Literature DB >> 34802404

Cancer Treatment Evolution from Traditional Methods to Stem Cells and Gene Therapy.

Wenhua He1, Qingxuan Li1, Yan Lu1, Dingyue Ju1, Yu Gu1, Kai Zhao1, Chuanming Dong1.   

Abstract

BACKGROUND: Cancer, a malignant tumor, is caused by the failure of the mechanism that controls cell growth and proliferation. Late clinical symptoms often manifest as lumps, pain, ulcers, and bleeding. Systemic symptoms include weight loss, fatigue, and loss of appetite. It is a major disease that threatens human life and health. How to treat cancer is a long-standing problem that needs to be overcome in the history of medicine.
METHODS: Traditional tumor treatment methods are poorly targeted, and the side effects of treatment seriously damage the physical and mental health of patients. In recent years, with the advancement of medical science and technology, the research on gene combined with mesenchymal stem cells to treat tumors has been intensified. Mesenchymal stem cells carry genes to target cancer cells, which can achieve better therapeutic effects. DISCUSSION: In this study, we systematically review the cancer treatment evolution from traditional methods to novel approaches that include immunotherapy, nanotherapy, stem cell theapy, and gene therapy. We provide the latest review of the application status, clinical trials, and development prospects of mesenchymal stem cells and gene therapy for cancer, as well as their integration in cancer treatment. Mesenchymal stem cells are effective carriers carrying genes and provide new clinical ideas for tumor treatment.
CONCLUSION: This review focuses on the current status, application prospects, and challenges of mesenchymal stem cell combined gene therapy for cancer and provides new ideas for clinical research. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  Gene therapy; cancer; clinical trials; gene carriers; mesenchymal stem cells; viral vectors

Mesh:

Year:  2022        PMID: 34802404     DOI: 10.2174/1566523221666211119110755

Source DB:  PubMed          Journal:  Curr Gene Ther        ISSN: 1566-5232            Impact factor:   4.676


  1 in total

1.  Prediction of Tumor Mutation Load in Colorectal Cancer Histopathological Images Based on Deep Learning.

Authors:  Yongguang Liu; Kaimei Huang; Yachao Yang; Yan Wu; Wei Gao
Journal:  Front Oncol       Date:  2022-05-24       Impact factor: 5.738

  1 in total

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