treat common symptoms such as cancerous cirrhosis and cerebral infarction, but there are still many problems to be solved in the clinical application of molecular targeted cross-linked immunotherapy. In this paper, the molecular targeted inoculation and secondary immune metabolism pathways of tumor therapy were studied, and their development prospects were prospected. The results showed that the application of molecular biology combined with molecular targeted cell model had a good effect in tumor patients. The experiment looked at the metabolism of cancer cells and found that there were differences between the metabolic rate and the overall average, but most were above the average metabolic rate of 11. This indicates that the immunotherapy effect of this model is better.

KEYWORDS

Tumor Therapy, Molecular Targeting, Adoptive Therapy, Cellular Immunity

CITE THIS PAPER

Yunhao Lei, A New Paradigm in Tumor Therapy: Molecularly Targeted-Adoptive Cell Immunotherapy. Transactions on Cancer (2024) Vol. 5: 39-46. DOI: http://dx.doi.org/10.23977/tranc.2024.050106.

REFERENCES

[1] Shuang Guo, Yuwei Liu, Yue Sun, Hanxiao Zhou, Yue Gao, Peng Wang, Hui Zhi, Yakun Zhang, Jing Gan, Shangwei Ning:Metabolic-Related Gene Prognostic Index for Predicting Prognosis, Immunotherapy Response, and Candidate Drugs in Ovarian Cancer. J. Chem. Inf. Model. 64(3): 1066-1080 (2024).
[2] Khalil M, Abbass M A. Neoadjuvant immunotherapy in microsatellite unstable colorectal cancer: Are we in the era of nonoperative management? [J].Journal of Surgical Oncology. 127(8):1296-1299(2023).
[3] Yunfang Wei, Yingzhen Su: Using machine learning and RNA to enhance the efficacy of anti-tumor immunotherapy. Evol. Intell. 16(5): 1555-1563 (2023).
[4] Qiong Wu, Jun Wang, Zongqiong Sun, Lei Xiao, Wenhao Ying, Jun Shi: Immunotherapy Efficacy Prediction for Non-Small Cell Lung Cancer Using Multi-View Adaptive Weighted Graph Convolutional Networks. IEEE J. Biomed. Health Informatics 27(11): 5564-5575 (2023).
[5] Gulnur Ungan, Anne-Flore Lavandier, Jacques Rouanet, Constance Hordonneau, Benoit Chauveau, Bruno Pereira, Louis Boyer, Jean-Marc Garcier, Sandrine Mansard, Adrien Bartoli, Benoît Magnin: Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification. Int. J. Comput. Assist. Radiol. Surg. 17(10): 1867-1877 (2022).
[6] Levente Kovács, Tamás Ferenci, Balázs Gombos, András Füredi, Imre J. Rudas, Gergely Szakács, Dániel András Drexler:Positive Impulsive Control of Tumor Therapy - A Cyber-Medical Approach. IEEE Trans. Syst. Man Cybern. Syst. 54(1): 597-608 (2024).
[7] Yuyue Zhang, Liqi Xie, Yueping Dong, Jicai Huang, Shigui Ruan, Yasuhiro Takeuchi:Bifurcation Analysis in a Tumor-Immune System Interaction Model with Dendritic Cell Therapy and Immune Response Delay. SIAM J. Appl. Math. 83(5): 1892-1914 (2023).
[8] Asghar Mesbahi, Maryam Sadeghian, Aisan Mesbahi, Henry M. Smilowitz, James F. Hainfeld: In silico analysis of optimum photon energy spectra and beam parameters for iodine nanoparticle-aided orthovoltage radiation therapy of brain tumors. Simul. 99(6): 539-552 (2023).
[9] Márton György Almásy, András Hörömpo, Dániel Kiss, Gábor Kertész:A review on modeling tumor dynamics and agent reward functions in reinforcement learning based therapy optimization. J. Intell. Fuzzy Syst. 43(6): 6939-6946 (2022).
[10] Ahad Mohammadi, Leonardo Bianchi, Sanzhar Korganbayev, Martina De Landro, Paola Saccomandi: Thermomechanical Modeling of Laser Ablation Therapy of Tumors: Sensitivity Analysis and Optimization of Influential Variables. IEEE Trans. Biomed. Eng. 69(1): 302-313 (2022).
[11] Runze Wang, Zehua Zhang, Yueqin Zhang, Zhongyuan Jiang, Shilin Sun, Guixiang Ma: MolHF : Molecular Heterogeneous Attributes Fusion for Drug-Target Affinity Prediction on Heterogeneity. IEICE Trans. Inf. Syst. 106(5): 697-706 (2023).
[12] Yazdan Maghsoud, Vindi M. Jayasinghe-Arachchige, Pratibha Kumari, G. Andrés Cisneros, Jin Liu:Leveraging QM/MM and Molecular Dynamics Simulations to Decipher the Reaction Mechanism of the Cas9 HNH Domain to Investigate Off-Target Effects. J. Chem. Inf. Model. 63(21): 6834-6850 (2023).
[13] Qichang Zhao, Guihua Duan, Haochen Zhao, Kai Zheng, Yaohang Li, Jianxin Wang:GIFDTI: Prediction of Drug-Target Interactions Based on Global Molecular and Intermolecular Interaction Representation Learning. IEEE ACM Trans. Comput. Biol. Bioinform. 20(3): 1943-1952 (2023).
[14] Xu Bao, Qingfeng Shen, Yanfei Zhu, Wence Zhang: Relative Localization for Silent Absorbing Target in Diffusive Molecular Communication System. IEEE Internet Things J. 9(7): 5009-5018 (2022).
[15] Kexin Huang, Cao Xiao, Lucas M. Glass, Jimeng Sun: MolTrans: Molecular Interaction Transformer for drug-target interaction prediction. Bioinform. 37(6): 830-836 (2021).
[16] Bhanu Sharma: Pharmacokinetic and molecular docking studies of natural plant compounds of Hibiscus sabdariffa to design antihypertensive compounds targeting AT2R. Int. J. Comput. Biol. Drug Des. 14(1): 32-42 (2021).
[17] Elif Onur, Tuba Denkçeken: Integrative analysis of molecular genetic targets and pathways in colorectal cancer through screening large-scale microarray data. Int. J. Data Min. Bioinform. 26(1/2): 81-98 (2021).
[18] John Barrow, William Hurst, Joakim Edman, Natasja Ariesen, Caspar Krampe: Virtual reality for biochemistry education: the cellular factory. Educ. Inf. Technol. 29(2): 1647-1672 (2024).
[19] Mohammed S. Elbasheir, Rashid A. Saeed, Salaheldin Edam: Electromagnetic field exposure boundary analysis at the near field for multi-technology cellular base station site. IET Commun. 18(1): 11-27 (2024).
[20] Nishattasnim Liza, Daniel J. Coe, Yuhui Lu, Enrique P. Blair: Ab initio studies of counterion effects in molecular quantum-dot cellular automata. J. Comput. Chem. 45(7): 392-404 (2024).

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